From 345f0f17c71f5b774ef1ce8c69c95508d4019a1f Mon Sep 17 00:00:00 2001
From: Alex <67700732+akrztrk@users.noreply.github.com>
Date: Mon, 30 Oct 2023 12:11:11 +0100
Subject: [PATCH] update md files  (#729)

---
 .../C-K-Loan/2021-03-29-pos_clinical_en.md    |   2 +-
 ...21-03-29-recognize_entities_posology_en.md |   2 -
 ...assifier_binary_rct_biobert_pipeline_en.md |  29 ---
 ...rt_token_classifier_ner_jsl_pipeline_en.md |  33 +--
 ...-clinical_deidentification_augmented_es.md |  90 +-------
 ...2023-06-13-clinical_deidentification_de.md |  66 +-----
 ...2023-06-13-clinical_deidentification_es.md |  94 +-------
 ...2023-06-13-clinical_deidentification_fr.md |  91 +-------
 ...6-13-clinical_deidentification_glove_en.md |  46 +---
 ...2023-06-13-clinical_deidentification_it.md |  88 +-------
 ...2023-06-13-clinical_deidentification_pt.md | 104 +--------
 ...2023-06-13-clinical_deidentification_ro.md |  44 ----
 ...06-13-clinical_deidentification_slim_en.md |  47 +---
 .../2023-06-13-icd10_icd9_mapping_en.md       |  33 +--
 .../2023-06-13-icd10cm_snomed_mapping_en.md   |  31 +--
 .../2023-06-13-icd10cm_umls_mapping_en.md     |  31 +--
 .../2023-06-13-icdo_snomed_mapping_en.md      |  32 +--
 .../2023-06-13-mesh_umls_mapping_en.md        |  32 +--
 ...-ner_deid_generic_augmented_pipeline_en.md |  27 +--
 ...23-06-13-ner_deid_subentity_pipeline_ar.md |  32 +--
 .../2023-06-13-ner_medication_pipeline_en.md  |  37 +---
 .../2023-06-13-ner_profiling_biobert_en.md    |  34 +--
 ...6-13-re_bodypart_directions_pipeline_en.md |  29 ---
 ...3-re_bodypart_proceduretest_pipeline_en.md |  30 ---
 ...man_phenotype_gene_clinical_pipeline_en.md |  27 +--
 ...re_temporal_events_clinical_pipeline_en.md |  27 +--
 ...al_events_enriched_clinical_pipeline_en.md |  27 +--
 ...-13-re_test_problem_finding_pipeline_en.md |  31 ---
 ...3-06-13-re_test_result_date_pipeline_en.md |  30 ---
 ...23-06-13-recognize_entities_posology_en.md |  32 +--
 .../2023-06-13-rxnorm_mesh_mapping_en.md      |  25 +--
 .../2023-06-13-rxnorm_ndc_mapping_en.md       |  31 +--
 .../2023-06-13-rxnorm_umls_mapping_en.md      |  32 +--
 .../2023-06-13-snomed_icd10cm_mapping_en.md   |  31 +--
 .../2023-06-13-snomed_icdo_mapping_en.md      |  32 +--
 .../2023-06-13-snomed_umls_mapping_en.md      |  32 +--
 ...assifier_binary_rct_biobert_pipeline_en.md |  60 -----
 ...classifier_ade_tweet_binary_pipeline_en.md |  55 -----
 ...fier_disease_mentions_tweet_pipeline_es.md |  55 -----
 ...ier_drug_development_trials_pipeline_en.md |  36 +--
 ...sifier_negation_uncertainty_pipeline_es.md |  55 -----
 ...n_classifier_ner_ade_binary_pipeline_en.md |  55 -----
 ...rt_token_classifier_ner_ade_pipeline_en.md |  34 +--
 ...token_classifier_ner_anatem_pipeline_en.md |  56 -----
 ...oken_classifier_ner_anatomy_pipeline_en.md |  51 +----
 ...ken_classifier_ner_bacteria_pipeline_en.md |  33 +--
 ...n_classifier_ner_bc2gm_gene_pipeline_en.md |  55 -----
 ...fier_ner_bc4chemd_chemicals_pipeline_en.md |  55 -----
 ...sifier_ner_bc5cdr_chemicals_pipeline_en.md |  55 -----
 ...assifier_ner_bc5cdr_disease_pipeline_en.md |  55 -----
 ...token_classifier_ner_bionlp_pipeline_en.md |  33 +--
 ...ken_classifier_ner_cellular_pipeline_en.md |  33 +--
 ...en_classifier_ner_chemicals_pipeline_en.md |  34 +--
 ...ken_classifier_ner_chemprot_pipeline_en.md |  33 +--
 ...ken_classifier_ner_clinical_pipeline_en.md |  34 +--
 ...r_clinical_trials_abstracts_pipeline_en.md |  55 -----
 ...r_clinical_trials_abstracts_pipeline_es.md |  55 -----
 ...t_token_classifier_ner_deid_pipeline_en.md |  33 +--
 ..._token_classifier_ner_drugs_pipeline_en.md |  34 +--
 ...ssifier_ner_jnlpba_cellular_pipeline_en.md |  55 -----
 ...rt_token_classifier_ner_jsl_pipeline_en.md |  68 +-----
 ...ken_classifier_ner_jsl_slim_pipeline_en.md |  34 +--
 ...sifier_ner_linnaeus_species_pipeline_en.md |  54 -----
 ...assifier_ner_living_species_pipeline_en.md |  55 -----
 ...assifier_ner_living_species_pipeline_es.md |  55 -----
 ...assifier_ner_living_species_pipeline_it.md |  55 -----
 ...assifier_ner_living_species_pipeline_pt.md |  55 -----
 ...classifier_ner_ncbi_disease_pipeline_en.md |  55 -----
 ...ken_classifier_ner_pathogen_pipeline_en.md |  55 -----
 ...oken_classifier_ner_species_pipeline_en.md |  55 -----
 ...ken_classifier_pharmacology_pipeline_es.md |  55 -----
 ...-clinical_deidentification_augmented_es.md | 181 +--------------
 ...2023-06-17-clinical_deidentification_de.md | 133 +----------
 ...2023-06-17-clinical_deidentification_en.md |  49 +---
 ...2023-06-17-clinical_deidentification_es.md | 186 +---------------
 ...2023-06-17-clinical_deidentification_fr.md | 183 +--------------
 ...cal_deidentification_glove_augmented_en.md |  30 +--
 ...6-17-clinical_deidentification_glove_en.md |  91 +-------
 ...2023-06-17-clinical_deidentification_it.md | 177 +--------------
 ...2023-06-17-clinical_deidentification_pt.md | 209 +-----------------
 ...2023-06-17-clinical_deidentification_ro.md |  90 --------
 ...06-17-clinical_deidentification_slim_en.md |  99 +--------
 ...-06-17-clinical_deidentification_wip_en.md |  50 +----
 .../2023-06-17-explain_clinical_doc_ade_en.md |  38 +---
 ...2023-06-17-explain_clinical_doc_carp_en.md |  70 +-----
 .../2023-06-17-explain_clinical_doc_era_en.md |  68 +-----
 ...6-17-explain_clinical_doc_medication_en.md |  69 +-----
 ...06-17-explain_clinical_doc_radiology_en.md |  37 +---
 .../2023-06-17-icd10_icd9_mapping_en.md       |  61 +----
 .../2023-06-17-icd10cm_snomed_mapping_en.md   |  61 +----
 .../2023-06-17-icd10cm_umls_mapping_en.md     |  61 +----
 .../2023-06-17-icdo_snomed_mapping_en.md      |  61 +----
 ...-06-17-jsl_ner_wip_clinical_pipeline_en.md |  33 +--
 ...-jsl_ner_wip_greedy_biobert_pipeline_en.md |  33 +--
 ...jsl_ner_wip_greedy_clinical_pipeline_en.md |  34 +--
 ...l_ner_wip_modifier_clinical_pipeline_en.md |  33 +--
 ...l_rd_ner_wip_greedy_biobert_pipeline_en.md |  34 +--
 ..._rd_ner_wip_greedy_clinical_pipeline_en.md |  34 +--
 .../2023-06-17-mesh_umls_mapping_en.md        |  61 +----
 ...7-ner_abbreviation_clinical_pipeline_en.md |  33 +--
 .../2023-06-17-ner_ade_biobert_pipeline_en.md |  33 +--
 ...2023-06-17-ner_ade_clinical_pipeline_en.md |  33 +--
 ...-06-17-ner_ade_clinicalbert_pipeline_en.md |  33 +--
 ...23-06-17-ner_ade_healthcare_pipeline_en.md |  33 +--
 ...3-06-17-ner_anatomy_biobert_pipeline_en.md |  51 +----
 ...-ner_anatomy_coarse_biobert_pipeline_en.md |  33 +--
 ...23-06-17-ner_anatomy_coarse_pipeline_en.md |  34 +--
 .../2023-06-17-ner_anatomy_pipeline_en.md     |  51 +----
 ...06-17-ner_bacterial_species_pipeline_en.md |  33 +--
 ...-06-17-ner_biomedical_bc2gm_pipeline_en.md |  33 +--
 ...23-06-17-ner_bionlp_biobert_pipeline_en.md |  33 +--
 .../2023-06-17-ner_bionlp_pipeline_en.md      |  33 +--
 ...3-06-17-ner_cancer_genetics_pipeline_en.md |  32 +--
 ...-06-17-ner_cellular_biobert_pipeline_en.md |  33 +--
 .../2023-06-17-ner_cellular_pipeline_en.md    |  33 +--
 ...23-06-17-ner_chemd_clinical_pipeline_en.md |  54 -----
 .../2023-06-17-ner_chemicals_pipeline_en.md   |  33 +--
 ...-06-17-ner_chemprot_biobert_pipeline_en.md |  33 +--
 ...06-17-ner_chemprot_clinical_pipeline_en.md |  33 +--
 .../2023-06-17-ner_chexpert_pipeline_en.md    |  34 +--
 ...023-06-17-ner_clinical_bert_pipeline_ro.md |  54 -----
 ...-06-17-ner_clinical_biobert_pipeline_en.md |  33 +--
 ...23-06-17-ner_clinical_large_pipeline_en.md |  33 +--
 .../2023-06-17-ner_clinical_pipeline_en.md    |  34 +--
 .../2023-06-17-ner_clinical_pipeline_ro.md    |  53 -----
 ...r_clinical_trials_abstracts_pipeline_en.md |  34 +--
 ...r_clinical_trials_abstracts_pipeline_es.md |  54 -----
 ...2023-06-17-ner_covid_trials_pipeline_en.md |  55 -----
 ...23-06-17-ner_deid_augmented_pipeline_en.md |  35 +--
 ...2023-06-17-ner_deid_biobert_pipeline_en.md |  35 +--
 ...7-ner_deid_enriched_biobert_pipeline_en.md |  33 +--
 ...023-06-17-ner_deid_enriched_pipeline_en.md |  33 +--
 ...-ner_deid_generic_augmented_pipeline_en.md |  58 +----
 ...06-17-ner_deid_generic_bert_pipeline_ro.md |  75 -------
 ...6-17-ner_deid_generic_glove_pipeline_en.md |  55 -----
 ...2023-06-17-ner_deid_generic_pipeline_ar.md |  25 +--
 ...2023-06-17-ner_deid_generic_pipeline_de.md |  34 +--
 ...2023-06-17-ner_deid_generic_pipeline_it.md |  54 -----
 ...2023-06-17-ner_deid_generic_pipeline_ro.md |  74 -------
 .../2023-06-17-ner_deid_large_pipeline_en.md  |  33 +--
 ...023-06-17-ner_deid_sd_large_pipeline_en.md |  33 +--
 .../2023-06-17-ner_deid_sd_pipeline_en.md     |  33 +--
 ...id_subentity_augmented_i2b2_pipeline_en.md |  33 +--
 ...er_deid_subentity_augmented_pipeline_en.md |  33 +--
 ...-17-ner_deid_subentity_bert_pipeline_ro.md |  75 -------
 ...17-ner_deid_subentity_glove_pipeline_en.md |  55 -----
 ...23-06-17-ner_deid_subentity_pipeline_ar.md |  63 ------
 ...23-06-17-ner_deid_subentity_pipeline_de.md |  34 +--
 ...23-06-17-ner_deid_subentity_pipeline_it.md |  55 -----
 ...23-06-17-ner_deid_subentity_pipeline_ro.md |  75 -------
 ...23-06-17-ner_deid_synthetic_pipeline_en.md |  55 -----
 ...023-06-17-ner_deidentify_dl_pipeline_en.md |  34 +--
 .../2023-06-17-ner_diag_proc_pipeline_es.md   |  55 -----
 ...-06-17-ner_diseases_biobert_pipeline_en.md |  34 +--
 ...23-06-17-ner_diseases_large_pipeline_en.md |  35 +--
 .../2023-06-17-ner_diseases_pipeline_en.md    |  34 +--
 ...06-17-ner_drugprot_clinical_pipeline_en.md |  33 +--
 ...2023-06-17-ner_drugs_greedy_pipeline_en.md |  34 +--
 .../2023-06-17-ner_drugs_large_pipeline_en.md |  35 +--
 .../2023-06-17-ner_drugs_pipeline_en.md       |  33 +--
 ...-06-17-ner_eu_clinical_case_pipeline_en.md |  63 ------
 ...-06-17-ner_eu_clinical_case_pipeline_es.md |  63 ------
 ...-06-17-ner_eu_clinical_case_pipeline_eu.md |  63 ------
 ...-06-17-ner_eu_clinical_case_pipeline_fr.md |  63 ------
 ...7-ner_eu_clinical_condition_pipeline_en.md |  31 ---
 ...7-ner_eu_clinical_condition_pipeline_es.md |  63 ------
 ...7-ner_eu_clinical_condition_pipeline_eu.md |  63 ------
 ...7-ner_eu_clinical_condition_pipeline_fr.md |  67 ------
 ...7-ner_eu_clinical_condition_pipeline_it.md |  66 ------
 ...r_events_admission_clinical_pipeline_en.md |  33 +--
 ...23-06-17-ner_events_biobert_pipeline_en.md |  33 +--
 ...3-06-17-ner_events_clinical_pipeline_en.md |  34 +--
 ...06-17-ner_events_healthcare_pipeline_en.md |  33 +--
 ...-06-17-ner_genetic_variants_pipeline_en.md |  33 +--
 .../2023-06-17-ner_healthcare_pipeline_de.md  |  55 -----
 .../2023-06-17-ner_healthcare_pipeline_en.md  |  34 +--
 ...3-06-17-ner_healthcare_slim_pipeline_de.md |  55 -----
 ...uman_phenotype_gene_biobert_pipeline_en.md |  35 +--
 ...man_phenotype_gene_clinical_pipeline_en.md |  33 +--
 ..._human_phenotype_go_biobert_pipeline_en.md |  33 +--
 ...human_phenotype_go_clinical_pipeline_en.md |  34 +--
 .../2023-06-17-ner_jsl_biobert_pipeline_en.md |  33 +--
 ...17-ner_jsl_enriched_biobert_pipeline_en.md |  34 +--
 ...2023-06-17-ner_jsl_enriched_pipeline_en.md |  33 +--
 ...6-17-ner_jsl_greedy_biobert_pipeline_en.md |  34 +--
 .../2023-06-17-ner_jsl_greedy_pipeline_en.md  |  35 +--
 .../2023-06-17-ner_jsl_pipeline_en.md         |  33 +--
 .../2023-06-17-ner_jsl_slim_pipeline_en.md    |  33 +--
 ...6-17-ner_living_species_300_pipeline_es.md |  55 -----
 ...-17-ner_living_species_bert_pipeline_es.md |  54 -----
 ...-17-ner_living_species_bert_pipeline_fr.md |  55 -----
 ...-17-ner_living_species_bert_pipeline_it.md |  55 -----
 ...-17-ner_living_species_bert_pipeline_pt.md |  55 -----
 ...-17-ner_living_species_bert_pipeline_ro.md |  54 -----
 ...-ner_living_species_biobert_pipeline_en.md |  55 -----
 ...23-06-17-ner_living_species_pipeline_ca.md |  55 -----
 ...23-06-17-ner_living_species_pipeline_en.md |  55 -----
 ...23-06-17-ner_living_species_pipeline_es.md |  55 -----
 ...23-06-17-ner_living_species_pipeline_fr.md |  56 +----
 ...23-06-17-ner_living_species_pipeline_gl.md |  55 -----
 ...23-06-17-ner_living_species_pipeline_it.md |  55 -----
 ...23-06-17-ner_living_species_pipeline_pt.md |  55 -----
 ...-ner_living_species_roberta_pipeline_es.md |  55 -----
 ...-ner_living_species_roberta_pipeline_pt.md |  55 -----
 ...7-ner_measurements_clinical_pipeline_en.md |  34 +--
 .../2023-06-17-ner_medication_pipeline_en.md  |  66 +-----
 ...6-17-ner_medmentions_coarse_pipeline_en.md |  35 +--
 ...17-ner_nature_nero_clinical_pipeline_en.md |  54 -----
 ...17-ner_negation_uncertainty_pipeline_es.md |  55 -----
 .../2023-06-17-ner_neoplasms_pipeline_es.md   |  54 -----
 .../2023-06-17-ner_nihss_pipeline_en.md       |  33 +--
 ..._anatomy_general_healthcare_pipeline_en.md |  63 ------
 ...er_oncology_anatomy_general_pipeline_en.md |  53 -----
 ...cology_biomarker_healthcare_pipeline_en.md |  55 -----
 ...6-17-ner_oncology_biomarker_pipeline_en.md |  55 -----
 ...7-ner_oncology_demographics_pipeline_en.md |  55 -----
 ...6-17-ner_oncology_diagnosis_pipeline_en.md |  54 -----
 ...06-17-ner_oncology_posology_pipeline_en.md |  55 -----
 ...ology_response_to_treatment_pipeline_en.md |  55 -----
 ...023-06-17-ner_oncology_test_pipeline_en.md |  55 -----
 ...-06-17-ner_oncology_therapy_pipeline_en.md |  63 ------
 ...2023-06-17-ner_oncology_tnm_pipeline_en.md |  55 -----
 ...pecific_posology_healthcare_pipeline_en.md |  63 ------
 ...ncology_unspecific_posology_pipeline_en.md |  55 -----
 .../2023-06-17-ner_pathogen_pipeline_en.md    |  34 +--
 ...2023-06-17-ner_pharmacology_pipeline_es.md |  55 -----
 ...-06-17-ner_posology_biobert_pipeline_en.md |  33 +--
 ...7-ner_posology_experimental_pipeline_en.md |  41 +---
 ...3-06-17-ner_posology_greedy_pipeline_en.md |  35 +--
 ...-17-ner_posology_healthcare_pipeline_en.md |  34 +--
 ...-ner_posology_large_biobert_pipeline_en.md |  35 +--
 ...23-06-17-ner_posology_large_pipeline_en.md |  34 +--
 .../2023-06-17-ner_posology_pipeline_en.md    |  34 +--
 ...23-06-17-ner_posology_small_pipeline_en.md |  34 +--
 .../2023-06-17-ner_profiling_biobert_en.md    |  63 +-----
 .../2023-06-17-ner_radiology_pipeline_en.md   |  34 +--
 ...-ner_radiology_wip_clinical_pipeline_en.md |  34 +--
 ...17-ner_risk_factors_biobert_pipeline_en.md |  58 +----
 ...2023-06-17-ner_risk_factors_pipeline_en.md |  57 +----
 ...023-06-17-ner_sdoh_mentions_pipeline_en.md |   2 -
 ...-17-ner_supplement_clinical_pipeline_en.md |  54 -----
 ...023-06-17-nerdl_tumour_demo_pipeline_en.md |  54 -----
 ...23-06-17-oncology_biomarker_pipeline_en.md |  70 +-----
 ...6-17-re_bodypart_directions_pipeline_en.md |  60 -----
 ...7-re_bodypart_proceduretest_pipeline_en.md |  60 -----
 ...man_phenotype_gene_clinical_pipeline_en.md |  56 +----
 ...re_temporal_events_clinical_pipeline_en.md |  56 +----
 ...al_events_enriched_clinical_pipeline_en.md |  55 +----
 ...-17-re_test_problem_finding_pipeline_en.md |  61 -----
 ...3-06-17-re_test_result_date_pipeline_en.md |  60 -----
 ...23-06-17-recognize_entities_posology_en.md |  64 +-----
 .../2023-06-17-rxnorm_mesh_mapping_en.md      |  52 +----
 .../2023-06-17-rxnorm_ndc_mapping_en.md       |  62 +-----
 .../2023-06-17-rxnorm_umls_mapping_en.md      |  62 +-----
 .../2023-06-17-snomed_icd10cm_mapping_en.md   |  62 +-----
 .../2023-06-17-snomed_icdo_mapping_en.md      |  61 +----
 .../2023-06-17-snomed_umls_mapping_en.md      |  63 +-----
 ...3-06-17-spellcheck_clinical_pipeline_en.md |  37 +---
 .../2023-06-19-ner_profiling_clinical_en.md   |  34 +--
 ...2023-06-22-clinical_deidentification_ar.md |  92 --------
 .../2023-06-22-cvx_resolver_pipeline_en.md    |  32 +--
 ...2023-06-22-icd10cm_resolver_pipeline_en.md |  32 +--
 .../2023-06-22-icd9_resolver_pipeline_en.md   |  34 +--
 ...t_binary_classifier_biobert_pipeline_en.md |  33 ---
 ...2-rct_binary_classifier_use_pipeline_en.md |  33 ---
 ...ummarizer_biomedical_pubmed_pipeline_en.md |  29 +--
 ...r_clinical_guidelines_large_pipeline_en.md |  85 +------
 ...izer_clinical_jsl_augmented_pipeline_en.md |  45 ----
 ...-22-summarizer_clinical_jsl_pipeline_en.md |  46 +---
 ...-summarizer_clinical_laymen_pipeline_en.md |  74 +------
 ...mmarizer_clinical_questions_pipeline_en.md |  34 +--
 ...6-22-summarizer_generic_jsl_pipeline_en.md |  46 +---
 ...-06-22-summarizer_radiology_pipeline_en.md |  61 +----
 ...s_disease_syndrome_resolver_pipeline_en.md |  28 +--
 ...23-06-23-umls_drug_resolver_pipeline_en.md |  28 +--
 ...mls_major_concepts_resolver_pipeline_en.md |  28 +--
 ...r_oncology_anatomy_granular_pipeline_en.md |  27 ---
 .../2023-06-26-ner_oncology_pipeline_en.md    |  31 +--
 ...23-06-26-oncology_diagnosis_pipeline_en.md |  39 +---
 .../2022-03-01-sbiobertresolve_atc_en_2_4.md  |   1 +
 .../2022-03-01-sbiobertresolve_atc_en_3_0.md  |   1 +
 ...-03-03-clinical_deidentification_en_2_4.md |   1 +
 ...-03-03-clinical_deidentification_en_3_0.md |   4 +-
 ...ssertion_dl_biobert_scope_L10R10_en_3_0.md |   4 +-
 ...ssertion_dl_biobert_scope_L10R10_en_2_4.md |   5 +-
 ...1-explain_clinical_doc_radiology_en_3_0.md |   1 +
 ...-explain_clinical_doc_medication_en_3_0.md |   1 +
 ...tresolve_rxnorm_action_treatment_en_2_4.md |   1 +
 ...esolve_icd10cm_slim_billable_hcc_en_3_0.md |   6 -
 ...tresolve_icd10cm_slim_normalized_en_3_0.md |   3 -
 ...22-ner_biomedical_bc2gm_pipeline_en_3_0.md |   1 +
 ...inical_trials_abstracts_pipeline_en_3_0.md |   1 +
 ...022-07-29-ner_living_species_300_es_3_0.md |   1 +
 .../2022-08-12-ner_clinical_bert_ro_3_0.md    |   1 +
 ...2022-08-15-ner_deid_generic_bert_ro_3_0.md |   1 +
 ...2022-09-14-clinical_deidentification_en.md |   1 +
 ...cal_deidentification_glove_augmented_en.md |   1 +
 .../2022-11-22-ner_clinical_bert_ro.md        |   1 +
 .../2022-11-22-ner_deid_generic_bert_ro.md    |   1 +
 .../2022-11-22-ner_living_species_300_es.md   |   1 +
 .../2023-01-06-redl_clinical_biobert_en.md    |   1 +
 .../Cabir40/2023-02-26-biogpt_pubmed_qa_en.md |   2 +-
 ...7-ner_eu_clinical_condition_pipeline_en.md |   1 +
 ...7-ner_eu_clinical_condition_pipeline_eu.md |   1 +
 .../2023-03-07-ner_jsl_slim_pipeline_en.md    |   3 +-
 ...-03-08-ner_eu_clinical_case_pipeline_en.md |   1 +
 ...-03-08-ner_eu_clinical_case_pipeline_es.md |   1 +
 ...-03-08-ner_eu_clinical_case_pipeline_eu.md |   1 +
 ...-03-08-ner_eu_clinical_case_pipeline_fr.md |   1 +
 ...8-ner_eu_clinical_condition_pipeline_es.md |   1 +
 ...8-ner_eu_clinical_condition_pipeline_fr.md |   1 +
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 ...r_clinical_trials_abstracts_pipeline_es.md |  54 -----
 ...2023-06-16-ner_covid_trials_pipeline_en.md |  55 -----
 ...23-06-16-ner_deid_augmented_pipeline_en.md |  34 +--
 ...2023-06-16-ner_deid_biobert_pipeline_en.md |  35 +--
 ...6-ner_deid_enriched_biobert_pipeline_en.md |  35 +--
 ...023-06-16-ner_deid_enriched_pipeline_en.md |  33 +--
 ...-ner_deid_generic_augmented_pipeline_en.md |  55 +----
 ...06-16-ner_deid_generic_bert_pipeline_ro.md |  73 ------
 ...2023-06-16-ner_deid_generic_pipeline_de.md |  35 +--
 ...2023-06-16-ner_deid_generic_pipeline_ro.md |  75 -------
 .../2023-06-16-ner_deid_large_pipeline_en.md  |  35 +--
 ...023-06-16-ner_deid_sd_large_pipeline_en.md |  35 +--
 .../2023-06-16-ner_deid_sd_pipeline_en.md     |  33 +--
 ...id_subentity_augmented_i2b2_pipeline_en.md |  34 +--
 ...er_deid_subentity_augmented_pipeline_en.md |  33 +--
 ...-16-ner_deid_subentity_bert_pipeline_ro.md |  73 ------
 ...23-06-16-ner_deid_subentity_pipeline_de.md |  34 +--
 ...23-06-16-ner_deid_subentity_pipeline_ro.md |  73 ------
 ...023-06-16-ner_deidentify_dl_pipeline_en.md |  34 +--
 ...-06-16-ner_diseases_biobert_pipeline_en.md |  33 +--
 ...23-06-16-ner_diseases_large_pipeline_en.md |  34 +--
 .../2023-06-16-ner_diseases_pipeline_en.md    |  34 +--
 ...06-16-ner_drugprot_clinical_pipeline_en.md |  34 +--
 ...2023-06-16-ner_drugs_greedy_pipeline_en.md |  34 +--
 .../2023-06-16-ner_drugs_large_pipeline_en.md |  34 +--
 .../2023-06-16-ner_drugs_pipeline_en.md       |  36 +--
 ...-06-16-ner_eu_clinical_case_pipeline_en.md |  62 ------
 ...-06-16-ner_eu_clinical_case_pipeline_es.md |  62 ------
 ...-06-16-ner_eu_clinical_case_pipeline_eu.md |  61 -----
 ...-06-16-ner_eu_clinical_case_pipeline_fr.md |  63 ------
 ...6-ner_eu_clinical_condition_pipeline_en.md |  31 ---
 ...6-ner_eu_clinical_condition_pipeline_es.md |  61 -----
 ...6-ner_eu_clinical_condition_pipeline_eu.md |  61 -----
 ...6-ner_eu_clinical_condition_pipeline_fr.md |  65 ------
 ...6-ner_eu_clinical_condition_pipeline_it.md |  66 ------
 ...r_events_admission_clinical_pipeline_en.md |  34 +--
 ...23-06-16-ner_events_biobert_pipeline_en.md |  34 +--
 ...3-06-16-ner_events_clinical_pipeline_en.md |  34 +--
 ...06-16-ner_events_healthcare_pipeline_en.md |  34 +--
 ...-06-16-ner_genetic_variants_pipeline_en.md |  33 +--
 .../2023-06-16-ner_healthcare_pipeline_en.md  |  33 +--
 ...uman_phenotype_gene_biobert_pipeline_en.md |  33 +--
 ...man_phenotype_gene_clinical_pipeline_en.md |  34 +--
 ..._human_phenotype_go_biobert_pipeline_en.md |  33 +--
 ...human_phenotype_go_clinical_pipeline_en.md |  33 +--
 .../2023-06-16-ner_jsl_biobert_pipeline_en.md |  33 +--
 ...16-ner_jsl_enriched_biobert_pipeline_en.md |  34 +--
 ...2023-06-16-ner_jsl_enriched_pipeline_en.md |  33 +--
 ...6-16-ner_jsl_greedy_biobert_pipeline_en.md |  33 +--
 .../2023-06-16-ner_jsl_greedy_pipeline_en.md  |  35 +--
 .../Cabir40/2023-06-16-ner_jsl_pipeline_en.md |  35 +--
 .../2023-06-16-ner_jsl_slim_pipeline_en.md    |  33 +--
 ...6-16-ner_living_species_300_pipeline_es.md |  53 -----
 ...6-ner_measurements_clinical_pipeline_en.md |  35 +--
 .../2023-06-16-ner_medication_pipeline_en.md  |  66 +-----
 ...6-16-ner_medmentions_coarse_pipeline_en.md |  35 +--
 ...16-ner_negation_uncertainty_pipeline_es.md |  53 -----
 .../2023-06-16-ner_nihss_pipeline_en.md       |  34 +--
 ..._anatomy_general_healthcare_pipeline_en.md |  61 -----
 ...er_oncology_anatomy_general_pipeline_en.md |  53 -----
 ...cology_biomarker_healthcare_pipeline_en.md |  53 -----
 ...6-16-ner_oncology_biomarker_pipeline_en.md |  53 -----
 ...6-ner_oncology_demographics_pipeline_en.md |  54 -----
 ...6-16-ner_oncology_diagnosis_pipeline_en.md |  53 -----
 ...06-16-ner_oncology_posology_pipeline_en.md |  53 -----
 ...ology_response_to_treatment_pipeline_en.md |  53 -----
 ...023-06-16-ner_oncology_test_pipeline_en.md |  53 -----
 ...-06-16-ner_oncology_therapy_pipeline_en.md |  57 -----
 ...2023-06-16-ner_oncology_tnm_pipeline_en.md |  55 -----
 ...pecific_posology_healthcare_pipeline_en.md |  63 ------
 .../2023-06-16-ner_pathogen_pipeline_en.md    |  35 ---
 ...-06-16-ner_posology_biobert_pipeline_en.md |  35 ---
 ...6-ner_posology_experimental_pipeline_en.md |  41 ----
 ...3-06-16-ner_posology_greedy_pipeline_en.md |  36 +--
 ...-16-ner_posology_healthcare_pipeline_en.md |  35 +--
 ...-ner_posology_large_biobert_pipeline_en.md |  36 +--
 ...23-06-16-ner_posology_large_pipeline_en.md |  36 +--
 .../2023-06-16-ner_posology_pipeline_en.md    |  35 ---
 ...23-06-16-ner_posology_small_pipeline_en.md |  34 +--
 .../2023-06-16-ner_profiling_biobert_en.md    |  65 +-----
 .../2023-06-16-ner_radiology_pipeline_en.md   |  35 +--
 ...-ner_radiology_wip_clinical_pipeline_en.md |  35 +--
 ...16-ner_risk_factors_biobert_pipeline_en.md |  58 +----
 ...2023-06-16-ner_risk_factors_pipeline_en.md |  59 +----
 ...023-06-16-ner_sdoh_mentions_pipeline_en.md |   4 -
 ...23-06-16-oncology_biomarker_pipeline_en.md |  71 +-----
 ...2023-06-16-oncology_general_pipeline_en.md |   3 -
 ...2023-06-16-oncology_therapy_pipeline_en.md |  37 +---
 ...6-16-re_bodypart_directions_pipeline_en.md |  62 ------
 ...6-re_bodypart_proceduretest_pipeline_en.md |  62 ------
 ...man_phenotype_gene_clinical_pipeline_en.md |  57 +----
 ...re_temporal_events_clinical_pipeline_en.md |  57 +----
 ...al_events_enriched_clinical_pipeline_en.md |  57 +----
 ...-16-re_test_problem_finding_pipeline_en.md |  61 -----
 ...3-06-16-re_test_result_date_pipeline_en.md |  62 ------
 ...23-06-16-recognize_entities_posology_en.md |  66 +-----
 .../2023-06-16-rxnorm_mesh_mapping_en.md      |  53 +----
 .../2023-06-16-rxnorm_ndc_mapping_en.md       |  61 +----
 .../2023-06-16-rxnorm_umls_mapping_en.md      |  63 +-----
 .../2023-06-16-snomed_icd10cm_mapping_en.md   |  62 +-----
 .../2023-06-16-snomed_icdo_mapping_en.md      |  62 +-----
 .../2023-06-16-snomed_umls_mapping_en.md      |  63 +-----
 ...3-06-16-spellcheck_clinical_pipeline_en.md |  39 +---
 ...classifier_ade_tweet_binary_pipeline_en.md |  54 -----
 ...fier_disease_mentions_tweet_pipeline_es.md |  53 -----
 ...sifier_negation_uncertainty_pipeline_es.md |  53 -----
 ...n_classifier_ner_ade_binary_pipeline_en.md |  53 -----
 ...token_classifier_ner_anatem_pipeline_en.md |  53 -----
 ...n_classifier_ner_bc2gm_gene_pipeline_en.md |  54 -----
 ...fier_ner_bc4chemd_chemicals_pipeline_en.md |  53 -----
 ...sifier_ner_bc5cdr_chemicals_pipeline_en.md |  54 -----
 ...assifier_ner_bc5cdr_disease_pipeline_en.md |  53 -----
 ...r_clinical_trials_abstracts_pipeline_en.md |  54 -----
 ...r_clinical_trials_abstracts_pipeline_es.md |  54 -----
 ...ssifier_ner_jnlpba_cellular_pipeline_en.md |  53 -----
 ...sifier_ner_linnaeus_species_pipeline_en.md |  53 -----
 ...assifier_ner_living_species_pipeline_en.md |  53 -----
 ...assifier_ner_living_species_pipeline_es.md |  53 -----
 ...assifier_ner_living_species_pipeline_it.md |  53 -----
 ...assifier_ner_living_species_pipeline_pt.md |  53 -----
 ...classifier_ner_ncbi_disease_pipeline_en.md |  54 -----
 ...ken_classifier_ner_pathogen_pipeline_en.md |  53 -----
 ...oken_classifier_ner_species_pipeline_en.md |  53 -----
 ...ken_classifier_pharmacology_pipeline_es.md |  54 -----
 ...23-06-17-ner_chemd_clinical_pipeline_en.md |  54 -----
 ...6-17-ner_deid_generic_glove_pipeline_en.md |  53 -----
 ...2023-06-17-ner_deid_generic_pipeline_ar.md |  25 +--
 ...2023-06-17-ner_deid_generic_pipeline_it.md |  54 -----
 ...17-ner_deid_subentity_glove_pipeline_en.md |  53 -----
 ...23-06-17-ner_deid_subentity_pipeline_ar.md |  61 -----
 ...23-06-17-ner_deid_subentity_pipeline_it.md |  53 -----
 ...23-06-17-ner_deid_synthetic_pipeline_en.md |  54 -----
 .../2023-06-17-ner_diag_proc_pipeline_es.md   |  53 -----
 .../2023-06-17-ner_healthcare_pipeline_de.md  |  54 -----
 ...3-06-17-ner_healthcare_slim_pipeline_de.md |  54 -----
 ...-17-ner_living_species_bert_pipeline_es.md |  53 -----
 ...-17-ner_living_species_bert_pipeline_fr.md |  54 -----
 ...-17-ner_living_species_bert_pipeline_it.md |  53 -----
 ...-17-ner_living_species_bert_pipeline_pt.md |  53 -----
 ...-17-ner_living_species_bert_pipeline_ro.md |  54 -----
 ...-ner_living_species_biobert_pipeline_en.md |  53 -----
 ...23-06-17-ner_living_species_pipeline_ca.md |  54 -----
 ...23-06-17-ner_living_species_pipeline_en.md |  54 -----
 ...23-06-17-ner_living_species_pipeline_es.md |  55 -----
 ...23-06-17-ner_living_species_pipeline_fr.md |  53 -----
 ...23-06-17-ner_living_species_pipeline_gl.md |  54 -----
 ...23-06-17-ner_living_species_pipeline_it.md |  54 -----
 ...23-06-17-ner_living_species_pipeline_pt.md |  53 -----
 ...-ner_living_species_roberta_pipeline_es.md |  54 -----
 ...-ner_living_species_roberta_pipeline_pt.md |  53 -----
 ...17-ner_nature_nero_clinical_pipeline_en.md |  53 -----
 .../2023-06-17-ner_neoplasms_pipeline_es.md   |  53 -----
 ...ncology_unspecific_posology_pipeline_en.md |  53 -----
 ...2023-06-17-ner_pharmacology_pipeline_es.md |  53 -----
 ...-17-ner_supplement_clinical_pipeline_en.md |  53 -----
 ...023-06-17-nerdl_tumour_demo_pipeline_en.md |  53 -----
 .../2023-06-19-ner_profiling_clinical_en.md   |  33 +--
 ..._classifier_vop_hcp_consult_pipeline_en.md |   2 -
 ...sifier_vop_drug_side_effect_pipeline_en.md |   2 -
 ..._classifier_vop_self_report_pipeline_en.md |   3 -
 ..._classifier_vop_side_effect_pipeline_en.md |   2 -
 ...lassifier_vop_sound_medical_pipeline_en.md |   2 -
 ...2023-06-22-clinical_deidentification_ar.md |  92 +-------
 .../2023-06-22-cvx_resolver_pipeline_en.md    |  31 +--
 ...2023-06-22-icd10cm_resolver_pipeline_en.md |  32 +--
 .../2023-06-22-icd9_resolver_pipeline_en.md   |  34 +--
 .../2023-06-22-ner_vop_anatomy_pipeline_en.md |   3 -
 ...06-22-ner_vop_clinical_dept_pipeline_en.md |   3 -
 ...t_binary_classifier_biobert_pipeline_en.md |  33 ---
 ...2-rct_binary_classifier_use_pipeline_en.md |  33 ---
 ...ummarizer_biomedical_pubmed_pipeline_en.md |  30 +--
 ...r_clinical_guidelines_large_pipeline_en.md |  86 +------
 ...izer_clinical_jsl_augmented_pipeline_en.md |  45 +---
 ...-22-summarizer_clinical_jsl_pipeline_en.md |  46 +---
 ...mmarizer_clinical_questions_pipeline_en.md |  33 +--
 ...6-22-summarizer_generic_jsl_pipeline_en.md |  45 +---
 ...-06-22-summarizer_radiology_pipeline_en.md |  61 +----
 ...s_disease_syndrome_resolver_pipeline_en.md |  30 +--
 ...23-06-23-umls_drug_resolver_pipeline_en.md |  28 +--
 ...mls_major_concepts_resolver_pipeline_en.md |  27 +--
 ...r_oncology_anatomy_granular_pipeline_en.md |  27 ---
 .../2023-06-26-ner_oncology_pipeline_en.md    |  32 +--
 ...23-06-26-oncology_diagnosis_pipeline_en.md |  38 +---
 ...3-08-17-clinical_notes_qa_large_onnx_en.md |   1 +
 ...ulticlassifierdl_respiratory_disease_en.md |   4 +-
 .../2023-06-11-ner_profiling_biobert_en.md    |  33 +--
 ...classifier_ade_tweet_binary_pipeline_en.md |  28 +--
 ...fier_disease_mentions_tweet_pipeline_es.md |  28 +--
 ...sifier_negation_uncertainty_pipeline_es.md |  27 +--
 ...n_classifier_ner_ade_binary_pipeline_en.md |  28 +--
 ...token_classifier_ner_anatem_pipeline_en.md |  27 +--
 ...n_classifier_ner_bc2gm_gene_pipeline_en.md |  27 ---
 ...fier_ner_bc4chemd_chemicals_pipeline_en.md |  28 +--
 ...sifier_ner_bc5cdr_chemicals_pipeline_en.md |  27 +--
 ...assifier_ner_bc5cdr_disease_pipeline_en.md |  28 +--
 ...r_clinical_trials_abstracts_pipeline_en.md |  28 +--
 ...r_clinical_trials_abstracts_pipeline_es.md |  27 +--
 ...ssifier_ner_jnlpba_cellular_pipeline_en.md |  28 +--
 ...sifier_ner_linnaeus_species_pipeline_en.md |  27 +--
 ...assifier_ner_living_species_pipeline_en.md |  27 +--
 ...assifier_ner_living_species_pipeline_es.md |  29 +--
 ...assifier_ner_living_species_pipeline_it.md |  28 +--
 ...assifier_ner_living_species_pipeline_pt.md |  27 +--
 ...classifier_ner_ncbi_disease_pipeline_en.md |  28 +--
 ...ken_classifier_ner_pathogen_pipeline_en.md |  27 +--
 ...oken_classifier_ner_species_pipeline_en.md |  27 +--
 ...ken_classifier_pharmacology_pipeline_es.md |  27 +--
 ...2023-06-13-explain_clinical_doc_carp_en.md |  35 +--
 .../2023-06-13-explain_clinical_doc_era_en.md |  35 +--
 ...6-13-explain_clinical_doc_medication_en.md |  35 +--
 .../2023-06-13-icd10_icd9_mapping_en.md       |  31 +--
 .../2023-06-13-icd10cm_umls_mapping_en.md     |  31 +--
 .../2023-06-13-icdo_snomed_mapping_en.md      |  32 +--
 .../2023-06-13-mesh_umls_mapping_en.md        |  31 +--
 ...23-06-13-ner_chemd_clinical_pipeline_en.md |  27 +--
 ...023-06-13-ner_clinical_bert_pipeline_ro.md |  27 +--
 .../2023-06-13-ner_clinical_pipeline_ro.md    |  27 +--
 ...r_clinical_trials_abstracts_pipeline_es.md |  28 +--
 ...2023-06-13-ner_covid_trials_pipeline_en.md |  27 +--
 ...06-13-ner_deid_generic_bert_pipeline_ro.md |  38 +---
 ...6-13-ner_deid_generic_glove_pipeline_en.md |  27 +--
 ...2023-06-13-ner_deid_generic_pipeline_it.md |  27 +--
 ...2023-06-13-ner_deid_generic_pipeline_ro.md |  37 +---
 ...-13-ner_deid_subentity_bert_pipeline_ro.md |  37 +---
 ...13-ner_deid_subentity_glove_pipeline_en.md |  27 +--
 ...23-06-13-ner_deid_subentity_pipeline_it.md |  27 +--
 ...23-06-13-ner_deid_subentity_pipeline_ro.md |  37 +---
 ...23-06-13-ner_deid_synthetic_pipeline_en.md |  27 +--
 .../2023-06-13-ner_diag_proc_pipeline_es.md   |  27 +--
 ...-06-13-ner_eu_clinical_case_pipeline_en.md |  31 +--
 ...-06-13-ner_eu_clinical_case_pipeline_es.md |  32 +--
 ...-06-13-ner_eu_clinical_case_pipeline_eu.md |  31 +--
 ...-06-13-ner_eu_clinical_case_pipeline_fr.md |  31 +--
 ...3-ner_eu_clinical_condition_pipeline_es.md |  32 +--
 ...3-ner_eu_clinical_condition_pipeline_eu.md |  31 +--
 ...3-ner_eu_clinical_condition_pipeline_fr.md |  33 +--
 ...3-ner_eu_clinical_condition_pipeline_it.md |  34 +--
 .../2023-06-13-ner_healthcare_pipeline_de.md  |  27 +--
 ...3-06-13-ner_healthcare_slim_pipeline_de.md |  27 ---
 ...6-13-ner_living_species_300_pipeline_es.md |  27 +--
 ...-13-ner_living_species_bert_pipeline_es.md |  27 +--
 ...-13-ner_living_species_bert_pipeline_fr.md |  27 +--
 ...-13-ner_living_species_bert_pipeline_it.md |  27 +--
 ...-13-ner_living_species_bert_pipeline_pt.md |  28 +--
 ...-13-ner_living_species_bert_pipeline_ro.md |  28 +--
 ...-ner_living_species_biobert_pipeline_en.md |  27 +--
 ...23-06-13-ner_living_species_pipeline_ca.md |  29 +--
 ...23-06-13-ner_living_species_pipeline_en.md |  27 +--
 ...23-06-13-ner_living_species_pipeline_es.md |  27 +--
 ...23-06-13-ner_living_species_pipeline_fr.md |  27 +--
 ...23-06-13-ner_living_species_pipeline_gl.md |  27 +--
 ...23-06-13-ner_living_species_pipeline_it.md |  27 +--
 ...23-06-13-ner_living_species_pipeline_pt.md |  27 +--
 ...-ner_living_species_roberta_pipeline_es.md |  27 +--
 ...-ner_living_species_roberta_pipeline_pt.md |  28 +--
 ...13-ner_nature_nero_clinical_pipeline_en.md |  28 +--
 ...13-ner_negation_uncertainty_pipeline_es.md |  29 +--
 .../2023-06-13-ner_neoplasms_pipeline_es.md   |  27 +--
 ..._anatomy_general_healthcare_pipeline_en.md |  31 +--
 ...er_oncology_anatomy_general_pipeline_en.md |  27 ---
 ...cology_biomarker_healthcare_pipeline_en.md |  27 +--
 ...6-13-ner_oncology_biomarker_pipeline_en.md |  27 +--
 ...3-ner_oncology_demographics_pipeline_en.md |  27 +--
 ...6-13-ner_oncology_diagnosis_pipeline_en.md |  28 +--
 ...06-13-ner_oncology_posology_pipeline_en.md |  28 +--
 ...ology_response_to_treatment_pipeline_en.md |  26 +--
 ...023-06-13-ner_oncology_test_pipeline_en.md |  28 +--
 ...-06-13-ner_oncology_therapy_pipeline_en.md |  33 +--
 ...2023-06-13-ner_oncology_tnm_pipeline_en.md |  27 ---
 ...pecific_posology_healthcare_pipeline_en.md |  31 +--
 ...ncology_unspecific_posology_pipeline_en.md |  28 +--
 ...2023-06-13-ner_pharmacology_pipeline_es.md |  28 +--
 .../2023-06-13-ner_profiling_biobert_en.md    |  32 +--
 ...-13-ner_supplement_clinical_pipeline_en.md |  28 +--
 ...023-06-13-nerdl_tumour_demo_pipeline_en.md |  28 +--
 ...23-06-13-oncology_biomarker_pipeline_en.md |  35 +--
 ...2023-06-13-oncology_general_pipeline_en.md |  39 +---
 ...6-13-re_bodypart_directions_pipeline_en.md |  30 ---
 ...3-re_bodypart_proceduretest_pipeline_en.md |  30 ---
 ...man_phenotype_gene_clinical_pipeline_en.md |  29 +--
 ...re_temporal_events_clinical_pipeline_en.md |  28 +--
 ...al_events_enriched_clinical_pipeline_en.md |  29 +--
 ...-13-re_test_problem_finding_pipeline_en.md |  34 ---
 ...3-06-13-re_test_result_date_pipeline_en.md |  31 ---
 .../2023-06-13-rxnorm_ndc_mapping_en.md       |  31 +--
 .../2023-06-13-snomed_icd10cm_mapping_en.md   |  32 +--
 .../2023-06-13-snomed_icdo_mapping_en.md      |  32 +--
 .../2023-06-13-snomed_umls_mapping_en.md      |  33 +--
 .../2023-06-15-ner_profiling_biobert_en.md    |  63 ------
 ...assifier_binary_rct_biobert_pipeline_en.md |  60 -----
 ...classifier_ade_tweet_binary_pipeline_en.md |  54 -----
 ...fier_disease_mentions_tweet_pipeline_es.md |  54 -----
 ...ier_drug_development_trials_pipeline_en.md |  35 ---
 ...sifier_negation_uncertainty_pipeline_es.md |  54 -----
 ...n_classifier_ner_ade_binary_pipeline_en.md |  54 -----
 ...rt_token_classifier_ner_ade_pipeline_en.md |  52 +----
 ...token_classifier_ner_anatem_pipeline_en.md |  55 -----
 ...oken_classifier_ner_anatomy_pipeline_en.md |  53 +----
 ...ken_classifier_ner_bacteria_pipeline_en.md |  34 +--
 ...n_classifier_ner_bc2gm_gene_pipeline_en.md |  55 -----
 ...fier_ner_bc4chemd_chemicals_pipeline_en.md |  55 -----
 ...sifier_ner_bc5cdr_chemicals_pipeline_en.md |  55 -----
 ...assifier_ner_bc5cdr_disease_pipeline_en.md |  55 -----
 ...token_classifier_ner_bionlp_pipeline_en.md |  35 ---
 ...ken_classifier_ner_cellular_pipeline_en.md |  34 +--
 ...en_classifier_ner_chemicals_pipeline_en.md |  35 +--
 ...ken_classifier_ner_chemprot_pipeline_en.md |  33 +--
 ...ken_classifier_ner_clinical_pipeline_en.md |  34 +--
 ...r_clinical_trials_abstracts_pipeline_en.md |  55 -----
 ...r_clinical_trials_abstracts_pipeline_es.md |  55 -----
 ...t_token_classifier_ner_deid_pipeline_en.md |  33 +--
 ..._token_classifier_ner_drugs_pipeline_en.md |  33 +--
 ...ssifier_ner_jnlpba_cellular_pipeline_en.md |  55 -----
 ...rt_token_classifier_ner_jsl_pipeline_en.md |  68 +-----
 ...ken_classifier_ner_jsl_slim_pipeline_en.md |  33 +--
 ...sifier_ner_linnaeus_species_pipeline_en.md |  55 -----
 ...assifier_ner_living_species_pipeline_en.md |  55 -----
 ...assifier_ner_living_species_pipeline_es.md |  55 -----
 ...assifier_ner_living_species_pipeline_it.md |  54 -----
 ...assifier_ner_living_species_pipeline_pt.md |  55 -----
 ...classifier_ner_ncbi_disease_pipeline_en.md |  55 -----
 ...ken_classifier_ner_pathogen_pipeline_en.md |  55 -----
 ...oken_classifier_ner_species_pipeline_en.md |  55 -----
 ...ken_classifier_pharmacology_pipeline_es.md |  55 -----
 ...-clinical_deidentification_augmented_es.md | 181 +--------------
 ...2023-06-16-clinical_deidentification_de.md | 134 +----------
 ...2023-06-16-clinical_deidentification_en.md |  50 +----
 ...2023-06-16-clinical_deidentification_es.md | 186 +---------------
 ...2023-06-16-clinical_deidentification_fr.md | 183 +--------------
 ...cal_deidentification_glove_augmented_en.md |  29 +--
 ...6-16-clinical_deidentification_glove_en.md |  91 +-------
 ...2023-06-16-clinical_deidentification_it.md | 177 +--------------
 ...2023-06-16-clinical_deidentification_pt.md | 209 +-----------------
 ...2023-06-16-clinical_deidentification_ro.md |  90 --------
 ...06-16-clinical_deidentification_slim_en.md | 100 +--------
 ...-06-16-clinical_deidentification_wip_en.md |  50 +----
 .../2023-06-16-explain_clinical_doc_ade_en.md |  36 +--
 ...2023-06-16-explain_clinical_doc_carp_en.md |  69 +-----
 .../2023-06-16-explain_clinical_doc_era_en.md |  68 +-----
 ...6-16-explain_clinical_doc_medication_en.md |  70 +-----
 ...06-16-explain_clinical_doc_radiology_en.md |  35 +--
 .../2023-06-16-icd10_icd9_mapping_en.md       |  62 +-----
 .../2023-06-16-icd10cm_snomed_mapping_en.md   |  62 +-----
 .../2023-06-16-icd10cm_umls_mapping_en.md     |  61 +----
 .../2023-06-16-icdo_snomed_mapping_en.md      |  61 +----
 ...-06-16-jsl_ner_wip_clinical_pipeline_en.md |  33 +--
 ...-jsl_ner_wip_greedy_biobert_pipeline_en.md |  33 +--
 ...jsl_ner_wip_greedy_clinical_pipeline_en.md |  33 +--
 ...l_ner_wip_modifier_clinical_pipeline_en.md |  33 +--
 ...l_rd_ner_wip_greedy_biobert_pipeline_en.md |  33 +--
 ..._rd_ner_wip_greedy_clinical_pipeline_en.md |  33 +--
 .../2023-06-16-mesh_umls_mapping_en.md        |  62 +-----
 ...6-ner_abbreviation_clinical_pipeline_en.md |  33 +--
 .../2023-06-16-ner_ade_biobert_pipeline_en.md |  33 +--
 ...2023-06-16-ner_ade_clinical_pipeline_en.md |  33 +--
 ...-06-16-ner_ade_clinicalbert_pipeline_en.md |  34 +--
 ...23-06-16-ner_ade_healthcare_pipeline_en.md |  33 +--
 ...3-06-16-ner_anatomy_biobert_pipeline_en.md |  51 +----
 ...-ner_anatomy_coarse_biobert_pipeline_en.md |  33 +--
 ...23-06-16-ner_anatomy_coarse_pipeline_en.md |  33 +--
 .../2023-06-16-ner_anatomy_pipeline_en.md     |  51 +----
 ...06-16-ner_bacterial_species_pipeline_en.md |  33 +--
 .../2023-06-16-ner_biomarker_pipeline_en.md   |  33 +--
 ...-06-16-ner_biomedical_bc2gm_pipeline_en.md |  34 +--
 ...23-06-16-ner_bionlp_biobert_pipeline_en.md |  34 +--
 .../2023-06-16-ner_bionlp_pipeline_en.md      |  33 +--
 ...3-06-16-ner_cancer_genetics_pipeline_en.md |  33 +--
 ...-06-16-ner_cellular_biobert_pipeline_en.md |  33 +--
 .../2023-06-16-ner_cellular_pipeline_en.md    |  33 +--
 ...23-06-16-ner_chemd_clinical_pipeline_en.md |  55 -----
 .../2023-06-16-ner_chemicals_pipeline_en.md   |  34 +--
 ...-06-16-ner_chemprot_biobert_pipeline_en.md |  33 +--
 ...06-16-ner_chemprot_clinical_pipeline_en.md |  33 +--
 .../2023-06-16-ner_chexpert_pipeline_en.md    |  33 +--
 ...023-06-16-ner_clinical_bert_pipeline_ro.md |  55 -----
 ...-06-16-ner_clinical_biobert_pipeline_en.md |  34 +--
 ...23-06-16-ner_clinical_large_pipeline_en.md |  33 +--
 .../2023-06-16-ner_clinical_pipeline_en.md    |  34 +--
 .../2023-06-16-ner_clinical_pipeline_ro.md    |  55 -----
 ...r_clinical_trials_abstracts_pipeline_en.md |  33 +--
 ...r_clinical_trials_abstracts_pipeline_es.md |  55 -----
 ...2023-06-16-ner_covid_trials_pipeline_en.md |  55 -----
 ...23-06-16-ner_deid_augmented_pipeline_en.md |  33 +--
 ...2023-06-16-ner_deid_biobert_pipeline_en.md |  33 +--
 ...6-ner_deid_enriched_biobert_pipeline_en.md |  34 +--
 ...023-06-16-ner_deid_enriched_pipeline_en.md |  33 +--
 ...-ner_deid_generic_augmented_pipeline_en.md |  57 +----
 ...06-16-ner_deid_generic_bert_pipeline_ro.md |  76 -------
 ...6-16-ner_deid_generic_glove_pipeline_en.md |  55 -----
 ...2023-06-16-ner_deid_generic_pipeline_ar.md |  23 --
 ...2023-06-16-ner_deid_generic_pipeline_de.md |  34 +--
 ...2023-06-16-ner_deid_generic_pipeline_it.md |  54 -----
 ...2023-06-16-ner_deid_generic_pipeline_ro.md |  76 -------
 .../2023-06-16-ner_deid_large_pipeline_en.md  |  33 +--
 ...023-06-16-ner_deid_sd_large_pipeline_en.md |  34 +--
 .../2023-06-16-ner_deid_sd_pipeline_en.md     |  33 +--
 ...id_subentity_augmented_i2b2_pipeline_en.md |  34 +--
 ...er_deid_subentity_augmented_pipeline_en.md |  33 +--
 ...-16-ner_deid_subentity_bert_pipeline_ro.md |  75 -------
 ...16-ner_deid_subentity_glove_pipeline_en.md |  55 -----
 ...23-06-16-ner_deid_subentity_pipeline_ar.md |  64 ------
 ...23-06-16-ner_deid_subentity_pipeline_de.md |  35 +--
 ...23-06-16-ner_deid_subentity_pipeline_it.md |  55 -----
 ...23-06-16-ner_deid_subentity_pipeline_ro.md |  75 -------
 ...23-06-16-ner_deid_synthetic_pipeline_en.md |  55 -----
 ...023-06-16-ner_deidentify_dl_pipeline_en.md |  35 +--
 .../2023-06-16-ner_diag_proc_pipeline_es.md   |  55 -----
 ...-06-16-ner_diseases_biobert_pipeline_en.md |  33 +--
 ...23-06-16-ner_diseases_large_pipeline_en.md |  35 +--
 .../2023-06-16-ner_diseases_pipeline_en.md    |  33 +--
 ...06-16-ner_drugprot_clinical_pipeline_en.md |  34 +--
 ...2023-06-16-ner_drugs_greedy_pipeline_en.md |  33 +--
 .../2023-06-16-ner_drugs_large_pipeline_en.md |  33 +--
 .../2023-06-16-ner_drugs_pipeline_en.md       |  33 +--
 ...-06-16-ner_eu_clinical_case_pipeline_en.md |  63 ------
 ...-06-16-ner_eu_clinical_case_pipeline_es.md |  63 ------
 ...-06-16-ner_eu_clinical_case_pipeline_eu.md |  63 ------
 ...-06-16-ner_eu_clinical_case_pipeline_fr.md |  63 ------
 ...6-ner_eu_clinical_condition_pipeline_en.md |  31 +--
 ...6-ner_eu_clinical_condition_pipeline_es.md |  63 ------
 ...6-ner_eu_clinical_condition_pipeline_eu.md |  63 ------
 ...6-ner_eu_clinical_condition_pipeline_fr.md |  67 ------
 ...6-ner_eu_clinical_condition_pipeline_it.md |  67 ------
 ...r_events_admission_clinical_pipeline_en.md |  34 +--
 ...23-06-16-ner_events_biobert_pipeline_en.md |  35 +--
 ...3-06-16-ner_events_clinical_pipeline_en.md |  33 +--
 ...06-16-ner_events_healthcare_pipeline_en.md |  34 +--
 ...-06-16-ner_genetic_variants_pipeline_en.md |  34 +--
 .../2023-06-16-ner_healthcare_pipeline_de.md  |  56 -----
 .../2023-06-16-ner_healthcare_pipeline_en.md  |  33 +--
 ...3-06-16-ner_healthcare_slim_pipeline_de.md |  55 -----
 ...uman_phenotype_gene_biobert_pipeline_en.md |  34 +--
 ...man_phenotype_gene_clinical_pipeline_en.md |  34 +--
 ..._human_phenotype_go_biobert_pipeline_en.md |  34 +--
 ...human_phenotype_go_clinical_pipeline_en.md |  33 +--
 .../2023-06-16-ner_jsl_biobert_pipeline_en.md |  33 +--
 ...16-ner_jsl_enriched_biobert_pipeline_en.md |  34 +--
 ...2023-06-16-ner_jsl_enriched_pipeline_en.md |  34 +--
 ...6-16-ner_jsl_greedy_biobert_pipeline_en.md |  33 +--
 .../2023-06-16-ner_jsl_greedy_pipeline_en.md  |  33 +--
 .../2023-06-16-ner_jsl_pipeline_en.md         |  33 +--
 .../2023-06-16-ner_jsl_slim_pipeline_en.md    |  33 +--
 ...6-16-ner_living_species_300_pipeline_es.md |  55 -----
 ...-16-ner_living_species_bert_pipeline_es.md |  55 -----
 ...-16-ner_living_species_bert_pipeline_fr.md |  55 -----
 ...-16-ner_living_species_bert_pipeline_it.md |  55 -----
 ...-16-ner_living_species_bert_pipeline_pt.md |  55 -----
 ...-16-ner_living_species_bert_pipeline_ro.md |  55 -----
 ...-ner_living_species_biobert_pipeline_en.md |  55 -----
 ...23-06-16-ner_living_species_pipeline_ca.md |  55 -----
 ...23-06-16-ner_living_species_pipeline_en.md |  55 -----
 ...23-06-16-ner_living_species_pipeline_es.md |  55 -----
 ...23-06-16-ner_living_species_pipeline_fr.md |  55 -----
 ...23-06-16-ner_living_species_pipeline_gl.md |  55 -----
 ...23-06-16-ner_living_species_pipeline_it.md |  55 -----
 ...23-06-16-ner_living_species_pipeline_pt.md |  55 -----
 ...-ner_living_species_roberta_pipeline_es.md |  55 -----
 ...-ner_living_species_roberta_pipeline_pt.md |  55 -----
 ...6-ner_measurements_clinical_pipeline_en.md |  34 +--
 .../2023-06-16-ner_medication_pipeline_en.md  |  67 +-----
 ...6-16-ner_medmentions_coarse_pipeline_en.md |  34 +--
 ...16-ner_nature_nero_clinical_pipeline_en.md |  55 -----
 ...16-ner_negation_uncertainty_pipeline_es.md |  55 -----
 .../2023-06-16-ner_neoplasms_pipeline_es.md   |  56 -----
 .../2023-06-16-ner_nihss_pipeline_en.md       |  34 +--
 ..._anatomy_general_healthcare_pipeline_en.md |  63 ------
 ...er_oncology_anatomy_general_pipeline_en.md |  55 -----
 ...cology_biomarker_healthcare_pipeline_en.md |  55 -----
 ...6-16-ner_oncology_biomarker_pipeline_en.md |  55 -----
 ...6-ner_oncology_demographics_pipeline_en.md |  55 -----
 ...6-16-ner_oncology_diagnosis_pipeline_en.md |  54 -----
 ...06-16-ner_oncology_posology_pipeline_en.md |  55 -----
 ...ology_response_to_treatment_pipeline_en.md |  55 -----
 ...023-06-16-ner_oncology_test_pipeline_en.md |  55 -----
 ...-06-16-ner_oncology_therapy_pipeline_en.md |  63 ------
 ...2023-06-16-ner_oncology_tnm_pipeline_en.md |  55 -----
 ...pecific_posology_healthcare_pipeline_en.md |  63 ------
 ...ncology_unspecific_posology_pipeline_en.md |  55 -----
 .../2023-06-16-ner_pathogen_pipeline_en.md    |  34 +--
 ...2023-06-16-ner_pharmacology_pipeline_es.md |  55 -----
 ...-06-16-ner_posology_biobert_pipeline_en.md |  33 +--
 ...6-ner_posology_experimental_pipeline_en.md |  39 +---
 ...3-06-16-ner_posology_greedy_pipeline_en.md |  35 +--
 ...-16-ner_posology_healthcare_pipeline_en.md |  33 +--
 ...-ner_posology_large_biobert_pipeline_en.md |  33 +--
 ...23-06-16-ner_posology_large_pipeline_en.md |  33 +--
 .../2023-06-16-ner_posology_pipeline_en.md    |  34 +--
 ...23-06-16-ner_posology_small_pipeline_en.md |  33 +--
 .../2023-06-16-ner_profiling_biobert_en.md    |  63 +-----
 .../2023-06-16-ner_radiology_pipeline_en.md   |  33 +--
 ...-ner_radiology_wip_clinical_pipeline_en.md |  33 +--
 ...16-ner_risk_factors_biobert_pipeline_en.md |  57 +----
 ...2023-06-16-ner_risk_factors_pipeline_en.md |  57 +----
 ...-16-ner_supplement_clinical_pipeline_en.md |  55 -----
 ...023-06-16-nerdl_tumour_demo_pipeline_en.md |  55 -----
 ...23-06-16-oncology_biomarker_pipeline_en.md |  69 +-----
 ...2023-06-16-oncology_therapy_pipeline_en.md |  35 +--
 ...6-16-re_bodypart_directions_pipeline_en.md |  60 -----
 ...6-re_bodypart_proceduretest_pipeline_en.md |  60 -----
 ...man_phenotype_gene_clinical_pipeline_en.md |  55 +----
 ...re_temporal_events_clinical_pipeline_en.md |  56 +----
 ...al_events_enriched_clinical_pipeline_en.md |  55 +----
 ...-16-re_test_problem_finding_pipeline_en.md |  60 -----
 ...3-06-16-re_test_result_date_pipeline_en.md |  61 -----
 ...23-06-16-recognize_entities_posology_en.md |  64 +-----
 .../2023-06-16-rxnorm_mesh_mapping_en.md      |  51 +----
 .../2023-06-16-rxnorm_ndc_mapping_en.md       |  61 +----
 .../2023-06-16-rxnorm_umls_mapping_en.md      |  61 +----
 .../2023-06-16-snomed_icd10cm_mapping_en.md   |  61 +----
 .../2023-06-16-snomed_icdo_mapping_en.md      |  61 +----
 .../2023-06-16-snomed_umls_mapping_en.md      |  61 +----
 ...3-06-16-spellcheck_clinical_pipeline_en.md |  38 +---
 .../2023-06-19-ner_profiling_clinical_en.md   |  32 +--
 ..._classifier_vop_hcp_consult_pipeline_en.md |  24 --
 ...lassifier_vop_sound_medical_pipeline_en.md |  19 +-
 ...2023-06-22-clinical_deidentification_ar.md |  92 +-------
 .../2023-06-22-cvx_resolver_pipeline_en.md    |  33 +--
 ...2023-06-22-icd10cm_resolver_pipeline_en.md |  33 +--
 .../2023-06-22-icd9_resolver_pipeline_en.md   |  35 +--
 ...t_binary_classifier_biobert_pipeline_en.md |  33 ---
 ...2-rct_binary_classifier_use_pipeline_en.md |  32 ---
 ...ummarizer_biomedical_pubmed_pipeline_en.md |  29 +--
 ...r_clinical_guidelines_large_pipeline_en.md |  86 +------
 ...izer_clinical_jsl_augmented_pipeline_en.md |  45 +---
 ...-22-summarizer_clinical_jsl_pipeline_en.md |  45 +---
 ...-summarizer_clinical_laymen_pipeline_en.md |  73 +-----
 ...mmarizer_clinical_questions_pipeline_en.md |  34 +--
 ...6-22-summarizer_generic_jsl_pipeline_en.md |  45 +---
 ...-06-22-summarizer_radiology_pipeline_en.md |  61 -----
 ...s_disease_syndrome_resolver_pipeline_en.md |  28 +--
 ...23-06-23-umls_drug_resolver_pipeline_en.md |  27 +--
 ...mls_major_concepts_resolver_pipeline_en.md |  27 +--
 ...r_oncology_anatomy_granular_pipeline_en.md |  28 +--
 .../2023-06-26-ner_oncology_pipeline_en.md    |  31 +--
 ...23-06-26-oncology_diagnosis_pipeline_en.md |  38 +---
 ...-01-04-bert_token_classifier_ner_ade_en.md |  39 ++--
 1162 files changed, 887 insertions(+), 42074 deletions(-)

diff --git a/docs/_posts/C-K-Loan/2021-03-29-pos_clinical_en.md b/docs/_posts/C-K-Loan/2021-03-29-pos_clinical_en.md
index 37a376a639..4e57c5eaac 100644
--- a/docs/_posts/C-K-Loan/2021-03-29-pos_clinical_en.md
+++ b/docs/_posts/C-K-Loan/2021-03-29-pos_clinical_en.md
@@ -53,6 +53,7 @@ A [Part of Speech](https://en.wikipedia.org/wiki/Part_of_speech) classifier pred
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler =  new DocumentAssembler().setInputCol("text").setOutputCol("document")
 tokenizer          =  new Tokenizer().setInputCols("document").setOutputCol("token")
@@ -86,7 +87,6 @@ nlu.load('pos.clinical').predict("POS assigns each token in a sentence a grammat
 +------------------------------------------+
 |[NN, NNS, PND, NN, II, DD, NN, DD, JJ, NN]|
 +------------------------------------------+
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2021-03-29-recognize_entities_posology_en.md b/docs/_posts/C-K-Loan/2021-03-29-recognize_entities_posology_en.md
index 4b3df1e86c..712921b60c 100644
--- a/docs/_posts/C-K-Loan/2021-03-29-recognize_entities_posology_en.md
+++ b/docs/_posts/C-K-Loan/2021-03-29-recognize_entities_posology_en.md
@@ -78,8 +78,6 @@ result_df
 +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |[[named_entity, 0, 2, O, [word -> The, confidence -> 1.0], []], [named_entity, 4, 10, O, [word -> patient, confidence -> 0.9993], []], [named_entity, 12, 14, O, [word -> was, confidence -> 1.0], []], [named_entity, 16, 25, O, [word -> perscriped, confidence -> 0.9985], []], [named_entity, 27, 30, B-Strength, [word -> 50MG, confidence -> 0.9966], []], [named_entity, 32, 40, B-Drug, [word -> penicilin, confidence -> 0.9934], []], [named_entity, 42, 44, O, [word -> for, confidence -> 0.9999], []], [named_entity, 46, 47, O, [word -> is, confidence -> 0.9468], []], [named_entity, 49, 56, O, [word -> headache, confidence -> 0.9805], []]]|
 +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
index 6f53435c4b..d68fbd0ba6 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
@@ -59,45 +59,16 @@ nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abs
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |True|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md
index 236abcee54..42c0b2b5d4 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                        |   begin |   end | ner_label    |   confidence |
 |---:|:---------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 21-day-old                       |      17 |    26 | Age          |     0.999456 |
@@ -123,9 +95,6 @@ Results
 | 22 | fussy                            |     574 |   578 | Symptom      |     0.997592 |
 | 23 | over the past 2 days             |     580 |   599 | Date_Time    |     0.994786 |
 | 24 | albuterol                        |     642 |   650 | Drug         |     0.999735 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_augmented_es.md b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_augmented_es.md
index 560b39ffc8..2e3d0e8998 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_augmented_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_augmented_es.md
@@ -36,6 +36,7 @@ The PHI information will be masked and obfuscated in the resulting text. The pip
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from johnsnowlabs import *
 
@@ -121,97 +122,11 @@ Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servic
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
 
-deid_pipeline = PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical_augmented").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Datos <SEX>.
@@ -383,9 +298,6 @@ Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura trata
 F. 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra.
 Francisco José Roca Bermúdez Hospital 12 de Octubre Servicio de Endocrinología y Nutrición Calle Ramón y Cajal s/n 03129 Zaragoza - Alicante (Portugal) Correo electrónico: richard@company.it
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_de.md b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_de.md
index 81dd530612..9aed91291c 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_de.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_de.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from **German** medical
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -95,73 +96,11 @@ Adresse : St.Johann-Straße 13 19300
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "de", "clinical/models")
-
-sample = """Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-"""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification","de","clinical/models")
-
-val sample = "Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.clinical").predict("""Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Zusammenfassung : <PATIENT> wird am Morgen des <DATE> ins <HOSPITAL> eingeliefert.
@@ -209,9 +148,6 @@ Kontonummer: 192837465738
 SSN : 1310011981M454
 Lizenznummer: XX123456
 Adresse : Klingelhöferring 31206
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_es.md b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_es.md
index bb9210671e..362f4184eb 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_es.md
@@ -34,6 +34,7 @@ This pipeline is trained with sciwiki_300d embeddings and can be used to deident
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from johnsnowlabs import *
 
@@ -121,99 +122,10 @@ Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servic
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Datos del paciente.
@@ -385,10 +297,6 @@ Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura trata
 El paciente 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra.
 Reinaldo Manjón Malo Barcelona Servicio de Endocrinología y Nutrición Valencia km 12,500 28905 Bilbao - Madrid (Barcelona) Correo electrónico: quintanasalome@example.net
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_fr.md b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_fr.md
index 5b9ca88857..62756764e4 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_fr.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_fr.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts in Fr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -120,98 +121,11 @@ COURRIEL : mariebreton@chb.fr
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = """COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = "COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"
-
-val result = deid_pipeline.annotate(sample)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("fr.deid_obfuscated").predict("""COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 COMPTE-RENDU D'HOSPITALISATION
@@ -310,9 +224,6 @@ Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des tr
 PSA de 1,16 ng/ml.
 ADDRESSÉ À : Dr Tristan-Gilbert Poulain - CENTRE HOSPITALIER D'ORTHEZ Service D'Endocrinologie et de Nutrition - 6, avenue Pages, 37443 Sainte Antoine
 COURRIEL : massecatherine@bouygtel.fr
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_glove_en.md b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_glove_en.md
index 3d6685d051..120b8aa723 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_glove_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_glove_en.md
@@ -34,6 +34,7 @@ This pipeline is trained with lightweight `glove_100d` embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -74,52 +75,10 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_glove", "en", "clinical/models")
-
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_glove","en","clinical/models")
-
-val result = deid_pipeline.annotate("Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. Dr. John Green, ID: 1231511863, IP 203.120.223.13. He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.glove_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -151,9 +110,6 @@ Dr. Dr Worley Colonel, ID: ZJ:9570208, IP 005.005.005.005.
 He is a 67 male was admitted to the ST. LUKE'S HOSPITAL AT THE VINTAGE for cystectomy on 06-02-1981.
 Patient's VIN : 3CCCC22DDDD333888, SSN SSN-618-77-1042, Driver's license W693817528998.
 Phone 0496 46 46 70, 3100 weston rd, Shattuck, E-MAIL: Freddi@hotmail.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_it.md b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_it.md
index 400b48c883..a04ca2fbdc 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_it.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_it.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts in It
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -117,95 +118,11 @@ EMAIL: bferrabosco@poste.it""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = """RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"""
 
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = "RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("it.deid.clinical").predict("""RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 RAPPORTO DI RICOVERO
@@ -301,9 +218,6 @@ PSA di 1,16 ng/ml.
 INDIRIZZATO A: Dott.
 Antonio Rusticucci - ASL 7 DI CARBONIA AZIENDA U.S.L. N. 7, Dipartimento di Endocrinologia e Nutrizione - Via Giorgio 0 Appartamento 26, 03461 Sesto Raimondo
 EMAIL: murat.g@jsl.com
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_pt.md b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_pt.md
index 45df52f47c..513474652e 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_pt.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_pt.md
@@ -34,6 +34,7 @@ This pipeline is trained with `w2v_cc_300d` portuguese embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,111 +134,11 @@ Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@ho
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = """Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = "Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("pt.deid.clinical").predict("""Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Dados do <SEX>.
@@ -353,9 +254,6 @@ O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta r
 A tomografia computorizada abdominal é normal.
 A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
 Referido por: Carlos Melo - Avenida Dos Aliados, 56, 22 Espanha E-mail: maria.prado@jacob.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_ro.md b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_ro.md
index c137ee9b00..9802f44da9 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_ro.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_ro.md
@@ -75,52 +75,11 @@ Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
 
-result = deid_pipeline.annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-val sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("ro.deid.clinical").predict("""Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Medic : Dr. <DOCTOR>, C.N.P : <IDNUM>, Data setului de analize: <DATE>
@@ -152,9 +111,6 @@ Varsta : 91, Nume si Prenume : Dragomir Emilia
 Tel: 0248 551 376, E-mail : tudorsmaranda@kappa.ro,
 Licență : T003485962M, Înmatriculare : AR-65-UPQ, Cont : KHHO5029180812813651, 
 Centrul Medical de Evaluare si Recuperare pentru Copii si Tineri Cristian Serban Buzias Aleea Voinea Curcani, 328479
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_slim_en.md b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_slim_en.md
index 68d1814a45..d4266ab28f 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_slim_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-clinical_deidentification_slim_en.md
@@ -34,6 +34,7 @@ This pipeline is trained with lightweight `glove_100d` embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,49 +79,6 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_slim", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_slim","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_slim").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
@@ -159,9 +117,6 @@ Dr. Dr Rosalba Hill, ID: JY:3489547, IP 005.005.005.005.
 He is a 79 male was admitted to the JOHN MUIR MEDICAL CENTER-CONCORD CAMPUS for cystectomy on 01-25-1997.
 Patient's VIN : 3CCCC22DDDD333888, SSN SSN-289-37-4495, Driver's license S99983662.
 Phone 04.32.52.27.90, North Adrienne, Colorado Springs, E-MAIL: Rawland@google.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-icd10_icd9_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-icd10_icd9_mapping_en.md
index fc4a420ba3..5ab6a5d2f7 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-icd10_icd9_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-icd10_icd9_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10_icd9_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,12 @@ nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-
 |    | icd10_code          | icd9_code          |
 |---:|:--------------------|:-------------------|
 |  0 | Z833 | A0100 | A000 | V180 | 0020 | 0010 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-icd10cm_snomed_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-icd10cm_snomed_mapping_en.md
index 5e77cd752b..5ea00a6cb8 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-icd10cm_snomed_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-icd10cm_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10cm_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,14 @@ nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | icd10cm_code          | snomed_code                              |
 |---:|:----------------------|:-----------------------------------------|
 |  0 | R079 | N4289 | M62830 | 161972006 | 22035000 | 16410651000119105 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-icd10cm_umls_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-icd10cm_umls_mapping_en.md
index 92f89f8786..14d43f13f0 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-icd10cm_umls_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-icd10cm_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps ICD10CM codes to UMLS codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,43 +59,13 @@ nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 {'icd10cm': ['M89.50', 'R82.2', 'R09.01'],
 'umls': ['C4721411', 'C0159076', 'C0004044']}
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-icdo_snomed_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-icdo_snomed_mapping_en.md
index bb6c52763e..6ed4955d3c 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-icdo_snomed_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-icdo_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icdo_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | icdo_code                | snomed_code                    |
 |---:|:-------------------------|:-------------------------------|
 |  0 | 8120/1 | 8170/3 | 8380/3 | 45083001 | 25370001 | 30289006 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-mesh_umls_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-mesh_umls_mapping_en.md
index 2426d472a0..23efa1a1fa 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-mesh_umls_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-mesh_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `mesh_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | mesh_code                   | umls_code                      |
 |---:|:----------------------------|:-------------------------------|
 |  0 | C028491 | D019326 | C579867 | C0043904 | C0045010 | C3696376 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-ner_deid_generic_augmented_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-ner_deid_generic_augmented_pipeline_en.md
index 640b105429..7282fe7e77 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-ner_deid_generic_augmented_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-ner_deid_generic_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_augmented](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -56,34 +57,11 @@ nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-```scala
-val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-------------------------------------------------+---------+
 |chunk                                            |ner_label|
 +-------------------------------------------------+---------+
@@ -99,9 +77,6 @@ Results
 |Cocke County Baptist Hospital. 0295 Keats Street.|LOCATION |
 |(302) 786-5227                                   |CONTACT  |
 +-------------------------------------------------+---------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-ner_deid_subentity_pipeline_ar.md b/docs/_posts/C-K-Loan/2023-06-13-ner_deid_subentity_pipeline_ar.md
index 23df577607..1ce18292a3 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-ner_deid_subentity_pipeline_ar.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-ner_deid_subentity_pipeline_ar.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -57,38 +58,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-text= '''
-ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +---------------+--------+
 |chunks         |entities|
 +---------------+--------+
@@ -104,10 +78,6 @@ Results
 |ليلى           |PATIENT |
 |35             |AGE     |
 +---------------+--------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-ner_medication_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-ner_medication_pipeline_en.md
index eaa4053e64..368cbc94c3 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-ner_medication_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-ner_medication_pipeline_en.md
@@ -34,6 +34,7 @@ A pretrained pipeline to detect medication entities. It was built on the top of
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -50,42 +51,11 @@ val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline",
 
 val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
 ```
-
-{:.nlu-block}
-```python
-| ner_chunk          | entity   |
-|:-------------------|:---------|
-| metformin 1000 MG  | DRUG     |
-| glipizide 2.5 MG   | DRUG     |
-| Fragmin 5000 units | DRUG     |
-| Xenaderm           | DRUG     |
-| OxyContin 30 mg    | DRUG     |
-```
 </div>
 
-{:.model-param}
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
+## Results
 
-ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."""
-
-result = ner_medication_pipeline.fullAnnotate([text])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
-```
-
-{:.nlu-block}
-```python
+```bash
 | ner_chunk          | entity   |
 |:-------------------|:---------|
 | metformin 1000 MG  | DRUG     |
@@ -94,7 +64,6 @@ val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed me
 | Xenaderm           | DRUG     |
 | OxyContin 30 mg    | DRUG     |
 ```
-</div>
 
 {:.model-param}
 ## Model Information
diff --git a/docs/_posts/C-K-Loan/2023-06-13-ner_profiling_biobert_en.md b/docs/_posts/C-K-Loan/2023-06-13-ner_profiling_biobert_en.md
index 13a14d9388..868b62bcc1 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-ner_profiling_biobert_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-ner_profiling_biobert_en.md
@@ -38,6 +38,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -65,40 +66,11 @@ import nlu
 nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
 
 ```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 ******************** ner_diseases_biobert Model Results ******************** 
 
 [('gestational diabetes mellitus', 'Disease'), ('type two diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('HTG-induced pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('poor appetite', 'Disease'), ('vomiting', 'Disease')]
@@ -122,10 +94,6 @@ Results
 ******************** ner_risk_factors_biobert Model Results ******************** 
 
 [('diabetes mellitus', 'DIABETES'), ('subsequent type two diabetes mellitus', 'DIABETES'), ('obesity', 'OBESE')]
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-re_bodypart_directions_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-re_bodypart_directions_pipeline_en.md
index 1728b224b0..b80dd71881 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-re_bodypart_directions_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-re_bodypart_directions_pipeline_en.md
@@ -59,36 +59,11 @@ nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 
 | index | relations | entity1                     | entity1_begin | entity1_end | chunk1     | entity2                     | entity2_end | entity2_end | chunk2        | confidence |
 |-------|-----------|-----------------------------|---------------|-------------|------------|-----------------------------|-------------|-------------|---------------|------------|
@@ -101,10 +76,6 @@ Results
 | 6     | 0         | Direction                   | 54            | 57          | left       | Internal_organ_or_component | 81          | 93          | basil ganglia | 0.97616416 |
 | 7     | 0         | Internal_organ_or_component | 59            | 68          | cerebellum | Direction                   | 75          | 79          | right         | 0.953046   |
 | 8     | 1         | Direction                   | 75            | 79          | right      | Internal_organ_or_component | 81          | 93          | basil ganglia | 1.0        |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-re_bodypart_proceduretest_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-re_bodypart_proceduretest_pipeline_en.md
index bde6f4f2fa..d7eaaa6098 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-re_bodypart_proceduretest_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-re_bodypart_proceduretest_pipeline_en.md
@@ -59,44 +59,14 @@ nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations | entity1                      | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2              | confidence |
 |-------|-----------|------------------------------|---------------|-------------|--------|---------|-------------|-------------|---------------------|------------|
 | 0     | 1         | External_body_part_or_region | 94            | 98          | chest  | Test    | 117         | 135         | portable ultrasound | 1.0        |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md
index b4f67e8238..55b3759469 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_human_phenotype_gene_clinica
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
 
@@ -56,34 +57,11 @@ nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobo
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    |   relation | entity1   |   entity1_begin |   entity1_end | chunk1              | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+===========+=================+===============+=====================+===========+=================+===============+=====================+==============+
@@ -91,9 +69,6 @@ Results
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 |          1 | HP        |              23 |            36 | microphthalmia      | GENE      |             110 |           114 | TENM3               |     0.999999 |
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-re_temporal_events_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-re_temporal_events_clinical_pipeline_en.md
index 7f5e378eac..d937be62f1 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-re_temporal_events_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-re_temporal_events_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_clinical](ht
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
 
@@ -56,34 +57,11 @@ nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    | relation   | entity1    |   entity1_begin |   entity1_end | chunk1                   | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+============+=================+===============+==========================+===========+=================+===============+=====================+==============+
@@ -91,9 +69,6 @@ Results
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 | OVERLAP    | DATE       |             171 |           179 | that time                | PROBLEM   |             201 |           219 | any specific injury |     0.956654 |
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md
index 91b6a05dcc..361c3fbb16 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_enriched_cli
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
 
@@ -56,34 +57,11 @@ nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 5
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |    | relation   | entity1   |   entity1_begin |   entity1_end | chunk1                                        | entity2    |   entity2_begin |   entity2_end | chunk2                   |   confidence |
 +====+============+===========+=================+===============+===============================================+============+=================+===============+==========================+==============+
@@ -91,9 +69,6 @@ Results
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |  1 | AFTER      | DATE      |             171 |           179 | that time                                     | PROBLEM    |             201 |           219 | any specific injury      |     0.577288 |
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-re_test_problem_finding_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-re_test_problem_finding_pipeline_en.md
index 6eecdab3a5..3fd31b082c 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-re_test_problem_finding_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-re_test_problem_finding_pipeline_en.md
@@ -59,44 +59,13 @@ nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | 1            | PROCEDURE    | biopsy              | SYMPTOM      |  lesion | 
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-re_test_result_date_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-13-re_test_result_date_pipeline_en.md
index e47990e359..5db177a1b4 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-re_test_result_date_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-re_test_result_date_pipeline_en.md
@@ -59,46 +59,16 @@ nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised ches
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | O            | TEST         | chest X-ray         | MEASUREMENTS |  93%    | 
 | 1     | O            | TEST         | CT scan             | MEASUREMENTS |  93%    |
 | 2     | is_result_of | TEST         | SpO2                | MEASUREMENTS |  93%    |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-recognize_entities_posology_en.md b/docs/_posts/C-K-Loan/2023-06-13-recognize_entities_posology_en.md
index db0cd0d43d..7399069692 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-recognize_entities_posology_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-recognize_entities_posology_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_posology`. It will only extract medication entities.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
 
@@ -61,38 +62,11 @@ She was seen by the endocrinology service and discharged on 40 units of insulin
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
-
-res = pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-```scala
-val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "clinical/models")
 
-val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")(0)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunk            |   begin |   end | entity    |
 |---:|:-----------------|--------:|------:|:----------|
 |  0 | metformin        |      83 |    91 | DRUG      |
@@ -104,10 +78,6 @@ Results
 |  6 | 12 units         |     309 |   316 | DOSAGE    |
 |  7 | insulin lispro   |     321 |   334 | DRUG      |
 |  8 | with meals       |     336 |   345 | FREQUENCY |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-rxnorm_mesh_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-rxnorm_mesh_mapping_en.md
index 4d5f04e1ab..93fe412bd4 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-rxnorm_mesh_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-rxnorm_mesh_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps RxNorm codes to MeSH codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline 
 pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
@@ -54,32 +55,11 @@ nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline 
-pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-pipeline.annotate("1191 6809 47613")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-val result = pipeline.annotate("1191 6809 47613")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 {'rxnorm': ['1191', '6809', '47613'],
 'mesh': ['D001241', 'D008687', 'D019355']}
 
@@ -97,9 +77,6 @@ Note:
 | D001241    | Aspirin             |
 | D008687    | Metformin           |
 | D019355    | Calcium Citrate     |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-rxnorm_ndc_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-rxnorm_ndc_mapping_en.md
index 07bb62ec23..1a6c4269b2 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-rxnorm_ndc_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-rxnorm_ndc_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps RXNORM codes to NDC codes without using any text d
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,45 +59,15 @@ nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1652674 259934)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1652674 259934)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 {'document': ['1652674 259934'],
 'package_ndc': ['62135-0625-60', '13349-0010-39'],
 'product_ndc': ['46708-0499', '13349-0010'],
 'rxnorm_code': ['1652674', '259934']}
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-rxnorm_umls_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-rxnorm_umls_mapping_en.md
index 4e0a095120..6384557f2a 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-rxnorm_umls_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-rxnorm_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `rxnorm_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1161611 315677)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1161611 315677)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | rxnorm_code      | umls_code           |
 |---:|:-----------------|:--------------------|
 |  0 | 1161611 | 315677 | C3215948 | C0984912 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-snomed_icd10cm_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-snomed_icd10cm_mapping_en.md
index 7ced2088d9..6009727dea 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-snomed_icd10cm_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-snomed_icd10cm_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icd10cm_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,14 @@ nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | snomed_code                             | icd10cm_code               |
 |---:|:----------------------------------------|:---------------------------|
 |  0 | 128041000119107 | 292278006 | 293072005 | K22.70 | T43.595 | T37.1X5 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-snomed_icdo_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-snomed_icdo_mapping_en.md
index b56e1d78d7..dc13714e94 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-snomed_icdo_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-snomed_icdo_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icdo_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | snomed_code                   | icdo_code                |
 |---:|:------------------------------|:-------------------------|
 |  0 | 10376009 | 2026006 | 26638004 | 8050/2 | 9014/0 | 8322/0 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-13-snomed_umls_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-13-snomed_umls_mapping_en.md
index be0388cb69..a73f817e7d 100644
--- a/docs/_posts/C-K-Loan/2023-06-13-snomed_umls_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-13-snomed_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | snomed_code                      | umls_code                      |
 |---:|:---------------------------------|:-------------------------------|
 |  0 | 733187009 | 449433008 | 51264003 | C4546029 | C3164619 | C0271267 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
index 41e7a98859..435910acfb 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
@@ -59,75 +59,15 @@ nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abs
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |True|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ade_tweet_binary_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ade_tweet_binary_pipeline_en.md
index 0c4b76c365..7365444301 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ade_tweet_binary_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ade_tweet_binary_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ade_tweet
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +69,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | depressed        |      44 |    52 | ADE         |     0.999755 |
 |  1 | angry            |      73 |    77 | ADE         |     0.999608 |
 |  2 | insulin blocking |      97 |   112 | ADE         |     0.738712 |
 |  3 | sugar crashes    |     147 |   159 | ADE         |     0.993742 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_disease_mentions_tweet_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
index 41eec96e6c..57f2bbbd91 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_disease_m
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-text = '''El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-val text = "El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-text = '''El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-val text = "El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Neumonía en el pulmón |      41 |    61 | ENFERMEDAD  |     0.999969 |
@@ -125,12 +76,6 @@ Results
 |  2 | Faringitis aguda      |      94 |   109 | ENFERMEDAD  |     0.999969 |
 |  3 | infección de orina    |     113 |   130 | ENFERMEDAD  |     0.999969 |
 |  4 | Gripe                 |     150 |   154 | ENFERMEDAD  |     0.999983 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_drug_development_trials_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_drug_development_trials_pipeline_en.md
index a82f2718d7..903216c64b 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_drug_development_trials_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_drug_development_trials_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_drug_deve
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,42 +61,10 @@ import nlu
 nlu.load("en.classify.token_bert.druge_developement.pipeline").predict("""In June 2003, the median overall survival  with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response  ( CR ) , partial response  ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with  topotecan,  and 11, 61, 66 and 32 patients, respectively, without topotecan.""")
 ```
 
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_drug_development_trials_pipeline", "en", "clinical/models")
-
-text = '''In June 2003, the median overall survival  with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response  ( CR ) , partial response  ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with  topotecan,  and 11, 61, 66 and 32 patients, respectively, without topotecan.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_drug_development_trials_pipeline", "en", "clinical/models")
-
-val text = "In June 2003, the median overall survival  with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response  ( CR ) , partial response  ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with  topotecan,  and 11, 61, 66 and 32 patients, respectively, without topotecan."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.druge_developement.pipeline").predict("""In June 2003, the median overall survival  with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response  ( CR ) , partial response  ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with  topotecan,  and 11, 61, 66 and 32 patients, respectively, without topotecan.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------|--------:|------:|:--------------|-------------:|
 |  0 | June 2003               |       3 |    11 | DATE          |     0.996034 |
@@ -118,9 +87,6 @@ Results
 | 17 | 66                      |     301 |   302 | Patient_Count |     0.998066 |
 | 18 | 32 patients             |     308 |   318 | Patient_Count |     0.996285 |
 | 19 | without topotecan       |     335 |   351 | Trial_Group   |     0.971218 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_negation_uncertainty_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_negation_uncertainty_pipeline_es.md
index 360117d731..124fb2184f 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_negation_uncertainty_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_negation_uncertainty_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_negation_
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                                              |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | probable                                               |      16 |    23 | UNC         |     0.999994 |
@@ -128,12 +79,6 @@ Results
 |  5 | se realizó paracentesis control por escasez de liquido |     178 |   231 | NSCO        |     0.999995 |
 |  6 | susceptible de                                         |     293 |   306 | UNC         |     0.999986 |
 |  7 | ca basocelular perlado                                 |     308 |   329 | USCO        |     0.99999  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ade_binary_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ade_binary_pipeline_en.md
index 9d871acfc2..ace0090c87 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ade_binary_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ade_binary_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ade_b
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,23 +69,11 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk     |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | depressed     |      44 |    52 | ADE         |     0.990846 |
 |  1 | angry         |      73 |    77 | ADE         |     0.972025 |
 |  2 | sugar crashes |     147 |   159 | ADE         |     0.933623 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ade_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ade_pipeline_en.md
index ba3d2476b4..b3aa11da31 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ade_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ade_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ade](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,45 +63,12 @@ nlu.load("en.classify.token_bert.ade_pipeline").predict("""Both the erbA IRES an
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_pipeline", "en", "clinical/models")
-
-text = '''Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_pipeline", "en", "clinical/models")
-
-val text = "Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.ade_pipeline").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 | ner_chunk   | begin   | end   | ner_label   | confidence   |
 |-------------|---------|-------|-------------|--------------|
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_anatem_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_anatem_pipeline_en.md
index 3f38e9222c..de73a167db 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_anatem_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_anatem_pipeline_en.md
@@ -50,50 +50,6 @@ val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline
 
 val text = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-text = '''Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-val text = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-text = '''Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-val text = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
-
 val result = pipeline.fullAnnotate(text)
 ```
 
@@ -112,12 +68,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------|--------:|------:|:------------|-------------:|
 |  0 | Malignant cells        |       0 |    14 | Anatomy     |     0.999951 |
@@ -126,12 +76,6 @@ Results
 |  3 | breast                 |     343 |   348 | Anatomy     |     0.999842 |
 |  4 | ovarian                |     351 |   357 | Anatomy     |     0.99998  |
 |  5 | prostate cancer        |     364 |   378 | Anatomy     |     0.999968 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_anatomy_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_anatomy_pipeline_en.md
index a847184f9c..2f8b02d36a 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_anatomy_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_anatomy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_anato
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -80,58 +81,11 @@ Dermatologic: She has got redness along her right great toe, but no bleeding or
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_anatomy_pipeline", "en", "clinical/models")
-
-text = '''This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatomy_pipeline", "en", "clinical/models")
-
-val text = "This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.anatomy_pipeline").predict("""This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label              |   confidence |
 |---:|:-----------------|--------:|------:|:-----------------------|-------------:|
 |  0 | great            |     320 |   324 | Multi-tissue_structure |     0.693343 |
@@ -154,9 +108,6 @@ Results
 | 17 | great            |    1017 |  1021 | Multi-tissue_structure |     0.818323 |
 | 18 | toe              |    1023 |  1025 | Organism_subdivision   |     0.341098 |
 | 19 | toenails         |    1031 |  1038 | Organism_subdivision   |     0.75016  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bacteria_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bacteria_pipeline_en.md
index 20d17e7a5e..396831918e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bacteria_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bacteria_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bacte
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,16 @@ nlu.load("en.classify.token_bert.bacteria_ner.pipeline").predict("""Based on the
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bacteria_pipeline", "en", "clinical/models")
-
-text = '''Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bacteria_pipeline", "en", "clinical/models")
-
-val text = "Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T))."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.bacteria_ner.pipeline").predict("""Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | SMSP (T)                |      73 |    80 | SPECIES     |     0.99985  |
 |  1 | Methanoregula formicica |     167 |   189 | SPECIES     |     0.999787 |
 |  2 | SMSP (T)                |     222 |   229 | SPECIES     |     0.999871 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
index 92c73f1cce..9d1be32262 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc2gm
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-text = '''ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-val text = "ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-text = '''ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-val text = "ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label    |   confidence |
 |---:|:------------|--------:|------:|:-------------|-------------:|
 |  0 | ROCK-I      |       0 |     5 | GENE/PROTEIN |     0.999978 |
@@ -129,12 +80,6 @@ Results
 |  6 | Rho         |     225 |   227 | GENE/PROTEIN |     0.999976 |
 |  7 | boxA        |     247 |   250 | GENE/PROTEIN |     0.999837 |
 |  8 | rut sites   |     256 |   264 | GENE/PROTEIN |     0.99115  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
index 4f664d86af..6931d89b86 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc4ch
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                       |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------|--------:|------:|:------------|-------------:|
 |  0 | triterpenes                     |      33 |    43 | CHEM        |     0.99999  |
@@ -133,12 +84,6 @@ Results
 | 10 | 4 - hydroxybenzoic acid         |     184 |   206 | CHEM        |     0.999973 |
 | 11 | gallic and protocatechuic acids |     209 |   239 | CHEM        |     0.999984 |
 | 12 | isocorilagin                    |     245 |   256 | CHEM        |     0.999985 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
index 82d10d2afa..0939a6264c 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc5cd
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                 |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | amphetamine               |     128 |   138 | CHEM        |     0.999973 |
@@ -127,12 +78,6 @@ Results
 |  4 | kanamycin                 |     350 |   358 | CHEM        |     0.999978 |
 |  5 | colistin                  |     362 |   369 | CHEM        |     0.999942 |
 |  6 | povidone-iodine           |     375 |   389 | CHEM        |     0.999977 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
index 3fd18b689b..cda89a693e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc5cd
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +69,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | interstitial cystitis |      61 |    81 | DISEASE     |     0.999746 |
 |  1 | mastocytosis          |     129 |   140 | DISEASE     |     0.999132 |
 |  2 | cystitis              |     209 |   216 | DISEASE     |     0.999912 |
 |  3 | prostate cancer       |     355 |   369 | DISEASE     |     0.999781 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bionlp_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bionlp_pipeline_en.md
index 47b85eecff..9e69e411e2 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bionlp_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_bionlp_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bionl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.biolp.pipeline").predict("""Both the erbA IRES
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bionlp_pipeline", "en", "clinical/models")
-
-text = '''Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bionlp_pipeline", "en", "clinical/models")
-
-val text = "Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.biolp.pipeline").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label              |   confidence |
 |---:|:--------------------|--------:|------:|:-----------------------|-------------:|
 |  0 | erbA IRES           |       9 |    17 | Organism               |     0.999188 |
@@ -107,9 +79,6 @@ Results
 |  6 | erbA/myb IRES virus |     140 |   158 | Organism               |     0.999751 |
 |  7 | erbA IRES virus     |     236 |   250 | Organism               |     0.999749 |
 |  8 | blastoderm          |     259 |   268 | Cell                   |     0.999897 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_cellular_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_cellular_pipeline_en.md
index 8d712b21bd..f28ca6d631 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_cellular_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_cellular_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_cellu
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.cellular_pipeline").predict("""Detection of var
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_cellular_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_cellular_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.cellular_pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                   |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | intracellular signaling proteins            |      27 |    58 | protein     |     0.999477 |
@@ -119,9 +91,6 @@ Results
 | 18 | GAD                                         |     791 |   793 | protein     |     0.999684 |
 | 19 | reporter gene                               |     848 |   860 | DNA         |     0.998856 |
 | 20 | Tax                                         |     863 |   865 | protein     |     0.999717 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_chemicals_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_chemicals_pipeline_en.md
index a9e78073ac..8f34422a33 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_chemicals_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_chemicals_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_chemi
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.classify.token_bert.chemicals_pipeline").predict("""The results hav
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.chemicals_pipeline").predict("""The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                   |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------|--------:|------:|:------------|-------------:|
 |  0 | p - choloroaniline          |      40 |    57 | CHEM        |     0.999986 |
@@ -103,9 +74,6 @@ Results
 |  2 | kanamycin                   |     169 |   177 | CHEM        |     0.999985 |
 |  3 | colistin                    |     181 |   188 | CHEM        |     0.999982 |
 |  4 | povidone - iodine           |     194 |   210 | CHEM        |     0.99998  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_chemprot_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_chemprot_pipeline_en.md
index abf20f830a..4947c7e34c 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_chemprot_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_chemprot_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_chemp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.chemprot_pipeline").predict("""Keratinocyte gro
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_chemprot_pipeline", "en", "clinical/models")
-
-text = '''Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_chemprot_pipeline", "en", "clinical/models")
-
-val text = "Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.chemprot_pipeline").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk    |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | Keratinocyte |       0 |    11 | GENE-Y      |     0.999147 |
@@ -105,9 +77,6 @@ Results
 |  4 | fibroblast   |      38 |    47 | GENE-Y      |     0.999753 |
 |  5 | growth       |      49 |    54 | GENE-Y      |     0.999771 |
 |  6 | factor       |      56 |    61 | GENE-Y      |     0.999742 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_pipeline_en.md
index aeaa50b102..9244cdce27 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.classify.token_bert.clinical_pipeline").predict("""A 28-year-old fe
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_pipeline", "en", "clinical/models")
-
-text = '''A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG . She had been on dapagliflozin for six months at the time of presentation . Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness , guarding , or rigidity . Pertinent laboratory findings on admission were : serum glucose 111 mg/dl , bicarbonate 18 mmol/l , anion gap 20 , creatinine 0.4 mg/dL , triglycerides 508 mg/dL , total cholesterol 122 mg/dL , glycated hemoglobin ( HbA1c ) 10% , and venous pH 7.27 . Serum lipase was normal at 43 U/L . Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia . The patient was initially admitted for starvation ketosis , as she reported poor oral intake for three days prior to admission . However , serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL , the anion gap was still elevated at 21 , serum bicarbonate was 16 mmol/L , triglyceride level peaked at 2050 mg/dL , and lipase was 52 U/L . The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again . The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL , within 24 hours . Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely . She had close follow-up with endocrinology post discharge .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_pipeline", "en", "clinical/models")
-
-val text = "A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG . She had been on dapagliflozin for six months at the time of presentation . Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness , guarding , or rigidity . Pertinent laboratory findings on admission were : serum glucose 111 mg/dl , bicarbonate 18 mmol/l , anion gap 20 , creatinine 0.4 mg/dL , triglycerides 508 mg/dL , total cholesterol 122 mg/dL , glycated hemoglobin ( HbA1c ) 10% , and venous pH 7.27 . Serum lipase was normal at 43 U/L . Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia . The patient was initially admitted for starvation ketosis , as she reported poor oral intake for three days prior to admission . However , serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL , the anion gap was still elevated at 21 , serum bicarbonate was 16 mmol/L , triglyceride level peaked at 2050 mg/dL , and lipase was 52 U/L . The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again . The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL , within 24 hours . Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely . She had close follow-up with endocrinology post discharge ."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.clinical_pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG . She had been on dapagliflozin for six months at the time of presentation . Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness , guarding , or rigidity . Pertinent laboratory findings on admission were : serum glucose 111 mg/dl , bicarbonate 18 mmol/l , anion gap 20 , creatinine 0.4 mg/dL , triglycerides 508 mg/dL , total cholesterol 122 mg/dL , glycated hemoglobin ( HbA1c ) 10% , and venous pH 7.27 . Serum lipase was normal at 43 U/L . Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia . The patient was initially admitted for starvation ketosis , as she reported poor oral intake for three days prior to admission . However , serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL , the anion gap was still elevated at 21 , serum bicarbonate was 16 mmol/L , triglyceride level peaked at 2050 mg/dL , and lipase was 52 U/L . The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again . The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL , within 24 hours . Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely . She had close follow-up with endocrinology post discharge .""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | gestational diabetes mellitus |      39 |    67 | PROBLEM     |     0.999895 |
@@ -123,9 +94,6 @@ Results
 | 22 | Physical examination          |     739 |   758 | TEST        |     0.985332 |
 | 23 | dry oral mucosa               |     796 |   810 | PROBLEM     |     0.991374 |
 | 24 | her abdominal examination     |     830 |   854 | TEST        |     0.999292 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
index a3191bd247..35435e6c63 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-text = '''This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-val text = "This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-text = '''This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-val text = "This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label          |   confidence |
 |---:|:-----------------|--------:|------:|:-------------------|-------------:|
 |  0 | open-label       |       5 |    14 | CTDesign           |     0.742075 |
@@ -138,12 +89,6 @@ Results
 | 15 | GLA              |     356 |   358 | Drug               |     0.972978 |
 | 16 | NPH              |     363 |   365 | Drug               |     0.989424 |
 | 17 | bedtime          |     370 |   376 | DrugTime           |     0.936016 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
index 8f73c4ed1f..ac6430ce0e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | suplementación          |      13 |    26 | PROC        |     0.999993 |
@@ -132,12 +83,6 @@ Results
 |  9 | pp                      |     337 |   338 | CHEM        |     0.999889 |
 | 10 | diálisis                |     388 |   395 | PROC        |     0.999993 |
 | 11 | función  residual       |     398 |   414 | PROC        |     0.999948 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_deid_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_deid_pipeline_en.md
index 298017ec35..b533a2b824 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_deid_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_deid_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_deid]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.ner_deid.pipeline").predict("""A. Record date :
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_deid_pipeline", "en", "clinical/models")
-
-text = '''A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_deid_pipeline", "en", "clinical/models")
-
-val text = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.ner_deid.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      17 |    26 | DATE          |     0.957256 |
@@ -107,9 +79,6 @@ Results
 |  6 | 0295 Keats Street             |     145 |   161 | STREET        |     0.997889 |
 |  7 | 302) 786-5227                 |     174 |   186 | PHONE         |     0.970114 |
 |  8 | Brothers Coal-Mine            |     253 |   270 | ORGANIZATION  |     0.998911 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_drugs_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_drugs_pipeline_en.md
index 8ba7e88e80..2d212e09a4 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_drugs_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_drugs_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_drugs
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.classify.token_bert.ner_ade.pipeline").predict("""The human KCNJ9 (
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_drugs_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_drugs_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.ner_ade.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | potassium      |      92 |   100 | DrugChem    |     0.990254 |
@@ -106,9 +77,6 @@ Results
 |  5 | vinorelbine    |    1343 |  1353 | DrugChem    |     0.999991 |
 |  6 | anthracyclines |    1390 |  1403 | DrugChem    |     0.99999  |
 |  7 | taxanes        |    1409 |  1415 | DrugChem    |     0.999946 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
index 9ee312b02f..f72b35e7ff 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jnlpb
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-text = '''The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-val text = "The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-text = '''The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-val text = "The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                             |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | protein kinase C                      |      39 |    54 | protein     |     0.993263 |
@@ -135,12 +86,6 @@ Results
 | 12 | tyrosine kinases                      |     732 |   747 | protein     |     0.999636 |
 | 13 | p95vav                                |     834 |   839 | protein     |     0.999842 |
 | 14 | hematopoietic and trophoblast cells   |     876 |   910 | cell_type   |     0.999709 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jsl_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jsl_pipeline_en.md
index 46402ebc23..a44babbf28 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jsl_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,71 +63,10 @@ nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                        |   begin |   end | ner_label    |   confidence |
 |---:|:---------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 21-day-old                       |      17 |    26 | Age          |     0.999456 |
@@ -154,12 +94,6 @@ Results
 | 22 | fussy                            |     574 |   578 | Symptom      |     0.997592 |
 | 23 | over the past 2 days             |     580 |   599 | Date_Time    |     0.994786 |
 | 24 | albuterol                        |     642 |   650 | Drug         |     0.999735 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jsl_slim_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jsl_slim_pipeline_en.md
index 8072d77f40..ea348ae75c 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jsl_slim_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_jsl_slim_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl_s
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.classify.token_bert.jsl_slim.pipeline").predict("""HISTORY: 30-year
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_slim_pipeline", "en", "clinical/models")
-
-text = '''HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_slim_pipeline", "en", "clinical/models")
-
-val text = "HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.jsl_slim.pipeline").predict("""HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label    |   confidence |
 |---:|:-----------------|--------:|------:|:-------------|-------------:|
 |  0 | HISTORY:         |       0 |     7 | Header       |     0.994786 |
@@ -108,9 +79,6 @@ Results
 |  7 | her mother       |     213 |   222 | Demographics |     0.997765 |
 |  8 | age 58           |     227 |   232 | Age          |     0.997636 |
 |  9 | breast cancer    |     270 |   282 | Oncological  |     0.999452 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_linnaeus_species_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
index 159434da45..9a19c1a6ea 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_linna
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-text = '''First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-val text = "First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-text = '''First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-val text = "First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                 |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | chicken                   |      20 |    26 | SPECIES     |     0.998697 |
@@ -125,12 +77,6 @@ Results
 |  2 | Xenopus laevis            |      82 |    95 | SPECIES     |     0.999918 |
 |  3 | Drosophila melanogaster   |     102 |   124 | SPECIES     |     0.999925 |
 |  4 | Schizosaccharomyces pombe |     134 |   158 | SPECIES     |     0.999881 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_en.md
index fa901ea571..045961802d 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |     0.986743 |
@@ -127,12 +78,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |     0.962562 |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |     0.999028 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |     0.999852 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_es.md
index 3078553972..bb4c8c87e7 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |     0.999294 |
@@ -131,12 +82,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |     0.999971 |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |     0.99997  |
 | 10 | padres         |     728 |   733 | HUMAN       |     0.999971 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_it.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_it.md
index 2727e4bad8..656a515602 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_it.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_it.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |     0.999699 |
@@ -130,12 +81,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |     0.999616 |
 |  8 | HIV              |     523 |   525 | SPECIES     |     0.999383 |
 |  9 | paziente         |     634 |   641 | HUMAN       |     0.99977  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_pt.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_pt.md
index 8b03beae52..b722377a98 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_pt.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_living_species_pipeline_pt.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |     0.999888 |
@@ -126,12 +77,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |     0.999365 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |     0.982236 |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |     0.996602 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ncbi_disease_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
index 0133b59b26..597158aca8 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ncbi_
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-text = '''Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-val text = "Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-text = '''Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-val text = "Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------|--------:|------:|:------------|-------------:|
 |  0 | Kniest dysplasia       |       0 |    15 | Disease     |     0.999886 |
@@ -126,12 +77,6 @@ Results
 |  3 | midface hypoplasia     |     120 |   137 | Disease     |     0.999911 |
 |  4 | myopia                 |     147 |   152 | Disease     |     0.999894 |
 |  5 | hearing loss           |     159 |   170 | Disease     |     0.999351 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_pathogen_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_pathogen_pipeline_en.md
index 4bb101b84d..4b9862489d 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_pathogen_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_pathogen_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_patho
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-text = '''Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-val text = "Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-text = '''Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-val text = "Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label        |   confidence |
 |---:|:----------------|--------:|------:|:-----------------|-------------:|
 |  0 | Racecadotril    |       0 |    11 | Medicine         |     0.986453 |
@@ -135,12 +86,6 @@ Results
 | 12 | rabies virus    |     381 |   392 | Pathogen         |     0.738198 |
 | 13 | Lyssavirus      |     395 |   404 | Pathogen         |     0.979239 |
 | 14 | Ephemerovirus   |     410 |   422 | Pathogen         |     0.992292 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_species_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_species_pipeline_en.md
index 73573a1419..34c3711b76 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_species_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_ner_species_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_speci
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-text = '''As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-val text = "As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) ."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-text = '''As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-val text = "As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) ."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | 6C (T)                  |      57 |    62 | SPECIES     |     0.998955 |
@@ -126,12 +77,6 @@ Results
 |  3 | DSM 18155 (T)           |     188 |   200 | SPECIES     |     0.997657 |
 |  4 | Thiomonas perometabolis |     206 |   228 | SPECIES     |     0.999614 |
 |  5 | DSM 18570 (T)           |     230 |   242 | SPECIES     |     0.997146 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_pharmacology_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_pharmacology_pipeline_es.md
index 81c29bb75b..4a943c9366 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_pharmacology_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-bert_token_classifier_pharmacology_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_pharmacol
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label     |   confidence |
 |---:|:----------------|--------:|------:|:--------------|-------------:|
 |  0 | creatinkinasa   |      32 |    44 | PROTEINAS     |     0.999973 |
@@ -132,12 +83,6 @@ Results
 |  9 | Interleukina II |     232 |   246 | PROTEINAS     |     0.999965 |
 | 10 | Dacarbacina     |     249 |   259 | NORMALIZABLES |     0.999988 |
 | 11 | Interferon alfa |     263 |   277 | PROTEINAS     |     0.999961 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_augmented_es.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_augmented_es.md
index aa6948cc7c..7fe97b7266 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_augmented_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_augmented_es.md
@@ -36,6 +36,7 @@ The PHI information will be masked and obfuscated in the resulting text. The pip
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from johnsnowlabs import *
 
@@ -121,185 +122,11 @@ Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servic
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical_augmented").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical_augmented").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Datos <SEX>.
@@ -471,12 +298,6 @@ Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura trata
 F. 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra.
 Francisco José Roca Bermúdez Hospital 12 de Octubre Servicio de Endocrinología y Nutrición Calle Ramón y Cajal s/n 03129 Zaragoza - Alicante (Portugal) Correo electrónico: richard@company.it
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_de.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_de.md
index 3de2252372..cd77121d47 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_de.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_de.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from **German** medical
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -95,137 +96,11 @@ Adresse : St.Johann-Straße 13 19300
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "de", "clinical/models")
-
-sample = """Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-"""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification","de","clinical/models")
-
-val sample = "Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300"
 
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.clinical").predict("""Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "de", "clinical/models")
-
-sample = """Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-"""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification","de","clinical/models")
-
-val sample = "Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.clinical").predict("""Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Zusammenfassung : <PATIENT> wird am Morgen des <DATE> ins <HOSPITAL> eingeliefert.
@@ -273,12 +148,6 @@ Kontonummer: 192837465738
 SSN : 1310011981M454
 Lizenznummer: XX123456
 Adresse : Klingelhöferring 31206
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_en.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_en.md
index d8eb3859e5..e14fa99d52 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_en.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts. The
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,56 +79,11 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -159,9 +115,6 @@ Dr. Dr Felice Lacer, IDXO:4884578, IP 444.444.444.444.
 He is a 75 male was admitted to the MADISON VALLEY MEDICAL CENTER for cystectomy on 07-01-1972.
 Patient's VIN : 2BBBB11BBBB222999, SSN SSN-814-86-1962, Driver's license P055567317431.
 Phone 0381-6762484, Budaörsi út 14., New brunswick, E-MAIL: Reba@google.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_es.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_es.md
index bf4d941cd2..a94d4acda7 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_es.md
@@ -34,6 +34,7 @@ This pipeline is trained with sciwiki_300d embeddings and can be used to deident
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from johnsnowlabs import *
 
@@ -121,189 +122,11 @@ Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servic
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Datos del paciente.
@@ -475,13 +298,6 @@ Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura trata
 El paciente 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra.
 Reinaldo Manjón Malo Barcelona Servicio de Endocrinología y Nutrición Valencia km 12,500 28905 Bilbao - Madrid (Barcelona) Correo electrónico: quintanasalome@example.net
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_fr.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_fr.md
index 4c288ae58d..d334519c6e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_fr.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_fr.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts in Fr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -120,187 +121,11 @@ COURRIEL : mariebreton@chb.fr
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = """COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"""
 
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = "COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("fr.deid_obfuscated").predict("""COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = """COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = "COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("fr.deid_obfuscated").predict("""COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 COMPTE-RENDU D'HOSPITALISATION
@@ -399,12 +224,6 @@ Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des tr
 PSA de 1,16 ng/ml.
 ADDRESSÉ À : Dr Tristan-Gilbert Poulain - CENTRE HOSPITALIER D'ORTHEZ Service D'Endocrinologie et de Nutrition - 6, avenue Pages, 37443 Sainte Antoine
 COURRIEL : massecatherine@bouygtel.fr
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_glove_augmented_en.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_glove_augmented_en.md
index 9016c0815c..eaf8bd28c7 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_glove_augmented_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_glove_augmented_en.md
@@ -36,6 +36,7 @@ It's different to `clinical_deidentification_glove` in the way it manages PHONE
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,36 +61,10 @@ nlu.load("en.deid.glove_augmented.pipeline").predict("""Record date : 2093-01-13
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_glove_augmented", "en", "clinical/models")
-
-deid_pipeline.annotate("""Record date : 2093-01-13, David Hale, M.D. IP: 203.120.223.13. The driver's license no:A334455B. the SSN: 324598674 and e-mail: hale@gmail.com. Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93. PCP : Oliveira, 25 years old. Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification_glove_augmented", "en", "clinical/models")
-
-val result = pipeline.annotate("""Record date : 2093-01-13, David Hale, M.D. IP: 203.120.223.13. The driver's license no:A334455B. the SSN: 324598674 and e-mail: hale@gmail.com. Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93. PCP : Oliveira, 25 years old. Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.glove_augmented.pipeline").predict("""Record date : 2093-01-13, David Hale, M.D. IP: 203.120.223.13. The driver's license no:A334455B. the SSN: 324598674 and e-mail: hale@gmail.com. Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93. PCP : Oliveira, 25 years old. Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 {'masked': ['Record date : <DATE>, <DOCTOR>, M.D.',
     'IP: <IPADDR>.',
     "The driver's license no: <LICENSE>.",
@@ -138,9 +113,6 @@ Results
     'Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93.',
     'PCP : Oliveira, 25 years old.',
     "Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286."]}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_glove_en.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_glove_en.md
index f376a4e866..959b02b12f 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_glove_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_glove_en.md
@@ -34,6 +34,7 @@ This pipeline is trained with lightweight `glove_100d` embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -74,95 +75,11 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_glove", "en", "clinical/models")
-
 
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_glove","en","clinical/models")
-
-val result = deid_pipeline.annotate("Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. Dr. John Green, ID: 1231511863, IP 203.120.223.13. He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.glove_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_glove", "en", "clinical/models")
-
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_glove","en","clinical/models")
-
-val result = deid_pipeline.annotate("Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. Dr. John Green, ID: 1231511863, IP 203.120.223.13. He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.glove_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -194,12 +111,6 @@ Dr. Dr Worley Colonel, ID: ZJ:9570208, IP 005.005.005.005.
 He is a 67 male was admitted to the ST. LUKE'S HOSPITAL AT THE VINTAGE for cystectomy on 06-02-1981.
 Patient's VIN : 3CCCC22DDDD333888, SSN SSN-618-77-1042, Driver's license W693817528998.
 Phone 0496 46 46 70, 3100 weston rd, Shattuck, E-MAIL: Freddi@hotmail.com.
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_it.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_it.md
index 435ca9e2dd..0eab50ac16 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_it.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_it.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts in It
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -117,181 +118,11 @@ EMAIL: bferrabosco@poste.it""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = """RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = "RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("it.deid.clinical").predict("""RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it""")
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = """RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = "RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("it.deid.clinical").predict("""RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 RAPPORTO DI RICOVERO
@@ -387,12 +218,6 @@ PSA di 1,16 ng/ml.
 INDIRIZZATO A: Dott.
 Antonio Rusticucci - ASL 7 DI CARBONIA AZIENDA U.S.L. N. 7, Dipartimento di Endocrinologia e Nutrizione - Via Giorgio 0 Appartamento 26, 03461 Sesto Raimondo
 EMAIL: murat.g@jsl.com
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_pt.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_pt.md
index a709749c2f..6fb7cb71c8 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_pt.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_pt.md
@@ -34,6 +34,7 @@ This pipeline is trained with `w2v_cc_300d` portuguese embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,213 +134,11 @@ Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@ho
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = """Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = "Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("pt.deid.clinical").predict("""Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-""")
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = """Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = "Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("pt.deid.clinical").predict("""Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Dados do <SEX>.
@@ -455,12 +254,6 @@ O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta r
 A tomografia computorizada abdominal é normal.
 A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
 Referido por: Carlos Melo - Avenida Dos Aliados, 56, 22 Espanha E-mail: maria.prado@jacob.com.
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_ro.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_ro.md
index 15c0b02f71..d4d9b67069 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_ro.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_ro.md
@@ -75,95 +75,11 @@ Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-val sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("ro.deid.clinical").predict("""Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-val sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("ro.deid.clinical").predict("""Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Medic : Dr. <DOCTOR>, C.N.P : <IDNUM>, Data setului de analize: <DATE>
@@ -195,12 +111,6 @@ Varsta : 91, Nume si Prenume : Dragomir Emilia
 Tel: 0248 551 376, E-mail : tudorsmaranda@kappa.ro,
 Licență : T003485962M, Înmatriculare : AR-65-UPQ, Cont : KHHO5029180812813651, 
 Centrul Medical de Evaluare si Recuperare pentru Copii si Tineri Cristian Serban Buzias Aleea Voinea Curcani, 328479
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_slim_en.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_slim_en.md
index bac990c098..95365ad15b 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_slim_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_slim_en.md
@@ -34,6 +34,7 @@ This pipeline is trained with lightweight `glove_100d` embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,103 +79,11 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-deid_pipeline = PretrainedPipeline("clinical_deidentification_slim", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_slim","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_slim").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_slim", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_slim","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_slim").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -206,12 +115,6 @@ Dr. Dr Rosalba Hill, ID: JY:3489547, IP 005.005.005.005.
 He is a 79 male was admitted to the JOHN MUIR MEDICAL CENTER-CONCORD CAMPUS for cystectomy on 01-25-1997.
 Patient's VIN : 3CCCC22DDDD333888, SSN SSN-289-37-4495, Driver's license S99983662.
 Phone 04.32.52.27.90, North Adrienne, Colorado Springs, E-MAIL: Rawland@google.com.
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_wip_en.md b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_wip_en.md
index c64de252bc..1d225f9692 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_wip_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-clinical_deidentification_wip_en.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts. The
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,56 +79,10 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_wip", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_wip","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.clinical_wip").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -159,9 +114,6 @@ Dr. Dr Felice Lacer, IDXO:4884578, IP 444.444.444.444.
 He is a 75 male was admitted to the MADISON VALLEY MEDICAL CENTER for cystectomy on 07-01-1972.
 Patient's VIN : 2BBBB11BBBB222999, SSN SSN-814-86-1962, Driver's license P055567317431.
 Phone 0381-6762484, Budaörsi út 14., New brunswick, E-MAIL: Reba@google.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_ade_en.md b/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_ade_en.md
index ae4e6d1e61..61309c4d7b 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_ade_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_ade_en.md
@@ -34,6 +34,7 @@ A pipeline for Adverse Drug Events (ADE) with `ner_ade_biobert`, `assertion_dl_b
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,43 +63,10 @@ nlu.load("en.explain_doc.clinical_ade").predict("""Been taking Lipitor for 15 ye
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_ade", "en", "clinical/models")
-
-text = """Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_ade", "en", "clinical/models")
-
-val text = """Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"""
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.clinical_ade").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
-Class: True
-
 NER_Assertion:
 |    | chunk                   | entitiy    | assertion   |
 |----|-------------------------|------------|-------------|
@@ -114,10 +82,6 @@ Relations:
 | 1  | cramps                        | ADE        | Lipitor     | DRUG    |        0 |
 | 2  | severe fatigue                | ADE        | voltaren    | DRUG    |        0 |
 | 3  | cramps                        | ADE        | voltaren    | DRUG    |        1 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_carp_en.md b/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_carp_en.md
index db7a0188e5..ba3ead56e1 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_carp_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_carp_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_clinical`, `assertion_dl`, `re_clinical` and `ner_posology`
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,72 +63,10 @@ nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history o
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-val text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-val text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |   | chunks                        | ner_clinical | assertion | posology_chunk   | ner_posology | relations |
 |---|-------------------------------|--------------|-----------|------------------|--------------|-----------|
 | 0 | gestational diabetes mellitus | PROBLEM      | present   | metformin        | Drug         | TrAP      |
@@ -137,13 +76,6 @@ Results
 | 4 | poor appetite                 | PROBLEM      | present   | insulin glargine | Drug         | TrCP      |
 | 5 | vomiting                      | PROBLEM      | present   | at night         | Frequency    | TrAP      |
 | 6 | insulin glargine              | TREATMENT    | present   | 12 units         | Dosage       | TrAP      |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_era_en.md b/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_era_en.md
index d8c7ed1d26..f460bb9897 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_era_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_era_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_clinical_events`, `assertion_dl` and `re_temporal_events_cl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,71 +63,11 @@ nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Ho
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-val text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
 
-text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-val text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 
 |    | relation   | entity1       |   entity1_begin |   entity1_end | chunk1                    | entity2       |   entity2_begin |   entity2_end | chunk2                        |   confidence |
 |---:|:-----------|:--------------|----------------:|--------------:|:--------------------------|:--------------|----------------:|--------------:|:------------------------------|-------------:|
@@ -138,13 +79,6 @@ Results
 |  5 | OVERLAP    | DATE          |              45 |            54 | 2 days ago                | PROBLEM       |              74 |           102 | gestational diabetes mellitus |     0.996954 |
 |  6 | BEFORE     | EVIDENTIAL    |             119 |           124 | denied                    | PROBLEM       |             126 |           129 | pain                          |     1        |
 |  7 | BEFORE     | EVIDENTIAL    |             119 |           124 | denied                    | PROBLEM       |             135 |           146 | any headache                  |     1        |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_medication_en.md b/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_medication_en.md
index d4d5a55356..56f1516387 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_medication_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_medication_en.md
@@ -34,6 +34,7 @@ A pipeline for detecting posology entities with the `ner_posology_large` NER mod
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,72 +63,11 @@ nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient i
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 +----+----------------+------------+
 |    | chunks         | entities   |
 |---:|:---------------|:-----------|
@@ -164,13 +104,6 @@ Results
 | DRUG-ROUTE     | DRUG      | Lantus     | ROUTE     | subcutaneously |
 | DRUG-FREQUENCY | DRUG      | Lantus     | FREQUENCY | at bedtime     |
 +----------------+-----------+------------+-----------+----------------+
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_radiology_en.md b/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_radiology_en.md
index f71fea5ba2..cfd286f4b9 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_radiology_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-explain_clinical_doc_radiology_en.md
@@ -34,6 +34,7 @@ A pipeline for detecting posology entities with the `ner_radiology` NER model, a
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,9 @@ nlu.load("en.explain_doc.clinical_radiology.pipeline").predict("""Bilateral brea
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_radiology", "en", "clinical/models")
-
-text = """Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_radiology", "en", "clinical/models")
-
-val text = """Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."""
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.clinical_radiology.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-
 +----+------------------------------------------+---------------------------+
 |    | chunks                                   | entities                  |
 |---:|:-----------------------------------------|:--------------------------|
@@ -132,10 +101,6 @@ Results
 |       1 | ImagingTest     | ultrasound            | ImagingFindings           | ovoid mass |
 |       0 | ImagingFindings | benign fibrous tissue | Disease_Syndrome_Disorder | lipoma     |
 +---------+-----------------+-----------------------+---------------------------+------------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-icd10_icd9_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-icd10_icd9_mapping_en.md
index 3c1091d9e4..f13858e1af 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-icd10_icd9_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-icd10_icd9_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10_icd9_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | icd10_code          | icd9_code          |
 |---:|:--------------------|:-------------------|
 |  0 | Z833 | A0100 | A000 | V180 | 0020 | 0010 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-icd10cm_snomed_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-icd10cm_snomed_mapping_en.md
index 03eb72f72c..ed47b5ff40 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-icd10cm_snomed_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-icd10cm_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10cm_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | icd10cm_code          | snomed_code                              |
 |---:|:----------------------|:-----------------------------------------|
 |  0 | R079 | N4289 | M62830 | 161972006 | 22035000 | 16410651000119105 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-icd10cm_umls_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-icd10cm_umls_mapping_en.md
index 869e432bd6..21c54cdf07 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-icd10cm_umls_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-icd10cm_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps ICD10CM codes to UMLS codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,73 +59,13 @@ nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 {'icd10cm': ['M89.50', 'R82.2', 'R09.01'],
 'umls': ['C4721411', 'C0159076', 'C0004044']}
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-icdo_snomed_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-icdo_snomed_mapping_en.md
index 783cdd7ea2..a1b3283fc7 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-icdo_snomed_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-icdo_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icdo_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | icdo_code                | snomed_code                    |
 |---:|:-------------------------|:-------------------------------|
 |  0 | 8120/1 | 8170/3 | 8380/3 | 45083001 | 25370001 | 30289006 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_clinical_pipeline_en.md
index 1be298566e..130a89b426 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_clinical](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl_wip_clinical.pipeline").predict("""The patient is a 21-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_wip_clinical.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                 |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     0.9984   |
@@ -123,9 +95,6 @@ Results
 | 22 | 5 to 10 minutes                           |     459 |   473 | Duration                     |     0.152125 |
 | 23 | his                                       |     488 |   490 | Gender                       |     0.9987   |
 | 24 | respiratory congestion                    |     492 |   513 | VS_Finding                   |     0.6458   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_greedy_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_greedy_biobert_pipeline_en.md
index e11d61ad57..37518f3ab2 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_greedy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_greedy_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.greedy_wip_biobert.pipeline").predict("""The patient is a 2
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.greedy_wip_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                      |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     1        |
@@ -123,9 +95,6 @@ Results
 | 22 | He                                             |     516 |   517 | Gender                       |     0.9998   |
 | 23 | tired                                          |     550 |   554 | Symptom                      |     0.8912   |
 | 24 | fussy                                          |     569 |   573 | Symptom                      |     0.9541   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_greedy_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_greedy_clinical_pipeline_en.md
index 5242f81a35..23bea0c9b4 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_greedy_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_greedy_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_greedy_clinical](ht
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.wip_greedy_clinical.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.wip_greedy_clinical.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                     |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     0.9817   |
@@ -123,9 +94,6 @@ Results
 | 22 | his                                            |     488 |   490 | Gender                       |     0.9904   |
 | 23 | respiratory congestion                         |     492 |   513 | Symptom                      |     0.5294   |
 | 24 | He                                             |     516 |   517 | Gender                       |     0.9989   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_modifier_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_modifier_clinical_pipeline_en.md
index 5866ef572c..3f3ead4d62 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_modifier_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-jsl_ner_wip_modifier_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_modifier_clinical](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.wip_modifier_clinical.pipeline").predict("""The patient is
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_modifier_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_modifier_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.wip_modifier_clinical.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                     |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     0.9817   |
@@ -123,9 +95,6 @@ Results
 | 22 | his                                            |     488 |   490 | Gender                       |     0.9904   |
 | 23 | respiratory congestion                         |     492 |   513 | Symptom                      |     0.5294   |
 | 24 | He                                             |     516 |   517 | Gender                       |     0.9989   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
index 67af1fae49..cf03171d2e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_rd_ner_wip_greedy_biobert](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.wip_greedy_biobert.pipeline").predict("""Bilateral breast u
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_rd_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_rd_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.wip_greedy_biobert.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Bilateral             |       0 |     8 | Direction                 |     0.9875   |
@@ -112,9 +83,6 @@ Results
 | 11 | internal color flow   |     294 |   312 | ImagingFindings           |     0.3726   |
 | 12 | benign fibrous tissue |     334 |   354 | ImagingFindings           |     0.484533 |
 | 13 | lipoma                |     361 |   366 | Disease_Syndrome_Disorder |     0.8955   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
index f1a4f80fb2..95df40eda5 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_rd_ner_wip_greedy_clinical]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.jsl_rd_wip_greedy.pipeline").predict("""The patient is a 21
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_rd_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_rd_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_rd_wip_greedy.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                  |   begin |   end | ner_label      |   confidence |
 |---:|:----------------------------|--------:|------:|:---------------|-------------:|
 |  0 | 21-day-old                  |      17 |    26 | Age            |     0.9913   |
@@ -123,9 +94,6 @@ Results
 | 22 | respiratory congestion      |     492 |   513 | Symptom        |     0.25015  |
 | 23 | He                          |     516 |   517 | Gender         |     0.9998   |
 | 24 | tired                       |     550 |   554 | Symptom        |     0.8179   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-mesh_umls_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-mesh_umls_mapping_en.md
index 28419dcf0f..0386b8cf61 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-mesh_umls_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-mesh_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `mesh_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | mesh_code                   | umls_code                      |
 |---:|:----------------------------|:-------------------------------|
 |  0 | C028491 | D019326 | C579867 | C0043904 | C0045010 | C3696376 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_abbreviation_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_abbreviation_clinical_pipeline_en.md
index 1dc2e4758e..eb40e54214 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_abbreviation_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_abbreviation_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_abbreviation_clinical](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.clinical-abbreviation.pipeline").predict("""Gravid with est
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_abbreviation_clinical_pipeline", "en", "clinical/models")
-
-text = '''Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_abbreviation_clinical_pipeline", "en", "clinical/models")
-
-val text = "Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical-abbreviation.pipeline").predict("""Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | CBC          |     126 |   128 | ABBR        |            1 |
 |  1 | AB           |     159 |   160 | ABBR        |            1 |
 |  2 | VDRL         |     189 |   192 | ABBR        |            1 |
 |  3 | HIV          |     247 |   249 | ABBR        |            1 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_ade_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_ade_biobert_pipeline_en.md
index 5049f9f3b7..829b5e97d7 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_ade_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_ade_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_biobert](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.biobert_ade.pipeline").predict("""Been taking Lipitor for 1
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_biobert_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_biobert_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biobert_ade.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |       0.9996 |
 |  1 | severe fatigue |      52 |    65 | ADE         |       0.7588 |
 |  2 | voltaren       |      97 |   104 | DRUG        |       0.998  |
 |  3 | cramps         |     152 |   157 | ADE         |       0.9258 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_ade_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_ade_clinical_pipeline_en.md
index 58f6cef354..d48cf7e8b8 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_ade_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_ade_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_clinical](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.ade_clinical.pipeline").predict("""Been taking Lipitor for
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_clinical_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_clinical_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.ade_clinical.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |      0.9969  |
 |  1 | severe fatigue |      52 |    65 | ADE         |      0.48995 |
 |  2 | voltaren       |      97 |   104 | DRUG        |      0.9889  |
 |  3 | cramps         |     152 |   157 | ADE         |      0.7472  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_ade_clinicalbert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_ade_clinicalbert_pipeline_en.md
index 0ecd6f2b94..26e4c0e306 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_ade_clinicalbert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_ade_clinicalbert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_clinicalbert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.clinical_bert_ade.pipeline").predict("""Been taking Lipitor
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_clinicalbert_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_clinicalbert_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_bert_ade.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |       0.9975 |
 |  1 | severe fatigue |      52 |    65 | ADE         |       0.7094 |
 |  2 | voltaren       |      97 |   104 | DRUG        |       0.9202 |
 |  3 | cramps         |     152 |   157 | ADE         |       0.5992 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_ade_healthcare_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_ade_healthcare_pipeline_en.md
index ff0bef2695..0fe0cf6daf 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_ade_healthcare_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_ade_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_healthcare](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.healthcare_ade.pipeline").predict("""Been taking Lipitor fo
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_healthcare_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_healthcare_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.healthcare_ade.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |      0.998   |
 |  1 | severe fatigue |      52 |    65 | ADE         |      0.67055 |
 |  2 | voltaren       |      97 |   104 | DRUG        |      0.9255  |
 |  3 | cramps         |     152 |   157 | ADE         |      0.9392  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_biobert_pipeline_en.md
index f861974531..bef7b679fc 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_biobert](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -80,58 +81,11 @@ Dermatologic: She has got redness along her right great toe, but no bleeding or
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_biobert_pipeline", "en", "clinical/models")
-
-text = '''This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_biobert_pipeline", "en", "clinical/models")
-
-val text = "This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatomy_biobert.pipeline").predict("""This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label              |   confidence |
 |---:|:-----------------|--------:|------:|:-----------------------|-------------:|
 |  0 | right            |     314 |   318 | Organism_subdivision   |      0.9948  |
@@ -154,9 +108,6 @@ Results
 | 17 | foot             |     999 |  1002 | Organism_subdivision   |      0.9831  |
 | 18 | toe              |    1023 |  1025 | Organism_subdivision   |      0.9653  |
 | 19 | toenails         |    1031 |  1038 | Organism_subdivision   |      0.9999  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_coarse_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_coarse_biobert_pipeline_en.md
index 98cf54ac07..2c8ac33305 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_coarse_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_coarse_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_coarse_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,46 +63,14 @@ nlu.load("en.med_ner.anatomy_coarse_biobert.pipeline").predict("""content in the
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_coarse_biobert_pipeline", "en", "clinical/models")
-
-text = '''content in the lung tissue'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_coarse_biobert_pipeline", "en", "clinical/models")
-
-val text = "content in the lung tissue"
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatomy_coarse_biobert.pipeline").predict("""content in the lung tissue""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label   |   confidence |
 |---:|:------------|--------:|------:|:------------|-------------:|
 |  0 | lung tissue |      15 |    25 | Anatomy     |      0.99155 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_coarse_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_coarse_pipeline_en.md
index f8d876ad75..b335b54704 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_coarse_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_coarse_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_coarse](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,46 +63,13 @@ nlu.load("en.med_ner.anatomy_coarse.pipeline").predict("""content in the lung ti
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_coarse_pipeline", "en", "clinical/models")
-
-text = '''content in the lung tissue'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_coarse_pipeline", "en", "clinical/models")
-
-val text = "content in the lung tissue"
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatomy_coarse.pipeline").predict("""content in the lung tissue""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | lung tissue  |      15 |    25 | Anatomy     |      0.99655 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_pipeline_en.md
index 23c2a7514e..95bd554235 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_anatomy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy](https://nlp.johnsn
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -80,58 +81,11 @@ Dermatologic: She has got redness along the lateral portion of her right great t
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_pipeline", "en", "clinical/models")
-
-text = '''This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along the lateral portion of her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_pipeline", "en", "clinical/models")
-
-val text = "This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along the lateral portion of her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatom.pipeline").predict("""This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along the lateral portion of her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label              |   confidence |
 |---:|:--------------------|--------:|------:|:-----------------------|-------------:|
 |  0 | skin                |     374 |   377 | Organ                  |      1       |
@@ -140,9 +94,6 @@ Results
 |  3 | Mucous membranes    |     716 |   731 | Tissue                 |      0.90445 |
 |  4 | bowel               |     802 |   806 | Organ                  |      0.9648  |
 |  5 | skin                |     956 |   959 | Organ                  |      0.9999  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_bacterial_species_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_bacterial_species_pipeline_en.md
index 1586c94748..7889b96bfd 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_bacterial_species_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_bacterial_species_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_bacterial_species](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,16 @@ nlu.load("en.med_ner.bacterial_species.pipeline").predict("""Based on these gene
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_bacterial_species_pipeline", "en", "clinical/models")
-
-text = '''Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_bacterial_species_pipeline", "en", "clinical/models")
-
-val text = "Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T))."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.bacterial_species.pipeline").predict("""Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | SMSP (T)                |      73 |    80 | SPECIES     |     0.9725   |
 |  1 | Methanoregula formicica |     167 |   189 | SPECIES     |     0.97935  |
 |  2 | SMSP (T)                |     222 |   229 | SPECIES     |     0.991975 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_biomedical_bc2gm_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_biomedical_bc2gm_pipeline_en.md
index ff196ff560..c55d6c6abc 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_biomedical_bc2gm_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_biomedical_bc2gm_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_biomedical_bc2gm](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,16 @@ nlu.load("en.med_ner.biomedical_bc2gm.pipeline").predict("""Immunohistochemical
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_biomedical_bc2gm_pipeline", "en", "clinical/models")
-
-text = '''Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_biomedical_bc2gm_pipeline", "en", "clinical/models")
-
-val text = "Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biomedical_bc2gm.pipeline").predict("""Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label    |   confidence |
 |---:|:-------------|--------:|------:|:-------------|-------------:|
 |  0 | S-100        |      46 |    50 | GENE_PROTEIN |       0.9911 |
 |  1 | HMB-45       |      89 |    94 | GENE_PROTEIN |       0.9944 |
 |  2 | cytokeratin  |     131 |   141 | GENE_PROTEIN |       0.9951 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_bionlp_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_bionlp_biobert_pipeline_en.md
index 456f200b37..59d6808869 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_bionlp_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_bionlp_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_bionlp_biobert](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.bionlp_biobert.pipeline").predict("""Both the erbA IRES and
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_bionlp_biobert_pipeline", "en", "clinical/models")
-
-text = '''Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_bionlp_biobert_pipeline", "en", "clinical/models")
-
-val text = "Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay"
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.bionlp_biobert.pipeline").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label              |   confidence |
 |---:|:--------------------|--------:|------:|:-----------------------|-------------:|
 |  0 | erbA                |       9 |    12 | Gene_or_gene_product   |      1       |
@@ -109,9 +81,6 @@ Results
 |  8 | erbA                |     236 |   239 | Gene_or_gene_product   |      0.9977  |
 |  9 | IRES virus          |     241 |   250 | Organism               |      0.9911  |
 | 10 | blastoderm          |     259 |   268 | Cell                   |      0.9941  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_bionlp_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_bionlp_pipeline_en.md
index 3e4069f194..5ce50a806f 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_bionlp_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_bionlp_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_bionlp](https://nlp.johnsno
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.bionlp.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_bionlp_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_bionlp_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.bionlp.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label            |   confidence |
 |---:|:-----------------------|--------:|------:|:---------------------|-------------:|
 |  0 | human                  |       4 |     8 | Organism             |     0.9996   |
@@ -109,9 +81,6 @@ Results
 |  8 | fat andskeletal muscle |     749 |   770 | Tissue               |     0.955433 |
 |  9 | KCNJ9                  |     801 |   805 | Gene_or_gene_product |     0.9172   |
 | 10 | Type II                |     940 |   946 | Gene_or_gene_product |     0.98845  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_cancer_genetics_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_cancer_genetics_pipeline_en.md
index 6f81f69edb..2e3bf3a30d 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_cancer_genetics_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_cancer_genetics_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_cancer_genetics](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,39 +63,11 @@ nlu.load("en.med_ner.cancer_genetics.pipeline").predict("""The human KCNJ9 (Kir
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_cancer_genetics_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_cancer_genetics_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.cancer_genetics.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
 
 |    | ner_chunk                                                               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------------------|--------:|------:|:------------|-------------:|
@@ -110,9 +83,6 @@ Results
 |  9 | KCNJ9 gene                                                              |     801 |   810 | DNA         |     0.95605  |
 | 10 | KCNJ9 protein                                                           |     868 |   880 | protein     |     0.844    |
 | 11 | locus                                                                   |     931 |   935 | DNA         |     0.9685   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_cellular_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_cellular_biobert_pipeline_en.md
index 180263ffea..1d1e845674 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_cellular_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_cellular_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_cellular_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.cellular_biobert.pipeline").predict("""Detection of various
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_cellular_biobert_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_cellular_biobert_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.cellular_biobert.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                   |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | intracellular signaling proteins            |      27 |    58 | protein     |     0.673333 |
@@ -118,9 +90,6 @@ Results
 | 17 | GAD                                         |     791 |   793 | protein     |     0.6432   |
 | 18 | reporter gene                               |     848 |   860 | DNA         |     0.61005  |
 | 19 | Tax                                         |     863 |   865 | protein     |     0.99     |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_cellular_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_cellular_pipeline_en.md
index d7af732d36..cf6104e050 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_cellular_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_cellular_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_cellular](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.cellular.pipeline").predict("""Detection of various other i
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_cellular_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_cellular_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.cellular.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                   |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | intracellular signaling proteins                            |      27 |    58 | protein     |     0.763367 |
@@ -118,9 +90,6 @@ Results
 | 17 | GAD                                                         |     791 |   793 | protein     |     0.9932   |
 | 18 | reporter gene                                               |     848 |   860 | DNA         |     0.78715  |
 | 19 | Tax                                                         |     863 |   865 | protein     |     0.9986   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_chemd_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_chemd_clinical_pipeline_en.md
index 9de56acc69..8c1d6f3827 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_chemd_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_chemd_clinical_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_chemd_clinical](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-text = '''Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-val text = "Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-text = '''Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-val text = "Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                         |   begin |   end | ner_label    |   confidence |
 |---:|:-----------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | Lystabactins                       |      65 |    76 | FAMILY       |     0.9841   |
@@ -134,12 +86,6 @@ Results
 | 11 | amino acid                         |     602 |   611 | FAMILY       |     0.4204   |
 | 12 | 4,8-diamino-3-hydroxyoctanoic acid |     614 |   647 | SYSTEMATIC   |     0.9124   |
 | 13 | LySta                              |     650 |   654 | ABBREVIATION |     0.9193   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_chemicals_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_chemicals_pipeline_en.md
index a0e7622711..6aa6f91e40 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_chemicals_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_chemicals_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemicals](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.chemicals.pipeline").predict("""The results have shown that
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chemicals.pipeline").predict("""The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                  |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------|--------:|------:|:------------|-------------:|
 |  0 | p - choloroaniline          |      40 |    57 | CHEM        |     0.935767 |
@@ -103,9 +75,6 @@ Results
 |  2 | kanamycin                   |     168 |   176 | CHEM        |     0.9824   |
 |  3 | colistin                    |     180 |   187 | CHEM        |     0.9911   |
 |  4 | povidone - iodine           |     193 |   209 | CHEM        |     0.8111   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_chemprot_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_chemprot_biobert_pipeline_en.md
index f9b87559da..8e0cf17b33 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_chemprot_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_chemprot_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemprot_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.chemprot_biobert.pipeline").predict("""Keratinocyte growth
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemprot_biobert_pipeline", "en", "clinical/models")
-
-text = '''Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemprot_biobert_pipeline", "en", "clinical/models")
-
-val text = "Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chemprot_biobert.pipeline").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk    |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | Keratinocyte |       0 |    11 | GENE-Y      |       0.894  |
@@ -105,9 +77,6 @@ Results
 |  4 | fibroblast   |      38 |    47 | GENE-Y      |       0.3905 |
 |  5 | growth       |      49 |    54 | GENE-Y      |       0.7109 |
 |  6 | factor       |      56 |    61 | GENE-Y      |       0.8693 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_chemprot_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_chemprot_clinical_pipeline_en.md
index 736b4ddb74..e3c0eac25f 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_chemprot_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_chemprot_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemprot_clinical](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.chemprot_clinical.pipeline").predict("""Keratinocyte growth
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemprot_clinical_pipeline", "en", "clinical/models")
-
-text = '''Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemprot_clinical_pipeline", "en", "clinical/models")
-
-val text = "Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chemprot_clinical.pipeline").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk    |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | Keratinocyte |       0 |    11 | GENE-Y      |       0.7433 |
@@ -105,9 +77,6 @@ Results
 |  4 | fibroblast   |      38 |    47 | GENE-Y      |       0.5111 |
 |  5 | growth       |      49 |    54 | GENE-Y      |       0.4559 |
 |  6 | factor       |      56 |    61 | GENE-Y      |       0.5213 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_chexpert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_chexpert_pipeline_en.md
index ed2d5037d8..c51370b881 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_chexpert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_chexpert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chexpert](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.chexpert.pipeline").predict("""FINAL REPORT HISTORY : Chest
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chexpert_pipeline", "en", "clinical/models")
-
-text = '''FINAL REPORT HISTORY : Chest tube leak , to assess for pneumothorax. FINDINGS : In comparison with study of ___ , the endotracheal tube and Swan - Ganz catheter have been removed . The left chest tube remains in place and there is no evidence of pneumothorax. Mild atelectatic changes are seen at the left base.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chexpert_pipeline", "en", "clinical/models")
-
-val text = "FINAL REPORT HISTORY : Chest tube leak , to assess for pneumothorax. FINDINGS : In comparison with study of ___ , the endotracheal tube and Swan - Ganz catheter have been removed . The left chest tube remains in place and there is no evidence of pneumothorax. Mild atelectatic changes are seen at the left base."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chexpert.pipeline").predict("""FINAL REPORT HISTORY : Chest tube leak , to assess for pneumothorax. FINDINGS : In comparison with study of ___ , the endotracheal tube and Swan - Ganz catheter have been removed . The left chest tube remains in place and there is no evidence of pneumothorax. Mild atelectatic changes are seen at the left base.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | endotracheal |     118 |   129 | OBS         |       0.9881 |
@@ -112,9 +83,6 @@ Results
 | 11 | changes      |     277 |   283 | OBS         |       0.9984 |
 | 12 | left         |     301 |   304 | ANAT        |       0.9999 |
 | 13 | base         |     306 |   309 | ANAT        |       0.9999 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_bert_pipeline_ro.md b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_bert_pipeline_ro.md
index 4bd986770b..7bd253102d 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_bert_pipeline_ro.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_bert_pipeline_ro.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_clinical_bert](https://nlp.
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-text = '''Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-val text = "Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-text = '''Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-val text = "Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-bass
 |    | ner_chunks                        |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Angio CT                          |      12 |    19 | Imaging_Test              |     0.96415  |
@@ -144,12 +96,6 @@ bass
 | 21 | cardiotoracica                    |     461 |   474 | Body_Part                 |     0.9344   |
 | 22 | achizitii secventiale prospective |     479 |   511 | Imaging_Technique         |     0.966833 |
 | 23 | 100/min                           |     546 |   552 | Pulse                     |     0.9128   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_biobert_pipeline_en.md
index 6df1a1f2dd..a84e1a593a 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.clinical_biobert.pipeline").predict("""The patient is a 21-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                           |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | congestion                                          |      62 |    71 | PROBLEM     |     0.5069   |
@@ -112,9 +84,6 @@ Results
 | 11 | albuterol treatments                                |     637 |   656 | TREATMENT   |     0.8917   |
 | 12 | His urine output                                    |     675 |   690 | TEST        |     0.7114   |
 | 13 | any diarrhea                                        |     832 |   843 | PROBLEM     |     0.73595  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_large_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_large_pipeline_en.md
index b56d06cf79..9a977cf773 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_large_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.clinical_large.pipeline").predict("""The human KCNJ9 (Kir 3
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_large_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_large_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_large.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                   |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | the G-protein-activated inwardly rectifying potassium (GIRK |      48 |   106 | TREATMENT   |     0.6926   |
@@ -118,9 +90,6 @@ Results
 | 17 | the standard therapy                                        |    1067 |  1086 | TREATMENT   |     0.757767 |
 | 18 | anthracyclines                                              |    1125 |  1138 | TREATMENT   |     0.9999   |
 | 19 | taxanes                                                     |    1144 |  1150 | TREATMENT   |     0.9999   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_pipeline_en.md
index 237f3742f6..aded43e25a 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.clinical.pipeline").predict("""The human KCNJ9 (Kir 3.3, GI
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                   |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | the G-protein-activated inwardly rectifying potassium (GIRK |      48 |   106 | TREATMENT   |     0.6926   |
@@ -118,9 +89,6 @@ Results
 | 17 | the standard therapy                                        |    1067 |  1086 | TREATMENT   |     0.757767 |
 | 18 | anthracyclines                                              |    1125 |  1138 | TREATMENT   |     0.9999   |
 | 19 | taxanes                                                     |    1144 |  1150 | TREATMENT   |     0.9999   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_pipeline_ro.md b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_pipeline_ro.md
index f513967008..25a5ac24f7 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_pipeline_ro.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_pipeline_ro.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_clinical](https://nlp.johns
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-text = ''' Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-val text = " Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-text = ''' Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-val text = " Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                        |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Angio CT                          |      13 |    20 | Imaging_Test              |     0.92675  |
@@ -145,12 +98,6 @@ Results
 | 22 | cardiotoracica                    |     455 |   468 | Body_Part                 |     0.9995   |
 | 23 | achizitii secventiale prospective |     473 |   505 | Imaging_Technique         |     0.8514   |
 | 24 | 100/min                           |     540 |   546 | Pulse                     |     0.8501   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_trials_abstracts_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_trials_abstracts_pipeline_en.md
index 58e338598c..aceffb4b81 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_trials_abstracts_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_trials_abstracts_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_trials_abstracts](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.clinical_trials_abstracts.pipe").predict("""A one-year, ran
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-text = '''A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes. In a multicentre, open, randomised study, 570 patients with Type 2 diabetes, aged 34 - 80 years, were treated for 52 weeks with insulin glargine or NPH insulin given once daily at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-val text = "A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes. In a multicentre, open, randomised study, 570 patients with Type 2 diabetes, aged 34 - 80 years, were treated for 52 weeks with insulin glargine or NPH insulin given once daily at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_trials_abstracts.pipe").predict("""A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes. In a multicentre, open, randomised study, 570 patients with Type 2 diabetes, aged 34 - 80 years, were treated for 52 weeks with insulin glargine or NPH insulin given once daily at bedtime.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label          |   confidence |
 |---:|:-----------------|--------:|------:|:-------------------|-------------:|
 |  0 | randomised       |      12 |    21 | CTDesign           |     0.9996   |
@@ -115,9 +86,6 @@ Results
 | 14 | NPH insulin      |     300 |   310 | Drug               |     0.97955  |
 | 15 | once daily       |     318 |   327 | DrugTime           |     0.999    |
 | 16 | bedtime          |     332 |   338 | DrugTime           |     0.9937   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_trials_abstracts_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_trials_abstracts_pipeline_es.md
index 58b9c13cb7..7cad1e122e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_trials_abstracts_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_clinical_trials_abstracts_pipeline_es.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_clinical_trials_abstracts](
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | suplementación                |      13 |    26 | PROC        |     0.9987   |
@@ -132,12 +84,6 @@ Results
 |  9 | pp                            |     337 |   338 | CHEM        |     0.96     |
 | 10 | diálisis                      |     388 |   395 | PROC        |     0.9982   |
 | 11 | función  residual             |     398 |   414 | PROC        |     0.73045  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_covid_trials_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_covid_trials_pipeline_en.md
index bdd57d4f0d..192c876f51 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_covid_trials_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_covid_trials_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_covid_trials](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-text = '''In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-val text = "In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-text = '''In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-val text = "In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                          |   begin |   end | ner_label                 |   confidence |
 |---:|:------------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | December 2019                       |       3 |    15 | Date                      |     0.99655  |
@@ -140,12 +91,6 @@ Results
 | 17 | CDC                                 |     547 |   549 | Institution               |     0.8296   |
 | 18 | 2020                                |     848 |   851 | Date                      |     0.9997   |
 | 19 | COVID‑19 vaccine                    |     864 |   879 | Vaccine_Name              |     0.87505  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_augmented_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_augmented_pipeline_en.md
index 8c8ecf0033..7fa5392d29 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_augmented_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_augmented](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.deid.ner_augmented.pipeline").predict("""HISTORY OF PRESENT ILLNESS
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_augmented_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_augmented_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.ner_augmented.pipeline").predict("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | Smith           |      32 |    36 | NAME        |     0.9998   |
@@ -110,9 +80,6 @@ Results
 |  9 | Hart            |    1221 |  1224 | NAME        |     0.9996   |
 | 10 | Smith           |    1231 |  1235 | NAME        |     0.9998   |
 | 11 | 02/07/2003      |    1329 |  1338 | DATE        |     0.9997   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_biobert_pipeline_en.md
index 11a917f697..7ac4ee3f61 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_biobert](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.deid.ner_biobert.pipeline").predict("""A. Record date : 2093-01-13,
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_biobert_pipeline", "en", "clinical/models")
-
-text = '''A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_biobert_pipeline", "en", "clinical/models")
-
-val text = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.ner_biobert.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      17 |    26 | DATE        |      0.981   |
@@ -107,9 +77,6 @@ Results
 |  6 | Keats Street                  |     150 |   161 | LOCATION    |      0.77305 |
 |  7 | Phone                         |     164 |   168 | LOCATION    |      0.7083  |
 |  8 | Brothers                      |     253 |   260 | LOCATION    |      0.9447  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_enriched_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_enriched_biobert_pipeline_en.md
index a868eca786..0da14ec19e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_enriched_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_enriched_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_enriched_biobert](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.ner_enriched_biobert.pipeline").predict("""A. Record date : 20
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_enriched_biobert_pipeline", "en", "clinical/models")
-
-text = '''A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_enriched_biobert_pipeline", "en", "clinical/models")
-
-val text = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.ner_enriched_biobert.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label    |   confidence |
 |---:|:------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 2093-01-13                    |      17 |    26 | DATE         |     0.9267   |
@@ -106,9 +78,6 @@ Results
 |  5 | 0295 Keats Street             |     145 |   161 | STREET       |     0.592433 |
 |  6 | 302) 786-5227                 |     174 |   186 | PHONE        |     0.846833 |
 |  7 | Brothers Coal-Mine            |     253 |   270 | ORGANIZATION |     0.45085  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_enriched_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_enriched_pipeline_en.md
index e6e7c5eabb..7ce473456d 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_enriched_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_enriched_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_enriched](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.deid_enriched.pipeline").predict("""HISTORY OF PRESENT ILLN
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_enriched_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_enriched_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_enriched.pipeline").predict("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | Smith         |      32 |    36 | PATIENT     |      0.9997  |
@@ -108,9 +80,6 @@ Results
 |  7 | Hart          |    1221 |  1224 | DOCTOR      |      0.9985  |
 |  8 | Smith         |    1231 |  1235 | PATIENT     |      0.9992  |
 |  9 | 02/07/2003    |    1329 |  1338 | DATE        |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_augmented_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_augmented_pipeline_en.md
index 4bf9a6ce45..8e160ca310 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_augmented_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_augmented](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -56,60 +57,10 @@ nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-```scala
-val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-```scala
-val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-+-------------------------------------------------+---------+
 |chunk                                            |ner_label|
 +-------------------------------------------------+---------+
 |2093-01-13                                       |DATE     |
@@ -123,13 +74,6 @@ Results
 |1-11-2000                                        |DATE     |
 |Cocke County Baptist Hospital. 0295 Keats Street.|LOCATION |
 |(302) 786-5227                                   |CONTACT  |
-+-------------------------------------------------+---------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_bert_pipeline_ro.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_bert_pipeline_ro.md
index cae16c6ee6..cde01fad48 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_bert_pipeline_ro.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_bert_pipeline_ro.md
@@ -34,70 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_bert](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -147,12 +84,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | LOCATION    |     0.99352  |
@@ -167,12 +98,6 @@ Results
 |  9 | Agota Evelyn Tımar           |     191 |   210 | NAME        |     0.859975 |
 |    | C                            |         |       |             |              |
 | 10 | 2450502264401                |     218 |   230 | ID          |     1        |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_glove_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_glove_pipeline_en.md
index e47fb06889..b94283ff8e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_glove_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_glove_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_glove](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     1        |
@@ -131,12 +82,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.8586   |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.948667 |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9972   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_ar.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_ar.md
index 91900322b2..c3600aca4a 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_ar.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_ar.md
@@ -34,24 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ar", "clinical/models")
-text = '''ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-''
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "ar", "clinical/models")
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-"
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ar", "clinical/models")
@@ -68,12 +51,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-## Results
 
-```bash
-Results
 
+## Results
 
+```bash
 +---------------+----------------------+
 |chunks         |entities          |
 +---------------+----------------------+
@@ -88,9 +70,6 @@ Results
 |أميرة أحمد         |NAME          |
 |ليلى                 |NAME          |
 +---------------+---------------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_de.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_de.md
index 89077b036a..9717285ef1 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_de.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_de.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("de.med_ner.deid_generic.pipeline").predict("""Michael Berger wird am M
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "de", "clinical/models")
-
-text = '''Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "de", "clinical/models")
-
-val text = "Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.med_ner.deid_generic.pipeline").predict("""Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | Michael Berger            |       0 |    13 | NAME        |     0.99555  |
@@ -104,9 +75,6 @@ Results
 |  3 | Bad Kissingen             |      84 |    96 | LOCATION    |     0.90785  |
 |  4 | Berger                    |     117 |   122 | NAME        |     0.935    |
 |  5 | 76                        |     128 |   129 | AGE         |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_it.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_it.md
index d9615a692a..9a58cd9890 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_it.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_it.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +70,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   | confidence   |
 |---:|:----------------------|--------:|------:|:------------|:-------------|
 |  0 | Gastone Montanariello |       9 |    29 | NAME        |              |
 |  1 | 49                    |      32 |    33 | AGE         |              |
 |  2 | Ospedale San Camillo  |      55 |    74 | LOCATION    |              |
 |  3 | marzo 2015            |     128 |   137 | DATE        |              |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_ro.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_ro.md
index 42338228cc..f6ab43c845 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_ro.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_generic_pipeline_ro.md
@@ -32,72 +32,10 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -147,12 +85,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | LOCATION    |     0.88326  |
@@ -164,12 +96,6 @@ Results
 |  6 | 77                           |     179 |   180 | AGE         |     1        |
 |  7 | Agota Evelyn Tımar           |     190 |   207 | NAME        |     0.832933 |
 |  8 | 2450502264401                |     217 |   229 | ID          |     1        |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_large_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_large_pipeline_en.md
index 4b73dbdc11..5fc3f167a8 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_large_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_large](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.deid_large.pipeline").predict("""HISTORY OF PRESENT ILLNESS
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_large_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_large_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_large.pipeline").predict("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | Smith           |      32 |    36 | NAME        |     0.9998   |
@@ -110,9 +82,6 @@ Results
 |  9 | Hart            |    1221 |  1224 | NAME        |     0.9995   |
 | 10 | Smith           |    1231 |  1235 | NAME        |     0.9998   |
 | 11 | 02/07/2003      |    1329 |  1338 | DATE        |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_sd_large_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_sd_large_pipeline_en.md
index 5207737c36..f13a071a30 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_sd_large_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_sd_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_sd_large](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.med_ner_large.pipeline").predict("""Record date : 2093-01-13 ,
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_sd_large_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_sd_large_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.med_ner_large.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     0.9999   |
@@ -109,9 +81,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.795975 |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.741567 |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.984    |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_sd_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_sd_pipeline_en.md
index aed5b7d5c0..0594178cc0 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_sd_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_sd_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_sd](https://nlp.johnsn
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.sd.pipeline").predict("""Record date : 2093-01-13 , David Hale
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_sd_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_sd_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.sd.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     0.9952   |
@@ -109,9 +81,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.84345  |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.775333 |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9492   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_augmented_i2b2_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_augmented_i2b2_pipeline_en.md
index d30f3c0500..aac10dd632 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_augmented_i2b2_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_augmented_i2b2_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_augmented_i2
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.subentity_ner_augmented_i2b2.pipeline").predict("""Record date
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_augmented_i2b2_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_augmented_i2b2_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.subentity_ner_augmented_i2b2.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |     0.9997   |
@@ -109,9 +81,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |     0.863775 |
 |  9 | 0295 Keats Street             |     195 |   211 | STREET        |     0.754533 |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |     0.9697   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_augmented_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_augmented_pipeline_en.md
index e380ee898e..b15dc06464 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_augmented_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_augmented](h
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.subentity_ner_augmented.pipeline").predict("""Record date : 20
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_augmented_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_augmented_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.subentity_ner_augmented.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |      1       |
@@ -109,9 +81,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |      0.97485 |
 |  9 | 0295 Keats Street             |     195 |   211 | STREET        |      0.8209  |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |      0.9541  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_bert_pipeline_ro.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_bert_pipeline_ro.md
index ec5508e787..d6b9c1798b 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_bert_pipeline_ro.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_bert_pipeline_ro.md
@@ -34,70 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -147,12 +84,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | HOSPITAL    |     0.84306  |
@@ -165,12 +96,6 @@ Results
 |  7 | 77                           |     180 |   181 | AGE         |     1        |
 |  8 | Agota Evelyn Tımar           |     191 |   208 | DOCTOR      |     0.803667 |
 |  9 | 2450502264401                |     218 |   230 | IDNUM       |     0.9995   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_glove_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_glove_pipeline_en.md
index af3713de55..0a40758eaa 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_glove_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_glove_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_glove](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |     1        |
@@ -131,12 +82,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |     0.731325 |
 |  9 | 0295 Keats Street             |     195 |   211 | STREET        |     0.737067 |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |     0.9882   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_ar.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_ar.md
index 18bb94f55f..189a119db7 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_ar.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_ar.md
@@ -34,56 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-text= '''
-ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
 
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-text= '''
-ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -123,13 +74,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 +---------------+--------+
 |chunks         |entities|
 +---------------+--------+
@@ -145,13 +89,6 @@ Results
 |ليلى           |PATIENT |
 |35             |AGE     |
 +---------------+--------+
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_de.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_de.md
index e5063937d5..8e0372f37d 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_de.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_de.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("de.deid.ner_subentity.pipeline").predict("""Michael Berger wird am Mor
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "de", "clinical/models")
-
-text = '''Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "de", "clinical/models")
-
-val text = "Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.ner_subentity.pipeline").predict("""Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Michael Berger        |       0 |    13 | PATIENT     |      0.99685 |
@@ -104,9 +75,6 @@ Results
 |  3 | Bad Kissingen         |      84 |    96 | CITY        |      0.69685 |
 |  4 | Berger                |     117 |   122 | PATIENT     |      0.5764  |
 |  5 | 76                    |     128 |   129 | AGE         |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_it.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_it.md
index 53453fc64a..ccf6fea7e5 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_it.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_it.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +69,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   | confidence   |
 |---:|:----------------------|--------:|------:|:------------|:-------------|
 |  0 | Gastone Montanariello |       9 |    29 | PATIENT     |              |
 |  1 | 49                    |      32 |    33 | AGE         |              |
 |  2 | Ospedale San Camillo  |      55 |    74 | HOSPITAL    |              |
 |  3 | marzo 2015            |     128 |   137 | DATE        |              |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_ro.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_ro.md
index a2c59f3f0d..b5caa4896d 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_ro.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_subentity_pipeline_ro.md
@@ -34,70 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -147,12 +84,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | HOSPITAL    |      0.5594  |
@@ -165,12 +96,6 @@ Results
 |  7 | 77                           |     180 |   181 | AGE         |      1       |
 |  8 | Agota Evelyn Tımar           |     191 |   208 | DOCTOR      |      0.8149  |
 |  9 | 2450502264401                |     218 |   230 | IDNUM       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_synthetic_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_synthetic_pipeline_en.md
index 2bb06ce2f7..a79218fc23 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deid_synthetic_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deid_synthetic_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_synthetic](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     1        |
@@ -131,12 +82,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.968825 |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.7831   |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9985   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_deidentify_dl_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_deidentify_dl_pipeline_en.md
index 0af6f645d2..d0b89fc368 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_deidentify_dl_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_deidentify_dl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deidentify_dl](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.deidentify.pipeline").predict("""Record date : 2093-01-13 ,
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deidentify_dl_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deidentify_dl_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deidentify.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |      0.9999  |
@@ -109,9 +80,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |      0.9466  |
 |  9 | Keats Street                  |     200 |   211 | STREET        |      0.91485 |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |      0.7415  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_diag_proc_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_diag_proc_pipeline_es.md
index f919a03edb..83bfbb0af7 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_diag_proc_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_diag_proc_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_diag_proc](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                             |   begin |   end | ner_label     |   confidence |
 |---:|:--------------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | ENFERMEDAD                            |      12 |    21 | DIAGNOSTICO   |      0.9989  |
@@ -131,12 +82,6 @@ Results
 |  8 | enfermedad de las arterias coronarias |     934 |   970 | DIAGNOSTICO   |      0.75594 |
 |  9 | estenosada                            |    1010 |  1019 | DIAGNOSTICO   |      0.9288  |
 | 10 | LAD                                   |    1068 |  1070 | DIAGNOSTICO   |      0.9365  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_biobert_pipeline_en.md
index 17d7911887..6bd1a343c8 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_diseases_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,16 @@ nlu.load("en.med_ner.diseases_biobert.pipeline").predict("""Indomethacin resulte
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diseases_biobert_pipeline", "en", "clinical/models")
-
-text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diseases_biobert_pipeline", "en", "clinical/models")
-
-val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.diseases_biobert.pipeline").predict("""Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | interstitial cystitis |      61 |    81 | Disease     |      0.99655 |
 |  1 | mastocytosis          |     129 |   140 | Disease     |      0.8569  |
 |  2 | cystitis              |     209 |   216 | Disease     |      0.9717  |
 |  3 | prostate cancer       |     355 |   369 | Disease     |      0.85965 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_large_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_large_pipeline_en.md
index 1ee327c5c3..3371503917 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_large_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_diseases_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,13 @@ nlu.load("en.med_ner.diseases_large.pipeline").predict("""Detection of various o
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diseases_large_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diseases_large_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.diseases_large.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | T-cell leukemia |     136 |   150 | Disease     |      0.93585 |
 |  1 | T-cell leukemia |     402 |   416 | Disease     |      0.9567  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_pipeline_en.md
index bb1d95de3a..a382e4d0d4 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_diseases_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_diseases](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.diseases.pipeline").predict("""Detection of various other i
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diseases_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diseases_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.diseases.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | T-cell leukemia |     136 |   150 | Disease     |      0.92015 |
 |  1 | T-cell leukemia |     402 |   416 | Disease     |      0.94145 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_drugprot_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_drugprot_clinical_pipeline_en.md
index f885985acd..defa3e2d17 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_drugprot_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_drugprot_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugprot_clinical](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,15 @@ nlu.load("en.med_ner.clinical_drugprot.pipeline").predict("""Anabolic effects of
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugprot_clinical_pipeline", "en", "clinical/models")
-
-text = '''Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugprot_clinical_pipeline", "en", "clinical/models")
-
-val text = "Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_drugprot.pipeline").predict("""Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | clenbuterol         |      20 |    30 | CHEMICAL    |      0.9691  |
 |  1 | beta 2-adrenoceptor |      67 |    85 | GENE        |      0.89855 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_greedy_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_greedy_pipeline_en.md
index 93c934e0e2..36c3f6f1dd 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_greedy_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_greedy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugs_greedy](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.drugs_greedy.pipeline").predict("""DOSAGE AND ADMINISTRATIO
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugs_greedy_pipeline", "en", "clinical/models")
-
-text = '''DOSAGE AND ADMINISTRATION The initial dosage of hydrocortisone tablets may vary from 20 mg to 240 mg of hydrocortisone per day depending on the specific disease entity being treated.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugs_greedy_pipeline", "en", "clinical/models")
-
-val text = "DOSAGE AND ADMINISTRATION The initial dosage of hydrocortisone tablets may vary from 20 mg to 240 mg of hydrocortisone per day depending on the specific disease entity being treated."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.drugs_greedy.pipeline").predict("""DOSAGE AND ADMINISTRATION The initial dosage of hydrocortisone tablets may vary from 20 mg to 240 mg of hydrocortisone per day depending on the specific disease entity being treated.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                         |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------------|--------:|------:|:------------|-------------:|
 |  0 | hydrocortisone tablets            |      48 |    69 | DRUG        |       0.9923 |
 |  1 | 20 mg to 240 mg of hydrocortisone |      85 |   117 | DRUG        |       0.7361 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_large_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_large_pipeline_en.md
index fde5f979f9..10ee6d55d1 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_large_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugs_large](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.drugs_large.pipeline").predict("""The patient is a 40-year-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugs_large_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. He has been advised Aspirin 81 milligrams QDay. Humulin N. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugs_large_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. He has been advised Aspirin 81 milligrams QDay. Humulin N. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain.."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.drugs_large.pipeline").predict("""The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. He has been advised Aspirin 81 milligrams QDay. Humulin N. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain..""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                        |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Aspirin 81 milligrams            |     306 |   326 | DRUG        |     0.8401   |
@@ -103,9 +73,6 @@ Results
 |  2 | insulin 50 units                 |     345 |   360 | DRUG        |     0.847067 |
 |  3 | HCTZ 50 mg                       |     370 |   379 | DRUG        |     0.875567 |
 |  4 | Nitroglycerin 1/150 sublingually |     387 |   418 | DRUG        |     0.845967 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_pipeline_en.md
index e34663f4bc..a0862c2719 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_drugs_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugs](https://nlp.johnsnow
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.drugs.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugs_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugs_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.drugs.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | potassium      |      92 |   100 | DrugChem    |       0.5346 |
@@ -105,9 +77,6 @@ Results
 |  4 | vinorelbine    |    1343 |  1353 | DrugChem    |       0.9815 |
 |  5 | anthracyclines |    1390 |  1403 | DrugChem    |       0.9447 |
 |  6 | taxanes        |    1409 |  1415 | DrugChem    |       0.6213 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_en.md
index c0a38fed63..a1c50df0b7 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_en.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-val text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-val text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                         |   begin |   end | entities           |   confidence |
 |---:|:-------------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | A 3-year-old boy               |       1 |    16 | patient            |     0.733133 |
@@ -162,12 +105,6 @@ Results
 | 25 | revealed                       |     628 |   635 | clinical_event     |     0.9989   |
 | 26 | spindle cell proliferation     |     637 |   662 | clinical_condition |     0.4487   |
 | 27 | the submucosal layer           |     667 |   686 | bodypart           |     0.523    |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_es.md
index b7a950305f..408cd83b2c 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_es.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-val text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-val text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                       |   begin |   end | entities           |   confidence |
 |---:|:-----------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Un niño de 3 años            |       1 |    17 | patient            |     0.68856  |
@@ -170,12 +113,6 @@ Results
 | 33 | proliferación                |     711 |   723 | clinical_event     |     0.9996   |
 | 34 | células fusiformes           |     728 |   745 | bodypart           |     0.7001   |
 | 35 | la capa submucosa            |     750 |   766 | bodypart           |     0.641267 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_eu.md b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_eu.md
index 72fa750731..efaaa5907e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_eu.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_eu.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-val text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-val text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                   |   begin |   end | entities           |   confidence |
 |---:|:-------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | 3 urteko mutiko bat      |       1 |    19 | patient            |     0.813975 |
@@ -175,12 +118,6 @@ Results
 | 38 | utzi                     |     701 |   704 | clinical_event     |     0.925    |
 | 39 | mukosaren azpiko zelulen |     711 |   734 | bodypart           |     0.754933 |
 | 40 | ugaltzea                 |     736 |   743 | clinical_event     |     0.9989   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_fr.md b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_fr.md
index d716e32757..8e747bfd99 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_fr.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_case_pipeline_fr.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-val text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-val text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                                                |   begin |   end | entities           |   confidence |
 |---:|:------------------------------------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Un garçon de 3 ans                                    |       1 |    18 | patient            |     0.58786  |
@@ -166,12 +109,6 @@ Results
 | 29 | prolifération                                         |     735 |   747 | clinical_event     |     0.6767   |
 | 30 | cellules fusiformes                                   |     752 |   770 | bodypart           |     0.5233   |
 | 31 | la couche sous-muqueuse                               |     777 |   799 | bodypart           |     0.6755   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_en.md
index 0893542ef9..4ac7a3fa36 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_en.md
@@ -34,32 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "en", "clinical/models")
-
-text = "
-Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "en", "clinical/models")
 
-val text = "
-Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -87,9 +62,6 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                  |   begin |   end | entities           |   confidence |
 |---:|:------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Hyperparathyroidism     |       1 |    19 | clinical_condition |     0.9375   |
@@ -100,9 +72,6 @@ Results
 |  5 | fractures               |     281 |   289 | clinical_condition |     0.9726   |
 |  6 | anesthesia              |     305 |   314 | clinical_condition |     0.991    |
 |  7 | mandibular fracture     |     330 |   348 | clinical_condition |     0.54925  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_es.md
index c286fc9481..569095a533 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_es.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-val text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-val text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks               |   begin |   end | entities           |   confidence |
 |---:|:---------------------|--------:|------:|:-------------------|-------------:|
 |  0 | cicatriz             |      37 |    44 | clinical_condition |      0.9883  |
@@ -139,12 +82,6 @@ Results
 |  2 | signos               |     170 |   175 | clinical_condition |      0.9862  |
 |  3 | irritación           |     180 |   189 | clinical_condition |      0.9975  |
 |  4 | hernias inguinales   |     214 |   231 | clinical_condition |      0.7543  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_eu.md b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_eu.md
index a2e1fbc0ce..6b4fa505b1 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_eu.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_eu.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-val text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-val text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks     |   begin |   end | entities           |   confidence |
 |---:|:-----------|--------:|------:|:-------------------|-------------:|
 |  0 | mina       |      98 |   101 | clinical_condition |       0.8754 |
@@ -141,12 +84,6 @@ Results
 |  4 | hantura    |     203 |   209 | clinical_condition |       0.8805 |
 |  5 | Polakiuria |     256 |   265 | clinical_condition |       0.6683 |
 |  6 | sintomarik |     345 |   354 | clinical_condition |       0.9632 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_fr.md b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_fr.md
index 496ef4a521..7bd07a6aa9 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_fr.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_fr.md
@@ -34,62 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-val text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-val text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,12 +78,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                   |   begin |   end | entities           |   confidence |
 |---:|:-------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | ulcérations              |      47 |    57 | clinical_condition |       0.9995 |
@@ -148,12 +87,6 @@ Results
 |  4 | apyrexie                 |     261 |   268 | clinical_condition |       0.9963 |
 |  5 | anasarque                |     353 |   361 | clinical_condition |       0.9973 |
 |  6 | décompensation cardiaque |     409 |   432 | clinical_condition |       0.8948 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_it.md b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_it.md
index c29b398aef..d8860a5f75 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_it.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_eu_clinical_condition_pipeline_it.md
@@ -32,64 +32,10 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-val text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-val text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,12 +79,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                 |   begin |   end | entities           |   confidence |
 |---:|:-----------------------|--------:|------:|:-------------------|-------------:|
 |  0 | dolore epigastrico     |      30 |    47 | clinical_condition |      0.90845 |
@@ -147,12 +87,6 @@ Results
 |  3 | edema                  |     188 |   192 | clinical_condition |      1       |
 |  4 | fistola transfinterica |     294 |   315 | clinical_condition |      0.97785 |
 |  5 | infiammazione          |     372 |   384 | clinical_condition |      0.9996  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_events_admission_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_events_admission_clinical_pipeline_en.md
index b4b6bcffc2..30407af905 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_events_admission_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_events_admission_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_admission_clinical](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,16 @@ nlu.load("en.med_ner.events_admission_clinical.pipeline").predict("""The patient
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_admission_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_admission_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.events_admission_clinical.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk          |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | OCCURRENCE    |       0.6219 |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |       0.812  |
 |  2 | last evening       |      44 |    55 | TIME          |       0.9534 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_events_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_events_biobert_pipeline_en.md
index ca59757883..5b738c6482 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_events_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_events_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_biobert](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,16 @@ nlu.load("en.med_ner.biobert_events.pipeline").predict("""The patient presented
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biobert_events.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk          |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | OCCURRENCE    |     0.5019   |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |     0.695333 |
 |  2 | last evening       |      44 |    55 | DATE          |     0.7621   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_events_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_events_clinical_pipeline_en.md
index 599156f632..050548b1b1 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_events_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_events_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_clinical](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,15 @@ nlu.load("en.med_ner.events_clinical.pipeline").predict("""The patient presented
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.events_clinical.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk          |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | OCCURRENCE    |     0.7132   |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |     0.723267 |
 |  2 | last evening       |      44 |    55 | DATE          |     0.90555  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_events_healthcare_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_events_healthcare_pipeline_en.md
index e55e85e481..6f62b92cbc 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_events_healthcare_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_events_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_healthcare](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,16 @@ nlu.load("en.med_ner.healthcare_events.pipeline").predict("""The patient present
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_healthcare_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_healthcare_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.healthcare_events.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks         |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | EVIDENTIAL    |     0.6769   |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |     0.835967 |
 |  2 | last evening       |      44 |    55 | DATE          |     0.59135  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_genetic_variants_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_genetic_variants_pipeline_en.md
index c1f65ed381..3efb19b160 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_genetic_variants_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_genetic_variants_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_genetic_variants](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.genetic_variants.pipeline").predict("""The mutation pattern
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_genetic_variants_pipeline", "en", "clinical/models")
-
-text = '''The mutation pattern of mitochondrial DNA (mtDNA) in mainland Chinese patients with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) has been rarely reported, though previous data suggested that the mutation pattern of MELAS could be different among geographically localized populations. We presented the results of comprehensive mtDNA mutation analysis in 92 unrelated Chinese patients with MELAS (85 with classic MELAS and 7 with MELAS/Leigh syndrome (LS) overlap syndrome). The mtDNA A3243G mutation was the most common causal genotype in this patient group (79/92 and 85.9%). The second common gene mutation was G13513A (7/92 and 7.6%). Additionally, we identified T10191C (p.S45P) in ND3, A11470C (p. K237N) in ND4, T13046C (p.M237T) in ND5 and a large-scale deletion (13025-13033:14417-14425) involving partial ND5 and ND6 subunits of complex I in one patient each. Among them, A11470C, T13046C and the single deletion were novel mutations. In summary, patients with mutations affecting mitochondrially encoded complex I (MTND) reached 12.0% (11/92) in this group. It is noteworthy that all seven patients with MELAS/LS overlap syndrome were associated with MTND mutations. Our data emphasize the important role of MTND mutations in the pathogenicity of MELAS, especially MELAS/LS overlap syndrome.PURPOSE: Genes in the complement pathway, including complement factor H (CFH), C2/BF, and C3, have been reported to be associated with age-related macular degeneration (AMD). Genetic variants, single-nucleotide polymorphisms (SNPs), in these genes were geno-typed for a case-control association study in a mainland Han Chinese population. METHODS: One hundred and fifty-eight patients with wet AMD, 80 patients with soft drusen, and 220 matched control subjects were recruited among Han Chinese in mainland China. Seven SNPs in CFH and two SNPs in C2, CFB', and C3 were genotyped using the ABI SNaPshot method. A deletion of 84,682 base pairs covering the CFHR1 and CFHR3 genes was detected by direct polymerase chain reaction and gel electrophoresis. RESULTS: Four SNPs, including rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), in CFH showed a significant association with wet AMD in the cohort of this study. A haplotype containing these four SNPs (CATA) significantly increased protection of wet AMD with a P value of 0.0005 and an odds ratio of 0.29 (95% confidence interval: 0.15-0.60). Unlike in other populations, rs2274700 and rs1410996 did not show a significant association with AMD in the Chinese population of this study. None of the SNPs in CFH showed a significant association with drusen, and none of the SNPs in CFH, C2, CFB, and C3 showed a significant association with either wet AMD or drusen in the cohort of this study. The CFHR1 and CFHR3 deletion was not polymorphic in the Chinese population and was not associated with wet AMD or drusen. CONCLUSION: This study showed that SNPs rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), but not rs7535263, rs1410996, or rs2274700, in CFH were significantly associated with wet AMD in a mainland Han Chinese population. This study showed that CFH was more likely to be AMD susceptibility gene at Chr.1q31 based on the finding that the CFHR1 and CFHR3 deletion was not polymorphic in the cohort of this study, and none of the SNPs that were significantly associated with AMD in a white population in C2, CFB, and C3 genes showed a significant association with AMD.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_genetic_variants_pipeline", "en", "clinical/models")
-
-val text = "The mutation pattern of mitochondrial DNA (mtDNA) in mainland Chinese patients with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) has been rarely reported, though previous data suggested that the mutation pattern of MELAS could be different among geographically localized populations. We presented the results of comprehensive mtDNA mutation analysis in 92 unrelated Chinese patients with MELAS (85 with classic MELAS and 7 with MELAS/Leigh syndrome (LS) overlap syndrome). The mtDNA A3243G mutation was the most common causal genotype in this patient group (79/92 and 85.9%). The second common gene mutation was G13513A (7/92 and 7.6%). Additionally, we identified T10191C (p.S45P) in ND3, A11470C (p. K237N) in ND4, T13046C (p.M237T) in ND5 and a large-scale deletion (13025-13033:14417-14425) involving partial ND5 and ND6 subunits of complex I in one patient each. Among them, A11470C, T13046C and the single deletion were novel mutations. In summary, patients with mutations affecting mitochondrially encoded complex I (MTND) reached 12.0% (11/92) in this group. It is noteworthy that all seven patients with MELAS/LS overlap syndrome were associated with MTND mutations. Our data emphasize the important role of MTND mutations in the pathogenicity of MELAS, especially MELAS/LS overlap syndrome.PURPOSE: Genes in the complement pathway, including complement factor H (CFH), C2/BF, and C3, have been reported to be associated with age-related macular degeneration (AMD). Genetic variants, single-nucleotide polymorphisms (SNPs), in these genes were geno-typed for a case-control association study in a mainland Han Chinese population. METHODS: One hundred and fifty-eight patients with wet AMD, 80 patients with soft drusen, and 220 matched control subjects were recruited among Han Chinese in mainland China. Seven SNPs in CFH and two SNPs in C2, CFB', and C3 were genotyped using the ABI SNaPshot method. A deletion of 84,682 base pairs covering the CFHR1 and CFHR3 genes was detected by direct polymerase chain reaction and gel electrophoresis. RESULTS: Four SNPs, including rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), in CFH showed a significant association with wet AMD in the cohort of this study. A haplotype containing these four SNPs (CATA) significantly increased protection of wet AMD with a P value of 0.0005 and an odds ratio of 0.29 (95% confidence interval: 0.15-0.60). Unlike in other populations, rs2274700 and rs1410996 did not show a significant association with AMD in the Chinese population of this study. None of the SNPs in CFH showed a significant association with drusen, and none of the SNPs in CFH, C2, CFB, and C3 showed a significant association with either wet AMD or drusen in the cohort of this study. The CFHR1 and CFHR3 deletion was not polymorphic in the Chinese population and was not associated with wet AMD or drusen. CONCLUSION: This study showed that SNPs rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), but not rs7535263, rs1410996, or rs2274700, in CFH were significantly associated with wet AMD in a mainland Han Chinese population. This study showed that CFH was more likely to be AMD susceptibility gene at Chr.1q31 based on the finding that the CFHR1 and CFHR3 deletion was not polymorphic in the cohort of this study, and none of the SNPs that were significantly associated with AMD in a white population in C2, CFB, and C3 genes showed a significant association with AMD."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.genetic_variants.pipeline").predict("""The mutation pattern of mitochondrial DNA (mtDNA) in mainland Chinese patients with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) has been rarely reported, though previous data suggested that the mutation pattern of MELAS could be different among geographically localized populations. We presented the results of comprehensive mtDNA mutation analysis in 92 unrelated Chinese patients with MELAS (85 with classic MELAS and 7 with MELAS/Leigh syndrome (LS) overlap syndrome). The mtDNA A3243G mutation was the most common causal genotype in this patient group (79/92 and 85.9%). The second common gene mutation was G13513A (7/92 and 7.6%). Additionally, we identified T10191C (p.S45P) in ND3, A11470C (p. K237N) in ND4, T13046C (p.M237T) in ND5 and a large-scale deletion (13025-13033:14417-14425) involving partial ND5 and ND6 subunits of complex I in one patient each. Among them, A11470C, T13046C and the single deletion were novel mutations. In summary, patients with mutations affecting mitochondrially encoded complex I (MTND) reached 12.0% (11/92) in this group. It is noteworthy that all seven patients with MELAS/LS overlap syndrome were associated with MTND mutations. Our data emphasize the important role of MTND mutations in the pathogenicity of MELAS, especially MELAS/LS overlap syndrome.PURPOSE: Genes in the complement pathway, including complement factor H (CFH), C2/BF, and C3, have been reported to be associated with age-related macular degeneration (AMD). Genetic variants, single-nucleotide polymorphisms (SNPs), in these genes were geno-typed for a case-control association study in a mainland Han Chinese population. METHODS: One hundred and fifty-eight patients with wet AMD, 80 patients with soft drusen, and 220 matched control subjects were recruited among Han Chinese in mainland China. Seven SNPs in CFH and two SNPs in C2, CFB', and C3 were genotyped using the ABI SNaPshot method. A deletion of 84,682 base pairs covering the CFHR1 and CFHR3 genes was detected by direct polymerase chain reaction and gel electrophoresis. RESULTS: Four SNPs, including rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), in CFH showed a significant association with wet AMD in the cohort of this study. A haplotype containing these four SNPs (CATA) significantly increased protection of wet AMD with a P value of 0.0005 and an odds ratio of 0.29 (95% confidence interval: 0.15-0.60). Unlike in other populations, rs2274700 and rs1410996 did not show a significant association with AMD in the Chinese population of this study. None of the SNPs in CFH showed a significant association with drusen, and none of the SNPs in CFH, C2, CFB, and C3 showed a significant association with either wet AMD or drusen in the cohort of this study. The CFHR1 and CFHR3 deletion was not polymorphic in the Chinese population and was not associated with wet AMD or drusen. CONCLUSION: This study showed that SNPs rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), but not rs7535263, rs1410996, or rs2274700, in CFH were significantly associated with wet AMD in a mainland Han Chinese population. This study showed that CFH was more likely to be AMD susceptibility gene at Chr.1q31 based on the finding that the CFHR1 and CFHR3 deletion was not polymorphic in the cohort of this study, and none of the SNPs that were significantly associated with AMD in a white population in C2, CFB, and C3 genes showed a significant association with AMD.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label       |   confidence |
 |---:|:-------------|--------:|------:|:----------------|-------------:|
 |  0 | A3243G       |     527 |   532 | DNAMutation     |       1      |
@@ -121,9 +93,6 @@ Results
 | 20 | rs7535263    |    3108 |  3116 | SNP             |       1      |
 | 21 | rs1410996    |    3119 |  3127 | SNP             |       1      |
 | 22 | rs2274700    |    3133 |  3141 | SNP             |       1      |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_pipeline_de.md b/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_pipeline_de.md
index 3c4a720c08..6ba2723dd5 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_pipeline_de.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_pipeline_de.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_healthcare](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk         |   begin |   end | ner_label             |   confidence |
 |---:|:------------------|--------:|------:|:----------------------|-------------:|
 |  0 | Kleinzellige      |       4 |    15 | MEASUREMENT           |       0.6897 |
@@ -136,12 +87,6 @@ Results
 | 13 | Hauptbronchus     |     195 |   207 | BODY_PART             |       0.9864 |
 | 14 | mittlere          |     223 |   230 | MEASUREMENT           |       0.9651 |
 | 15 | Prävalenz         |     232 |   240 | MEDICAL_CONDITION     |       0.9833 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_pipeline_en.md
index 6762e181fe..346b7734bc 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_healthcare](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.healthcare_pipeline").predict("""A 28-year-old female with
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_pipeline", "en", "clinical/models")
-
-text = '''A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "en", "clinical/models")
-
-val text = "A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG ."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.healthcare_pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG .""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | gestational diabetes mellitus |      39 |    67 | PROBLEM     |     0.938233 |
@@ -118,9 +89,6 @@ Results
 | 17 | atorvastatin                  |     625 |   636 | TREATMENT   |     0.9993   |
 | 18 | gemfibrozil                   |     642 |   652 | TREATMENT   |     0.9997   |
 | 19 | HTG                           |     658 |   660 | PROBLEM     |     0.9927   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_slim_pipeline_de.md b/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_slim_pipeline_de.md
index ec010aa1b8..b1216a720c 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_slim_pipeline_de.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_healthcare_slim_pipeline_de.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_healthcare_slim](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                          |   begin |   end | ner_label         |   confidence |
 |---:|:-----------------------------------|--------:|------:|:------------------|-------------:|
 |  0 | Bronchialkarzinom                  |      17 |    33 | MEDICAL_CONDITION |       0.9988 |
@@ -130,12 +81,6 @@ Results
 |  7 | Lunge                              |     179 |   183 | BODY_PART         |       0.9729 |
 |  8 | Hauptbronchus                      |     195 |   207 | BODY_PART         |       0.9987 |
 |  9 | Prävalenz                          |     232 |   240 | MEDICAL_CONDITION |       0.9986 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_gene_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_gene_biobert_pipeline_en.md
index ecf2e1a023..3de9801b92 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_gene_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_gene_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_gene_biober
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.human_phenotype_gene_biobert.pipeline").predict("""Here we
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_gene_biobert_pipeline", "en", "clinical/models")
-
-text = '''Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_gene_biobert_pipeline", "en", "clinical/models")
-
-val text = "Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3)."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.human_phenotype_gene_biobert.pipeline").predict("""Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | type             |      29 |    32 | GENE        |       0.9977 |
@@ -103,9 +73,6 @@ Results
 |  2 | polyuria         |      91 |    98 | HP          |       0.9955 |
 |  3 | nephrocalcinosis |     101 |   116 | HP          |       0.995  |
 |  4 | hypokalemia      |     122 |   132 | HP          |       0.9986 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_gene_clinical_pipeline_en.md
index dd0cec1a67..fc4426ac12 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_gene_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_gene_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_gene_clinic
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.human_phnotype_gene_clinical.pipeline").predict("""Here we
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-text = '''Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-val text = "Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3)."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.human_phnotype_gene_clinical.pipeline").predict("""Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | type             |      29 |    32 | GENE        |       0.9837 |
@@ -103,9 +75,6 @@ Results
 |  2 | polyuria         |      91 |    98 | HP          |       0.9964 |
 |  3 | nephrocalcinosis |     101 |   116 | HP          |       0.9979 |
 |  4 | hypokalemia      |     122 |   132 | HP          |       0.9952 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_go_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_go_biobert_pipeline_en.md
index 91ea0ad352..b067fd172e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_go_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_go_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_go_biobert]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,15 @@ nlu.load("en.med_ner.phenotype_go_biobert.pipeline").predict("""Another disease
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_go_biobert_pipeline", "en", "clinical/models")
-
-text = '''Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_go_biobert_pipeline", "en", "clinical/models")
-
-val text = "Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.phenotype_go_biobert.pipeline").predict("""Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------|--------:|------:|:------------|-------------:|
 |  0 | tumor                    |      39 |    43 | HP          |     1        |
 |  1 | tricarboxylic acid cycle |      79 |   102 | GO          |     0.999867 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_go_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_go_clinical_pipeline_en.md
index bebc12534f..03aefc5d44 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_go_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_human_phenotype_go_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_go_clinical
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.human_phenotype_clinical.pipeline").predict("""Another dise
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_go_clinical_pipeline", "en", "clinical/models")
-
-text = '''Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_go_clinical_pipeline", "en", "clinical/models")
-
-val text = "Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.human_phenotype_clinical.pipeline").predict("""Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------|--------:|------:|:------------|-------------:|
 |  0 | tumor                    |      39 |    43 | HP          |     0.9996   |
 |  1 | tricarboxylic acid cycle |      79 |   102 | GO          |     0.994633 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_biobert_pipeline_en.md
index a5e17f89f3..3ff4f94bad 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_biobert](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl_biobert.pipeline").predict("""The patient is a 21-day-o
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                 |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     1        |
@@ -123,9 +95,6 @@ Results
 | 22 | his                                       |     488 |   490 | Gender                       |     0.9573   |
 | 23 | respiratory congestion                    |     492 |   513 | Symptom                      |     0.5144   |
 | 24 | He                                        |     516 |   517 | Gender                       |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_enriched_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_enriched_biobert_pipeline_en.md
index d29e804921..d7d5ef02cf 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_enriched_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_enriched_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_enriched_biobert](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.jsl_enriched_biobert.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_enriched_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_enriched_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_enriched_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                   |   begin |   end | ner_label    |   confidence |
 |---:|:----------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 21-day-old                  |      17 |    26 | Age          |     1        |
@@ -123,9 +94,6 @@ Results
 | 22 | denies                      |     825 |   830 | Negation     |     0.9841   |
 | 23 | diarrhea                    |     836 |   843 | Symptom_Name |     0.6033   |
 | 24 | His                         |     846 |   848 | Gender       |     0.8459   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_enriched_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_enriched_pipeline_en.md
index 8f5df473c4..b414b8cc25 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_enriched_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_enriched_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_enriched](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl_enriched.pipeline").predict("""The patient is a 21-day-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_enriched_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_enriched_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_enriched.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     0.9993   |
@@ -137,9 +109,6 @@ Results
 | 36 | diarrhea                                  |     836 |   843 | Symptom                      |     0.9995   |
 | 37 | His                                       |     846 |   848 | Gender                       |     0.9998   |
 | 38 | bowel                                     |     850 |   854 | Internal_organ_or_component  |     0.9675   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_greedy_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_greedy_biobert_pipeline_en.md
index 56fdde408d..08792011cf 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_greedy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_greedy_biobert](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.biobert_jsl_greedy.pipeline").predict("""The patient is a 2
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_greedy_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_greedy_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biobert_jsl_greedy.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                      |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     1        |
@@ -123,9 +94,6 @@ Results
 | 22 | He                                             |     516 |   517 | Gender                       |     0.9998   |
 | 23 | tired                                          |     550 |   554 | Symptom                      |     0.8912   |
 | 24 | fussy                                          |     569 |   573 | Symptom                      |     0.9541   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_greedy_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_greedy_pipeline_en.md
index 55583e4eb9..a2e67011b1 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_greedy_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_greedy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_greedy](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.jsl_greedy.pipeline").predict("""The patient is a 21-day-ol
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_greedy_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_greedy_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_greedy.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                     |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     0.9817   |
@@ -123,9 +93,6 @@ Results
 | 22 | his                                            |     488 |   490 | Gender                       |     0.9904   |
 | 23 | respiratory congestion                         |     492 |   513 | Symptom                      |     0.5294   |
 | 24 | He                                             |     516 |   517 | Gender                       |     0.9989   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_pipeline_en.md
index 280ca0bc56..50dfb177ba 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl](https://nlp.johnsnowla
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl.pipeline").predict("""The patient is a 21-day-old Cauca
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     0.997    |
@@ -139,9 +111,6 @@ Results
 | 38 | diarrhea                                  |     908 |   915 | Symptom                      |     0.9956   |
 | 39 | His                                       |     918 |   920 | Gender                       |     0.9997   |
 | 40 | bowel                                     |     922 |   926 | Internal_organ_or_component  |     0.9218   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_slim_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_slim_pipeline_en.md
index e23aefa4b7..3f1db34f5b 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_slim_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_jsl_slim_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_slim](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -65,40 +66,11 @@ nlu.load("en.med_ner.jsl_slim.pipeline").predict("""Hyperparathyroidism was cons
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_slim_pipeline", "en", "clinical/models")
-
-text = "Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture."
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_slim_pipeline", "en", "clinical/models")
-
-val text = "Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_slim.pipeline").predict("""Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                               |   begin |   end | entities                  |   confidence |
 |---:|:-------------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Hyperparathyroidism                  |       0 |    18 | Disease_Syndrome_Disorder |     0.9977   |
@@ -112,9 +84,6 @@ Results
 |  8 | fractures under general anesthesia   |     280 |   313 | Drug                      |     0.79585  |
 |  9 | He                                   |     316 |   317 | Demographics              |     0.9992   |
 | 10 | sustained mandibular fracture        |     319 |   347 | Disease_Syndrome_Disorder |     0.662467 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_300_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_300_pipeline_es.md
index 069ff68946..4bd47605b4 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_300_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_300_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_300](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.92045 |
@@ -131,12 +82,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      0.9963  |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_es.md
index 303b2ce91f..db07510b56 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_es.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.98915 |
@@ -131,12 +83,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      1       |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_fr.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_fr.md
index a36a86f8bb..b7d44c28c8 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_fr.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_fr.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                       |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Femme                            |       0 |     4 | HUMAN       |     1        |
@@ -134,12 +85,6 @@ Results
 | 11 | virus de la varicelle et du zona |     518 |   549 | SPECIES     |     0.985429 |
 | 12 | parvovirus B19                   |     554 |   567 | SPECIES     |     0.98595  |
 | 13 | Brucella                         |     636 |   643 | SPECIES     |     0.9995   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_it.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_it.md
index a735e22031..c759ac4f79 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_it.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_it.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |      0.9997  |
@@ -130,12 +81,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |      0.9745  |
 |  8 | HIV              |     523 |   525 | SPECIES     |      0.9838  |
 |  9 | paziente         |     634 |   641 | HUMAN       |      0.9994  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_pt.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_pt.md
index 75b464d9be..4852d1f7ce 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_pt.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_pt.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |       0.9849 |
@@ -126,12 +77,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |       0.9912 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |       0.9909 |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |       0.9778 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_ro.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_ro.md
index 1503e7e466..e244fd967f 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_ro.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_bert_pipeline_ro.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-text = '''O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-val text = "O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-text = '''O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-val text = "O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks           |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------|--------:|------:|:------------|-------------:|
 |  0 | femeie               |       2 |     7 | HUMAN       |     0.9998   |
@@ -129,12 +81,6 @@ Results
 |  6 | enterovirus          |     804 |   814 | SPECIES     |     0.9984   |
 |  7 | parvovirus B19       |     819 |   832 | SPECIES     |     0.99255  |
 |  8 | fetală               |     932 |   937 | HUMAN       |     0.9994   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_biobert_pipeline_en.md
index 34467b6fda..d59a3f3b64 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_biobert_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |       0.9999 |
@@ -127,12 +78,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |       0.9926 |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |       0.8422 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |       0.9929 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_ca.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_ca.md
index 23663a2988..e27341c044 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_ca.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_ca.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-text = '''Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-val text = "Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-text = '''Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-val text = "Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | Dona                    |       0 |     3 | HUMAN       |     1        |
@@ -135,12 +86,6 @@ Results
 | 12 | virus varicel·la zoster |     717 |   739 | SPECIES     |     0.778333 |
 | 13 | parvovirus B19          |     743 |   756 | SPECIES     |     0.9138   |
 | 14 | Brucella                |     847 |   854 | SPECIES     |     0.9483   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_en.md
index e1669b6815..ccdb1fb91a 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |     0.9993   |
@@ -127,12 +78,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |     0.8838   |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |     0.748667 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |     0.9847   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_es.md
index 74cc25f2ba..eb43222cbf 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |       0.9926 |
@@ -131,12 +82,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |       0.9997 |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |       0.9998 |
 | 10 | padres         |     728 |   733 | HUMAN       |       0.9992 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_fr.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_fr.md
index 7e0118c2df..ba602fa3dc 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_fr.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_fr.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                       |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Femme                            |       0 |     4 | HUMAN       |     1        |
@@ -133,13 +85,7 @@ Results
 | 10 | virus d'Epstein Barr             |     496 |   515 | SPECIES     |     0.788667 |
 | 11 | virus de la varicelle et du zona |     518 |   549 | SPECIES     |     0.788543 |
 | 12 | parvovirus B19                   |     554 |   567 | SPECIES     |     0.9341   |
-| 13 | Brucella                         |     636 |   643 | SPECIES     |     0.9993   |
-
-
-{:.model-param}
-
-
-{:.model-param}
+| 13 | Brucella                         |     636 |   643 | SPECIES     |     0.9993   |}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_gl.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_gl.md
index 54c8dd29e0..7a7d9abc37 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_gl.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_gl.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-text = '''Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-val text = "Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-text = '''Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-val text = "Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Muller                |       0 |     5 | HUMAN       |      0.9998  |
@@ -127,12 +78,6 @@ Results
 |  4 | herpética             |     437 |   445 | SPECIES     |      0.9592  |
 |  5 | púbico                |     551 |   556 | HUMAN       |      0.7293  |
 |  6 | Staphylococcus aureus |     644 |   664 | SPECIES     |      0.87005 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_it.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_it.md
index 8b5a99fd77..cdebe18b5c 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_it.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_it.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |       0.9992 |
@@ -130,12 +81,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |       0.991  |
 |  8 | HIV              |     523 |   525 | SPECIES     |       0.991  |
 |  9 | paziente         |     634 |   641 | HUMAN       |       0.9978 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_pt.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_pt.md
index c5779a8065..d61f4aad7d 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_pt.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_pipeline_pt.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |       0.9991 |
@@ -126,12 +77,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |       0.9847 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |       0.91   |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |       0.9996 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_roberta_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_roberta_pipeline_es.md
index 6928be0c61..ba7fa66c5e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_roberta_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_roberta_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_roberta](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.93805 |
@@ -131,12 +82,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      0.9985  |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_roberta_pipeline_pt.md b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_roberta_pipeline_pt.md
index 1eaf17db0a..1bee7be43f 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_roberta_pipeline_pt.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_living_species_roberta_pipeline_pt.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_roberta](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-text = '''Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-val text = "Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-text = '''Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-val text = "Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | Mulher        |       0 |     5 | HUMAN       |       0.9975 |
@@ -127,12 +78,6 @@ Results
 |  4 | HBV           |     360 |   362 | SPECIES     |       0.9911 |
 |  5 | HCV           |     365 |   367 | SPECIES     |       0.9858 |
 |  6 | sífilis       |     384 |   390 | SPECIES     |       0.8898 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_measurements_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_measurements_clinical_pipeline_en.md
index 2cf31ed6b8..bd6062557e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_measurements_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_measurements_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_measurements_clinical](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.clinical_measurements.pipeline").predict("""Bilateral breas
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_measurements_clinical_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_measurements_clinical_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_measurements.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label    |   confidence |
 |---:|:----------------|--------:|------:|:-------------|-------------:|
 |  0 | 0.5 x 0.5 x 0.4 |     113 |   127 | Measurements |      0.98748 |
 |  1 | cm              |     129 |   130 | Units        |      0.9996  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_medication_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_medication_pipeline_en.md
index 3b9bd955a6..288860627f 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_medication_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_medication_pipeline_en.md
@@ -34,39 +34,7 @@ A pretrained pipeline to detect medication entities. It was built on the top of
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."""
-
-result = ner_medication_pipeline.fullAnnotate([text])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
-```
 
-{:.nlu-block}
-```python
-| ner_chunk          | entity   |
-|:-------------------|:---------|
-| metformin 1000 MG  | DRUG     |
-| glipizide 2.5 MG   | DRUG     |
-| Fragmin 5000 units | DRUG     |
-| Xenaderm           | DRUG     |
-| OxyContin 30 mg    | DRUG     |
-```
-</div>
-
-{:.model-param}
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -84,41 +52,12 @@ val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline",
 val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
 ```
 
-{:.nlu-block}
-```python
-| ner_chunk          | entity   |
-|:-------------------|:---------|
-| metformin 1000 MG  | DRUG     |
-| glipizide 2.5 MG   | DRUG     |
-| Fragmin 5000 units | DRUG     |
-| Xenaderm           | DRUG     |
-| OxyContin 30 mg    | DRUG     |
-```
 </div>
 
-{:.model-param}
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."""
 
-result = ner_medication_pipeline.fullAnnotate([text])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+## Results
 
-val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
-```
-
-{:.nlu-block}
-```python
+```bash
 | ner_chunk          | entity   |
 |:-------------------|:---------|
 | metformin 1000 MG  | DRUG     |
@@ -127,7 +66,6 @@ val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed me
 | Xenaderm           | DRUG     |
 | OxyContin 30 mg    | DRUG     |
 ```
-</div>
 
 {:.model-param}
 ## Model Information
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_medmentions_coarse_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_medmentions_coarse_pipeline_en.md
index 3f1e9d26b7..835a19297d 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_medmentions_coarse_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_medmentions_coarse_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_medmentions_coarse](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.medmentions_coarse.pipeline").predict("""he patient is a 21
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_medmentions_coarse_pipeline", "en", "clinical/models")
-
-text = '''he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_medmentions_coarse_pipeline", "en", "clinical/models")
-
-val text = "he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.medmentions_coarse.pipeline").predict("""he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                  |   begin |   end | ner_label                            |   confidence |
 |---:|:----------------------------|--------:|------:|:-------------------------------------|-------------:|
 |  0 | Caucasian                   |      27 |    35 | Population_Group                     |     0.8439   |
@@ -123,9 +93,6 @@ Results
 | 22 | bowel movements             |     921 |   935 | Biologic_Function                    |     0.29385  |
 | 23 | yellow                      |     941 |   946 | Qualitative_Concept                  |     0.742    |
 | 24 | colored                     |     948 |   954 | Qualitative_Concept                  |     0.275    |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_nature_nero_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_nature_nero_clinical_pipeline_en.md
index f792303ded..29813a55f6 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_nature_nero_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_nature_nero_clinical_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_nature_nero_clinical](https
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-text = '''he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-val text = "he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-text = '''he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-val text = "he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                                   |   begin |   end | ner_label             |   confidence |
 |---:|:---------------------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | perioral cyanosis                            |     236 |   252 | Medicalfinding        |     0.198    |
@@ -142,12 +94,6 @@ Results
 | 19 | diarrhea                                     |     835 |   842 | Medicalfinding        |     0.533    |
 | 20 | bowel movements                              |     849 |   863 | Biologicalprocess     |     0.2036   |
 | 21 | soft in nature                               |     888 |   901 | Biologicalprocess     |     0.170467 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_negation_uncertainty_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_negation_uncertainty_pipeline_es.md
index 7ffa292cb3..6db8d24861 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_negation_uncertainty_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_negation_uncertainty_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_negation_uncertainty](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +------------------------------------------------------+---------+
 |chunk                                                 |ner_label|
 +------------------------------------------------------+---------+
@@ -130,12 +81,6 @@ Results
 |susceptible de                                        |UNC      |
 |ca basocelular perlado                                |USCO     |
 +------------------------------------------------------+---------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_neoplasms_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_neoplasms_pipeline_es.md
index 455ea71253..59e3443583 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_neoplasms_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_neoplasms_pipeline_es.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_neoplasms](https://nlp.john
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,22 +70,10 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label            |   confidence |
 |---:|:------------|--------:|------:|:---------------------|-------------:|
 |  0 | cáncer      |     140 |   145 | MORFOLOGIA_NEOPLASIA |       0.9997 |
 |  1 | Multi-Link  |    1195 |  1204 | MORFOLOGIA_NEOPLASIA |       0.574  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_nihss_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_nihss_pipeline_en.md
index d739f176ee..6f7d710490 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_nihss_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_nihss_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_nihss](https://nlp.johnsnow
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.nihss_pipeline").predict("""Abdomen , soft , nontender . NI
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_nihss_pipeline", "en", "clinical/models")
-
-text = '''Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_nihss_pipeline", "en", "clinical/models")
-
-val text = "Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently"
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.nihss_pipeline").predict("""Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label       |   confidence |
 |---:|:-----------------|--------:|------:|:----------------|-------------:|
 |  0 | NIH stroke scale |      29 |    44 | NIHSS           |     0.973533 |
@@ -120,9 +92,6 @@ Results
 | 19 | three            |     258 |   262 | Measurement     |     0.8896   |
 | 20 | best language    |     268 |   280 | 9_BestLanguage  |     0.89415  |
 | 21 | two              |     286 |   288 | Measurement     |     0.949    |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_anatomy_general_healthcare_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_anatomy_general_healthcare_pipeline_en.md
index 111d7d8d9a..75d10f381a 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_anatomy_general_healthcare_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_anatomy_general_healthcare_pipeline_en.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_general_he
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,24 +75,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks   |   begin |   end | entities        |   confidence |
 |---:|:---------|--------:|------:|:----------------|-------------:|
 |  0 | left     |      37 |    40 | Direction       |       0.9948 |
 |  1 | breast   |      42 |    47 | Anatomical_Site |       0.5814 |
 |  2 | lungs    |      83 |    87 | Anatomical_Site |       0.9486 |
 |  3 | liver    |     100 |   104 | Anatomical_Site |       0.9646 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_anatomy_general_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_anatomy_general_pipeline_en.md
index 4502b8a942..7b736ef69e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_anatomy_general_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_anatomy_general_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_general](h
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +71,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label       |   confidence |
 |---:|:-------------|--------:|------:|:----------------|-------------:|
 |  0 | left         |      36 |    39 | Direction       |       0.9825 |
 |  1 | breast       |      41 |    46 | Anatomical_Site |       0.9005 |
 |  2 | lungs        |      82 |    86 | Anatomical_Site |       0.9735 |
 |  3 | liver        |      99 |   103 | Anatomical_Site |       0.9817 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_biomarker_healthcare_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_biomarker_healthcare_pipeline_en.md
index 0eb4df1017..2d941a715c 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_biomarker_healthcare_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_biomarker_healthcare_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_biomarker_healthca
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-text = '''he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-val text = "he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-text = '''he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-val text = "he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                                   |   begin |   end | entities         |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-----------------|-------------:|
 |  0 | negative                                 |      69 |    76 | Biomarker_Result |      1       |
@@ -138,12 +89,6 @@ Results
 | 15 | p53                                      |     244 |   246 | Biomarker        |      1       |
 | 16 | Ki-67 index                              |     253 |   263 | Biomarker        |      0.99865 |
 | 17 | 87%                                      |     275 |   277 | Biomarker_Result |      0.828   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_biomarker_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_biomarker_pipeline_en.md
index 842728957c..f26c3a6e9e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_biomarker_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_biomarker_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_biomarker](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                               |   begin |   end | ner_label        |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-----------------|-------------:|
 |  0 | negative                                 |      70 |    77 | Biomarker_Result |      0.9984  |
@@ -138,12 +89,6 @@ Results
 | 15 | p53                                      |     245 |   247 | Biomarker        |      1       |
 | 16 | Ki-67 index                              |     254 |   264 | Biomarker        |      0.99465 |
 | 17 | 87%                                      |     276 |   278 | Biomarker_Result |      0.9814  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_demographics_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_demographics_pipeline_en.md
index 1585d0990b..66baaa0d95 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_demographics_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_demographics_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_demographics](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old man with history of heavy smoking.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old man with history of heavy smoking."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old man with history of heavy smoking.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old man with history of heavy smoking."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,23 +69,11 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label      |   confidence |
 |---:|:--------------|--------:|------:|:---------------|-------------:|
 |  0 | 40-year-old   |      17 |    27 | Age            |       0.6743 |
 |  1 | man           |      29 |    31 | Gender         |       0.9365 |
 |  2 | heavy smoking |      49 |    61 | Smoking_Status |       0.7294 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_diagnosis_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_diagnosis_pipeline_en.md
index 18a9b2c31e..82a5fa81ec 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_diagnosis_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_diagnosis_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_oncology_diagnosis](https:/
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-text = '''Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-val text = "Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-text = '''Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-val text = "Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label         |   confidence |
 |---:|:-------------|--------:|------:|:------------------|-------------:|
 |  0 | tumor        |      44 |    48 | Tumor_Finding     |       0.9958 |
@@ -126,12 +78,6 @@ Results
 |  3 | ductal       |     119 |   124 | Histological_Type |       0.9996 |
 |  4 | carcinoma    |     126 |   134 | Cancer_Dx         |       0.9988 |
 |  5 | metastasis   |     181 |   190 | Metastasis        |       0.9996 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_posology_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_posology_pipeline_en.md
index e6bddfff64..f44615096e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_posology_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_posology_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_posology](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label      |   confidence |
 |---:|:-----------------|--------:|------:|:---------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy |      1       |
@@ -127,12 +78,6 @@ Results
 |  4 | six courses      |     106 |   116 | Cycle_Count    |      0.494   |
 |  5 | second cycle     |     150 |   161 | Cycle_Number   |      0.98675 |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_response_to_treatment_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_response_to_treatment_pipeline_en.md
index 870222ec3b..fa04d7ecce 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_response_to_treatment_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_response_to_treatment_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_response_to_treatm
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-text = '''She completed her first-line therapy, but some months later there was recurrence of the breast cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-val text = "She completed her first-line therapy, but some months later there was recurrence of the breast cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-text = '''She completed her first-line therapy, but some months later there was recurrence of the breast cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-val text = "She completed her first-line therapy, but some months later there was recurrence of the breast cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,21 +69,9 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label             |   confidence |
 |---:|:-------------|--------:|------:|:----------------------|-------------:|
 |  0 | recurrence   |      70 |    79 | Response_To_Treatment |       0.9767 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_test_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_test_pipeline_en.md
index a983538c2f..5c57073efc 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_test_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_test_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_test](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-text = ''' biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-val text = " biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-text = ''' biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-val text = " biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +69,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                |   begin |   end | ner_label      |   confidence |
 |---:|:--------------------------|--------:|------:|:---------------|-------------:|
 |  0 | biopsy                    |       1 |     6 | Pathology_Test |      0.9987  |
 |  1 | ultrasound guided         |      31 |    47 | Imaging_Test   |      0.87635 |
 |  2 | chest computed tomography |      67 |    91 | Imaging_Test   |      0.9176  |
 |  3 | CT                        |      94 |    95 | Imaging_Test   |      0.8294  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_therapy_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_therapy_pipeline_en.md
index 7fb1940665..0e5481fa07 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_therapy_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_therapy_pipeline_en.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_therapy](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                     |   begin |   end | ner_label             |   confidence |
 |---:|:-------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | mastectomy                     |      36 |    45 | Cancer_Surgery        |     0.9817   |
@@ -144,12 +87,6 @@ Results
 |  7 | 600 mg/m2                      |     381 |   389 | Dosage                |     0.64205  |
 |  8 | six courses                    |     397 |   407 | Cycle_Count           |     0.46815  |
 |  9 | first line                     |     413 |   422 | Line_Of_Therapy       |     0.95015  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_tnm_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_tnm_pipeline_en.md
index cb699ddb2f..6c97e320f7 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_tnm_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_tnm_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_tnm](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label         |   confidence |
 |---:|:-----------------|--------:|------:|:------------------|-------------:|
 |  0 | metastatic       |      24 |    33 | Metastasis        |     0.9999   |
@@ -126,12 +77,6 @@ Results
 |  3 | 4 cm             |     126 |   129 | Tumor_Description |     0.85105  |
 |  4 | tumor            |     131 |   135 | Tumor             |     0.9926   |
 |  5 | grade 2          |     141 |   147 | Tumor_Description |     0.89705  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_unspecific_posology_healthcare_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
index 14a2491f2e..bcfa765a03 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_unspecific_posolog
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks           |   begin |   end | entities             |   confidence |
 |---:|:-----------------|--------:|------:|:---------------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy       |     0.9999   |
@@ -141,12 +84,6 @@ Results
 |  4 | over six courses |     101 |   116 | Posology_Information |     0.689833 |
 |  5 | second cycle     |     150 |   161 | Posology_Information |     0.9906   |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy       |     0.9997   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_unspecific_posology_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_unspecific_posology_pipeline_en.md
index 9276a3f71a..2e403a6176 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_unspecific_posology_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_oncology_unspecific_posology_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_unspecific_posolog
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label            |   confidence |
 |---:|:-----------------|--------:|------:|:---------------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy       |      1       |
@@ -127,12 +78,6 @@ Results
 |  4 | over six courses |     101 |   116 | Posology_Information |      0.9078  |
 |  5 | second cycle     |     150 |   161 | Posology_Information |      0.9853  |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy       |      0.9998  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_pathogen_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_pathogen_pipeline_en.md
index 2dd5bed5bb..d9457d31c5 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_pathogen_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_pathogen_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_pathogen](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.pathogen.pipeline").predict("""Racecadotril is an antisecre
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_pathogen_pipeline", "en", "clinical/models")
-
-text = '''Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin.  This can progress to loss of skin color, a fast heart rate as it becomes more severe.  While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_pathogen_pipeline", "en", "clinical/models")
-
-val text = "Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin.  This can progress to loss of skin color, a fast heart rate as it becomes more severe.  While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.pathogen.pipeline").predict("""Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin.  This can progress to loss of skin color, a fast heart rate as it becomes more severe.  While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label        |   confidence |
 |---:|:----------------|--------:|------:|:-----------------|-------------:|
 |  0 | Racecadotril    |       0 |    11 | Medicine         |     0.9468   |
@@ -107,9 +78,6 @@ Results
 |  6 | rabies virus    |     383 |   394 | Pathogen         |     0.95685  |
 |  7 | Lyssavirus      |     397 |   406 | Pathogen         |     0.9694   |
 |  8 | Ephemerovirus   |     412 |   424 | Pathogen         |     0.6919   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_pharmacology_pipeline_es.md b/docs/_posts/C-K-Loan/2023-06-17-ner_pharmacology_pipeline_es.md
index 694c915f6f..dd9bedb2a8 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_pharmacology_pipeline_es.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_pharmacology_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_pharmacology](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label     |   confidence |
 |---:|:----------------|--------:|------:|:--------------|-------------:|
 |  0 | creatinkinasa   |      31 |    43 | PROTEINAS     |      0.9994  |
@@ -132,12 +83,6 @@ Results
 |  9 | Interleukina II |     231 |   245 | PROTEINAS     |      0.99955 |
 | 10 | Dacarbacina     |     248 |   258 | NORMALIZABLES |      0.9996  |
 | 11 | Interferon alfa |     262 |   276 | PROTEINAS     |      0.99935 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_biobert_pipeline_en.md
index 384c7b5b38..5c425c7190 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.posology_biobert.pipeline").predict("""The patient was pres
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_biobert.pipeline").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | 1                   |      27 |    27 | DOSAGE      |     0.9993   |
@@ -115,9 +87,6 @@ Results
 | 14 | metformin           |     261 |   269 | DRUG        |     0.9999   |
 | 15 | 1000 mg             |     271 |   277 | STRENGTH    |     0.91255  |
 | 16 | two times a day     |     279 |   293 | FREQUENCY   |     0.9969   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_experimental_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_experimental_pipeline_en.md
index a3b8cdab27..1a63fc8d9a 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_experimental_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_experimental_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_experimental](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -68,46 +69,9 @@ Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear th
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_experimental_pipeline", "en", "clinical/models")
-
-text = '''Y-90 Humanized Anti-Tac: 10 mCi (if a bone marrow transplant was part of the patient's previous therapy) or 15 mCi of yttrium labeled anti-TAC; followed by calcium trisodium Inj (Ca DTPA)..
-
-Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear the radioactive agent from the body.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_experimental_pipeline", "en", "clinical/models")
-
-val text = "Y-90 Humanized Anti-Tac: 10 mCi (if a bone marrow transplant was part of the patient's previous therapy) or 15 mCi of yttrium labeled anti-TAC; followed by calcium trisodium Inj (Ca DTPA)..
-
-Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear the radioactive agent from the body."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_experimental.pipeline").predict("""Y-90 Humanized Anti-Tac: 10 mCi (if a bone marrow transplant was part of the patient's previous therapy) or 15 mCi of yttrium labeled anti-TAC; followed by calcium trisodium Inj (Ca DTPA)..
-
-Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear the radioactive agent from the body.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------|--------:|------:|:------------|-------------:|
 |  0 | Anti-Tac                 |      15 |    22 | Drug        |     0.8797   |
@@ -119,9 +83,6 @@ Results
 |  6 | Ca-DTPA                  |     205 |   211 | Drug        |     0.9544   |
 |  7 | intravenously            |     234 |   246 | Route       |     0.9518   |
 |  8 | Days 1-3                 |     251 |   258 | Cycleday    |     0.83325  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_greedy_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_greedy_pipeline_en.md
index 99c77720ac..8aba45450b 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_greedy_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_greedy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_greedy](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.posology_greedy.pipeline").predict("""The patient was presc
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_greedy_pipeline", "en", "clinical/models")
-
-text = '''The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_greedy_pipeline", "en", "clinical/models")
-
-val text = "The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_greedy.pipeline").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                   |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 1 capsule of Advil 10 mg                    |      27 |    50 | DRUG        |     0.638183 |
@@ -107,9 +77,6 @@ Results
 |  6 | with meals                                  |     245 |   254 | FREQUENCY   |     0.79235  |
 |  7 | metformin 1000 mg                           |     261 |   277 | DRUG        |     0.707133 |
 |  8 | two times a day                             |     279 |   293 | FREQUENCY   |     0.700825 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_healthcare_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_healthcare_pipeline_en.md
index 384cee0ae2..81c9c1c923 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_healthcare_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_healthcare](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.posology.healthcare_pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_healthcare_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_healthcare_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology.healthcare_pipeline").predict("""The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk     |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | Aspirin       |     267 |   273 | Drug        |      0.9983  |
@@ -110,9 +81,6 @@ Results
 |  9 | Nitroglycerin |     337 |   349 | Drug        |      0.9927  |
 | 10 | 1/150         |     351 |   355 | Strength    |      0.9565  |
 | 11 | sublingually. |     357 |   369 | Route       |      0.72065 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_large_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_large_biobert_pipeline_en.md
index 7410efbd64..aec3f2067c 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_large_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_large_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_large_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.posology_biobert_large.pipeline").predict("""The patient wa
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_large_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_large_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_biobert_large.pipeline").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | 1                   |      27 |    27 | DOSAGE      |     0.9998   |
@@ -116,9 +86,6 @@ Results
 | 15 | metformin           |     261 |   269 | DRUG        |     1        |
 | 16 | 1000 mg             |     271 |   277 | STRENGTH    |     0.69955  |
 | 17 | two times a day     |     279 |   293 | FREQUENCY   |     0.758125 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_large_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_large_pipeline_en.md
index 8e0ff394a3..12fc88ee08 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_large_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.posoloy_large.pipeline").predict("""The patient is a 30-yea
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_large_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_large_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posoloy_large.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | insulin        |      59 |    65 | DRUG        |     0.9752   |
@@ -123,9 +94,6 @@ Results
 | 22 | 0.1 mg         |    1113 |  1118 | STRENGTH    |     0.9325   |
 | 23 | p.o.           |    1120 |  1123 | ROUTE       |     0.6783   |
 | 24 | daily          |    1125 |  1129 | FREQUENCY   |     0.9925   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_pipeline_en.md
index 106e7ed493..046d282cec 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.posology_pipeline").predict("""The patient is a 30-year-old
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | insulin        |      59 |    65 | DRUG        |      0.9759  |
@@ -123,9 +94,6 @@ Results
 | 22 | 0.1 mg         |    1113 |  1118 | STRENGTH    |      0.7658  |
 | 23 | p.o            |    1120 |  1122 | ROUTE       |      0.9982  |
 | 24 | daily          |    1125 |  1129 | FREQUENCY   |      0.9983  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_small_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_small_pipeline_en.md
index a940422260..9a51d45050 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_posology_small_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_posology_small_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_small](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.posology_small.pipeline").predict("""The patient is a 30-ye
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_small_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_small_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_small.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | insulin        |      59 |    65 | DRUG        |      0.9984  |
@@ -123,9 +94,6 @@ Results
 | 22 | 0.1 mg         |    1113 |  1118 | STRENGTH    |      0.99965 |
 | 23 | p.o            |    1120 |  1122 | ROUTE       |      0.999   |
 | 24 | daily          |    1125 |  1129 | FREQUENCY   |      0.9373  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_profiling_biobert_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_profiling_biobert_en.md
index 04e90bed87..301a78adf6 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_profiling_biobert_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_profiling_biobert_en.md
@@ -38,6 +38,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -67,66 +68,11 @@ nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 ******************** ner_diseases_biobert Model Results ******************** 
 
 [('gestational diabetes mellitus', 'Disease'), ('type two diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('HTG-induced pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('poor appetite', 'Disease'), ('vomiting', 'Disease')]
@@ -150,13 +96,6 @@ Results
 ******************** ner_risk_factors_biobert Model Results ******************** 
 
 [('diabetes mellitus', 'DIABETES'), ('subsequent type two diabetes mellitus', 'DIABETES'), ('obesity', 'OBESE')]
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_radiology_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_radiology_pipeline_en.md
index 0b5fe06d56..75fe2d1848 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_radiology_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_radiology_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_radiology](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.radiology.pipeline").predict("""Bilateral breast ultrasound
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_radiology_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_radiology_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.radiology.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                |   begin |   end | ner_label                 |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Bilateral breast                         |       0 |    15 | BodyPart                  |     0.945    |
@@ -110,9 +81,6 @@ Results
 |  9 | internal color flow                      |     294 |   312 | ImagingFindings           |     0.477233 |
 | 10 | benign fibrous tissue                    |     334 |   354 | ImagingFindings           |     0.524067 |
 | 11 | lipoma                                   |     361 |   366 | Disease_Syndrome_Disorder |     0.6081   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_radiology_wip_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_radiology_wip_clinical_pipeline_en.md
index b8726883a9..a6b8116d18 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_radiology_wip_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_radiology_wip_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_radiology_wip_clinical](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.radiology.clinical_wip.pipeline").predict("""Bilateral brea
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_radiology_wip_clinical_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_radiology_wip_clinical_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.radiology.clinical_wip.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Bilateral             |       0 |     8 | Direction                 |     0.9828   |
@@ -113,9 +84,6 @@ Results
 | 12 | internal color flow   |     294 |   312 | ImagingFindings           |     0.5153   |
 | 13 | benign fibrous tissue |     334 |   354 | ImagingFindings           |     0.394867 |
 | 14 | lipoma                |     361 |   366 | Disease_Syndrome_Disorder |     0.9142   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_risk_factors_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_risk_factors_biobert_pipeline_en.md
index 1499373c8d..572189c7da 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_risk_factors_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_risk_factors_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_risk_factors_biobert](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -86,64 +87,10 @@ FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_risk_factors_biobert_pipeline", "en", "clinical/models")
-
-text = '''ISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_risk_factors_biobert_pipeline", "en", "clinical/models")
-
-val text = "ISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother)."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.risk_factors_biobert.pipeline").predict("""ISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                |   begin |   end | ner_label    |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | diabetic                                 |     135 |   142 | DIABETES     |     0.9689   |
@@ -153,9 +100,6 @@ Results
 |  4 | hypertension                             |    1341 |  1352 | HYPERTENSION |     0.956    |
 |  5 | coronary artery disease                  |    1355 |  1377 | CAD          |     0.7962   |
 |  6 | Smokes 2 packs of cigarettes per day     |    1480 |  1515 | SMOKER       |     0.461643 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_risk_factors_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_risk_factors_pipeline_en.md
index c6ac8971a4..473ffce745 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_risk_factors_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_risk_factors_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_risk_factors](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -86,64 +87,11 @@ FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_risk_factors_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_risk_factors_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother)."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.risk_factors.pipeline").predict("""HISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
 
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                            |   begin |   end | ner_label    |   confidence |
 |---:|:-------------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | diabetic                             |     136 |   143 | DIABETES     |     0.9992   |
@@ -155,9 +103,6 @@ Results
 |  6 | ABC                                  |    1434 |  1436 | PHI          |     0.9999   |
 |  7 | Smokes 2 packs of cigarettes per day |    1481 |  1516 | SMOKER       |     0.634257 |
 |  8 | banker                               |    1530 |  1535 | PHI          |     0.9779   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_sdoh_mentions_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_sdoh_mentions_pipeline_en.md
index 13e55b491b..cadfccb75c 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_sdoh_mentions_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_sdoh_mentions_pipeline_en.md
@@ -62,7 +62,6 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-
 |    | chunks           |   begin |   end | entities         |   confidence |
 |---:|:-----------------|--------:|------:|:-----------------|-------------:|
 |  0 | married          |     123 |   129 | sdoh_community   |       0.9972 |
@@ -71,7 +70,6 @@ val result = pipeline.fullAnnotate(text)
 |  3 | alcohol          |     185 |   191 | behavior_alcohol |       0.9925 |
 |  4 | intravenous drug |     196 |   211 | behavior_drug    |       0.9803 |
 |  5 | smoking          |     230 |   236 | behavior_tobacco |       0.9997 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-ner_supplement_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-ner_supplement_clinical_pipeline_en.md
index ea2833dd52..d27f0eb5ef 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-ner_supplement_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-ner_supplement_clinical_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_supplement_clinical](https:
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-text = '''Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-val text = "Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-text = '''Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-val text = "Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +70,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | nervousness  |      42 |    52 | CONDITION   |      0.9999  |
 |  1 | night sleep  |      70 |    80 | BENEFIT     |      0.80775 |
 |  2 | hair         |     109 |   112 | BENEFIT     |      0.9997  |
 |  3 | nail growth  |     118 |   128 | BENEFIT     |      0.9997  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-nerdl_tumour_demo_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-nerdl_tumour_demo_pipeline_en.md
index cc16993a70..0325e195fd 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-nerdl_tumour_demo_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-nerdl_tumour_demo_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [nerdl_tumour_demo](https://nlp.
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,21 +70,9 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label    | confidence   |
 |---:|:-----------------|--------:|------:|:-------------|:-------------|
 |  0 | breast carcinoma |      35 |    50 | Localization |              |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-oncology_biomarker_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-oncology_biomarker_pipeline_en.md
index 0eb7dc87f1..f1f177d4a5 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-oncology_biomarker_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-oncology_biomarker_pipeline_en.md
@@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status and Relation E
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,72 +63,10 @@ nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was n
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk                          | ner_label        |
@@ -253,13 +192,6 @@ Results
 | ER                   | Biomarker        | negative                       | Biomarker_Result | O             |
 | PR                   | Biomarker        | negative                       | Biomarker_Result | O             |
 | negative             | Biomarker_Result | HER2                           | Oncogene         | is_finding_of |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-re_bodypart_directions_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-re_bodypart_directions_pipeline_en.md
index 67cced7da2..45e4ddfe84 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-re_bodypart_directions_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-re_bodypart_directions_pipeline_en.md
@@ -59,64 +59,11 @@ nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations | entity1                     | entity1_begin | entity1_end | chunk1     | entity2                     | entity2_end | entity2_end | chunk2        | confidence |
 |-------|-----------|-----------------------------|---------------|-------------|------------|-----------------------------|-------------|-------------|---------------|------------|
 | 0     | 1         | Direction                   | 35            | 39          | upper      | Internal_organ_or_component | 41          | 50          | brain stem    | 0.9999989  |
@@ -128,13 +75,6 @@ Results
 | 6     | 0         | Direction                   | 54            | 57          | left       | Internal_organ_or_component | 81          | 93          | basil ganglia | 0.97616416 |
 | 7     | 0         | Internal_organ_or_component | 59            | 68          | cerebellum | Direction                   | 75          | 79          | right         | 0.953046   |
 | 8     | 1         | Direction                   | 75            | 79          | right      | Internal_organ_or_component | 81          | 93          | basil ganglia | 1.0        |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-re_bodypart_proceduretest_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-re_bodypart_proceduretest_pipeline_en.md
index 9acccf2b48..3a5af04e08 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-re_bodypart_proceduretest_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-re_bodypart_proceduretest_pipeline_en.md
@@ -59,74 +59,14 @@ nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations | entity1                      | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2              | confidence |
 |-------|-----------|------------------------------|---------------|-------------|--------|---------|-------------|-------------|---------------------|------------|
 | 0     | 1         | External_body_part_or_region | 94            | 98          | chest  | Test    | 117         | 135         | portable ultrasound | 1.0        |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-re_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-re_human_phenotype_gene_clinical_pipeline_en.md
index 69ad33ba96..09fb6cc8b9 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-re_human_phenotype_gene_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-re_human_phenotype_gene_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_human_phenotype_gene_clinica
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
 
@@ -56,59 +57,10 @@ nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobo
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    |   relation | entity1   |   entity1_begin |   entity1_end | chunk1              | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+===========+=================+===============+=====================+===========+=================+===============+=====================+==============+
@@ -116,12 +68,6 @@ Results
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 |          1 | HP        |              23 |            36 | microphthalmia      | GENE      |             110 |           114 | TENM3               |     0.999999 |
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-re_temporal_events_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-re_temporal_events_clinical_pipeline_en.md
index 05f208262e..a9a2b79b7e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-re_temporal_events_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-re_temporal_events_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_clinical](ht
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
 
@@ -56,59 +57,10 @@ nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    | relation   | entity1    |   entity1_begin |   entity1_end | chunk1                   | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+============+=================+===============+==========================+===========+=================+===============+=====================+==============+
@@ -116,12 +68,6 @@ Results
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 | OVERLAP    | DATE       |             171 |           179 | that time                | PROBLEM   |             201 |           219 | any specific injury |     0.956654 |
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-re_temporal_events_enriched_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-re_temporal_events_enriched_clinical_pipeline_en.md
index 31ecb0c719..9242e5d43e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-re_temporal_events_enriched_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-re_temporal_events_enriched_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_enriched_cli
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
 
@@ -56,59 +57,11 @@ nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 5
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
 
 
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |    | relation   | entity1   |   entity1_begin |   entity1_end | chunk1                                        | entity2    |   entity2_begin |   entity2_end | chunk2                   |   confidence |
 +====+============+===========+=================+===============+===============================================+============+=================+===============+==========================+==============+
@@ -116,12 +69,6 @@ Results
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |  1 | AFTER      | DATE      |             171 |           179 | that time                                     | PROBLEM    |             201 |           219 | any specific injury      |     0.577288 |
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-re_test_problem_finding_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-re_test_problem_finding_pipeline_en.md
index 4ea67c9d13..7cee5850b4 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-re_test_problem_finding_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-re_test_problem_finding_pipeline_en.md
@@ -59,74 +59,13 @@ nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | 1            | PROCEDURE    | biopsy              | SYMPTOM      |  lesion | 
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-re_test_result_date_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-re_test_result_date_pipeline_en.md
index 8e1e701ac1..ce2856208f 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-re_test_result_date_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-re_test_result_date_pipeline_en.md
@@ -59,76 +59,16 @@ nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised ches
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | O            | TEST         | chest X-ray         | MEASUREMENTS |  93%    | 
 | 1     | O            | TEST         | CT scan             | MEASUREMENTS |  93%    |
 | 2     | is_result_of | TEST         | SpO2                | MEASUREMENTS |  93%    |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-recognize_entities_posology_en.md b/docs/_posts/C-K-Loan/2023-06-17-recognize_entities_posology_en.md
index 63aef55635..5162541c17 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-recognize_entities_posology_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-recognize_entities_posology_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_posology`. It will only extract medication entities.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
 
@@ -61,67 +62,11 @@ She was seen by the endocrinology service and discharged on 40 units of insulin
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
-
-res = pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-```scala
-val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "clinical/models")
-
-val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")(0)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
-
-res = pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-```scala
-val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "clinical/models")
 
-val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")(0)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunk            |   begin |   end | entity    |
 |---:|:-----------------|--------:|------:|:----------|
 |  0 | metformin        |      83 |    91 | DRUG      |
@@ -133,13 +78,6 @@ Results
 |  6 | 12 units         |     309 |   316 | DOSAGE    |
 |  7 | insulin lispro   |     321 |   334 | DRUG      |
 |  8 | with meals       |     336 |   345 | FREQUENCY |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-rxnorm_mesh_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-rxnorm_mesh_mapping_en.md
index 89ed6ee3bf..060aca2e3e 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-rxnorm_mesh_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-rxnorm_mesh_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps RxNorm codes to MeSH codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline 
 pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
@@ -54,55 +55,10 @@ nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline 
-pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-pipeline.annotate("1191 6809 47613")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-val result = pipeline.annotate("1191 6809 47613")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline 
-pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-pipeline.annotate("1191 6809 47613")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-val result = pipeline.annotate("1191 6809 47613")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 {'rxnorm': ['1191', '6809', '47613'],
 'mesh': ['D001241', 'D008687', 'D019355']}
 
@@ -120,12 +76,6 @@ Note:
 | D001241    | Aspirin             |
 | D008687    | Metformin           |
 | D019355    | Calcium Citrate     |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-rxnorm_ndc_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-rxnorm_ndc_mapping_en.md
index b6c86981ef..e38f32c3f8 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-rxnorm_ndc_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-rxnorm_ndc_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps RXNORM codes to NDC codes without using any text d
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,75 +59,14 @@ nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1652674 259934)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1652674 259934)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1652674 259934)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1652674 259934)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 {'document': ['1652674 259934'],
 'package_ndc': ['62135-0625-60', '13349-0010-39'],
 'product_ndc': ['46708-0499', '13349-0010'],
 'rxnorm_code': ['1652674', '259934']}
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-rxnorm_umls_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-rxnorm_umls_mapping_en.md
index 8953c68f21..c5bc8cd56b 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-rxnorm_umls_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-rxnorm_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `rxnorm_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,13 @@ nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1161611 315677)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1161611 315677)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1161611 315677)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1161611 315677)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | rxnorm_code      | umls_code           |
 |---:|:-----------------|:--------------------|
 |  0 | 1161611 | 315677 | C3215948 | C0984912 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-snomed_icd10cm_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-snomed_icd10cm_mapping_en.md
index 4998e9bae6..a518dc2c87 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-snomed_icd10cm_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-snomed_icd10cm_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icd10cm_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,13 @@ nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | snomed_code                             | icd10cm_code               |
 |---:|:----------------------------------------|:---------------------------|
 |  0 | 128041000119107 | 292278006 | 293072005 | K22.70 | T43.595 | T37.1X5 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-snomed_icdo_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-snomed_icdo_mapping_en.md
index 5ba38ca7f8..def188ec5c 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-snomed_icdo_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-snomed_icdo_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icdo_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | snomed_code                   | icdo_code                |
 |---:|:------------------------------|:-------------------------|
 |  0 | 10376009 | 2026006 | 26638004 | 8050/2 | 9014/0 | 8322/0 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-snomed_umls_mapping_en.md b/docs/_posts/C-K-Loan/2023-06-17-snomed_umls_mapping_en.md
index e099a20db9..5fc8932f9b 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-snomed_umls_mapping_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-snomed_umls_mapping_en.md
@@ -34,32 +34,7 @@ This pretrained pipeline is built on the top of `snomed_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
 
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -75,57 +50,21 @@ val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/mod
 val result = pipeline.fullAnnotate(733187009 449433008 51264003)
 ```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
 
 {:.nlu-block}
 ```python
 import nlu
 nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
 ```
+
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | snomed_code                      | umls_code                      |
 |---:|:---------------------------------|:-------------------------------|
 |  0 | 733187009 | 449433008 | 51264003 | C4546029 | C3164619 | C0271267 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-17-spellcheck_clinical_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-17-spellcheck_clinical_pipeline_en.md
index d4cc98649c..121bd2ea00 100644
--- a/docs/_posts/C-K-Loan/2023-06-17-spellcheck_clinical_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-17-spellcheck_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained medical spellchecker pipeline is built on the top of `spellcheck
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -66,44 +67,11 @@ nlu.load("en.spell.clinical.pipeline").predict("""Witth the hell of phisical ter
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("spellcheck_clinical_pipeline", "en", "clinical/models")
-example = ["Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.",
-           "With paint wel controlled on orall pain medications, she was discharged too reihabilitation facilitay.",
-           "Abdomen is sort, nontender, and nonintended.",
-           "Patient not showing pain or any wealth problems.",
-           "No cute distress"]
-pipeline.fullAnnotate(example)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("spellcheck_clinical_pipeline", "en", "clinical/models")
-val example = Array("Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.",
-           "With paint wel controlled on orall pain medications, she was discharged too reihabilitation facilitay.",
-           "Abdomen is sort, nontender, and nonintended.",
-           "Patient not showing pain or any wealth problems.",
-           "No cute distress")
-pipeline.fullAnnotate(example)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.spell.clinical.pipeline").predict("""Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 [{'checked': ['With','the','cell','of','physical','therapy','the','patient','was','ambulated','and','on','postoperative',',','the','patient','tolerating','a','post','surgical','soft','diet','.'],
   'document': ['Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.'],
   'token': ['Witth','the','hell','of','phisical','terapy','the','patient','was','imbulated','and','on','postoperative',',','the','impatient','tolerating','a','post','curgical','soft','diet','.']},
@@ -123,9 +91,6 @@ Results
  {'checked': ['No', 'acute', 'distress'],
   'document': ['No cute distress'],
   'token': ['No', 'cute', 'distress']}]
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-19-ner_profiling_clinical_en.md b/docs/_posts/C-K-Loan/2023-06-19-ner_profiling_clinical_en.md
index e7e18ca88f..d71a64fdf8 100644
--- a/docs/_posts/C-K-Loan/2023-06-19-ner_profiling_clinical_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-19-ner_profiling_clinical_en.md
@@ -38,6 +38,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -67,38 +68,11 @@ nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline("ner_profiling_clinical", "en", "clinical/models")
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_clinical", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
 
-nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 ******************** ner_jsl Model Results ******************** 
 
 [('28-year-old', 'Age'), ('female', 'Gender'), ('gestational diabetes mellitus', 'Diabetes'), ('eight years prior', 'RelativeDate'), ('subsequent', 'Modifier'), ('type two diabetes mellitus', 'Diabetes'), ('T2DM', 'Diabetes'), ('HTG-induced pancreatitis', 'Disease_Syndrome_Disorder'), ('three years prior', 'RelativeDate'), ('acute', 'Modifier'), ('hepatitis', 'Communicable_Disease'), ('obesity', 'Obesity'), ('body mass index', 'Symptom'), ('33.5 kg/m2', 'Weight'), ('one-week', 'Duration'), ('polyuria', 'Symptom'), ('polydipsia', 'Symptom'), ('poor appetite', 'Symptom'), ('vomiting', 'Symptom')]
@@ -122,12 +96,6 @@ Results
 ******************** ner_medmentions_coarse Model Results ******************** 
 
 [('female', 'Organism_Attribute'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('T2DM', 'Disease_or_Syndrome'), ('HTG-induced pancreatitis', 'Disease_or_Syndrome'), ('associated with', 'Qualitative_Concept'), ('acute hepatitis', 'Disease_or_Syndrome'), ('obesity', 'Disease_or_Syndrome'), ('body mass index', 'Clinical_Attribute'), ('BMI', 'Clinical_Attribute'), ('polyuria', 'Sign_or_Symptom'), ('polydipsia', 'Sign_or_Symptom'), ('poor appetite', 'Sign_or_Symptom'), ('vomiting', 'Sign_or_Symptom')]
-
-...
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-clinical_deidentification_ar.md b/docs/_posts/C-K-Loan/2023-06-22-clinical_deidentification_ar.md
index 0e7a95a6fa..7cfe8eeb09 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-clinical_deidentification_ar.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-clinical_deidentification_ar.md
@@ -34,92 +34,7 @@ This pipeline can be used to deidentify Arabic PHI information from medical text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ar", "clinical/models")
-
-text = '''
-
-ملاحظات سريرية - مريض الربو:
-
-التاريخ: 30 مايو 2023
-اسم المريضة: ليلى حسن
-
-تم تسجيل المريض في النظام باستخدام رقم الضمان الاجتماعي 123456789012.
-
-العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة
-الرمز البريدي: 54321
-البلد: المملكة العربية السعودية
-اسم المستشفى: مستشفى النور
-اسم الطبيب: د. أميرة أحمد
-
-تفاصيل الحالة:
-المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة.
-
-الخطة:
-
-تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج.
-يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة.
-يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة.
-يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو.
-تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح
-
-'''
-result = deid_pipeline.annotate(text)
 
-print("\nMasked with entity labels")
-print("-"*30)
-print("\n".join(result['masked_with_entity']))
-print("\nMasked with chars")
-print("-"*30)
-print("\n".join(result['masked_with_chars']))
-print("\nMasked with fixed length chars")
-print("-"*30)
-print("\n".join(result['masked_fixed_length_chars']))
-print("\nObfuscated")
-print("-"*30)
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification","ar","clinical/models")
-
-val text = '''
-
-ملاحظات سريرية - مريض الربو:
-
-التاريخ: 30 مايو 2023
-اسم المريضة: ليلى حسن
-
-تم تسجيل المريض في النظام باستخدام رقم الضمان الاجتماعي 123456789012.
-
-العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة
-الرمز البريدي: 54321
-البلد: المملكة العربية السعودية
-اسم المستشفى: مستشفى النور
-اسم الطبيب: د. أميرة أحمد
-
-تفاصيل الحالة:
-المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة.
-
-الخطة:
-
-تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج.
-يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة.
-يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة.
-يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو.
-تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح
-
-'''
-
-val result = deid_pipeline.annotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -207,10 +122,6 @@ val result = deid_pipeline.annotate(text)
 ## Results
 
 ```bash
-Results
-
-
-
 Masked with entity labels
 ------------------------------
 ملاحظات سريرية - مريض الربو:
@@ -306,9 +217,6 @@ Obfuscated
 يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة.
 يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو.
 تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-cvx_resolver_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-cvx_resolver_pipeline_en.md
index e8b6887790..1ec0b9f6b1 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-cvx_resolver_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-cvx_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities with their corresponding CVX codes. You
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,38 +61,10 @@ nlu.load("en.resolve.cvx_pipeline").predict("""The patient has a history of infl
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-resolver_pipeline = PretrainedPipeline("cvx_resolver_pipeline", "en", "clinical/models")
-
-text= "The patient has a history of influenza vaccine, tetanus and DTaP"
-
-result = resolver_pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val resolver_pipeline = new PretrainedPipeline("cvx_resolver_pipeline", "en", "clinical/models")
-
-val result = resolver_pipeline.fullAnnotate("The patient has a history of influenza vaccine, tetanus and DTaP")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.cvx_pipeline").predict("""The patient has a history of influenza vaccine, tetanus and DTaP""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-----------------+---------+--------+
 |chunk            |ner_chunk|cvx_code|
 +-----------------+---------+--------+
@@ -99,9 +72,6 @@ Results
 |tetanus          |Vaccine  |35      |
 |DTaP             |Vaccine  |20      |
 +-----------------+---------+--------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-icd10cm_resolver_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-icd10cm_resolver_pipeline_en.md
index 47e7287927..4d3bb161b6 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-icd10cm_resolver_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-icd10cm_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities with their corresponding ICD-10-CM codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,38 +61,10 @@ nlu.load("en.icd10cm_resolver.pipeline").predict("""A 28-year-old female with a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-resolver_pipeline = PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")
-
-text = """A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage"""
-
-result = resolver_pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val resolver_pipeline = new PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")
-
-val result = resolver_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm_resolver.pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-----------------------------+---------+------------+
 |chunk                        |ner_chunk|icd10cm_code|
 +-----------------------------+---------+------------+
@@ -99,9 +72,6 @@ Results
 |anisakiasis                  |PROBLEM  |B81.0       |
 |fetal and neonatal hemorrhage|PROBLEM  |P545        |
 +-----------------------------+---------+------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-icd9_resolver_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-icd9_resolver_pipeline_en.md
index f79d49e719..e6cd6988bf 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-icd9_resolver_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-icd9_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities with their corresponding ICD-9-CM codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,39 +61,10 @@ nlu.load("en.resolve.icd9.pipeline").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd9_resolver_pipeline", "en", "clinical/models")
-
-text= A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd9_resolver_pipeline", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.icd9.pipeline").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +-----------------------------+---------+---------+
 |chunk                        |ner_chunk|icd9_code|
 +-----------------------------+---------+---------+
@@ -100,10 +72,6 @@ Results
 |anisakiasis                  |PROBLEM  |127.1    |
 |fetal and neonatal hemorrhage|PROBLEM  |772      |
 +-----------------------------+---------+---------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md
index aa79348892..273549e252 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md
@@ -60,49 +60,16 @@ nlu.load("en.classify.rct_binary_biobert.pipeline").predict("""Abstract:Based on
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-
-pipeline = PretrainedPipeline("rct_binary_classifier_biobert_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rct_binary_classifier_biobert_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.rct_binary_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |true|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-rct_binary_classifier_use_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-rct_binary_classifier_use_pipeline_en.md
index e2dba72bb0..f1e257ce50 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-rct_binary_classifier_use_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-rct_binary_classifier_use_pipeline_en.md
@@ -59,48 +59,15 @@ nlu.load("en.classify.rct_binary_use.pipeline").predict("""Abstract:Based on the
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rct_binary_classifier_use_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rct_binary_classifier_use_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.rct_binary_use.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |true|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md
index 051c74f3d6..0a6678187b 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md
@@ -32,30 +32,11 @@ This pretrained pipeline is built on the top of [summarizer_biomedical_pubmed](h
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_biomedical_pubmed_pipeline", "en", "clinical/models")
-
-text = """Residual disease after initial surgery for ovarian cancer is the strongest prognostic factor for survival. However, the extent of surgical resection required to achieve optimal cytoreduction is controversial. Our goal was to estimate the effect of aggressive surgical resection on ovarian cancer patient survival.\n A retrospective cohort study of consecutive patients with International Federation of Gynecology and Obstetrics stage IIIC ovarian cancer undergoing primary surgery was conducted between January 1, 1994, and December 31, 1998. The main outcome measures were residual disease after cytoreduction, frequency of radical surgical resection, and 5-year disease-specific survival.\n The study comprised 194 patients, including 144 with carcinomatosis. The mean patient age and follow-up time were 64.4 and 3.5 years, respectively. After surgery, 131 (67.5%) of the 194 patients had less than 1 cm of residual disease (definition of optimal cytoreduction). Considering all patients, residual disease was the only independent predictor of survival; the need to perform radical procedures to achieve optimal cytoreduction was not associated with a decrease in survival. For the subgroup of patients with carcinomatosis, residual disease and the performance of radical surgical procedures were the only independent predictors. Disease-specific survival was markedly improved for patients with carcinomatosis operated on by surgeons who most frequently used radical procedures compared with those least likely to use radical procedures (44% versus 17%, P < .001).\n Overall, residual disease was the only independent predictor of survival. Minimizing residual disease through aggressive surgical resection was beneficial, especially in patients with carcinomatosis."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_biomedical_pubmed_pipeline", "en", "clinical/models")
 
-val text = """Residual disease after initial surgery for ovarian cancer is the strongest prognostic factor for survival. However, the extent of surgical resection required to achieve optimal cytoreduction is controversial. Our goal was to estimate the effect of aggressive surgical resection on ovarian cancer patient survival.\n A retrospective cohort study of consecutive patients with International Federation of Gynecology and Obstetrics stage IIIC ovarian cancer undergoing primary surgery was conducted between January 1, 1994, and December 31, 1998. The main outcome measures were residual disease after cytoreduction, frequency of radical surgical resection, and 5-year disease-specific survival.\n The study comprised 194 patients, including 144 with carcinomatosis. The mean patient age and follow-up time were 64.4 and 3.5 years, respectively. After surgery, 131 (67.5%) of the 194 patients had less than 1 cm of residual disease (definition of optimal cytoreduction). Considering all patients, residual disease was the only independent predictor of survival; the need to perform radical procedures to achieve optimal cytoreduction was not associated with a decrease in survival. For the subgroup of patients with carcinomatosis, residual disease and the performance of radical surgical procedures were the only independent predictors. Disease-specific survival was markedly improved for patients with carcinomatosis operated on by surgeons who most frequently used radical procedures compared with those least likely to use radical procedures (44% versus 17%, P < .001).\n Overall, residual disease was the only independent predictor of survival. Minimizing residual disease through aggressive surgical resection was beneficial, especially in patients with carcinomatosis."""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -79,15 +60,7 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-
 The results of this review suggest that aggressive ovarian cancer surgery is associated with a significant reduction in the risk of recurrence and a reduction in the number of radical versus conservative surgical resections. However, the results of this review are based on only one small trial. Further research is needed to determine the role of aggressive ovarian cancer surgery in women with stage IIIC ovarian cancer.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md
index f152ea71c7..caec4abf56 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_guidelines_
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -110,91 +111,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_guidelines_large_pipeline", "en", "clinical/models")
-
-text = """Clinical Guidelines for Breast Cancer:
-
-Breast cancer is the most common type of cancer among women. It occurs when the cells in the breast start growing abnormally, forming a lump or mass. This can result in the spread of cancerous cells to other parts of the body. Breast cancer may occur in both men and women but is more prevalent in women.
-
-The exact cause of breast cancer is unknown. However, several risk factors can increase your likelihood of developing breast cancer, such as:
-- A personal or family history of breast cancer
-- A genetic mutation, such as BRCA1 or BRCA2
-- Exposure to radiation
-- Age (most commonly occurring in women over 50)
-- Early onset of menstruation or late menopause
-- Obesity
-- Hormonal factors, such as taking hormone replacement therapy
-
-Breast cancer may not present symptoms during its early stages. Symptoms typically manifest as the disease progresses. Some notable symptoms include:
-- A lump or thickening in the breast or underarm area
-- Changes in the size or shape of the breast
-- Nipple discharge
-- Nipple changes in appearance, such as inversion or flattening
-- Redness or swelling in the breast
-
-Treatment for breast cancer depends on several factors, including the stage of the cancer, the location of the tumor, and the individual's overall health. Common treatment options include:
-- Surgery (such as lumpectomy or mastectomy)
-- Radiation therapy
-- Chemotherapy
-- Hormone therapy
-- Targeted therapy
-
-Early detection is crucial for the successful treatment of breast cancer. Women are advised to routinely perform self-examinations and undergo regular mammogram testing starting at age 40. If you notice any changes in your breast tissue, consult with your healthcare provider immediately.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_guidelines_large_pipeline", "en", "clinical/models")
-
-val text = """Clinical Guidelines for Breast Cancer:
-
-Breast cancer is the most common type of cancer among women. It occurs when the cells in the breast start growing abnormally, forming a lump or mass. This can result in the spread of cancerous cells to other parts of the body. Breast cancer may occur in both men and women but is more prevalent in women.
-
-The exact cause of breast cancer is unknown. However, several risk factors can increase your likelihood of developing breast cancer, such as:
-- A personal or family history of breast cancer
-- A genetic mutation, such as BRCA1 or BRCA2
-- Exposure to radiation
-- Age (most commonly occurring in women over 50)
-- Early onset of menstruation or late menopause
-- Obesity
-- Hormonal factors, such as taking hormone replacement therapy
-
-Breast cancer may not present symptoms during its early stages. Symptoms typically manifest as the disease progresses. Some notable symptoms include:
-- A lump or thickening in the breast or underarm area
-- Changes in the size or shape of the breast
-- Nipple discharge
-- Nipple changes in appearance, such as inversion or flattening
-- Redness or swelling in the breast
-
-Treatment for breast cancer depends on several factors, including the stage of the cancer, the location of the tumor, and the individual's overall health. Common treatment options include:
-- Surgery (such as lumpectomy or mastectomy)
-- Radiation therapy
-- Chemotherapy
-- Hormone therapy
-- Targeted therapy
 
-Early detection is crucial for the successful treatment of breast cancer. Women are advised to routinely perform self-examinations and undergo regular mammogram testing starting at age 40. If you notice any changes in your breast tissue, consult with your healthcare provider immediately.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 Overview of the disease: Breast cancer is the most common type of cancer among women, occurring when the cells in the breast start growing abnormally, forming a lump or mass. It can result in the spread of cancerous cells to other parts of the body. 
 
 Causes: The exact cause of breast cancer is unknown, but several risk factors can increase the likelihood of developing it, such as a personal or family history, a genetic mutation, exposure to radiation, age, early onset of menstruation or late menopause, obesity, and hormonal factors. 
@@ -202,10 +123,6 @@ Causes: The exact cause of breast cancer is unknown, but several risk factors ca
 Symptoms: Symptoms of breast cancer typically manifest as the disease progresses, including a lump or thickening in the breast or underarm area, changes in the size or shape of the breast, nipple discharge, nipple changes in appearance, and redness or swelling in the breast. 
 
 Treatment recommendations: Treatment for breast cancer depends on several factors, including the stage of the cancer, the location of the tumor, and the individual's overall health. Common treatment options include surgery, radiation therapy, chemotherapy, hormone therapy, and targeted therapy. Early detection is crucial for successful treatment of breast cancer. Women are advised to routinely perform self-examinations and undergo regular mammogram testing starting at age 40.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md
index 8f5f2d8c69..2559623d77 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md
@@ -34,44 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_jsl_augment
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_jsl_augmented_pipeline", "en", "clinical/models")
-
-text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_jsl_augmented_pipeline", "en", "clinical/models")
-
-val text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -111,15 +74,7 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-
 A 78-year-old female with hypertension, syncope, and spinal stenosis returns for a recheck. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema. Her medications include Atenolol, Premarin, calcium with vitamin D, multivitamin, aspirin, and TriViFlor. She also has Elocon cream and Synalar cream for rash.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_jsl_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_jsl_pipeline_en.md
index 202104f747..ada2107bb1 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_jsl_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_jsl_pipeline_en.md
@@ -34,44 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_jsl](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_jsl_pipeline", "en", "clinical/models")
 
-text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_jsl_pipeline", "en", "clinical/models")
-
-val text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -108,18 +71,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
-
 A 78-year-old female with hypertension, syncope, and spinal stenosis returns for recheck. She denies chest pain, palpations, orthopnea, nocturnal dyspnea, or edema. She is on multiple medications and has Elocon cream and Synalar cream for rash.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_laymen_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_laymen_pipeline_en.md
index 49730cb7b3..40ba276fb7 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_laymen_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_laymen_pipeline_en.md
@@ -34,72 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_laymen](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_laymen_pipeline", "en", "clinical/models")
-
-text = """
-Olivia Smith was seen in my office for evaluation for elective surgical weight loss on October 6, 2008. Olivia Smith is a 34-year-old female with a BMI of 43. She is 5'6" tall and weighs 267 pounds. She is motivated to attempt surgical weight loss because she has been overweight for over 20 years and wants to have more energy and improve her self-image. She is not only affected physically, but also socially by her weight. When she loses weight she always regains it and she always gains back more weight than she has lost. At one time, she lost 100 pounds and gained the weight back within a year. She has tried numerous commercial weight loss programs including Weight Watcher's for four months in 1992 with 15-pound weight loss, RS for two months in 1990 with six-pound weight loss, Slim Fast for six weeks in 2004 with eight-pound weight loss, an exercise program for two months in 2007 with a five-pound weight loss, Atkin's Diet for three months in 2008 with a ten-pound weight loss, and Dexatrim for one month in 2005 with a five-pound weight loss. She has also tried numerous fat reduction or fad diets. She was on Redux for nine months with a 100-pound weight loss.
-
-PAST MEDICAL HISTORY: She has a history of hypertension and shortness of breath.
-
-PAST SURGICAL HISTORY: Pertinent for cholecystectomy.
-
-PSYCHOLOGICAL HISTORY: Negative.
-
-SOCIAL HISTORY: She is single. She drinks alcohol once a week. She does not smoke.
-
-FAMILY HISTORY: Pertinent for obesity and hypertension.
-
-MEDICATIONS: Include Topamax 100 mg twice daily, Zoloft 100 mg twice daily, Abilify 5 mg daily, Motrin 800 mg daily, and a multivitamin.
 
-ALLERGIES: She has no known drug allergies.
-
-REVIEW OF SYSTEMS: Negative.
-
-PHYSICAL EXAM: This is a pleasant female in no acute distress. Alert and oriented x 3. HEENT: Normocephalic, atraumatic. Extraocular muscles intact, nonicteric sclerae. Chest is clear to auscultation bilaterally. Cardiovascular is normal sinus rhythm. Abdomen is obese, soft, nontender and nondistended. Extremities show no edema, clubbing or cyanosis.
-
-ASSESSMENT/PLAN: This is a 34-year-old female with a BMI of 43 who is interested in surgical weight via the gastric bypass as opposed to Lap-Band. Olivia Smith will be asking for a letter of medical necessity from Dr. Andrew Johnson. She will also see my nutritionist and social worker and have an upper endoscopy. Once this is completed, we will submit her to her insurance company for approval.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_laymen_pipeline", "en", "clinical/models")
-
-val text = """
-Olivia Smith was seen in my office for evaluation for elective surgical weight loss on October 6, 2008. Olivia Smith is a 34-year-old female with a BMI of 43. She is 5'6" tall and weighs 267 pounds. She is motivated to attempt surgical weight loss because she has been overweight for over 20 years and wants to have more energy and improve her self-image. She is not only affected physically, but also socially by her weight. When she loses weight she always regains it and she always gains back more weight than she has lost. At one time, she lost 100 pounds and gained the weight back within a year. She has tried numerous commercial weight loss programs including Weight Watcher's for four months in 1992 with 15-pound weight loss, RS for two months in 1990 with six-pound weight loss, Slim Fast for six weeks in 2004 with eight-pound weight loss, an exercise program for two months in 2007 with a five-pound weight loss, Atkin's Diet for three months in 2008 with a ten-pound weight loss, and Dexatrim for one month in 2005 with a five-pound weight loss. She has also tried numerous fat reduction or fad diets. She was on Redux for nine months with a 100-pound weight loss.
-
-PAST MEDICAL HISTORY: She has a history of hypertension and shortness of breath.
-
-PAST SURGICAL HISTORY: Pertinent for cholecystectomy.
-
-PSYCHOLOGICAL HISTORY: Negative.
-
-SOCIAL HISTORY: She is single. She drinks alcohol once a week. She does not smoke.
-
-FAMILY HISTORY: Pertinent for obesity and hypertension.
-
-MEDICATIONS: Include Topamax 100 mg twice daily, Zoloft 100 mg twice daily, Abilify 5 mg daily, Motrin 800 mg daily, and a multivitamin.
-
-ALLERGIES: She has no known drug allergies.
-
-REVIEW OF SYSTEMS: Negative.
-
-PHYSICAL EXAM: This is a pleasant female in no acute distress. Alert and oriented x 3. HEENT: Normocephalic, atraumatic. Extraocular muscles intact, nonicteric sclerae. Chest is clear to auscultation bilaterally. Cardiovascular is normal sinus rhythm. Abdomen is obese, soft, nontender and nondistended. Extremities show no edema, clubbing or cyanosis.
-
-ASSESSMENT/PLAN: This is a 34-year-old female with a BMI of 43 who is interested in surgical weight via the gastric bypass as opposed to Lap-Band. Olivia Smith will be asking for a letter of medical necessity from Dr. Andrew Johnson. She will also see my nutritionist and social worker and have an upper endoscopy. Once this is completed, we will submit her to her insurance company for approval.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -164,18 +99,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
-
 This is a clinical note about a 34-year-old woman who is interested in having weight loss surgery. She has been overweight for over 20 years and wants to have more energy and improve her self-image. She has tried many diets and weight loss programs, but has not been successful in keeping the weight off. She has a history of hypertension and shortness of breath, but is not allergic to any medications. She will have an upper endoscopy and will be contacted by a nutritionist and social worker. The plan is to have her weight loss surgery through the gastric bypass, rather than Lap-Band.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_questions_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_questions_pipeline_en.md
index e86676c7f3..426676a128 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_questions_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-summarizer_clinical_questions_pipeline_en.md
@@ -34,32 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_questions](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("summarizer_clinical_questions_pipeline", "en", "clinical/models")
-
-text = """
-Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_questions_pipeline", "en", "clinical/models")
-
-val text = """
-Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -84,18 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
-
 What are the treatments for hyperthyroidism?
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-summarizer_generic_jsl_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-summarizer_generic_jsl_pipeline_en.md
index 1edcf920d6..7b8043865b 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-summarizer_generic_jsl_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-summarizer_generic_jsl_pipeline_en.md
@@ -34,44 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_generic_jsl](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_generic_jsl_pipeline", "en", "clinical/models")
 
-text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_generic_jsl_pipeline", "en", "clinical/models")
-
-val text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -108,18 +71,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
-
 The patient is 78 years old and has hypertension. She has a history of chest pain, palpations, orthopedics, and spinal stenosis. She has a prescription of Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin, and TriViFlor 25 mg two pills daily.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-22-summarizer_radiology_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-22-summarizer_radiology_pipeline_en.md
index 54563ed8f0..be36671c8d 100644
--- a/docs/_posts/C-K-Loan/2023-06-22-summarizer_radiology_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-22-summarizer_radiology_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_radiology](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -86,72 +87,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_radiology_pipeline", "en", "clinical/models")
-
-text = """INDICATIONS: Peripheral vascular disease with claudication.
-
-RIGHT:
-1. Normal arterial imaging of right lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic.
-4. Ankle brachial index is 0.96.
-
-LEFT:
-1. Normal arterial imaging of left lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic throughout except in posterior tibial artery where it is biphasic.
-4. Ankle brachial index is 1.06.
-
-IMPRESSION: 
-Normal arterial imaging of both lower lobes.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_radiology_pipeline", "en", "clinical/models")
-
-val text = """INDICATIONS: Peripheral vascular disease with claudication.
-
-RIGHT:
-1. Normal arterial imaging of right lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic.
-4. Ankle brachial index is 0.96.
-
-LEFT:
-1. Normal arterial imaging of left lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic throughout except in posterior tibial artery where it is biphasic.
-4. Ankle brachial index is 1.06.
-
-IMPRESSION: 
-Normal arterial imaging of both lower lobes.
-"""
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 The patient has peripheral vascular disease with claudication. The right lower extremity shows normal arterial imaging, but the peak systolic velocity is normal. The arterial waveform is triphasic throughout, except for the posterior tibial artery, which is biphasic. The ankle brachial index is 0.96. The impression is normal arterial imaging of both lower lobes.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md
index 84f440f6e2..cd5fed3eae 100644
--- a/docs/_posts/C-K-Loan/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities (Diseases and Syndromes) with their corre
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,34 +59,11 @@ nlu.load("en.map_entity.umls_disease_syndrome_resolver").predict("""A 34-year-ol
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline= PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
-pipeline.annotate("A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline= PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
-val pipeline.annotate("A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.umls_disease_syndrome_resolver").predict("""A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-----------------------------+---------+---------+
 |chunk                        |ner_label|umls_code|
 +-----------------------------+---------+---------+
@@ -94,10 +72,6 @@ Results
 |acyclovir allergy            |PROBLEM  |C0571297 |
 |polyuria                     |PROBLEM  |C0018965 |
 +-----------------------------+---------+---------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-23-umls_drug_resolver_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-23-umls_drug_resolver_pipeline_en.md
index 1179bfe809..9c3be1d80d 100644
--- a/docs/_posts/C-K-Loan/2023-06-23-umls_drug_resolver_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-23-umls_drug_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities (Clinical Drugs) with their corresponding
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -56,43 +57,16 @@ nlu.load("en.map_entity.umls_drug_resolver").predict("""The patient was given Ad
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
-pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
-val pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.umls_drug_resolver").predict("""The patient was given Adapin 10 MG, coumadn 5 mg""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +------------+---------+---------+
 |chunk       |ner_label|umls_code|
 +------------+---------+---------+
 |Adapin 10 MG|DRUG     |C2930083 |
 |coumadn 5 mg|DRUG     |C2723075 |
 +------------+---------+---------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-23-umls_major_concepts_resolver_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-23-umls_major_concepts_resolver_pipeline_en.md
index 8c1bfb16e8..78719f6f2c 100644
--- a/docs/_posts/C-K-Loan/2023-06-23-umls_major_concepts_resolver_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-23-umls_major_concepts_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities (Clinical Major Concepts) with their corr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -56,34 +57,10 @@ nlu.load("en.map_entity.umls_major_concepts_resolver").predict("""The patient co
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline= PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
-pipeline.annotate("The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline= PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
-val pipeline.annotate("The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.umls_major_concepts_resolver").predict("""The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-----------+-----------------------------------+---------+
 |chunk      |ner_label                          |umls_code|
 +-----------+-----------------------------------+---------+
@@ -91,9 +68,6 @@ Results
 |stairs     |Daily_or_Recreational_Activity     |C4300351 |
 |Arthroscopy|Therapeutic_or_Preventive_Procedure|C0179144 |
 +-----------+-----------------------------------+---------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md
index 862e0ea47c..b9952227a7 100644
--- a/docs/_posts/C-K-Loan/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_granular](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_granular_pipeline", "en", "clinical/models")
-
-text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_granular_pipeline", "en", "clinical/models")
 
-val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -79,18 +58,12 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | left         |      36 |    39 | Direction   |       0.9981 |
 |  1 | breast       |      41 |    46 | Site_Breast |       0.9969 |
 |  2 | lungs        |      82 |    86 | Site_Lung   |       0.9978 |
 |  3 | liver        |      99 |   103 | Site_Liver  |       0.9999 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-26-ner_oncology_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-26-ner_oncology_pipeline_en.md
index d026ba9709..4fc4ceab40 100644
--- a/docs/_posts/C-K-Loan/2023-06-26-ner_oncology_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-26-ner_oncology_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,38 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_pipeline", "en", "clinical/models")
-
-text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_pipeline", "en", "clinical/models")
 
-val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                     |   begin |   end | ner_label             |   confidence |
 |---:|:-------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | left                           |      31 |    34 | Direction             |     0.9913   |
@@ -116,9 +90,6 @@ Results
 | 21 | 600 mg/m2                      |     390 |   398 | Dosage                |     0.9647   |
 | 22 | six courses                    |     406 |   416 | Cycle_Count           |     0.6798   |
 | 23 | first line                     |     422 |   431 | Line_Of_Therapy       |     0.9792   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/C-K-Loan/2023-06-26-oncology_diagnosis_pipeline_en.md b/docs/_posts/C-K-Loan/2023-06-26-oncology_diagnosis_pipeline_en.md
index 95ad5f389a..275c159a97 100644
--- a/docs/_posts/C-K-Loan/2023-06-26-oncology_diagnosis_pipeline_en.md
+++ b/docs/_posts/C-K-Loan/2023-06-26-oncology_diagnosis_pipeline_en.md
@@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status, Relation Extr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -65,44 +66,10 @@ According to her last CT, she has no lung metastases.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_diagnosis_pipeline", "en", "clinical/models")
-
-text = '''Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
-According to her last CT, she has no lung metastases.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_diagnosis_pipeline", "en", "clinical/models")
-
-val text = "Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
-According to her last CT, she has no lung metastases."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_diagnosis.pipeline").predict("""Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
-According to her last CT, she has no lung metastases.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk      | ner_label         |
@@ -199,10 +166,6 @@ Results
 | carcinoma  | Cancer_Dx         | 8010/3 | carcinoma         |
 | lung       | Site_Lung         | C34.9  | lung              |
 | metastases | Metastasis        | 8000/6 | tumor, metastatic |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2022-03-01-sbiobertresolve_atc_en_2_4.md b/docs/_posts/Cabir40/2022-03-01-sbiobertresolve_atc_en_2_4.md
index 7372c50a45..7337ec7696 100644
--- a/docs/_posts/Cabir40/2022-03-01-sbiobertresolve_atc_en_2_4.md
+++ b/docs/_posts/Cabir40/2022-03-01-sbiobertresolve_atc_en_2_4.md
@@ -37,6 +37,7 @@ This model maps drugs entities to ATC (Anatomic Therapeutic Chemical) codes usin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2022-03-01-sbiobertresolve_atc_en_3_0.md b/docs/_posts/Cabir40/2022-03-01-sbiobertresolve_atc_en_3_0.md
index d5dbacdab6..1f8eafaaad 100644
--- a/docs/_posts/Cabir40/2022-03-01-sbiobertresolve_atc_en_3_0.md
+++ b/docs/_posts/Cabir40/2022-03-01-sbiobertresolve_atc_en_3_0.md
@@ -37,6 +37,7 @@ This model maps drugs entities to ATC (Anatomic Therapeutic Chemical) codes usin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
       .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2022-03-03-clinical_deidentification_en_2_4.md b/docs/_posts/Cabir40/2022-03-03-clinical_deidentification_en_2_4.md
index d6f16772dd..2a94eacd4b 100644
--- a/docs/_posts/Cabir40/2022-03-03-clinical_deidentification_en_2_4.md
+++ b/docs/_posts/Cabir40/2022-03-03-clinical_deidentification_en_2_4.md
@@ -33,6 +33,7 @@ This pipeline can be used to deidentify PHI information from medical texts. The
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2022-03-03-clinical_deidentification_en_3_0.md b/docs/_posts/Cabir40/2022-03-03-clinical_deidentification_en_3_0.md
index 61b2f23044..7a71d6173d 100644
--- a/docs/_posts/Cabir40/2022-03-03-clinical_deidentification_en_3_0.md
+++ b/docs/_posts/Cabir40/2022-03-03-clinical_deidentification_en_3_0.md
@@ -41,6 +41,7 @@ This pipeline can be used to deidentify PHI information from medical texts. The
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -169,6 +170,3 @@ Phone 74 617 042, 1407 west stassney lane, Edmonton, E-MAIL: Carliss@hotmail.com
 - DeIdentificationModel
 - DeIdentificationModel
 - Finisher
-<!--stackedit_data:
-eyJoaXN0b3J5IjpbMTEyNzczMjk3NV19
--->
diff --git a/docs/_posts/Cabir40/2022-03-23-assertion_dl_biobert_scope_L10R10_en_3_0.md b/docs/_posts/Cabir40/2022-03-23-assertion_dl_biobert_scope_L10R10_en_3_0.md
index 1e5f989972..79539f98fd 100644
--- a/docs/_posts/Cabir40/2022-03-23-assertion_dl_biobert_scope_L10R10_en_3_0.md
+++ b/docs/_posts/Cabir40/2022-03-23-assertion_dl_biobert_scope_L10R10_en_3_0.md
@@ -46,6 +46,7 @@ This model is trained using `biobert_pubmed_base_cased` BERT token embeddings. I
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document = DocumentAssembler()\
     .setInputCol("text")\
@@ -215,6 +216,3 @@ possible                      126  36   75   0.7777778  0.6268657  0.6942149
 Macro-average                 3633 328  328  0.7967971  0.8105832  0.8036310
 Micro-average                 3633 328  328  0.9171926  0.9171926  0.9171926
 ```
-<!--stackedit_data:
-eyJoaXN0b3J5IjpbMTM3MTI5MjYyXX0=
--->
\ No newline at end of file
diff --git a/docs/_posts/Cabir40/2022-03-24-assertion_dl_biobert_scope_L10R10_en_2_4.md b/docs/_posts/Cabir40/2022-03-24-assertion_dl_biobert_scope_L10R10_en_2_4.md
index aee1d18518..a2119b6618 100644
--- a/docs/_posts/Cabir40/2022-03-24-assertion_dl_biobert_scope_L10R10_en_2_4.md
+++ b/docs/_posts/Cabir40/2022-03-24-assertion_dl_biobert_scope_L10R10_en_2_4.md
@@ -46,6 +46,7 @@ This model is trained using `biobert_pubmed_base_cased` BERT token embeddings. I
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document = DocumentAssembler()\
     .setInputCol("text")\
@@ -210,7 +211,3 @@ possible                      126  36   75   0.7777778  0.6268657  0.6942149
 Macro-average                 3633 328  328  0.7967971  0.8105832  0.8036310
 Micro-average                 3633 328  328  0.9171926  0.9171926  0.9171926
 ```
-<!--stackedit_data:
-eyJoaXN0b3J5IjpbMjgwMTU0NTg3LC02MTkwOTA1MDcsLTE4Nj
-YzNjg0NTBdfQ==
--->
\ No newline at end of file
diff --git a/docs/_posts/Cabir40/2022-03-31-explain_clinical_doc_radiology_en_3_0.md b/docs/_posts/Cabir40/2022-03-31-explain_clinical_doc_radiology_en_3_0.md
index 38d9b6fbd1..aa3f323c57 100644
--- a/docs/_posts/Cabir40/2022-03-31-explain_clinical_doc_radiology_en_3_0.md
+++ b/docs/_posts/Cabir40/2022-03-31-explain_clinical_doc_radiology_en_3_0.md
@@ -33,6 +33,7 @@ A pipeline for detecting radiology entities with the `ner_radiology` NER model,
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2022-04-01-explain_clinical_doc_medication_en_3_0.md b/docs/_posts/Cabir40/2022-04-01-explain_clinical_doc_medication_en_3_0.md
index 602d127ca3..ef27e13913 100644
--- a/docs/_posts/Cabir40/2022-04-01-explain_clinical_doc_medication_en_3_0.md
+++ b/docs/_posts/Cabir40/2022-04-01-explain_clinical_doc_medication_en_3_0.md
@@ -33,6 +33,7 @@ A pipeline for detecting posology entities with the `ner_posology_large` NER mod
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2022-04-25-sbiobertresolve_rxnorm_action_treatment_en_2_4.md b/docs/_posts/Cabir40/2022-04-25-sbiobertresolve_rxnorm_action_treatment_en_2_4.md
index 38dfe597d2..16dc31aff2 100644
--- a/docs/_posts/Cabir40/2022-04-25-sbiobertresolve_rxnorm_action_treatment_en_2_4.md
+++ b/docs/_posts/Cabir40/2022-04-25-sbiobertresolve_rxnorm_action_treatment_en_2_4.md
@@ -37,6 +37,7 @@ This model maps clinical entities and concepts (like drugs/ingredients) to RxNor
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 documentAssembler = DocumentAssembler()\
 .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2022-05-11-sbiobertresolve_icd10cm_slim_billable_hcc_en_3_0.md b/docs/_posts/Cabir40/2022-05-11-sbiobertresolve_icd10cm_slim_billable_hcc_en_3_0.md
index 527e9a0c6c..f30efa509a 100644
--- a/docs/_posts/Cabir40/2022-05-11-sbiobertresolve_icd10cm_slim_billable_hcc_en_3_0.md
+++ b/docs/_posts/Cabir40/2022-05-11-sbiobertresolve_icd10cm_slim_billable_hcc_en_3_0.md
@@ -202,8 +202,6 @@ nlu.load("en.resolve.icd10cm.slim_billable_hcc").predict("""A 28-year-old female
 |                             vomiting|PROBLEM|   R11.1|vomiting [Vomiting]:::vomiting [Vomiting, unspecified]:::intermi...|R11.1:::R11.10:::R11:::G43.A0:::G43.A:::R11.0::...| 0||0||0:::1||0||0:::0||0||0:::1||0||0:::0||0||0:::1...|
 |        a respiratory tract infection|PROBLEM|   J06.9|upper respiratory tract infection [Acute upper respiratory infec...|J06.9:::T17.9:::T17:::J04.10:::J22:::J98.8:::J9...| 1||0||0:::0||0||0:::0||0||0:::1||0||0:::1||0||0:::1...|
 +-------------------------------------+-------+--------+----------------------------------------------------------------------------------------------------+--------------------------------------------------+-------------------------------------------------------+
-
-
 ```
 
 
@@ -222,7 +220,3 @@ nlu.load("en.resolve.icd10cm.slim_billable_hcc").predict("""A 28-year-old female
 |Language:|en|
 |Size:|846.6 MB|
 |Case sensitive:|false|
-<!--stackedit_data:
-eyJoaXN0b3J5IjpbLTE1Nzk5NzQxNTcsLTE0MjY2MTg4OTNdfQ
-==
--->
\ No newline at end of file
diff --git a/docs/_posts/Cabir40/2022-05-12-sbiobertresolve_icd10cm_slim_normalized_en_3_0.md b/docs/_posts/Cabir40/2022-05-12-sbiobertresolve_icd10cm_slim_normalized_en_3_0.md
index 3bbaee1217..99e5fc0e5c 100644
--- a/docs/_posts/Cabir40/2022-05-12-sbiobertresolve_icd10cm_slim_normalized_en_3_0.md
+++ b/docs/_posts/Cabir40/2022-05-12-sbiobertresolve_icd10cm_slim_normalized_en_3_0.md
@@ -220,6 +220,3 @@ nlu.load("en.resolve.icd10cm.slim_normalized").predict("""A 28-year-old female w
 |Language:|en|
 |Size:|846.3 MB|
 |Case sensitive:|false|
-<!--stackedit_data:
-eyJoaXN0b3J5IjpbNDIzMjU0MjAzLC0xNjMwNjI1OTcxXX0=
--->
\ No newline at end of file
diff --git a/docs/_posts/Cabir40/2022-06-22-ner_biomedical_bc2gm_pipeline_en_3_0.md b/docs/_posts/Cabir40/2022-06-22-ner_biomedical_bc2gm_pipeline_en_3_0.md
index 604c5a47ea..b11467b797 100644
--- a/docs/_posts/Cabir40/2022-06-22-ner_biomedical_bc2gm_pipeline_en_3_0.md
+++ b/docs/_posts/Cabir40/2022-06-22-ner_biomedical_bc2gm_pipeline_en_3_0.md
@@ -33,6 +33,7 @@ This pretrained pipeline is built on the top of [ner_biomedical_bc2gm](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2022-06-27-ner_clinical_trials_abstracts_pipeline_en_3_0.md b/docs/_posts/Cabir40/2022-06-27-ner_clinical_trials_abstracts_pipeline_en_3_0.md
index f353032dcf..0958f37ca4 100644
--- a/docs/_posts/Cabir40/2022-06-27-ner_clinical_trials_abstracts_pipeline_en_3_0.md
+++ b/docs/_posts/Cabir40/2022-06-27-ner_clinical_trials_abstracts_pipeline_en_3_0.md
@@ -33,6 +33,7 @@ This pretrained pipeline is built on the top of [ner_clinical_trials_abstracts](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2022-07-29-ner_living_species_300_es_3_0.md b/docs/_posts/Cabir40/2022-07-29-ner_living_species_300_es_3_0.md
index 0367af5818..3185760efc 100644
--- a/docs/_posts/Cabir40/2022-07-29-ner_living_species_300_es_3_0.md
+++ b/docs/_posts/Cabir40/2022-07-29-ner_living_species_300_es_3_0.md
@@ -38,6 +38,7 @@ It is trained on the [LivingNER](https://temu.bsc.es/livingner/) corpus that is
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
     .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2022-08-12-ner_clinical_bert_ro_3_0.md b/docs/_posts/Cabir40/2022-08-12-ner_clinical_bert_ro_3_0.md
index 0d9fd98820..f70a6457eb 100644
--- a/docs/_posts/Cabir40/2022-08-12-ner_clinical_bert_ro_3_0.md
+++ b/docs/_posts/Cabir40/2022-08-12-ner_clinical_bert_ro_3_0.md
@@ -37,6 +37,7 @@ Extract clinical entities from Romanian clinical texts. This model is trained us
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 documentAssembler = DocumentAssembler()\
 .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2022-08-15-ner_deid_generic_bert_ro_3_0.md b/docs/_posts/Cabir40/2022-08-15-ner_deid_generic_bert_ro_3_0.md
index 61395a355b..ff000832bc 100644
--- a/docs/_posts/Cabir40/2022-08-15-ner_deid_generic_bert_ro_3_0.md
+++ b/docs/_posts/Cabir40/2022-08-15-ner_deid_generic_bert_ro_3_0.md
@@ -41,6 +41,7 @@ This NER model is trained with a combination of custom datasets with several dat
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 documentAssembler = DocumentAssembler()\
         .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2022-09-14-clinical_deidentification_en.md b/docs/_posts/Cabir40/2022-09-14-clinical_deidentification_en.md
index dc6322cc9a..183f6f8244 100644
--- a/docs/_posts/Cabir40/2022-09-14-clinical_deidentification_en.md
+++ b/docs/_posts/Cabir40/2022-09-14-clinical_deidentification_en.md
@@ -33,6 +33,7 @@ This pipeline can be used to deidentify PHI information from medical texts. The
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2022-09-16-clinical_deidentification_glove_augmented_en.md b/docs/_posts/Cabir40/2022-09-16-clinical_deidentification_glove_augmented_en.md
index 9a8b893725..6fd83c327b 100644
--- a/docs/_posts/Cabir40/2022-09-16-clinical_deidentification_glove_augmented_en.md
+++ b/docs/_posts/Cabir40/2022-09-16-clinical_deidentification_glove_augmented_en.md
@@ -35,6 +35,7 @@ It's different to `clinical_deidentification_glove` in the way it manages PHONE
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2022-11-22-ner_clinical_bert_ro.md b/docs/_posts/Cabir40/2022-11-22-ner_clinical_bert_ro.md
index 82ac81aeb4..2c4a6cb70e 100644
--- a/docs/_posts/Cabir40/2022-11-22-ner_clinical_bert_ro.md
+++ b/docs/_posts/Cabir40/2022-11-22-ner_clinical_bert_ro.md
@@ -36,6 +36,7 @@ Extract clinical entities from Romanian clinical texts. This model is trained us
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 documentAssembler = DocumentAssembler()\
 .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2022-11-22-ner_deid_generic_bert_ro.md b/docs/_posts/Cabir40/2022-11-22-ner_deid_generic_bert_ro.md
index 4790696d26..5f4600f36c 100644
--- a/docs/_posts/Cabir40/2022-11-22-ner_deid_generic_bert_ro.md
+++ b/docs/_posts/Cabir40/2022-11-22-ner_deid_generic_bert_ro.md
@@ -38,6 +38,7 @@ This NER model is trained with a combination of custom datasets with several dat
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 documentAssembler = DocumentAssembler()\
         .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2022-11-22-ner_living_species_300_es.md b/docs/_posts/Cabir40/2022-11-22-ner_living_species_300_es.md
index 364bb3c226..3b4c2dd5ce 100644
--- a/docs/_posts/Cabir40/2022-11-22-ner_living_species_300_es.md
+++ b/docs/_posts/Cabir40/2022-11-22-ner_living_species_300_es.md
@@ -38,6 +38,7 @@ It is trained on the [LivingNER](https://temu.bsc.es/livingner/) corpus that is
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = DocumentAssembler()\
     .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2023-01-06-redl_clinical_biobert_en.md b/docs/_posts/Cabir40/2023-01-06-redl_clinical_biobert_en.md
index 6b555618d3..b3f81c6397 100644
--- a/docs/_posts/Cabir40/2023-01-06-redl_clinical_biobert_en.md
+++ b/docs/_posts/Cabir40/2023-01-06-redl_clinical_biobert_en.md
@@ -37,6 +37,7 @@ Extract relations like `TrIP` : a certain treatment has improved a medical probl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 documenter = DocumentAssembler()\
 .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2023-02-26-biogpt_pubmed_qa_en.md b/docs/_posts/Cabir40/2023-02-26-biogpt_pubmed_qa_en.md
index 80d0d613ce..ac69872167 100644
--- a/docs/_posts/Cabir40/2023-02-26-biogpt_pubmed_qa_en.md
+++ b/docs/_posts/Cabir40/2023-02-26-biogpt_pubmed_qa_en.md
@@ -39,6 +39,7 @@ Types of questions are supported: `"short"` (producing yes/no/maybe) answers and
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = MultiDocumentAssembler()\
     .setInputCols("question", "context")\
@@ -114,7 +115,6 @@ val result = pipeline.fit(data).transform(data)
 +------------------------------------------------------------------------------------------------------------------------------------------------------+
 |[the results of the two experiments suggest that the visual indexeing theory does not fully explain the effects that spatial attention has on memory.]|
 +------------------------------------------------------------------------------------------------------------------------------------------------------+
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-07-ner_eu_clinical_condition_pipeline_en.md b/docs/_posts/Cabir40/2023-03-07-ner_eu_clinical_condition_pipeline_en.md
index 2ef78ce5b3..8a6237346f 100644
--- a/docs/_posts/Cabir40/2023-03-07-ner_eu_clinical_condition_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-07-ner_eu_clinical_condition_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-07-ner_eu_clinical_condition_pipeline_eu.md b/docs/_posts/Cabir40/2023-03-07-ner_eu_clinical_condition_pipeline_eu.md
index e1a3b2827a..47e228e7d2 100644
--- a/docs/_posts/Cabir40/2023-03-07-ner_eu_clinical_condition_pipeline_eu.md
+++ b/docs/_posts/Cabir40/2023-03-07-ner_eu_clinical_condition_pipeline_eu.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-07-ner_jsl_slim_pipeline_en.md b/docs/_posts/Cabir40/2023-03-07-ner_jsl_slim_pipeline_en.md
index c241d723ec..cb45f0d819 100644
--- a/docs/_posts/Cabir40/2023-03-07-ner_jsl_slim_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-07-ner_jsl_slim_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_jsl_slim](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -50,8 +51,6 @@ val pipeline = new PretrainedPipeline("ner_jsl_slim_pipeline", "en", "clinical/m
 val text = "Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture."
 
 val result = pipeline.fullAnnotate(text)
-
-
 ```
 
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_en.md b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_en.md
index cf156d984f..2132537899 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_es.md b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_es.md
index 489bfda50a..1ae7775096 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_eu.md b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_eu.md
index bd21aab10f..89c317d44b 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_eu.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_eu.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_fr.md b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_fr.md
index a59311de3d..be8645aa6c 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_fr.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_case_pipeline_fr.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_es.md b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_es.md
index 054ca7fff1..3453cc91a6 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_fr.md b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_fr.md
index e318020792..8463df54e1 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_fr.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_fr.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_it.md b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_it.md
index ffb085ca4b..b522946c8a 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_eu_clinical_condition_pipeline_it.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_general_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_general_healthcare_pipeline_en.md
index afa0ea6d0d..5e26f29588 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_general_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_general_healthcare_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_general_he
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_general_pipeline_en.md b/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_general_pipeline_en.md
index ecf9b83cd6..88102c099b 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_general_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_general_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_general](h
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_granular_pipeline_en.md b/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_granular_pipeline_en.md
index 4c213ed9cf..bc1eccfeaf 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_granular_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_oncology_anatomy_granular_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_granular](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_oncology_biomarker_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-03-08-ner_oncology_biomarker_healthcare_pipeline_en.md
index 77e226b1fc..d65dd4ef97 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_oncology_biomarker_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_oncology_biomarker_healthcare_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_biomarker_healthca
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_oncology_pipeline_en.md b/docs/_posts/Cabir40/2023-03-08-ner_oncology_pipeline_en.md
index e3506ed782..b4bc1a77d8 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_oncology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_oncology_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_oncology_posology_pipeline_en.md b/docs/_posts/Cabir40/2023-03-08-ner_oncology_posology_pipeline_en.md
index 4034f71ab0..a2b9fd2275 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_oncology_posology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_oncology_posology_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_posology](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-08-ner_oncology_unspecific_posology_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-03-08-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
index cbf21a4290..05b7812a39 100644
--- a/docs/_posts/Cabir40/2023-03-08-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-08-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_unspecific_posolog
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-biogpt_pubmed_qa_en.md b/docs/_posts/Cabir40/2023-03-09-biogpt_pubmed_qa_en.md
index 2767dd590e..ebf33c1775 100644
--- a/docs/_posts/Cabir40/2023-03-09-biogpt_pubmed_qa_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-biogpt_pubmed_qa_en.md
@@ -38,6 +38,7 @@ Types of questions are supported: `"short"` (producing yes/no/maybe) answers and
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = MultiDocumentAssembler()\
     .setInputCols("question", "context")\
diff --git a/docs/_posts/Cabir40/2023-03-09-medical_qa_biogpt_en.md b/docs/_posts/Cabir40/2023-03-09-medical_qa_biogpt_en.md
index 6f4e868005..da4f4989c5 100644
--- a/docs/_posts/Cabir40/2023-03-09-medical_qa_biogpt_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-medical_qa_biogpt_en.md
@@ -39,6 +39,7 @@ It can generate two types of answers, short and long. Types of questions are sup
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 document_assembler = MultiDocumentAssembler()\
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_clinical_bert_pipeline_ro.md b/docs/_posts/Cabir40/2023-03-09-ner_clinical_bert_pipeline_ro.md
index f6e91414b5..1bcc892c51 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_clinical_bert_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_clinical_bert_pipeline_ro.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_clinical_bert](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_clinical_pipeline_ro.md b/docs/_posts/Cabir40/2023-03-09-ner_clinical_pipeline_ro.md
index b85028a80e..d218d5dce7 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_clinical_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_clinical_pipeline_ro.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_clinical](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_clinical_trials_abstracts_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_clinical_trials_abstracts_pipeline_en.md
index d78101b0a5..a1f25f4e54 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_clinical_trials_abstracts_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_clinical_trials_abstracts_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_clinical_trials_abstracts](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_clinical_trials_abstracts_pipeline_es.md b/docs/_posts/Cabir40/2023-03-09-ner_clinical_trials_abstracts_pipeline_es.md
index 6a5a5a143b..03e1cbd242 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_clinical_trials_abstracts_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_clinical_trials_abstracts_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_clinical_trials_abstracts](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_covid_trials_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_covid_trials_pipeline_en.md
index f5b11db070..bdd5d6acf8 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_covid_trials_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_covid_trials_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_covid_trials](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_deid_generic_bert_pipeline_ro.md b/docs/_posts/Cabir40/2023-03-09-ner_deid_generic_bert_pipeline_ro.md
index 5b471a6ba7..057a84f1af 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_deid_generic_bert_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_deid_generic_bert_pipeline_ro.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_bert](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_deid_generic_pipeline_ro.md b/docs/_posts/Cabir40/2023-03-09-ner_deid_generic_pipeline_ro.md
index e97ca81f35..2b1fb9389e 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_deid_generic_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_deid_generic_pipeline_ro.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_deid_subentity_bert_pipeline_ro.md b/docs/_posts/Cabir40/2023-03-09-ner_deid_subentity_bert_pipeline_ro.md
index 8474aed0a2..37523170b0 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_deid_subentity_bert_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_deid_subentity_bert_pipeline_ro.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_deid_subentity_pipeline_ro.md b/docs/_posts/Cabir40/2023-03-09-ner_deid_subentity_pipeline_ro.md
index e46644b3e3..34b6311f9d 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_deid_subentity_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_deid_subentity_pipeline_ro.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_jsl_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_jsl_pipeline_en.md
index 571a15fd02..d2bfb56e7e 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_jsl_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_jsl_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_jsl](https://nlp.johnsnowla
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_living_species_300_pipeline_es.md b/docs/_posts/Cabir40/2023-03-09-ner_living_species_300_pipeline_es.md
index a3e927d6f9..3616edf641 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_living_species_300_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_living_species_300_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species_300](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_negation_uncertainty_pipeline_es.md b/docs/_posts/Cabir40/2023-03-09-ner_negation_uncertainty_pipeline_es.md
index 05887be1d5..f59c5526c5 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_negation_uncertainty_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_negation_uncertainty_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_negation_uncertainty](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_oncology_biomarker_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_oncology_biomarker_pipeline_en.md
index 02904d1157..edbe8fea19 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_oncology_biomarker_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_oncology_biomarker_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_biomarker](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_oncology_demographics_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_oncology_demographics_pipeline_en.md
index a139dcc5de..e4c92b4aaa 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_oncology_demographics_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_oncology_demographics_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_demographics](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_oncology_diagnosis_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_oncology_diagnosis_pipeline_en.md
index 0ed099a9b7..31481d153a 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_oncology_diagnosis_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_oncology_diagnosis_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_diagnosis](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_oncology_response_to_treatment_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_oncology_response_to_treatment_pipeline_en.md
index ab0bdfee37..37039ab956 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_oncology_response_to_treatment_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_oncology_response_to_treatment_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_response_to_treatm
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_oncology_test_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_oncology_test_pipeline_en.md
index 8733eac6da..7dfd297973 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_oncology_test_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_oncology_test_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_test](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_oncology_therapy_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_oncology_therapy_pipeline_en.md
index 792795855d..7a80ce8bef 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_oncology_therapy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_oncology_therapy_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_therapy](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_oncology_tnm_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_oncology_tnm_pipeline_en.md
index 42b2785b48..9f83e8923d 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_oncology_tnm_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_oncology_tnm_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_tnm](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_oncology_unspecific_posology_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_oncology_unspecific_posology_pipeline_en.md
index b942841068..5e64ac4829 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_oncology_unspecific_posology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_oncology_unspecific_posology_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_oncology_unspecific_posolog
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_pathogen_pipeline_en.md b/docs/_posts/Cabir40/2023-03-09-ner_pathogen_pipeline_en.md
index 94210302a5..a4de66eceb 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_pathogen_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_pathogen_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_pathogen](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-09-ner_pharmacology_pipeline_es.md b/docs/_posts/Cabir40/2023-03-09-ner_pharmacology_pipeline_es.md
index 11e1ea1cf3..3adc54d43b 100644
--- a/docs/_posts/Cabir40/2023-03-09-ner_pharmacology_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-09-ner_pharmacology_pipeline_es.md
@@ -28,10 +28,9 @@ This pretrained pipeline is built on the top of [ner_pharmacology](https://nlp.j
 
 ## How to use
 
-
-
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-10-umls_clinical_findings_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-03-10-umls_clinical_findings_resolver_pipeline_en.md
index 40d4b9a3b6..15d93e3526 100644
--- a/docs/_posts/Cabir40/2023-03-10-umls_clinical_findings_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-10-umls_clinical_findings_resolver_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline maps entities (Clinical Findings) with their correspond
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -99,4 +100,4 @@ nlu.load("en.map_entity.umls_clinical_findings_resolver").predict("""['HTG-induc
 - Chunk2Doc
 - BertSentenceEmbeddings
 - SentenceEntityResolverModel
-- ResolverMerger
\ No newline at end of file
+- ResolverMerger
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_augmented_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_augmented_pipeline_en.md
index f64db700d1..2968af9172 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_augmented_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_augmented_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_augmented](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -99,4 +100,4 @@ nlu.load("en.deid.ner_augmented.pipeline").predict("""HISTORY OF PRESENT ILLNESS
 - TokenizerModel
 - WordEmbeddingsModel
 - MedicalNerModel
-- NerConverterInternalModel
\ No newline at end of file
+- NerConverterInternalModel
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_enriched_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_enriched_pipeline_en.md
index 4d8c2abc8c..d14f6d4590 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_enriched_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_enriched_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_enriched](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -97,4 +98,4 @@ nlu.load("en.med_ner.deid_enriched.pipeline").predict("""HISTORY OF PRESENT ILLN
 - TokenizerModel
 - WordEmbeddingsModel
 - MedicalNerModel
-- NerConverterInternalModel
\ No newline at end of file
+- NerConverterInternalModel
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_glove_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_glove_pipeline_en.md
index 545fc256c7..379347ddfa 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_glove_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_glove_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_glove](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_pipeline_de.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_pipeline_de.md
index d68a6fb41a..4ab65609e4 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_pipeline_de.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_pipeline_de.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_pipeline_it.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_pipeline_it.md
index 1b257d4e39..dd9fed786d 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_generic_pipeline_it.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_large_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_large_pipeline_en.md
index f9a70993ef..7bd7bc27e1 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_large_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_large](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_sd_large_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_sd_large_pipeline_en.md
index d607ba776d..55d05e9d8c 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_sd_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_sd_large_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_sd_large](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_sd_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_sd_pipeline_en.md
index d189da9db1..a861541ed3 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_sd_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_sd_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_sd](https://nlp.johnsn
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -98,4 +99,4 @@ nlu.load("en.deid.sd.pipeline").predict("""Record date : 2093-01-13 , David Hale
 - TokenizerModel
 - WordEmbeddingsModel
 - MedicalNerModel
-- NerConverterInternalModel
\ No newline at end of file
+- NerConverterInternalModel
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_augmented_i2b2_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_augmented_i2b2_pipeline_en.md
index bd33140030..d23ce77e6f 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_augmented_i2b2_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_augmented_i2b2_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_augmented_i2
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_augmented_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_augmented_pipeline_en.md
index b24cbcd799..cc1710c3f7 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_augmented_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_augmented_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_augmented](h
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_glove_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_glove_pipeline_en.md
index 97387ef2e0..87b86f5fdc 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_glove_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_glove_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_glove](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_pipeline_de.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_pipeline_de.md
index 7be1b374f1..ea07f1d225 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_pipeline_de.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_pipeline_de.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_pipeline_it.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_pipeline_it.md
index 3c686f7676..bb90f0e02c 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_subentity_pipeline_it.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deid_synthetic_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deid_synthetic_pipeline_en.md
index be8266c2e6..43458a96ca 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deid_synthetic_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deid_synthetic_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_synthetic](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_deidentify_dl_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_deidentify_dl_pipeline_en.md
index ada3ece3e7..c068d79c29 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_deidentify_dl_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_deidentify_dl_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deidentify_dl](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_es.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_es.md
index 21fc1d58b2..ae7044153f 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_fr.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_fr.md
index b957865ee9..e6dc2b51a8 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_fr.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_fr.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_it.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_it.md
index 5ae3681639..87a9aca1d7 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_it.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_pt.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_pt.md
index 847b2c9c1e..2dcfa7b56a 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_pt.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_pt.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_ro.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_ro.md
index 1f88012adb..43683048ba 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_bert_pipeline_ro.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_ca.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_ca.md
index 4dba52e3d5..61d12430bf 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_ca.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_ca.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_en.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_en.md
index c5d51e2d29..57e6753aa9 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_es.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_es.md
index e4aa14e121..3a95029d55 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_fr.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_fr.md
index 5c7f54b2c2..19500cdc65 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_fr.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_fr.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_gl.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_gl.md
index a9283a8c7c..4bf248aac8 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_gl.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_gl.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_it.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_it.md
index 251a4c1d0c..8328be3916 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_it.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_pt.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_pt.md
index 108a817865..523f338e8d 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_pt.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_pipeline_pt.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_roberta_pipeline_es.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_roberta_pipeline_es.md
index 524c46087c..77e63fb7bf 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_roberta_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_roberta_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species_roberta](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-13-ner_living_species_roberta_pipeline_pt.md b/docs/_posts/Cabir40/2023-03-13-ner_living_species_roberta_pipeline_pt.md
index 97d01b0a24..d19eda8e74 100644
--- a/docs/_posts/Cabir40/2023-03-13-ner_living_species_roberta_pipeline_pt.md
+++ b/docs/_posts/Cabir40/2023-03-13-ner_living_species_roberta_pipeline_pt.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species_roberta](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-jsl_ner_wip_greedy_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-jsl_ner_wip_greedy_clinical_pipeline_en.md
index b618ec1d8a..4b1b5e1edc 100644
--- a/docs/_posts/Cabir40/2023-03-14-jsl_ner_wip_greedy_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-jsl_ner_wip_greedy_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_greedy_clinical](ht
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-jsl_ner_wip_modifier_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-jsl_ner_wip_modifier_clinical_pipeline_en.md
index 09b5e1499a..34ce32cd6a 100644
--- a/docs/_posts/Cabir40/2023-03-14-jsl_ner_wip_modifier_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-jsl_ner_wip_modifier_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_modifier_clinical](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
index f3bbd6cc0a..2ccf10c275 100644
--- a/docs/_posts/Cabir40/2023-03-14-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [jsl_rd_ner_wip_greedy_clinical]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_abbreviation_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_abbreviation_clinical_pipeline_en.md
index 3ad0b21e99..1664f1ac41 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_abbreviation_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_abbreviation_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_abbreviation_clinical](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_ade_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_ade_clinical_pipeline_en.md
index 076dff8078..b9b83331f2 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_ade_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_ade_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_ade_clinical](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_ade_clinicalbert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_ade_clinicalbert_pipeline_en.md
index 272a0c100f..3b7269ce7e 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_ade_clinicalbert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_ade_clinicalbert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_ade_clinicalbert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_ade_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_ade_healthcare_pipeline_en.md
index d66cbd5f8c..15ebb695ae 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_ade_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_ade_healthcare_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_ade_healthcare](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_bacterial_species_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_bacterial_species_pipeline_en.md
index 057f861770..c098a6a1b5 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_bacterial_species_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_bacterial_species_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_bacterial_species](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_biomarker_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_biomarker_pipeline_en.md
index 104ed3de97..67e12fabab 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_biomarker_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_biomarker_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_biomarker](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_biomedical_bc2gm_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_biomedical_bc2gm_pipeline_en.md
index 06739e67e7..7532af2b9c 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_biomedical_bc2gm_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_biomedical_bc2gm_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_biomedical_bc2gm](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_chemd_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_chemd_clinical_pipeline_en.md
index 5525abd0d9..e430204361 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_chemd_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_chemd_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_chemd_clinical](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_chemicals_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_chemicals_pipeline_en.md
index 98d48a71f3..1a2b1f7af3 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_chemicals_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_chemicals_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_chemicals](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_chexpert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_chexpert_pipeline_en.md
index 790cd8d1ad..bb6a741126 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_chexpert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_chexpert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_chexpert](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_diseases_large_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_diseases_large_pipeline_en.md
index 527ad101b4..6575eb18d2 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_diseases_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_diseases_large_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_diseases_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_drugprot_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_drugprot_clinical_pipeline_en.md
index 67159256cb..83ba78ca68 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_drugprot_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_drugprot_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_drugprot_clinical](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_events_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_events_healthcare_pipeline_en.md
index de4c6f9c17..a3a2fac952 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_events_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_events_healthcare_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_events_healthcare](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_genetic_variants_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_genetic_variants_pipeline_en.md
index e18d680975..056d8c6715 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_genetic_variants_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_genetic_variants_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_genetic_variants](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_healthcare_pipeline_en.md
index 624a728e3b..eab62672b0 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_healthcare_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_healthcare](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_jsl_enriched_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_jsl_enriched_pipeline_en.md
index 5fec3e10c5..3537455ee7 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_jsl_enriched_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_jsl_enriched_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_jsl_enriched](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_jsl_greedy_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_jsl_greedy_pipeline_en.md
index 58b24917e9..526b5e7e2d 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_jsl_greedy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_jsl_greedy_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_jsl_greedy](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_measurements_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_measurements_clinical_pipeline_en.md
index 9334db87fd..66adc3c830 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_measurements_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_measurements_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_measurements_clinical](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_medmentions_coarse_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_medmentions_coarse_pipeline_en.md
index d34c05295c..c29f201410 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_medmentions_coarse_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_medmentions_coarse_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_medmentions_coarse](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_nature_nero_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_nature_nero_clinical_pipeline_en.md
index 2ff999a486..6f976f61df 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_nature_nero_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_nature_nero_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_nature_nero_clinical](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_nihss_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_nihss_pipeline_en.md
index 510a0d54a3..8d32b21fa1 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_nihss_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_nihss_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_nihss](https://nlp.johnsnow
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_radiology_wip_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_radiology_wip_clinical_pipeline_en.md
index 628066eef8..200f25e4b0 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_radiology_wip_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_radiology_wip_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_radiology_wip_clinical](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-ner_supplement_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-ner_supplement_clinical_pipeline_en.md
index 0720f1a536..6045a541ef 100644
--- a/docs/_posts/Cabir40/2023-03-14-ner_supplement_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-ner_supplement_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_supplement_clinical](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-14-nerdl_tumour_demo_pipeline_en.md b/docs/_posts/Cabir40/2023-03-14-nerdl_tumour_demo_pipeline_en.md
index d1e2701354..b0d8c9466b 100644
--- a/docs/_posts/Cabir40/2023-03-14-nerdl_tumour_demo_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-14-nerdl_tumour_demo_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [nerdl_tumour_demo](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-jsl_ner_wip_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-jsl_ner_wip_clinical_pipeline_en.md
index 82cd5ca56d..bd9a30f283 100644
--- a/docs/_posts/Cabir40/2023-03-15-jsl_ner_wip_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-jsl_ner_wip_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_clinical](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_anatomy_coarse_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_anatomy_coarse_pipeline_en.md
index dfbc05cd76..56f5cb5820 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_anatomy_coarse_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_anatomy_coarse_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_coarse](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_anatomy_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_anatomy_pipeline_en.md
index 2268560788..63ce0f56a1 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_anatomy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_anatomy_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_anatomy](https://nlp.johnsn
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_bionlp_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_bionlp_pipeline_en.md
index 76d98950e9..1f006e1923 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_bionlp_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_bionlp_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_bionlp](https://nlp.johnsno
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_cancer_genetics_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_cancer_genetics_pipeline_en.md
index 416bb1c5ba..933171597b 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_cancer_genetics_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_cancer_genetics_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_cancer_genetics](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_cellular_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_cellular_pipeline_en.md
index bf3a2759a4..dc9b4600cc 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_cellular_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_cellular_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_cellular](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_chemprot_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_chemprot_clinical_pipeline_en.md
index 3f32c30459..21844de1ca 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_chemprot_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_chemprot_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_chemprot_clinical](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_clinical_large_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_clinical_large_pipeline_en.md
index 4734d54cb2..f8edaa6688 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_clinical_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_clinical_large_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_clinical_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_clinical_pipeline_en.md
index 48a5be652b..ba679b088a 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_clinical](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_diag_proc_pipeline_es.md b/docs/_posts/Cabir40/2023-03-15-ner_diag_proc_pipeline_es.md
index 24dc6d73b3..db7ebe0bdb 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_diag_proc_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_diag_proc_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_diag_proc](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_diseases_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_diseases_pipeline_en.md
index 7b8151c499..9e69fd2429 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_diseases_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_diseases_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_diseases](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_drugs_greedy_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_drugs_greedy_pipeline_en.md
index 9c42745c9e..dd64c5c96a 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_drugs_greedy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_drugs_greedy_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_drugs_greedy](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_drugs_large_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_drugs_large_pipeline_en.md
index 3653f21d6a..6560e6e0fd 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_drugs_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_drugs_large_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_drugs_large](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_drugs_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_drugs_pipeline_en.md
index a682c3ca50..004f4bcb31 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_drugs_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_drugs_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_drugs](https://nlp.johnsnow
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_events_admission_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_events_admission_clinical_pipeline_en.md
index 6a976294e6..09faa39650 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_events_admission_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_events_admission_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_events_admission_clinical](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_events_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_events_clinical_pipeline_en.md
index 7e84bcc71a..157d279237 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_events_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_events_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_events_clinical](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_healthcare_pipeline_de.md b/docs/_posts/Cabir40/2023-03-15-ner_healthcare_pipeline_de.md
index 7f66bbb1cb..0390423a49 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_healthcare_pipeline_de.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_healthcare_pipeline_de.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_healthcare](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_healthcare_slim_pipeline_de.md b/docs/_posts/Cabir40/2023-03-15-ner_healthcare_slim_pipeline_de.md
index 5be5f8b69a..61477aefc6 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_healthcare_slim_pipeline_de.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_healthcare_slim_pipeline_de.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_healthcare_slim](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_human_phenotype_gene_clinical_pipeline_en.md
index a2621d9bb2..6321341e1f 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_human_phenotype_gene_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_human_phenotype_gene_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_gene_clinic
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_human_phenotype_go_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_human_phenotype_go_clinical_pipeline_en.md
index 199d8e37b2..41c082441f 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_human_phenotype_go_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_human_phenotype_go_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_go_clinical
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_neoplasms_pipeline_es.md b/docs/_posts/Cabir40/2023-03-15-ner_neoplasms_pipeline_es.md
index 664afa4d32..7393d2fcb0 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_neoplasms_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_neoplasms_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_neoplasms](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_posology_experimental_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_posology_experimental_pipeline_en.md
index 91ef07edb9..6227b62c79 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_posology_experimental_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_posology_experimental_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_posology_experimental](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_posology_greedy_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_posology_greedy_pipeline_en.md
index c177ff0361..99880bdb70 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_posology_greedy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_posology_greedy_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_posology_greedy](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_posology_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_posology_healthcare_pipeline_en.md
index d426fafde0..f798fc7afe 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_posology_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_posology_healthcare_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_posology_healthcare](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_posology_large_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_posology_large_pipeline_en.md
index e6190d882b..214dbc0a4a 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_posology_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_posology_large_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_posology_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_posology_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_posology_pipeline_en.md
index e2fac60374..ffe38f029d 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_posology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_posology_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_posology](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_posology_small_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_posology_small_pipeline_en.md
index 8755952b56..3b1027913e 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_posology_small_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_posology_small_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_posology_small](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_radiology_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_radiology_pipeline_en.md
index 82cdeaa99c..2af6be47f2 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_radiology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_radiology_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_radiology](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-15-ner_risk_factors_pipeline_en.md b/docs/_posts/Cabir40/2023-03-15-ner_risk_factors_pipeline_en.md
index d7273ad10e..f42ef3b8e8 100644
--- a/docs/_posts/Cabir40/2023-03-15-ner_risk_factors_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-15-ner_risk_factors_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_risk_factors](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ade_tweet_binary_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ade_tweet_binary_pipeline_en.md
index c7c0bfb389..c0a26efc2d 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ade_tweet_binary_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ade_tweet_binary_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ade_tweet
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_disease_mentions_tweet_pipeline_es.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
index eac265c795..f38ac8ed32 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_disease_m
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_drug_development_trials_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_drug_development_trials_pipeline_en.md
index f863e597e7..27791cf515 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_drug_development_trials_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_drug_development_trials_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_drug_deve
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_negation_uncertainty_pipeline_es.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_negation_uncertainty_pipeline_es.md
index aa485fc809..17db1dd58a 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_negation_uncertainty_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_negation_uncertainty_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_negation_
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ade_binary_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ade_binary_pipeline_en.md
index 7cd752b7d5..e54425cade 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ade_binary_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ade_binary_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ade_b
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ade_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ade_pipeline_en.md
index 218fdccf24..5d68797530 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ade_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ade_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ade](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_anatem_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_anatem_pipeline_en.md
index 3d155402e9..76872e22c4 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_anatem_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_anatem_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_anate
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_anatomy_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_anatomy_pipeline_en.md
index 50721be5e3..4436b383af 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_anatomy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_anatomy_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_anato
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bacteria_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bacteria_pipeline_en.md
index 3e3d92d880..30551b6feb 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bacteria_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bacteria_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bacte
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
index 4ba11e7c68..36227b2def 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc2gm
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
index 4827d3ae91..d97d42ffbf 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc4ch
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
index ac598773a4..8ea9d9a22f 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc5cd
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
index d4cd9eadfe..6cba4e6523 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc5cd
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bionlp_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bionlp_pipeline_en.md
index 3658e1e627..015b2f2bff 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bionlp_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_bionlp_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bionl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_cellular_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_cellular_pipeline_en.md
index 147b5d264e..4040af3dc5 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_cellular_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_cellular_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_cellu
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_chemicals_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_chemicals_pipeline_en.md
index 802da6a18d..b7f56671ac 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_chemicals_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_chemicals_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_chemi
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_chemprot_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_chemprot_pipeline_en.md
index 6730823297..3c90e56464 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_chemprot_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_chemprot_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_chemp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_pipeline_en.md
index 02df25d81b..5ce0e9756f 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
index 4ab7ff41d6..bdebc7e7ce 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
index ccbb2e0e6e..19c729c7e2 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_deid_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_deid_pipeline_en.md
index 175d1d594c..4231f4531f 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_deid_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_deid_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_deid]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_drugs_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_drugs_pipeline_en.md
index 938166d5e8..4dee492a32 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_drugs_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_drugs_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_drugs
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
index cb311ef595..3dd789761c 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jnlpb
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jsl_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jsl_pipeline_en.md
index 595a7c235e..1876c792e6 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jsl_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jsl_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jsl_slim_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jsl_slim_pipeline_en.md
index fb481b6c4a..3d14399cfb 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jsl_slim_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_jsl_slim_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl_s
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_linnaeus_species_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
index 9728ce5242..7bbe90cd78 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_linna
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_en.md
index 28075791cd..fe7821e05e 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_es.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_es.md
index 6b31cf26bb..9da0fec4f0 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_it.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_it.md
index fcdee06e5c..6bd95e62f0 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_it.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_pt.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_pt.md
index 4c1bc5c692..6c8e3f6d32 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_pt.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_living_species_pipeline_pt.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ncbi_disease_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
index 33a43ecbcf..329753cae0 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ncbi_
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_pathogen_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_pathogen_pipeline_en.md
index 7ff84804ee..257b44a184 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_pathogen_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_pathogen_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_patho
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_species_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_species_pipeline_en.md
index eaa5ae3952..5d95e181b7 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_species_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_ner_species_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_speci
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_pharmacology_pipeline_es.md b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_pharmacology_pipeline_es.md
index 498c3cff02..f6d27482dd 100644
--- a/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_pharmacology_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-03-20-bert_token_classifier_pharmacology_pipeline_es.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_pharmacol
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-jsl_ner_wip_greedy_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-jsl_ner_wip_greedy_biobert_pipeline_en.md
index 7c586eade6..3b16a8a163 100644
--- a/docs/_posts/Cabir40/2023-03-20-jsl_ner_wip_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-jsl_ner_wip_greedy_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_greedy_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
index 3ee2713d2c..96213df782 100644
--- a/docs/_posts/Cabir40/2023-03-20-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [jsl_rd_ner_wip_greedy_biobert](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_ade_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_ade_biobert_pipeline_en.md
index 4a30938eee..bb5da6c6b0 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_ade_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_ade_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_ade_biobert](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_anatomy_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_anatomy_biobert_pipeline_en.md
index 4a9e9749b4..36a64441a8 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_anatomy_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_anatomy_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_biobert](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_anatomy_coarse_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_anatomy_coarse_biobert_pipeline_en.md
index 533280e592..908829f15c 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_anatomy_coarse_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_anatomy_coarse_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_coarse_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_bionlp_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_bionlp_biobert_pipeline_en.md
index bd2418bc0c..cd7d4b5076 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_bionlp_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_bionlp_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_bionlp_biobert](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_cellular_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_cellular_biobert_pipeline_en.md
index 53bfc89c67..47dda0a494 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_cellular_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_cellular_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_cellular_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_chemprot_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_chemprot_biobert_pipeline_en.md
index 37385450ea..e5945335fa 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_chemprot_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_chemprot_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_chemprot_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_clinical_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_clinical_biobert_pipeline_en.md
index 7714389ced..960737ba9c 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_clinical_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_clinical_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_clinical_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_deid_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_deid_biobert_pipeline_en.md
index e219041073..5d8623fd4f 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_deid_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_deid_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_biobert](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_deid_enriched_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_deid_enriched_biobert_pipeline_en.md
index f6d1e056bd..f2fd661c67 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_deid_enriched_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_deid_enriched_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_deid_enriched_biobert](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_diseases_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_diseases_biobert_pipeline_en.md
index b1050169e3..399a61c0de 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_diseases_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_diseases_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_diseases_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_events_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_events_biobert_pipeline_en.md
index 27ade03172..8ee9616075 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_events_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_events_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_events_biobert](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_human_phenotype_gene_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_human_phenotype_gene_biobert_pipeline_en.md
index 32f8346f1e..6a8422333f 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_human_phenotype_gene_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_human_phenotype_gene_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_gene_biober
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_human_phenotype_go_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_human_phenotype_go_biobert_pipeline_en.md
index f84e0fc8ad..c0b2a03c7c 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_human_phenotype_go_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_human_phenotype_go_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_go_biobert]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_jsl_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_jsl_biobert_pipeline_en.md
index d4016235ca..37d3d9e2bf 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_jsl_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_jsl_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_jsl_biobert](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_jsl_enriched_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_jsl_enriched_biobert_pipeline_en.md
index b1380004a7..f5bb1eceb3 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_jsl_enriched_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_jsl_enriched_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_jsl_enriched_biobert](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_jsl_greedy_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_jsl_greedy_biobert_pipeline_en.md
index e1ee4a8b53..108bc36411 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_jsl_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_jsl_greedy_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_jsl_greedy_biobert](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_living_species_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_living_species_biobert_pipeline_en.md
index f9894453fd..f1fe2f90ea 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_living_species_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_living_species_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_living_species_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_posology_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_posology_biobert_pipeline_en.md
index 77ffcfea02..1ffc176d91 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_posology_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_posology_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_posology_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_posology_large_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_posology_large_biobert_pipeline_en.md
index be3d92ffdb..8584b6c9dd 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_posology_large_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_posology_large_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_posology_large_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-20-ner_risk_factors_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-03-20-ner_risk_factors_biobert_pipeline_en.md
index eeddad14ae..df95cc58b1 100644
--- a/docs/_posts/Cabir40/2023-03-20-ner_risk_factors_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-20-ner_risk_factors_biobert_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of [ner_risk_factors_biobert](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-03-29-icd10_icd9_mapping_en.md b/docs/_posts/Cabir40/2023-03-29-icd10_icd9_mapping_en.md
index 78bc23e381..2ed8befa5e 100644
--- a/docs/_posts/Cabir40/2023-03-29-icd10_icd9_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-icd10_icd9_mapping_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of `icd10_icd9_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -59,11 +60,9 @@ nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
 ## Results
 
 ```bash
-
 |    | icd10_code          | icd9_code          |
 |---:|:--------------------|:-------------------|
 |  0 | Z833 | A0100 | A000 | V180 | 0020 | 0010 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-icd10cm_snomed_mapping_en.md b/docs/_posts/Cabir40/2023-03-29-icd10cm_snomed_mapping_en.md
index 2850582d6d..65a8b08893 100644
--- a/docs/_posts/Cabir40/2023-03-29-icd10cm_snomed_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-icd10cm_snomed_mapping_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of `icd10cm_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -59,11 +60,9 @@ nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here."
 ## Results
 
 ```bash
-
 |    | icd10cm_code          | snomed_code                              |
 |---:|:----------------------|:-----------------------------------------|
 |  0 | R079 | N4289 | M62830 | 161972006 | 22035000 | 16410651000119105 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-icd10cm_umls_mapping_en.md b/docs/_posts/Cabir40/2023-03-29-icd10cm_umls_mapping_en.md
index ad26503a45..b6eb14d618 100644
--- a/docs/_posts/Cabir40/2023-03-29-icd10cm_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-icd10cm_umls_mapping_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline maps ICD10CM codes to UMLS codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -59,10 +60,8 @@ nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
 ## Results
 
 ```bash
-
 {'icd10cm': ['M89.50', 'R82.2', 'R09.01'],
 'umls': ['C4721411', 'C0159076', 'C0004044']}
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-icd9_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-03-29-icd9_resolver_pipeline_en.md
index b828616819..400c85a750 100644
--- a/docs/_posts/Cabir40/2023-03-29-icd9_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-icd9_resolver_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline maps entities with their corresponding ICD-9-CM codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -61,7 +62,6 @@ nlu.load("en.resolve.icd9.pipeline").predict("""Put your text here.""")
 ## Results
 
 ```bash
-
 +-----------------------------+---------+---------+
 |chunk                        |ner_chunk|icd9_code|
 +-----------------------------+---------+---------+
@@ -69,7 +69,6 @@ nlu.load("en.resolve.icd9.pipeline").predict("""Put your text here.""")
 |anisakiasis                  |PROBLEM  |127.1    |
 |fetal and neonatal hemorrhage|PROBLEM  |772      |
 +-----------------------------+---------+---------+
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-icdo_snomed_mapping_en.md b/docs/_posts/Cabir40/2023-03-29-icdo_snomed_mapping_en.md
index 88bd618635..ea09c284f2 100644
--- a/docs/_posts/Cabir40/2023-03-29-icdo_snomed_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-icdo_snomed_mapping_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of `icdo_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -59,11 +60,9 @@ nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
 ## Results
 
 ```bash
-
 |    | icdo_code                | snomed_code                    |
 |---:|:-------------------------|:-------------------------------|
 |  0 | 8120/1 | 8170/3 | 8380/3 | 45083001 | 25370001 | 30289006 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-mesh_umls_mapping_en.md b/docs/_posts/Cabir40/2023-03-29-mesh_umls_mapping_en.md
index c6c4c78b7d..982d6bc313 100644
--- a/docs/_posts/Cabir40/2023-03-29-mesh_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-mesh_umls_mapping_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of `mesh_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -59,11 +60,9 @@ nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
 ## Results
 
 ```bash
-
 |    | mesh_code                   | umls_code                      |
 |---:|:----------------------------|:-------------------------------|
 |  0 | C028491 | D019326 | C579867 | C0043904 | C0045010 | C3696376 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-oncology_biomarker_pipeline_en.md b/docs/_posts/Cabir40/2023-03-29-oncology_biomarker_pipeline_en.md
index 98c15e8639..71a058d72d 100644
--- a/docs/_posts/Cabir40/2023-03-29-oncology_biomarker_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-oncology_biomarker_pipeline_en.md
@@ -32,6 +32,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status and Relation E
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -63,7 +64,6 @@ nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was n
 ## Results
 
 ```bash
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk                          | ner_label        |
@@ -189,7 +189,6 @@ nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was n
 | ER                   | Biomarker        | negative                       | Biomarker_Result | O             |
 | PR                   | Biomarker        | negative                       | Biomarker_Result | O             |
 | negative             | Biomarker_Result | HER2                           | Oncogene         | is_finding_of |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-oncology_diagnosis_pipeline_en.md b/docs/_posts/Cabir40/2023-03-29-oncology_diagnosis_pipeline_en.md
index a5f2acb0f5..1776225f21 100644
--- a/docs/_posts/Cabir40/2023-03-29-oncology_diagnosis_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-oncology_diagnosis_pipeline_en.md
@@ -32,6 +32,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status, Relation Extr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -66,7 +67,6 @@ According to her last CT, she has no lung metastases.""")
 ## Results
 
 ```bash
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk      | ner_label         |
@@ -163,7 +163,6 @@ According to her last CT, she has no lung metastases.""")
 | carcinoma  | Cancer_Dx         | 8010/3 | carcinoma         |
 | lung       | Site_Lung         | C34.9  | lung              |
 | metastases | Metastasis        | 8000/6 | tumor, metastatic |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-oncology_general_pipeline_en.md b/docs/_posts/Cabir40/2023-03-29-oncology_general_pipeline_en.md
index ea517413ec..b73975af45 100644
--- a/docs/_posts/Cabir40/2023-03-29-oncology_general_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-oncology_general_pipeline_en.md
@@ -32,6 +32,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status and Relation E
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -66,7 +67,6 @@ The tumor is positive for ER and PR.""")
 ## Results
 
 ```bash
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk          | ner_label        |
@@ -147,8 +147,6 @@ The tumor is positive for ER and PR.""")
 | tumor         | Tumor_Finding    | PR             | Biomarker     | O             |
 | positive      | Biomarker_Result | ER             | Biomarker     | is_finding_of |
 | positive      | Biomarker_Result | PR             | Biomarker     | is_finding_of |
-
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-oncology_therapy_pipeline_en.md b/docs/_posts/Cabir40/2023-03-29-oncology_therapy_pipeline_en.md
index f22e6be63d..2901a6e9b4 100644
--- a/docs/_posts/Cabir40/2023-03-29-oncology_therapy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-oncology_therapy_pipeline_en.md
@@ -32,6 +32,7 @@ This pipeline includes Named-Entity Recognition and Assertion Status models to e
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -63,7 +64,6 @@ nlu.load("en.oncology_therpay.pipeline").predict("""The patient underwent a mast
 ## Results
 
 ```bash
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk            | ner_label      |
@@ -120,7 +120,6 @@ nlu.load("en.oncology_therpay.pipeline").predict("""The patient underwent a mast
 | mastectomy       | Cancer_Surgery | Present_Or_Past |
 | adriamycin       | Chemotherapy   | Present_Or_Past |
 | cyclophosphamide | Chemotherapy   | Present_Or_Past |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-rxnorm_ndc_mapping_en.md b/docs/_posts/Cabir40/2023-03-29-rxnorm_ndc_mapping_en.md
index 9b04dbc7b0..3f6ff6626e 100644
--- a/docs/_posts/Cabir40/2023-03-29-rxnorm_ndc_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-rxnorm_ndc_mapping_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline maps RXNORM codes to NDC codes without using any text d
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -59,12 +60,10 @@ nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
 ## Results
 
 ```bash
-
 {'document': ['1652674 259934'],
 'package_ndc': ['62135-0625-60', '13349-0010-39'],
 'product_ndc': ['46708-0499', '13349-0010'],
 'rxnorm_code': ['1652674', '259934']}
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-rxnorm_umls_mapping_en.md b/docs/_posts/Cabir40/2023-03-29-rxnorm_umls_mapping_en.md
index 7391212877..314d275093 100644
--- a/docs/_posts/Cabir40/2023-03-29-rxnorm_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-rxnorm_umls_mapping_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of `rxnorm_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -59,11 +60,9 @@ nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
 ## Results
 
 ```bash
-
 |    | rxnorm_code      | umls_code           |
 |---:|:-----------------|:--------------------|
 |  0 | 1161611 | 315677 | C3215948 | C0984912 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-snomed_icd10cm_mapping_en.md b/docs/_posts/Cabir40/2023-03-29-snomed_icd10cm_mapping_en.md
index 582562b85d..616abf4c88 100644
--- a/docs/_posts/Cabir40/2023-03-29-snomed_icd10cm_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-snomed_icd10cm_mapping_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of `snomed_icd10cm_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -59,11 +60,9 @@ nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here."
 ## Results
 
 ```bash
-
 |    | snomed_code                             | icd10cm_code               |
 |---:|:----------------------------------------|:---------------------------|
 |  0 | 128041000119107 | 292278006 | 293072005 | K22.70 | T43.595 | T37.1X5 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-snomed_icdo_mapping_en.md b/docs/_posts/Cabir40/2023-03-29-snomed_icdo_mapping_en.md
index 1cc2c5c228..11cae450c0 100644
--- a/docs/_posts/Cabir40/2023-03-29-snomed_icdo_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-snomed_icdo_mapping_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of `snomed_icdo_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -59,11 +60,9 @@ nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
 ## Results
 
 ```bash
-
 |    | snomed_code                   | icdo_code                |
 |---:|:------------------------------|:-------------------------|
 |  0 | 10376009 | 2026006 | 26638004 | 8050/2 | 9014/0 | 8322/0 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-29-snomed_umls_mapping_en.md b/docs/_posts/Cabir40/2023-03-29-snomed_umls_mapping_en.md
index 15954f668a..3d2d0e9220 100644
--- a/docs/_posts/Cabir40/2023-03-29-snomed_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-03-29-snomed_umls_mapping_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline is built on the top of `snomed_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -59,11 +60,9 @@ nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
 ## Results
 
 ```bash
-
 |    | snomed_code                      | umls_code                      |
 |---:|:---------------------------------|:-------------------------------|
 |  0 | 733187009 | 449433008 | 51264003 | C4546029 | C3164619 | C0271267 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-03-30-summarizer_generic_jsl_en.md b/docs/_posts/Cabir40/2023-03-30-summarizer_generic_jsl_en.md
index 91e6e42859..1f8268b00b 100644
--- a/docs/_posts/Cabir40/2023-03-30-summarizer_generic_jsl_en.md
+++ b/docs/_posts/Cabir40/2023-03-30-summarizer_generic_jsl_en.md
@@ -96,13 +96,11 @@ val result = pipeline.fit(data).transform(data)
 ## Results
 
 ```bash
-"
 +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |result                                                                                                                                                                                                                                                                                                                                   |
 +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |[recheck. A 78-year-old female patient returns for recheck due to hypertension, syncope, and spinal stenosis. She has a history of heart failure, myocardial infarction, lymphoma, and asthma. She has been prescribed Atenolol, Premarin, calcium with vitamin D, multivitamin, aspirin, and TriViFlor. She has also been prescribed El]|
 +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-03-summarizer_biomedical_pubmed_en.md b/docs/_posts/Cabir40/2023-04-03-summarizer_biomedical_pubmed_en.md
index 3996d4af41..26861c9fc0 100644
--- a/docs/_posts/Cabir40/2023-04-03-summarizer_biomedical_pubmed_en.md
+++ b/docs/_posts/Cabir40/2023-04-03-summarizer_biomedical_pubmed_en.md
@@ -84,7 +84,6 @@ val result = pipeline.fit(data).transform(data)
 ## Results
 
 ```bash
-
 ['The results of this review suggest that aggressive ovarian cancer surgery is associated with a significant reduction in the risk of recurrence and a reduction in the number of radical versus conservative surgical resections. However, the results of this review are based on only one small trial. Further research is needed to determine the role of aggressive ovarian cancer surgery in women with stage IIIC ovarian cancer.']
 ```
 
diff --git a/docs/_posts/Cabir40/2023-04-03-summarizer_clinical_questions_en.md b/docs/_posts/Cabir40/2023-04-03-summarizer_clinical_questions_en.md
index f3394aea87..071f4d6e1c 100644
--- a/docs/_posts/Cabir40/2023-04-03-summarizer_clinical_questions_en.md
+++ b/docs/_posts/Cabir40/2023-04-03-summarizer_clinical_questions_en.md
@@ -87,9 +87,7 @@ val result = pipeline.fit(data).transform(data)
 ## Results
 
 ```bash
-
 ['What are the treatments for hyperthyroidism?']
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-03-text_generator_biomedical_biogpt_base_en.md b/docs/_posts/Cabir40/2023-04-03-text_generator_biomedical_biogpt_base_en.md
index b3286807a7..1bfcfc6bf3 100644
--- a/docs/_posts/Cabir40/2023-04-03-text_generator_biomedical_biogpt_base_en.md
+++ b/docs/_posts/Cabir40/2023-04-03-text_generator_biomedical_biogpt_base_en.md
@@ -79,11 +79,9 @@ val result = pipeline.fit(data).transform(data)
 ## Results
 
 ```bash
-
 [Covid 19 is a pandemic with high rates in both mortality ( around 5 to 8 percent in the United States ) and economic loss, which are likely related to the disruption of social life. The COVID - 19 crisis has caused a significant reduction in healthcare capacity and has led to an increased risk of infection in healthcare facilities and patients with underlying conditions, which has increased morbidity, increased mortality rates in patients, and increased healthcare costs. The COVID - 19 pandemic has also led to a significant increase in the number of patients with chronic diseases, which has led to an increase in the number of patients with chronic conditions who are at high cardiovascular ( cardiovascular diseases &#91; CDs &#93; ) risk and therefore require intensive care. &quot; This review will discuss the impact of this COVID pandemic in the healthcare system, the potential impact in healthcare providers caring and treating patients with CDs, and the potential impact on the healthcare system. The COVID Pandemias- A Review of the Current Literature. The COVID - 19 pandemic has resulted in a significant increase in the number of patients with cardiovascular disease ( CVD ). The number of patients with CVD is expected to increase by approximately 20 percent by the end of 2020. The number of patients with CVD will also increase by approximately 20 percent by the end of 2020]
 
 [The most common cause of stomach pain is peptic ulcer disease. The diagnosis of gastric ulcer is based on the presence and severity ( as determined by endoscopy ) of the ulcer, as confirmed on the basis ofendoscopic biopsy and gastric mucosal biopsy with urease tests, and by the presence of Helicobacter pylori. The treatment of gastric ulcer is based on the eradication of H. pylori. The aim of this study, conducted on the population aged over 40 in the city of Szczecin, was to determine the prevalence of H. pylori infection in patients with gastric ulcer and to assess the effectiveness of the eradication therapy. MATERIAL AND METHODS: The study involved patients aged over 40 who were admitted to the Gastroenterology Clinic of the Medical University of Szczecin with a diagnosis of gastric ulcer. The study was conducted on the population of patients with gastric ulcer, who were admitted to the Gastroenterology Clinic of the Medical University of Szczecin between January and December 2014. The study included patients with gastric ulcer who were admitted to the Gastroenterology Clinic of the Medical University of Szczecin between January and December 2014. The study was conducted on the population of patients aged over 40 who were admitted to the Gastroenterology Clinic of the] 
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-03-text_generator_generic_flan_base_en.md b/docs/_posts/Cabir40/2023-04-03-text_generator_generic_flan_base_en.md
index 57728f2101..685505fe76 100644
--- a/docs/_posts/Cabir40/2023-04-03-text_generator_generic_flan_base_en.md
+++ b/docs/_posts/Cabir40/2023-04-03-text_generator_generic_flan_base_en.md
@@ -81,9 +81,7 @@ val result = pipeline.fit(data).transform(data)
 ## Results
 
 ```bash
-
 ['the patient is admitted to the clinic with a severe back pain and a severe left - sided leg pain. The patient was diagnosed with a lumbar disc herniation and underwent a discectomy. The patient was discharged on the third postoperative day. The patient was followed up for a period of 6 months and was found to be asymptomatic. A rare case of a giant cell tumor of the sacrum. Giant cell tumors ( GCTs ) are benign, locally aggressive tumors that are most commonly found in the long bones of the extremities. They are rarely found in the spine. We report a case of a GCT of the sacrum in a young female patient. The patient presented with a history of progressive lower back pain and a palpable mass in the left buttock. The patient underwent a left hemilaminectomy and biopsy. The histopathological examination revealed a GCT. The patient was treated with a combination of surgery and radiation therapy. The patient was followed up for 2 years and no recurrence was observed. A rare case of a giant cell tumor of the sacrum. Giant cell tumors ( GCTs ) are benign, locally aggressive tumors that are most commonly found in the long bones of the extremities. They are rarely found in the spine. We report a case of a GCT']
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-03-text_generator_generic_jsl_base_en.md b/docs/_posts/Cabir40/2023-04-03-text_generator_generic_jsl_base_en.md
index 371ac89a6e..96287ca350 100644
--- a/docs/_posts/Cabir40/2023-04-03-text_generator_generic_jsl_base_en.md
+++ b/docs/_posts/Cabir40/2023-04-03-text_generator_generic_jsl_base_en.md
@@ -79,9 +79,7 @@ val result = pipeline.fit(data).transform(data)
 ## Results
 
 ```bash
-
 ['the patient is admitted to the clinic with a severe back pain and a severe left - sided leg pain. The patient was diagnosed with a lumbar disc herniation and underwent a discectomy. The patient was discharged on the third postoperative day. The patient was followed up for a period of 6 months and was found to be asymptomatic. A rare case of a giant cell tumor of the sacrum. Giant cell tumors ( GCTs ) are benign, locally aggressive tumors that are most commonly found in the long bones of the extremities. They are rarely found in the spine. We report a case of a GCT of the sacrum in a young female patient. The patient presented with a history of progressive lower back pain and a palpable mass in the left buttock. The patient underwent a left hemilaminectomy and biopsy. The histopathological examination revealed a GCT. The patient was treated with a combination of surgery and radiation therapy. The patient was followed up for 2 years and no recurrence was observed. A rare case of a giant cell tumor of the sacrum. Giant cell tumors ( GCTs ) are benign, locally aggressive tumors that are most commonly found in the long bones of the extremities. They are rarely found in the spine. We report a case of a GCT']
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-11-umls_clinical_findings_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-04-11-umls_clinical_findings_resolver_pipeline_en.md
index fb96d22d45..5f3b389369 100644
--- a/docs/_posts/Cabir40/2023-04-11-umls_clinical_findings_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-04-11-umls_clinical_findings_resolver_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline maps entities (Clinical Findings) with their correspond
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-04-11-umls_drug_substance_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-04-11-umls_drug_substance_resolver_pipeline_en.md
index a2659efd60..e1423a1905 100644
--- a/docs/_posts/Cabir40/2023-04-11-umls_drug_substance_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-04-11-umls_drug_substance_resolver_pipeline_en.md
@@ -32,6 +32,7 @@ This pretrained pipeline maps entities (Drug Substances) with their correspondin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -47,8 +48,11 @@ val pipeline = PretrainedPipeline("umls_drug_substance_resolver_pipeline", "en",
 val result = pipeline.annotate("The patient was given  metformin, lenvatinib and Magnesium hydroxide 100mg/1ml")
 ```
 
-{:.nlu-block}
-```python
+</div>
+
+## Results
+
+```bash
 +-----------------------------+---------+---------+
 |chunk                        |ner_label|umls_code|
 +-----------------------------+---------+---------+
@@ -57,7 +61,7 @@ val result = pipeline.annotate("The patient was given  metformin, lenvatinib and
 |Magnesium hydroxide 100mg/1ml|DRUG     |C1134402 |
 +-----------------------------+---------+---------+
 ```
-</div>
+
 
 {:.model-param}
 ## Model Information
diff --git a/docs/_posts/Cabir40/2023-04-12-biogpt_chat_jsl_en.md b/docs/_posts/Cabir40/2023-04-12-biogpt_chat_jsl_en.md
index 859d708193..70a624cc10 100644
--- a/docs/_posts/Cabir40/2023-04-12-biogpt_chat_jsl_en.md
+++ b/docs/_posts/Cabir40/2023-04-12-biogpt_chat_jsl_en.md
@@ -74,9 +74,7 @@ val result = pipeline.fit(data).transform(data)
 ## Results
 
 ```bash
-
 ['Asthma is itself an allergic disease due to cold or dust or pollen or grass etc. irrespective of the triggering factor. You can go for pulmonary function tests if not done. Treatment is mainly symptomatic which might require inhalation steroids, beta agonists, anticholinergics as MDI or rota haler as a regular treatment. To decrease the inflammation of bronchi and bronchioles, you might be given oral antihistamines with mast cell stabilizers (montelukast) and steroids (prednisolone) with nebulization and frequently steam inhalation. To decrease the bronchoconstriction caused by allergens, you might be given oral antihistamines with mast cell stabilizers (montelukast) and steroids (prednisolone) with nebulization and frequently steam inhalation. The best way to cure any allergy is a complete avoidance of allergen or triggering factor. Consult your pulmonologist for further advise.']
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-13-medication_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-04-13-medication_resolver_pipeline_en.md
index 86cd4a65dd..772560e21b 100644
--- a/docs/_posts/Cabir40/2023-04-13-medication_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-04-13-medication_resolver_pipeline_en.md
@@ -36,6 +36,7 @@ This pipeline can be used as Lightpipeline (with `annotate/fullAnnotate`). You c
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_ade_en.md b/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_ade_en.md
index 55b8b667be..87fbd9d807 100644
--- a/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_ade_en.md
+++ b/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_ade_en.md
@@ -32,6 +32,7 @@ A pipeline for Adverse Drug Events (ADE) with `ner_ade_biobert`, `assertion_dl_b
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -63,7 +64,6 @@ nlu.load("en.explain_doc.clinical_ade").predict("""Been taking Lipitor for 15 ye
 ## Results
 
 ```bash
-
 Class: True
 
 NER_Assertion:
@@ -81,7 +81,6 @@ Relations:
 | 1  | cramps                        | ADE        | Lipitor     | DRUG    |        0 |
 | 2  | severe fatigue                | ADE        | voltaren    | DRUG    |        0 |
 | 3  | cramps                        | ADE        | voltaren    | DRUG    |        1 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_carp_en.md b/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_carp_en.md
index 856507d817..3020fd32ff 100644
--- a/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_carp_en.md
+++ b/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_carp_en.md
@@ -32,6 +32,7 @@ A pipeline with `ner_clinical`, `assertion_dl`, `re_clinical` and `ner_posology`
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -63,7 +64,6 @@ nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history o
 ## Results
 
 ```bash
-
 |   | chunks                        | ner_clinical | assertion | posology_chunk   | ner_posology | relations |
 |---|-------------------------------|--------------|-----------|------------------|--------------|-----------|
 | 0 | gestational diabetes mellitus | PROBLEM      | present   | metformin        | Drug         | TrAP      |
@@ -73,7 +73,6 @@ nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history o
 | 4 | poor appetite                 | PROBLEM      | present   | insulin glargine | Drug         | TrCP      |
 | 5 | vomiting                      | PROBLEM      | present   | at night         | Frequency    | TrAP      |
 | 6 | insulin glargine              | TREATMENT    | present   | 12 units         | Dosage       | TrAP      |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_era_en.md b/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_era_en.md
index 2d1f7d2524..8dd9911c84 100644
--- a/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_era_en.md
+++ b/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_era_en.md
@@ -32,6 +32,7 @@ A pipeline with `ner_clinical_events`, `assertion_dl` and `re_temporal_events_cl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_medication_en.md b/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_medication_en.md
index 064f1aa789..a6f6827bd1 100644
--- a/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_medication_en.md
+++ b/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_medication_en.md
@@ -32,6 +32,7 @@ A pipeline for detecting posology entities with the `ner_posology_large` NER mod
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
diff --git a/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_radiology_en.md b/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_radiology_en.md
index 8e5892a73f..0e39599881 100644
--- a/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_radiology_en.md
+++ b/docs/_posts/Cabir40/2023-04-20-explain_clinical_doc_radiology_en.md
@@ -32,6 +32,7 @@ A pipeline for detecting posology entities with the `ner_radiology` NER model, a
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -63,7 +64,6 @@ nlu.load("en.explain_doc.clinical_radiology.pipeline").predict("""Bilateral brea
 ## Results
 
 ```bash
-
 +----+------------------------------------------+---------------------------+
 |    | chunks                                   | entities                  |
 |---:|:-----------------------------------------|:--------------------------|
@@ -99,7 +99,6 @@ nlu.load("en.explain_doc.clinical_radiology.pipeline").predict("""Bilateral brea
 |       1 | ImagingTest     | ultrasound            | ImagingFindings           | ovoid mass |
 |       0 | ImagingFindings | benign fibrous tissue | Disease_Syndrome_Disorder | lipoma     |
 +---------+-----------------+-----------------------+---------------------------+------------+
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-26-ner_profiling_biobert_en.md b/docs/_posts/Cabir40/2023-04-26-ner_profiling_biobert_en.md
index 952bdcc9e8..ee297f5047 100644
--- a/docs/_posts/Cabir40/2023-04-26-ner_profiling_biobert_en.md
+++ b/docs/_posts/Cabir40/2023-04-26-ner_profiling_biobert_en.md
@@ -66,7 +66,6 @@ nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a
 ## Results
 
 ```bash
-
 ******************** ner_diseases_biobert Model Results ******************** 
 
 [('gestational diabetes mellitus', 'Disease'), ('type two diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('HTG-induced pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('poor appetite', 'Disease'), ('vomiting', 'Disease')]
@@ -90,7 +89,6 @@ nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a
 ******************** ner_risk_factors_biobert Model Results ******************** 
 
 [('diabetes mellitus', 'DIABETES'), ('subsequent type two diabetes mellitus', 'DIABETES'), ('obesity', 'OBESE')]
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-28-ner_profiling_biobert_en.md b/docs/_posts/Cabir40/2023-04-28-ner_profiling_biobert_en.md
index c43de917f8..a8f646f31e 100644
--- a/docs/_posts/Cabir40/2023-04-28-ner_profiling_biobert_en.md
+++ b/docs/_posts/Cabir40/2023-04-28-ner_profiling_biobert_en.md
@@ -36,6 +36,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -68,7 +69,6 @@ nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a
 ## Results
 
 ```bash
-
 ******************** ner_diseases_biobert Model Results ******************** 
 
 [('gestational diabetes mellitus', 'Disease'), ('type two diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('HTG-induced pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('poor appetite', 'Disease'), ('vomiting', 'Disease')]
@@ -92,7 +92,6 @@ nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a
 ******************** ner_risk_factors_biobert Model Results ******************** 
 
 [('diabetes mellitus', 'DIABETES'), ('subsequent type two diabetes mellitus', 'DIABETES'), ('obesity', 'OBESE')]
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-04-28-ner_profiling_clinical_en.md b/docs/_posts/Cabir40/2023-04-28-ner_profiling_clinical_en.md
index 981496dad0..5a00e66d19 100644
--- a/docs/_posts/Cabir40/2023-04-28-ner_profiling_clinical_en.md
+++ b/docs/_posts/Cabir40/2023-04-28-ner_profiling_clinical_en.md
@@ -36,6 +36,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -68,7 +69,6 @@ nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a
 ## Results
 
 ```bash
-
 ******************** ner_jsl Model Results ******************** 
 
 [('28-year-old', 'Age'), ('female', 'Gender'), ('gestational diabetes mellitus', 'Diabetes'), ('eight years prior', 'RelativeDate'), ('subsequent', 'Modifier'), ('type two diabetes mellitus', 'Diabetes'), ('T2DM', 'Diabetes'), ('HTG-induced pancreatitis', 'Disease_Syndrome_Disorder'), ('three years prior', 'RelativeDate'), ('acute', 'Modifier'), ('hepatitis', 'Communicable_Disease'), ('obesity', 'Obesity'), ('body mass index', 'Symptom'), ('33.5 kg/m2', 'Weight'), ('one-week', 'Duration'), ('polyuria', 'Symptom'), ('polydipsia', 'Symptom'), ('poor appetite', 'Symptom'), ('vomiting', 'Symptom')]
@@ -94,7 +94,6 @@ nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a
 [('female', 'Organism_Attribute'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('T2DM', 'Disease_or_Syndrome'), ('HTG-induced pancreatitis', 'Disease_or_Syndrome'), ('associated with', 'Qualitative_Concept'), ('acute hepatitis', 'Disease_or_Syndrome'), ('obesity', 'Disease_or_Syndrome'), ('body mass index', 'Clinical_Attribute'), ('BMI', 'Clinical_Attribute'), ('polyuria', 'Sign_or_Symptom'), ('polydipsia', 'Sign_or_Symptom'), ('poor appetite', 'Sign_or_Symptom'), ('vomiting', 'Sign_or_Symptom')]
 
 ...
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-05-04-bert_token_classifier_ner_jsl_en.md b/docs/_posts/Cabir40/2023-05-04-bert_token_classifier_ner_jsl_en.md
index 4fcdef065f..abd5b873c0 100644
--- a/docs/_posts/Cabir40/2023-05-04-bert_token_classifier_ner_jsl_en.md
+++ b/docs/_posts/Cabir40/2023-05-04-bert_token_classifier_ner_jsl_en.md
@@ -192,7 +192,6 @@ val result = pipeline.fit(sample_text).transform(sample_text)
 ## Results
 
 ```bash
-
 +-----------------------------------------+----------------------------+
 |chunk                                    |ner_label                   |
 +-----------------------------------------+----------------------------+
@@ -217,7 +216,6 @@ val result = pipeline.fit(sample_text).transform(sample_text)
 |Baby-girl                                |Age                         |
 |decreased                                |Symptom                     |
 +-----------------------------------------+----------------------------+
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-05-04-ner_profiling_clinical_en.md b/docs/_posts/Cabir40/2023-05-04-ner_profiling_clinical_en.md
index 643b47febc..e1292cf2b9 100644
--- a/docs/_posts/Cabir40/2023-05-04-ner_profiling_clinical_en.md
+++ b/docs/_posts/Cabir40/2023-05-04-ner_profiling_clinical_en.md
@@ -36,6 +36,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -68,7 +69,6 @@ nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a
 ## Results
 
 ```bash
-
 ******************** ner_jsl Model Results ******************** 
 
 [('28-year-old', 'Age'), ('female', 'Gender'), ('gestational diabetes mellitus', 'Diabetes'), ('eight years prior', 'RelativeDate'), ('subsequent', 'Modifier'), ('type two diabetes mellitus', 'Diabetes'), ('T2DM', 'Diabetes'), ('HTG-induced pancreatitis', 'Disease_Syndrome_Disorder'), ('three years prior', 'RelativeDate'), ('acute', 'Modifier'), ('hepatitis', 'Communicable_Disease'), ('obesity', 'Obesity'), ('body mass index', 'Symptom'), ('33.5 kg/m2', 'Weight'), ('one-week', 'Duration'), ('polyuria', 'Symptom'), ('polydipsia', 'Symptom'), ('poor appetite', 'Symptom'), ('vomiting', 'Symptom')]
@@ -94,7 +94,6 @@ nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a
 [('female', 'Organism_Attribute'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('T2DM', 'Disease_or_Syndrome'), ('HTG-induced pancreatitis', 'Disease_or_Syndrome'), ('associated with', 'Qualitative_Concept'), ('acute hepatitis', 'Disease_or_Syndrome'), ('obesity', 'Disease_or_Syndrome'), ('body mass index', 'Clinical_Attribute'), ('BMI', 'Clinical_Attribute'), ('polyuria', 'Sign_or_Symptom'), ('polydipsia', 'Sign_or_Symptom'), ('poor appetite', 'Sign_or_Symptom'), ('vomiting', 'Sign_or_Symptom')]
 
 ...
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-05-15-biogpt_pubmed_qa_en.md b/docs/_posts/Cabir40/2023-05-15-biogpt_pubmed_qa_en.md
index 31b962f9b1..1c35c98cd8 100644
--- a/docs/_posts/Cabir40/2023-05-15-biogpt_pubmed_qa_en.md
+++ b/docs/_posts/Cabir40/2023-05-15-biogpt_pubmed_qa_en.md
@@ -109,7 +109,6 @@ val result = pipeline.fit(data).transform(data)
 +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |[the present study investigated whether directing spatial attention to one location in a visual array would enhance memory for the array features. participants memorized two]|
 +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-05-15-flan_t5_base_jsl_qa_en.md b/docs/_posts/Cabir40/2023-05-15-flan_t5_base_jsl_qa_en.md
index e8ea1500da..e53c64bcfd 100644
--- a/docs/_posts/Cabir40/2023-05-15-flan_t5_base_jsl_qa_en.md
+++ b/docs/_posts/Cabir40/2023-05-15-flan_t5_base_jsl_qa_en.md
@@ -93,7 +93,6 @@ val result = pipeline.fit(data).transform(data)
 +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |[The effect of directing attention on memory is that it can help to improve memory retention and recall. It can help to reduce the amount of time spent on tasks, such as focusing on one task at a time, or focusing on ]|
 +--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-05-17-medical_qa_biogpt_en.md b/docs/_posts/Cabir40/2023-05-17-medical_qa_biogpt_en.md
index ba988f9026..2ecc3810b1 100644
--- a/docs/_posts/Cabir40/2023-05-17-medical_qa_biogpt_en.md
+++ b/docs/_posts/Cabir40/2023-05-17-medical_qa_biogpt_en.md
@@ -38,6 +38,7 @@ It can generate two types of answers, short and long. Types of questions are sup
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = MultiDocumentAssembler()\
     .setInputCols("question", "context")\
@@ -118,7 +119,6 @@ val result = pipeline.fit(data).transform(data)
 +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |[The effect of directing attention on memory is that it can help to improve the accuracy and recall of a document. It can help to improve the accuracy of a document by allowing the user to quickly and easily access the information they need. It can also help to improve the overall efficiency of a document by allowing the user to quickly]|
 +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-08-ner_demographic_extended_healthcare_en.md b/docs/_posts/Cabir40/2023-06-08-ner_demographic_extended_healthcare_en.md
index be8ae6c748..0ab2038ab3 100644
--- a/docs/_posts/Cabir40/2023-06-08-ner_demographic_extended_healthcare_en.md
+++ b/docs/_posts/Cabir40/2023-06-08-ner_demographic_extended_healthcare_en.md
@@ -36,6 +36,7 @@ This model identifies healthcare mentions that refers to a situation where a pat
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 documentAssembler = DocumentAssembler()\
     .setInputCol("text")\
diff --git a/docs/_posts/Cabir40/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
index cdef8b618c..f903b79883 100644
--- a/docs/_posts/Cabir40/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
@@ -59,45 +59,16 @@ nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abs
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |True|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md
index 48a7af0870..2195fbb7fd 100644
--- a/docs/_posts/Cabir40/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-bert_token_classifier_ner_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                        |   begin |   end | ner_label    |   confidence |
 |---:|:---------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 21-day-old                       |      17 |    26 | Age          |     0.999456 |
@@ -123,9 +95,6 @@ Results
 | 22 | fussy                            |     574 |   578 | Symptom      |     0.997592 |
 | 23 | over the past 2 days             |     580 |   599 | Date_Time    |     0.994786 |
 | 24 | albuterol                        |     642 |   650 | Drug         |     0.999735 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_augmented_es.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_augmented_es.md
index f9be4ed44b..e659f9386c 100644
--- a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_augmented_es.md
+++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_augmented_es.md
@@ -36,6 +36,7 @@ The PHI information will be masked and obfuscated in the resulting text. The pip
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from johnsnowlabs import *
 
@@ -121,97 +122,11 @@ Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servic
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
 
-deid_pipeline = PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical_augmented").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Datos <SEX>.
@@ -383,9 +298,6 @@ Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura trata
 F. 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra.
 Francisco José Roca Bermúdez Hospital 12 de Octubre Servicio de Endocrinología y Nutrición Calle Ramón y Cajal s/n 03129 Zaragoza - Alicante (Portugal) Correo electrónico: richard@company.it
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_de.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_de.md
index c903ae3c02..2620031979 100644
--- a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_de.md
+++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_de.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from **German** medical
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -95,73 +96,10 @@ Adresse : St.Johann-Straße 13 19300
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "de", "clinical/models")
-
-sample = """Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-"""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification","de","clinical/models")
-
-val sample = "Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.clinical").predict("""Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Zusammenfassung : <PATIENT> wird am Morgen des <DATE> ins <HOSPITAL> eingeliefert.
@@ -209,9 +147,6 @@ Kontonummer: 192837465738
 SSN : 1310011981M454
 Lizenznummer: XX123456
 Adresse : Klingelhöferring 31206
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_es.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_es.md
index d3f003fe06..c7d10f5a30 100644
--- a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_es.md
+++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_es.md
@@ -34,6 +34,7 @@ This pipeline is trained with sciwiki_300d embeddings and can be used to deident
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from johnsnowlabs import *
 
@@ -121,99 +122,11 @@ Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servic
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Datos del paciente.
@@ -385,10 +298,6 @@ Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura trata
 El paciente 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra.
 Reinaldo Manjón Malo Barcelona Servicio de Endocrinología y Nutrición Valencia km 12,500 28905 Bilbao - Madrid (Barcelona) Correo electrónico: quintanasalome@example.net
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_fr.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_fr.md
index b02efdc672..acf8c3949d 100644
--- a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_fr.md
+++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_fr.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts in Fr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -120,98 +121,11 @@ COURRIEL : mariebreton@chb.fr
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = """COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = "COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"
-
-val result = deid_pipeline.annotate(sample)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("fr.deid_obfuscated").predict("""COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 COMPTE-RENDU D'HOSPITALISATION
@@ -310,9 +224,6 @@ Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des tr
 PSA de 1,16 ng/ml.
 ADDRESSÉ À : Dr Tristan-Gilbert Poulain - CENTRE HOSPITALIER D'ORTHEZ Service D'Endocrinologie et de Nutrition - 6, avenue Pages, 37443 Sainte Antoine
 COURRIEL : massecatherine@bouygtel.fr
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_glove_en.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_glove_en.md
index 4c1f935da4..e8d903db3c 100644
--- a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_glove_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_glove_en.md
@@ -34,6 +34,7 @@ This pipeline is trained with lightweight `glove_100d` embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -117,9 +118,6 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -151,9 +149,6 @@ Dr. Dr Worley Colonel, ID: ZJ:9570208, IP 005.005.005.005.
 He is a 67 male was admitted to the ST. LUKE'S HOSPITAL AT THE VINTAGE for cystectomy on 06-02-1981.
 Patient's VIN : 3CCCC22DDDD333888, SSN SSN-618-77-1042, Driver's license W693817528998.
 Phone 0496 46 46 70, 3100 weston rd, Shattuck, E-MAIL: Freddi@hotmail.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_it.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_it.md
index efa58b6d7f..2a2b55f06a 100644
--- a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_it.md
+++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_it.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts in It
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -117,95 +118,11 @@ EMAIL: bferrabosco@poste.it""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = """RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"""
 
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = "RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("it.deid.clinical").predict("""RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 RAPPORTO DI RICOVERO
@@ -301,9 +218,6 @@ PSA di 1,16 ng/ml.
 INDIRIZZATO A: Dott.
 Antonio Rusticucci - ASL 7 DI CARBONIA AZIENDA U.S.L. N. 7, Dipartimento di Endocrinologia e Nutrizione - Via Giorgio 0 Appartamento 26, 03461 Sesto Raimondo
 EMAIL: murat.g@jsl.com
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_pt.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_pt.md
index 53eac71b56..9949e1390e 100644
--- a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_pt.md
+++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_pt.md
@@ -34,6 +34,7 @@ This pipeline is trained with `w2v_cc_300d` portuguese embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,111 +134,9 @@ Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@ho
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = """Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = "Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("pt.deid.clinical").predict("""Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Dados do <SEX>.
@@ -353,9 +252,6 @@ O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta r
 A tomografia computorizada abdominal é normal.
 A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
 Referido por: Carlos Melo - Avenida Dos Aliados, 56, 22 Espanha E-mail: maria.prado@jacob.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_ro.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_ro.md
index 2e58041d5f..50d370c2ff 100644
--- a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_ro.md
+++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_ro.md
@@ -75,52 +75,9 @@ Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-val sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("ro.deid.clinical").predict("""Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Medic : Dr. <DOCTOR>, C.N.P : <IDNUM>, Data setului de analize: <DATE>
@@ -152,9 +109,6 @@ Varsta : 91, Nume si Prenume : Dragomir Emilia
 Tel: 0248 551 376, E-mail : tudorsmaranda@kappa.ro,
 Licență : T003485962M, Înmatriculare : AR-65-UPQ, Cont : KHHO5029180812813651, 
 Centrul Medical de Evaluare si Recuperare pentru Copii si Tineri Cristian Serban Buzias Aleea Voinea Curcani, 328479
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_slim_en.md b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_slim_en.md
index df31092c31..c1653fa64e 100644
--- a/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_slim_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-clinical_deidentification_slim_en.md
@@ -34,6 +34,7 @@ This pipeline is trained with lightweight `glove_100d` embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,56 +79,10 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_slim", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_slim","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_slim").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -159,9 +114,6 @@ Dr. Dr Rosalba Hill, ID: JY:3489547, IP 005.005.005.005.
 He is a 79 male was admitted to the JOHN MUIR MEDICAL CENTER-CONCORD CAMPUS for cystectomy on 01-25-1997.
 Patient's VIN : 3CCCC22DDDD333888, SSN SSN-289-37-4495, Driver's license S99983662.
 Phone 04.32.52.27.90, North Adrienne, Colorado Springs, E-MAIL: Rawland@google.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-icd10_icd9_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-icd10_icd9_mapping_en.md
index 3cc7f708bd..5d33f9d7fd 100644
--- a/docs/_posts/Cabir40/2023-06-13-icd10_icd9_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-icd10_icd9_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10_icd9_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | icd10_code          | icd9_code          |
 |---:|:--------------------|:-------------------|
 |  0 | Z833 | A0100 | A000 | V180 | 0020 | 0010 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-icd10cm_snomed_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-icd10cm_snomed_mapping_en.md
index 982774103a..58e6e85ca8 100644
--- a/docs/_posts/Cabir40/2023-06-13-icd10cm_snomed_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-icd10cm_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10cm_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | icd10cm_code          | snomed_code                              |
 |---:|:----------------------|:-----------------------------------------|
 |  0 | R079 | N4289 | M62830 | 161972006 | 22035000 | 16410651000119105 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-icd10cm_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-icd10cm_umls_mapping_en.md
index 8852980f46..a35e510fb8 100644
--- a/docs/_posts/Cabir40/2023-06-13-icd10cm_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-icd10cm_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps ICD10CM codes to UMLS codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,43 +59,12 @@ nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 {'icd10cm': ['M89.50', 'R82.2', 'R09.01'],
 'umls': ['C4721411', 'C0159076', 'C0004044']}
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-icdo_snomed_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-icdo_snomed_mapping_en.md
index 9f9a2d8eef..b0f16b1aa9 100644
--- a/docs/_posts/Cabir40/2023-06-13-icdo_snomed_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-icdo_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icdo_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | icdo_code                | snomed_code                    |
 |---:|:-------------------------|:-------------------------------|
 |  0 | 8120/1 | 8170/3 | 8380/3 | 45083001 | 25370001 | 30289006 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-mesh_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-mesh_umls_mapping_en.md
index 7f1d58098b..fe5b94a47b 100644
--- a/docs/_posts/Cabir40/2023-06-13-mesh_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-mesh_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `mesh_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | mesh_code                   | umls_code                      |
 |---:|:----------------------------|:-------------------------------|
 |  0 | C028491 | D019326 | C579867 | C0043904 | C0045010 | C3696376 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-ner_deid_generic_augmented_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-ner_deid_generic_augmented_pipeline_en.md
index 129b69a2d1..08e32dc1d4 100644
--- a/docs/_posts/Cabir40/2023-06-13-ner_deid_generic_augmented_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-ner_deid_generic_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_augmented](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -56,34 +57,10 @@ nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-```scala
-val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-------------------------------------------------+---------+
 |chunk                                            |ner_label|
 +-------------------------------------------------+---------+
@@ -99,9 +76,6 @@ Results
 |Cocke County Baptist Hospital. 0295 Keats Street.|LOCATION |
 |(302) 786-5227                                   |CONTACT  |
 +-------------------------------------------------+---------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-ner_deid_subentity_pipeline_ar.md b/docs/_posts/Cabir40/2023-06-13-ner_deid_subentity_pipeline_ar.md
index 621766be9c..bf5d9b5017 100644
--- a/docs/_posts/Cabir40/2023-06-13-ner_deid_subentity_pipeline_ar.md
+++ b/docs/_posts/Cabir40/2023-06-13-ner_deid_subentity_pipeline_ar.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -57,38 +58,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-text= '''
-ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +---------------+--------+
 |chunks         |entities|
 +---------------+--------+
@@ -104,10 +78,6 @@ Results
 |ليلى           |PATIENT |
 |35             |AGE     |
 +---------------+--------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-ner_medication_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-ner_medication_pipeline_en.md
index 3c8cf4496d..f14187f7c3 100644
--- a/docs/_posts/Cabir40/2023-06-13-ner_medication_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-ner_medication_pipeline_en.md
@@ -34,6 +34,7 @@ A pretrained pipeline to detect medication entities. It was built on the top of
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -50,42 +51,11 @@ val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline",
 
 val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
 ```
-
-{:.nlu-block}
-```python
-| ner_chunk          | entity   |
-|:-------------------|:---------|
-| metformin 1000 MG  | DRUG     |
-| glipizide 2.5 MG   | DRUG     |
-| Fragmin 5000 units | DRUG     |
-| Xenaderm           | DRUG     |
-| OxyContin 30 mg    | DRUG     |
-```
 </div>
 
-{:.model-param}
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
+## Results
 
-ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."""
-
-result = ner_medication_pipeline.fullAnnotate([text])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
-```
-
-{:.nlu-block}
-```python
+```bash
 | ner_chunk          | entity   |
 |:-------------------|:---------|
 | metformin 1000 MG  | DRUG     |
@@ -94,7 +64,6 @@ val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed me
 | Xenaderm           | DRUG     |
 | OxyContin 30 mg    | DRUG     |
 ```
-</div>
 
 {:.model-param}
 ## Model Information
diff --git a/docs/_posts/Cabir40/2023-06-13-re_bodypart_directions_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_bodypart_directions_pipeline_en.md
index 46198d8d2d..b7412783aa 100644
--- a/docs/_posts/Cabir40/2023-06-13-re_bodypart_directions_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-re_bodypart_directions_pipeline_en.md
@@ -59,37 +59,10 @@ nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations | entity1                     | entity1_begin | entity1_end | chunk1     | entity2                     | entity2_end | entity2_end | chunk2        | confidence |
 |-------|-----------|-----------------------------|---------------|-------------|------------|-----------------------------|-------------|-------------|---------------|------------|
 | 0     | 1         | Direction                   | 35            | 39          | upper      | Internal_organ_or_component | 41          | 50          | brain stem    | 0.9999989  |
@@ -101,10 +74,6 @@ Results
 | 6     | 0         | Direction                   | 54            | 57          | left       | Internal_organ_or_component | 81          | 93          | basil ganglia | 0.97616416 |
 | 7     | 0         | Internal_organ_or_component | 59            | 68          | cerebellum | Direction                   | 75          | 79          | right         | 0.953046   |
 | 8     | 1         | Direction                   | 75            | 79          | right      | Internal_organ_or_component | 81          | 93          | basil ganglia | 1.0        |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-re_bodypart_proceduretest_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_bodypart_proceduretest_pipeline_en.md
index 0c5d316dea..4f93d6c445 100644
--- a/docs/_posts/Cabir40/2023-06-13-re_bodypart_proceduretest_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-re_bodypart_proceduretest_pipeline_en.md
@@ -59,44 +59,13 @@ nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations | entity1                      | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2              | confidence |
 |-------|-----------|------------------------------|---------------|-------------|--------|---------|-------------|-------------|---------------------|------------|
 | 0     | 1         | External_body_part_or_region | 94            | 98          | chest  | Test    | 117         | 135         | portable ultrasound | 1.0        |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md
index 62a0635224..099d301625 100644
--- a/docs/_posts/Cabir40/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_human_phenotype_gene_clinica
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
 
@@ -56,34 +57,11 @@ nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobo
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    |   relation | entity1   |   entity1_begin |   entity1_end | chunk1              | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+===========+=================+===============+=====================+===========+=================+===============+=====================+==============+
@@ -91,9 +69,6 @@ Results
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 |          1 | HP        |              23 |            36 | microphthalmia      | GENE      |             110 |           114 | TENM3               |     0.999999 |
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-re_temporal_events_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_temporal_events_clinical_pipeline_en.md
index 1a2a14ca40..c4373907b0 100644
--- a/docs/_posts/Cabir40/2023-06-13-re_temporal_events_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-re_temporal_events_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_clinical](ht
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
 
@@ -56,34 +57,9 @@ nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    | relation   | entity1    |   entity1_begin |   entity1_end | chunk1                   | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+============+=================+===============+==========================+===========+=================+===============+=====================+==============+
@@ -91,9 +67,6 @@ Results
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 | OVERLAP    | DATE       |             171 |           179 | that time                | PROBLEM   |             201 |           219 | any specific injury |     0.956654 |
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md
index 71e8df6e4d..a1cefd0c37 100644
--- a/docs/_posts/Cabir40/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_enriched_cli
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
 
@@ -56,34 +57,11 @@ nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 5
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |    | relation   | entity1   |   entity1_begin |   entity1_end | chunk1                                        | entity2    |   entity2_begin |   entity2_end | chunk2                   |   confidence |
 +====+============+===========+=================+===============+===============================================+============+=================+===============+==========================+==============+
@@ -91,9 +69,6 @@ Results
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |  1 | AFTER      | DATE      |             171 |           179 | that time                                     | PROBLEM    |             201 |           219 | any specific injury      |     0.577288 |
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-re_test_problem_finding_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_test_problem_finding_pipeline_en.md
index 455b44b63d..97c7715d5e 100644
--- a/docs/_posts/Cabir40/2023-06-13-re_test_problem_finding_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-re_test_problem_finding_pipeline_en.md
@@ -59,44 +59,12 @@ nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | 1            | PROCEDURE    | biopsy              | SYMPTOM      |  lesion | 
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-re_test_result_date_pipeline_en.md b/docs/_posts/Cabir40/2023-06-13-re_test_result_date_pipeline_en.md
index 8a0c3c145f..7f525e584d 100644
--- a/docs/_posts/Cabir40/2023-06-13-re_test_result_date_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-re_test_result_date_pipeline_en.md
@@ -59,46 +59,15 @@ nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised ches
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | O            | TEST         | chest X-ray         | MEASUREMENTS |  93%    | 
 | 1     | O            | TEST         | CT scan             | MEASUREMENTS |  93%    |
 | 2     | is_result_of | TEST         | SpO2                | MEASUREMENTS |  93%    |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-recognize_entities_posology_en.md b/docs/_posts/Cabir40/2023-06-13-recognize_entities_posology_en.md
index bcb96298e4..e992f88d10 100644
--- a/docs/_posts/Cabir40/2023-06-13-recognize_entities_posology_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-recognize_entities_posology_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_posology`. It will only extract medication entities.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
 
@@ -61,38 +62,9 @@ She was seen by the endocrinology service and discharged on 40 units of insulin
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
-
-res = pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-```scala
-val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "clinical/models")
-
-val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")(0)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | chunk            |   begin |   end | entity    |
 |---:|:-----------------|--------:|------:|:----------|
 |  0 | metformin        |      83 |    91 | DRUG      |
@@ -104,10 +76,6 @@ Results
 |  6 | 12 units         |     309 |   316 | DOSAGE    |
 |  7 | insulin lispro   |     321 |   334 | DRUG      |
 |  8 | with meals       |     336 |   345 | FREQUENCY |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-rxnorm_mesh_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-rxnorm_mesh_mapping_en.md
index 336c56f022..3262706b62 100644
--- a/docs/_posts/Cabir40/2023-06-13-rxnorm_mesh_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-rxnorm_mesh_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps RxNorm codes to MeSH codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline 
 pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
@@ -54,32 +55,10 @@ nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline 
-pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-pipeline.annotate("1191 6809 47613")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-val result = pipeline.annotate("1191 6809 47613")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 {'rxnorm': ['1191', '6809', '47613'],
 'mesh': ['D001241', 'D008687', 'D019355']}
 
@@ -97,9 +76,6 @@ Note:
 | D001241    | Aspirin             |
 | D008687    | Metformin           |
 | D019355    | Calcium Citrate     |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-rxnorm_ndc_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-rxnorm_ndc_mapping_en.md
index 3bd7cd4a80..1982446b53 100644
--- a/docs/_posts/Cabir40/2023-06-13-rxnorm_ndc_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-rxnorm_ndc_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps RXNORM codes to NDC codes without using any text d
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,45 +59,13 @@ nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1652674 259934)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1652674 259934)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-
 {'document': ['1652674 259934'],
 'package_ndc': ['62135-0625-60', '13349-0010-39'],
 'product_ndc': ['46708-0499', '13349-0010'],
 'rxnorm_code': ['1652674', '259934']}
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-rxnorm_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-rxnorm_umls_mapping_en.md
index 41e6ab3658..cd567fa538 100644
--- a/docs/_posts/Cabir40/2023-06-13-rxnorm_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-rxnorm_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `rxnorm_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1161611 315677)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1161611 315677)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | rxnorm_code      | umls_code           |
 |---:|:-----------------|:--------------------|
 |  0 | 1161611 | 315677 | C3215948 | C0984912 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-snomed_icd10cm_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-snomed_icd10cm_mapping_en.md
index bb7b141366..acfbd7ddb1 100644
--- a/docs/_posts/Cabir40/2023-06-13-snomed_icd10cm_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-snomed_icd10cm_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icd10cm_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | snomed_code                             | icd10cm_code               |
 |---:|:----------------------------------------|:---------------------------|
 |  0 | 128041000119107 | 292278006 | 293072005 | K22.70 | T43.595 | T37.1X5 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-snomed_icdo_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-snomed_icdo_mapping_en.md
index 23e806b04a..6e6eef282f 100644
--- a/docs/_posts/Cabir40/2023-06-13-snomed_icdo_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-snomed_icdo_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icdo_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,14 @@ nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | snomed_code                   | icdo_code                |
 |---:|:------------------------------|:-------------------------|
 |  0 | 10376009 | 2026006 | 26638004 | 8050/2 | 9014/0 | 8322/0 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-13-snomed_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-13-snomed_umls_mapping_en.md
index bd24a041d5..b3852e801e 100644
--- a/docs/_posts/Cabir40/2023-06-13-snomed_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-13-snomed_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | snomed_code                      | umls_code                      |
 |---:|:---------------------------------|:-------------------------------|
 |  0 | 733187009 | 449433008 | 51264003 | C4546029 | C3164619 | C0271267 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
index 7585528703..10c5dd401e 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
@@ -57,13 +57,11 @@ val result = pipeline.annotate("""Abstract:Based on the American Society of Anes
 ## Results
 
 ```bash
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |True|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_drug_development_trials_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_drug_development_trials_pipeline_en.md
index 76f87b0812..46b22605a6 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_drug_development_trials_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_drug_development_trials_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_drug_deve
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.classify.token_bert.druge_developement.pipeline").predict("""In Jun
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_drug_development_trials_pipeline", "en", "clinical/models")
-
-text = '''In June 2003, the median overall survival  with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response  ( CR ) , partial response  ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with  topotecan,  and 11, 61, 66 and 32 patients, respectively, without topotecan.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_drug_development_trials_pipeline", "en", "clinical/models")
-
-val text = "In June 2003, the median overall survival  with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response  ( CR ) , partial response  ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with  topotecan,  and 11, 61, 66 and 32 patients, respectively, without topotecan."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.druge_developement.pipeline").predict("""In June 2003, the median overall survival  with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response  ( CR ) , partial response  ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with  topotecan,  and 11, 61, 66 and 32 patients, respectively, without topotecan.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------|--------:|------:|:--------------|-------------:|
 |  0 | June 2003               |       3 |    11 | DATE          |     0.996034 |
@@ -118,9 +88,6 @@ Results
 | 17 | 66                      |     301 |   302 | Patient_Count |     0.998066 |
 | 18 | 32 patients             |     308 |   318 | Patient_Count |     0.996285 |
 | 19 | without topotecan       |     335 |   351 | Trial_Group   |     0.971218 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_ade_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_ade_pipeline_en.md
index 7384f1a4b5..5948ce6b6a 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_ade_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_ade_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ade](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,45 +63,13 @@ nlu.load("en.classify.token_bert.ade_pipeline").predict("""Both the erbA IRES an
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_pipeline", "en", "clinical/models")
-
-text = '''Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_pipeline", "en", "clinical/models")
-
-val text = "Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.ade_pipeline").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 | ner_chunk   | begin   | end   | ner_label   | confidence   |
 |-------------|---------|-------|-------------|--------------|
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_anatomy_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_anatomy_pipeline_en.md
index a31dae4837..db6599d103 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_anatomy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_anatomy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_anato
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -80,58 +81,9 @@ Dermatologic: She has got redness along her right great toe, but no bleeding or
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_anatomy_pipeline", "en", "clinical/models")
-
-text = '''This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatomy_pipeline", "en", "clinical/models")
-
-val text = "This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.anatomy_pipeline").predict("""This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label              |   confidence |
 |---:|:-----------------|--------:|------:|:-----------------------|-------------:|
 |  0 | great            |     320 |   324 | Multi-tissue_structure |     0.693343 |
@@ -154,9 +106,6 @@ Results
 | 17 | great            |    1017 |  1021 | Multi-tissue_structure |     0.818323 |
 | 18 | toe              |    1023 |  1025 | Organism_subdivision   |     0.341098 |
 | 19 | toenails         |    1031 |  1038 | Organism_subdivision   |     0.75016  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_bacteria_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_bacteria_pipeline_en.md
index 39148872fc..45a7d82a07 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_bacteria_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_bacteria_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bacte
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,16 @@ nlu.load("en.classify.token_bert.bacteria_ner.pipeline").predict("""Based on the
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bacteria_pipeline", "en", "clinical/models")
-
-text = '''Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bacteria_pipeline", "en", "clinical/models")
-
-val text = "Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T))."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.bacteria_ner.pipeline").predict("""Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | SMSP (T)                |      73 |    80 | SPECIES     |     0.99985  |
 |  1 | Methanoregula formicica |     167 |   189 | SPECIES     |     0.999787 |
 |  2 | SMSP (T)                |     222 |   229 | SPECIES     |     0.999871 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_bionlp_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_bionlp_pipeline_en.md
index d407f3261f..a766e22214 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_bionlp_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_bionlp_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bionl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.classify.token_bert.biolp.pipeline").predict("""Both the erbA IRES
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bionlp_pipeline", "en", "clinical/models")
-
-text = '''Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bionlp_pipeline", "en", "clinical/models")
-
-val text = "Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.biolp.pipeline").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label              |   confidence |
 |---:|:--------------------|--------:|------:|:-----------------------|-------------:|
 |  0 | erbA IRES           |       9 |    17 | Organism               |     0.999188 |
@@ -107,9 +78,6 @@ Results
 |  6 | erbA/myb IRES virus |     140 |   158 | Organism               |     0.999751 |
 |  7 | erbA IRES virus     |     236 |   250 | Organism               |     0.999749 |
 |  8 | blastoderm          |     259 |   268 | Cell                   |     0.999897 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_cellular_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_cellular_pipeline_en.md
index d6c26539fc..6de1d13c04 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_cellular_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_cellular_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_cellu
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.cellular_pipeline").predict("""Detection of var
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_cellular_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_cellular_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.cellular_pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                   |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | intracellular signaling proteins            |      27 |    58 | protein     |     0.999477 |
@@ -119,9 +91,6 @@ Results
 | 18 | GAD                                         |     791 |   793 | protein     |     0.999684 |
 | 19 | reporter gene                               |     848 |   860 | DNA         |     0.998856 |
 | 20 | Tax                                         |     863 |   865 | protein     |     0.999717 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_chemicals_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_chemicals_pipeline_en.md
index 1f2c46f067..925616028b 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_chemicals_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_chemicals_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_chemi
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.chemicals_pipeline").predict("""The results hav
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.chemicals_pipeline").predict("""The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                   |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------|--------:|------:|:------------|-------------:|
 |  0 | p - choloroaniline          |      40 |    57 | CHEM        |     0.999986 |
@@ -103,9 +75,6 @@ Results
 |  2 | kanamycin                   |     169 |   177 | CHEM        |     0.999985 |
 |  3 | colistin                    |     181 |   188 | CHEM        |     0.999982 |
 |  4 | povidone - iodine           |     194 |   210 | CHEM        |     0.99998  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_chemprot_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_chemprot_pipeline_en.md
index f36d79edc5..80c121be5f 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_chemprot_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_chemprot_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_chemp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.classify.token_bert.chemprot_pipeline").predict("""Keratinocyte gro
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_chemprot_pipeline", "en", "clinical/models")
-
-text = '''Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_chemprot_pipeline", "en", "clinical/models")
-
-val text = "Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.chemprot_pipeline").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk    |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | Keratinocyte |       0 |    11 | GENE-Y      |     0.999147 |
@@ -105,9 +75,6 @@ Results
 |  4 | fibroblast   |      38 |    47 | GENE-Y      |     0.999753 |
 |  5 | growth       |      49 |    54 | GENE-Y      |     0.999771 |
 |  6 | factor       |      56 |    61 | GENE-Y      |     0.999742 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_clinical_pipeline_en.md
index a3607204df..3d72369690 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.classify.token_bert.clinical_pipeline").predict("""A 28-year-old fe
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_pipeline", "en", "clinical/models")
-
-text = '''A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG . She had been on dapagliflozin for six months at the time of presentation . Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness , guarding , or rigidity . Pertinent laboratory findings on admission were : serum glucose 111 mg/dl , bicarbonate 18 mmol/l , anion gap 20 , creatinine 0.4 mg/dL , triglycerides 508 mg/dL , total cholesterol 122 mg/dL , glycated hemoglobin ( HbA1c ) 10% , and venous pH 7.27 . Serum lipase was normal at 43 U/L . Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia . The patient was initially admitted for starvation ketosis , as she reported poor oral intake for three days prior to admission . However , serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL , the anion gap was still elevated at 21 , serum bicarbonate was 16 mmol/L , triglyceride level peaked at 2050 mg/dL , and lipase was 52 U/L . The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again . The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL , within 24 hours . Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely . She had close follow-up with endocrinology post discharge .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_pipeline", "en", "clinical/models")
-
-val text = "A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG . She had been on dapagliflozin for six months at the time of presentation . Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness , guarding , or rigidity . Pertinent laboratory findings on admission were : serum glucose 111 mg/dl , bicarbonate 18 mmol/l , anion gap 20 , creatinine 0.4 mg/dL , triglycerides 508 mg/dL , total cholesterol 122 mg/dL , glycated hemoglobin ( HbA1c ) 10% , and venous pH 7.27 . Serum lipase was normal at 43 U/L . Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia . The patient was initially admitted for starvation ketosis , as she reported poor oral intake for three days prior to admission . However , serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL , the anion gap was still elevated at 21 , serum bicarbonate was 16 mmol/L , triglyceride level peaked at 2050 mg/dL , and lipase was 52 U/L . The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again . The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL , within 24 hours . Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely . She had close follow-up with endocrinology post discharge ."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.clinical_pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG . She had been on dapagliflozin for six months at the time of presentation . Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness , guarding , or rigidity . Pertinent laboratory findings on admission were : serum glucose 111 mg/dl , bicarbonate 18 mmol/l , anion gap 20 , creatinine 0.4 mg/dL , triglycerides 508 mg/dL , total cholesterol 122 mg/dL , glycated hemoglobin ( HbA1c ) 10% , and venous pH 7.27 . Serum lipase was normal at 43 U/L . Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia . The patient was initially admitted for starvation ketosis , as she reported poor oral intake for three days prior to admission . However , serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL , the anion gap was still elevated at 21 , serum bicarbonate was 16 mmol/L , triglyceride level peaked at 2050 mg/dL , and lipase was 52 U/L . The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again . The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL , within 24 hours . Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely . She had close follow-up with endocrinology post discharge .""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | gestational diabetes mellitus |      39 |    67 | PROBLEM     |     0.999895 |
@@ -123,9 +94,6 @@ Results
 | 22 | Physical examination          |     739 |   758 | TEST        |     0.985332 |
 | 23 | dry oral mucosa               |     796 |   810 | PROBLEM     |     0.991374 |
 | 24 | her abdominal examination     |     830 |   854 | TEST        |     0.999292 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_deid_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_deid_pipeline_en.md
index 2b6aebd5aa..e65874d21f 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_deid_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_deid_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_deid]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.classify.token_bert.ner_deid.pipeline").predict("""A. Record date :
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_deid_pipeline", "en", "clinical/models")
-
-text = '''A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_deid_pipeline", "en", "clinical/models")
-
-val text = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.ner_deid.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      17 |    26 | DATE          |     0.957256 |
@@ -107,9 +78,6 @@ Results
 |  6 | 0295 Keats Street             |     145 |   161 | STREET        |     0.997889 |
 |  7 | 302) 786-5227                 |     174 |   186 | PHONE         |     0.970114 |
 |  8 | Brothers Coal-Mine            |     253 |   270 | ORGANIZATION  |     0.998911 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_drugs_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_drugs_pipeline_en.md
index 429a33b9a8..4db40ec843 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_drugs_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_drugs_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_drugs
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.ner_ade.pipeline").predict("""The human KCNJ9 (
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_drugs_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_drugs_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.ner_ade.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | potassium      |      92 |   100 | DrugChem    |     0.990254 |
@@ -106,9 +78,6 @@ Results
 |  5 | vinorelbine    |    1343 |  1353 | DrugChem    |     0.999991 |
 |  6 | anthracyclines |    1390 |  1403 | DrugChem    |     0.99999  |
 |  7 | taxanes        |    1409 |  1415 | DrugChem    |     0.999946 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_jsl_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_jsl_pipeline_en.md
index 6425c96e40..b1e24b574a 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_jsl_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,71 +63,10 @@ nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                        |   begin |   end | ner_label    |   confidence |
 |---:|:---------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 21-day-old                       |      17 |    26 | Age          |     0.999456 |
@@ -154,12 +94,6 @@ Results
 | 22 | fussy                            |     574 |   578 | Symptom      |     0.997592 |
 | 23 | over the past 2 days             |     580 |   599 | Date_Time    |     0.994786 |
 | 24 | albuterol                        |     642 |   650 | Drug         |     0.999735 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_jsl_slim_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_jsl_slim_pipeline_en.md
index 33d44ccc82..22e92c957d 100644
--- a/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_jsl_slim_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-bert_token_classifier_ner_jsl_slim_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl_s
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.classify.token_bert.jsl_slim.pipeline").predict("""HISTORY: 30-year
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_slim_pipeline", "en", "clinical/models")
-
-text = '''HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_slim_pipeline", "en", "clinical/models")
-
-val text = "HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.jsl_slim.pipeline").predict("""HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label    |   confidence |
 |---:|:-----------------|--------:|------:|:-------------|-------------:|
 |  0 | HISTORY:         |       0 |     7 | Header       |     0.994786 |
@@ -108,9 +79,6 @@ Results
 |  7 | her mother       |     213 |   222 | Demographics |     0.997765 |
 |  8 | age 58           |     227 |   232 | Age          |     0.997636 |
 |  9 | breast cancer    |     270 |   282 | Oncological  |     0.999452 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_augmented_es.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_augmented_es.md
index 667604cc69..eee3e1c23e 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_augmented_es.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_augmented_es.md
@@ -36,93 +36,7 @@ The PHI information will be masked and obfuscated in the resulting text. The pip
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical_augmented").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from johnsnowlabs import *
 
@@ -180,6 +94,7 @@ Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servic
 val result = deid_pipeline.annotate(sample)
 ```
 
+
 {:.nlu-block}
 ```python
 import nlu
@@ -204,102 +119,13 @@ Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologí
 Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""")
 ```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
 
-val result = deid_pipeline.annotate(sample)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical_augmented").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""")
-```
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Datos <SEX>.
@@ -471,12 +297,6 @@ Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura trata
 F. 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra.
 Francisco José Roca Bermúdez Hospital 12 de Octubre Servicio de Endocrinología y Nutrición Calle Ramón y Cajal s/n 03129 Zaragoza - Alicante (Portugal) Correo electrónico: richard@company.it
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_de.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_de.md
index 53ae1bee25..1396ae63b7 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_de.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_de.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from **German** medical
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -95,137 +96,10 @@ Adresse : St.Johann-Straße 13 19300
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "de", "clinical/models")
-
-sample = """Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-"""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification","de","clinical/models")
-
-val sample = "Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.clinical").predict("""Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "de", "clinical/models")
-
-sample = """Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-"""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification","de","clinical/models")
-
-val sample = "Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.clinical").predict("""Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Zusammenfassung : <PATIENT> wird am Morgen des <DATE> ins <HOSPITAL> eingeliefert.
@@ -273,12 +147,6 @@ Kontonummer: 192837465738
 SSN : 1310011981M454
 Lizenznummer: XX123456
 Adresse : Klingelhöferring 31206
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_en.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_en.md
index 0a8b181bbf..4a42354c6d 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_en.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts. The
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,56 +79,10 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -159,9 +114,6 @@ Dr. Dr Felice Lacer, IDXO:4884578, IP 444.444.444.444.
 He is a 75 male was admitted to the MADISON VALLEY MEDICAL CENTER for cystectomy on 07-01-1972.
 Patient's VIN : 2BBBB11BBBB222999, SSN SSN-814-86-1962, Driver's license P055567317431.
 Phone 0381-6762484, Budaörsi út 14., New brunswick, E-MAIL: Reba@google.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_es.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_es.md
index e7f63fef36..6f99b722f2 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_es.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_es.md
@@ -34,6 +34,7 @@ This pipeline is trained with sciwiki_300d embeddings and can be used to deident
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from johnsnowlabs import *
 
@@ -121,189 +122,10 @@ Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servic
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Datos del paciente.
@@ -475,13 +297,6 @@ Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura trata
 El paciente 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra.
 Reinaldo Manjón Malo Barcelona Servicio de Endocrinología y Nutrición Valencia km 12,500 28905 Bilbao - Madrid (Barcelona) Correo electrónico: quintanasalome@example.net
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_fr.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_fr.md
index 344d876819..aa19a2fb2b 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_fr.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_fr.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts in Fr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -120,187 +121,11 @@ COURRIEL : mariebreton@chb.fr
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = """COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"""
 
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = "COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("fr.deid_obfuscated").predict("""COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = """COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = "COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("fr.deid_obfuscated").predict("""COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 COMPTE-RENDU D'HOSPITALISATION
@@ -399,12 +224,6 @@ Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des tr
 PSA de 1,16 ng/ml.
 ADDRESSÉ À : Dr Tristan-Gilbert Poulain - CENTRE HOSPITALIER D'ORTHEZ Service D'Endocrinologie et de Nutrition - 6, avenue Pages, 37443 Sainte Antoine
 COURRIEL : massecatherine@bouygtel.fr
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_glove_augmented_en.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_glove_augmented_en.md
index af6134d694..92f035ddb3 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_glove_augmented_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_glove_augmented_en.md
@@ -36,6 +36,7 @@ It's different to `clinical_deidentification_glove` in the way it manages PHONE
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,36 +61,11 @@ nlu.load("en.deid.glove_augmented.pipeline").predict("""Record date : 2093-01-13
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_glove_augmented", "en", "clinical/models")
-
-deid_pipeline.annotate("""Record date : 2093-01-13, David Hale, M.D. IP: 203.120.223.13. The driver's license no:A334455B. the SSN: 324598674 and e-mail: hale@gmail.com. Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93. PCP : Oliveira, 25 years old. Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification_glove_augmented", "en", "clinical/models")
-
-val result = pipeline.annotate("""Record date : 2093-01-13, David Hale, M.D. IP: 203.120.223.13. The driver's license no:A334455B. the SSN: 324598674 and e-mail: hale@gmail.com. Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93. PCP : Oliveira, 25 years old. Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286.""")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.glove_augmented.pipeline").predict("""Record date : 2093-01-13, David Hale, M.D. IP: 203.120.223.13. The driver's license no:A334455B. the SSN: 324598674 and e-mail: hale@gmail.com. Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93. PCP : Oliveira, 25 years old. Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 {'masked': ['Record date : <DATE>, <DOCTOR>, M.D.',
     'IP: <IPADDR>.',
     "The driver's license no: <LICENSE>.",
@@ -138,9 +114,6 @@ Results
     'Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93.',
     'PCP : Oliveira, 25 years old.',
     "Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286."]}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_glove_en.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_glove_en.md
index 7b0b915f25..f9e811bf6a 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_glove_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_glove_en.md
@@ -34,6 +34,7 @@ This pipeline is trained with lightweight `glove_100d` embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -74,95 +75,11 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_glove", "en", "clinical/models")
-
 
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_glove","en","clinical/models")
-
-val result = deid_pipeline.annotate("Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. Dr. John Green, ID: 1231511863, IP 203.120.223.13. He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.glove_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_glove", "en", "clinical/models")
-
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_glove","en","clinical/models")
-
-val result = deid_pipeline.annotate("Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. Dr. John Green, ID: 1231511863, IP 203.120.223.13. He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.glove_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -194,12 +111,6 @@ Dr. Dr Worley Colonel, ID: ZJ:9570208, IP 005.005.005.005.
 He is a 67 male was admitted to the ST. LUKE'S HOSPITAL AT THE VINTAGE for cystectomy on 06-02-1981.
 Patient's VIN : 3CCCC22DDDD333888, SSN SSN-618-77-1042, Driver's license W693817528998.
 Phone 0496 46 46 70, 3100 weston rd, Shattuck, E-MAIL: Freddi@hotmail.com.
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_it.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_it.md
index 95dcba52d2..46f9803d80 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_it.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_it.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts in It
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -117,181 +118,10 @@ EMAIL: bferrabosco@poste.it""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = """RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = "RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("it.deid.clinical").predict("""RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = """RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = "RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("it.deid.clinical").predict("""RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 RAPPORTO DI RICOVERO
@@ -387,12 +217,6 @@ PSA di 1,16 ng/ml.
 INDIRIZZATO A: Dott.
 Antonio Rusticucci - ASL 7 DI CARBONIA AZIENDA U.S.L. N. 7, Dipartimento di Endocrinologia e Nutrizione - Via Giorgio 0 Appartamento 26, 03461 Sesto Raimondo
 EMAIL: murat.g@jsl.com
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_pt.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_pt.md
index 6988ab3054..ce5c16281f 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_pt.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_pt.md
@@ -34,6 +34,7 @@ This pipeline is trained with `w2v_cc_300d` portuguese embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,213 +134,11 @@ Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@ho
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = """Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = "Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("pt.deid.clinical").predict("""Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-""")
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = """Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = "Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("pt.deid.clinical").predict("""Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Dados do <SEX>.
@@ -455,12 +254,6 @@ O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta r
 A tomografia computorizada abdominal é normal.
 A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
 Referido por: Carlos Melo - Avenida Dos Aliados, 56, 22 Espanha E-mail: maria.prado@jacob.com.
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_ro.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_ro.md
index b23bed8ee9..19702bcb17 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_ro.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_ro.md
@@ -75,95 +75,11 @@ Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-val sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("ro.deid.clinical").predict("""Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-val sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("ro.deid.clinical").predict("""Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Medic : Dr. <DOCTOR>, C.N.P : <IDNUM>, Data setului de analize: <DATE>
@@ -195,12 +111,6 @@ Varsta : 91, Nume si Prenume : Dragomir Emilia
 Tel: 0248 551 376, E-mail : tudorsmaranda@kappa.ro,
 Licență : T003485962M, Înmatriculare : AR-65-UPQ, Cont : KHHO5029180812813651, 
 Centrul Medical de Evaluare si Recuperare pentru Copii si Tineri Cristian Serban Buzias Aleea Voinea Curcani, 328479
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_slim_en.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_slim_en.md
index 8f197400c9..3c4b5598dc 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_slim_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_slim_en.md
@@ -34,6 +34,7 @@ This pipeline is trained with lightweight `glove_100d` embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,102 +79,11 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_slim", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_slim","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_slim").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_slim", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_slim","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_slim").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
 
 Masked with entity labels
 ------------------------------
@@ -206,12 +116,6 @@ Dr. Dr Rosalba Hill, ID: JY:3489547, IP 005.005.005.005.
 He is a 79 male was admitted to the JOHN MUIR MEDICAL CENTER-CONCORD CAMPUS for cystectomy on 01-25-1997.
 Patient's VIN : 3CCCC22DDDD333888, SSN SSN-289-37-4495, Driver's license S99983662.
 Phone 04.32.52.27.90, North Adrienne, Colorado Springs, E-MAIL: Rawland@google.com.
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_wip_en.md b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_wip_en.md
index 1207eebd51..917d6333d2 100644
--- a/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_wip_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-clinical_deidentification_wip_en.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts. The
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,56 +79,9 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_wip", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_wip","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.clinical_wip").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -159,9 +113,6 @@ Dr. Dr Felice Lacer, IDXO:4884578, IP 444.444.444.444.
 He is a 75 male was admitted to the MADISON VALLEY MEDICAL CENTER for cystectomy on 07-01-1972.
 Patient's VIN : 2BBBB11BBBB222999, SSN SSN-814-86-1962, Driver's license P055567317431.
 Phone 0381-6762484, Budaörsi út 14., New brunswick, E-MAIL: Reba@google.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_ade_en.md b/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_ade_en.md
index 5ba50ed961..544033432a 100644
--- a/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_ade_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_ade_en.md
@@ -34,6 +34,7 @@ A pipeline for Adverse Drug Events (ADE) with `ner_ade_biobert`, `assertion_dl_b
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,9 @@ nlu.load("en.explain_doc.clinical_ade").predict("""Been taking Lipitor for 15 ye
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_ade", "en", "clinical/models")
-
-text = """Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_ade", "en", "clinical/models")
-
-val text = """Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"""
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.clinical_ade").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-
 Class: True
 
 NER_Assertion:
@@ -114,10 +83,6 @@ Relations:
 | 1  | cramps                        | ADE        | Lipitor     | DRUG    |        0 |
 | 2  | severe fatigue                | ADE        | voltaren    | DRUG    |        0 |
 | 3  | cramps                        | ADE        | voltaren    | DRUG    |        1 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_carp_en.md b/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_carp_en.md
index 31c459ab71..ee5df07c05 100644
--- a/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_carp_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_carp_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_clinical`, `assertion_dl`, `re_clinical` and `ner_posology`
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,72 +63,11 @@ nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history o
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-val text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-val text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |   | chunks                        | ner_clinical | assertion | posology_chunk   | ner_posology | relations |
 |---|-------------------------------|--------------|-----------|------------------|--------------|-----------|
 | 0 | gestational diabetes mellitus | PROBLEM      | present   | metformin        | Drug         | TrAP      |
@@ -137,13 +77,6 @@ Results
 | 4 | poor appetite                 | PROBLEM      | present   | insulin glargine | Drug         | TrCP      |
 | 5 | vomiting                      | PROBLEM      | present   | at night         | Frequency    | TrAP      |
 | 6 | insulin glargine              | TREATMENT    | present   | 12 units         | Dosage       | TrAP      |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_era_en.md b/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_era_en.md
index ab0ac3b1e7..fd50c9af04 100644
--- a/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_era_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_era_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_clinical_events`, `assertion_dl` and `re_temporal_events_cl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,72 +63,10 @@ nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Ho
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-val text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-val text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | relation   | entity1       |   entity1_begin |   entity1_end | chunk1                    | entity2       |   entity2_begin |   entity2_end | chunk2                        |   confidence |
 |---:|:-----------|:--------------|----------------:|--------------:|:--------------------------|:--------------|----------------:|--------------:|:------------------------------|-------------:|
 |  0 | AFTER      | OCCURRENCE    |               7 |            14 | admitted                  | CLINICAL_DEPT |              19 |            43 | The John Hopkins Hospital     |     0.963836 |
@@ -138,13 +77,6 @@ Results
 |  5 | OVERLAP    | DATE          |              45 |            54 | 2 days ago                | PROBLEM       |              74 |           102 | gestational diabetes mellitus |     0.996954 |
 |  6 | BEFORE     | EVIDENTIAL    |             119 |           124 | denied                    | PROBLEM       |             126 |           129 | pain                          |     1        |
 |  7 | BEFORE     | EVIDENTIAL    |             119 |           124 | denied                    | PROBLEM       |             135 |           146 | any headache                  |     1        |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_medication_en.md b/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_medication_en.md
index 52e9a64ee3..918f5ea950 100644
--- a/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_medication_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_medication_en.md
@@ -34,6 +34,7 @@ A pipeline for detecting posology entities with the `ner_posology_large` NER mod
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,72 +63,10 @@ nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient i
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 +----+----------------+------------+
 |    | chunks         | entities   |
 |---:|:---------------|:-----------|
@@ -164,13 +103,6 @@ Results
 | DRUG-ROUTE     | DRUG      | Lantus     | ROUTE     | subcutaneously |
 | DRUG-FREQUENCY | DRUG      | Lantus     | FREQUENCY | at bedtime     |
 +----------------+-----------+------------+-----------+----------------+
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_radiology_en.md b/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_radiology_en.md
index b9dca79581..d20469b39b 100644
--- a/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_radiology_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-explain_clinical_doc_radiology_en.md
@@ -34,6 +34,7 @@ A pipeline for detecting posology entities with the `ner_radiology` NER model, a
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,11 @@ nlu.load("en.explain_doc.clinical_radiology.pipeline").predict("""Bilateral brea
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_radiology", "en", "clinical/models")
-
-text = """Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_radiology", "en", "clinical/models")
-
-val text = """Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."""
 
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.clinical_radiology.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +----+------------------------------------------+---------------------------+
 |    | chunks                                   | entities                  |
 |---:|:-----------------------------------------|:--------------------------|
@@ -132,10 +103,6 @@ Results
 |       1 | ImagingTest     | ultrasound            | ImagingFindings           | ovoid mass |
 |       0 | ImagingFindings | benign fibrous tissue | Disease_Syndrome_Disorder | lipoma     |
 +---------+-----------------+-----------------------+---------------------------+------------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-icd10_icd9_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-icd10_icd9_mapping_en.md
index 3f18e50ce3..6e5a1d0937 100644
--- a/docs/_posts/Cabir40/2023-06-16-icd10_icd9_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-icd10_icd9_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10_icd9_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,13 @@ nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | icd10_code          | icd9_code          |
 |---:|:--------------------|:-------------------|
 |  0 | Z833 | A0100 | A000 | V180 | 0020 | 0010 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-icd10cm_snomed_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-icd10cm_snomed_mapping_en.md
index 162a716f7d..c352e88069 100644
--- a/docs/_posts/Cabir40/2023-06-16-icd10cm_snomed_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-icd10cm_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10cm_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,13 @@ nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | icd10cm_code          | snomed_code                              |
 |---:|:----------------------|:-----------------------------------------|
 |  0 | R079 | N4289 | M62830 | 161972006 | 22035000 | 16410651000119105 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-icd10cm_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-icd10cm_umls_mapping_en.md
index 8beb0ab07a..f77ca38b34 100644
--- a/docs/_posts/Cabir40/2023-06-16-icd10cm_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-icd10cm_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps ICD10CM codes to UMLS codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,73 +59,13 @@ nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 {'icd10cm': ['M89.50', 'R82.2', 'R09.01'],
 'umls': ['C4721411', 'C0159076', 'C0004044']}
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-icdo_snomed_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-icdo_snomed_mapping_en.md
index 81916cc38e..df7d1dca2d 100644
--- a/docs/_posts/Cabir40/2023-06-16-icdo_snomed_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-icdo_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icdo_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | icdo_code                | snomed_code                    |
 |---:|:-------------------------|:-------------------------------|
 |  0 | 8120/1 | 8170/3 | 8380/3 | 45083001 | 25370001 | 30289006 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_clinical_pipeline_en.md
index 1d3cb0ab5b..1535d09093 100644
--- a/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_clinical](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl_wip_clinical.pipeline").predict("""The patient is a 21-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_wip_clinical.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                 |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     0.9984   |
@@ -123,9 +95,6 @@ Results
 | 22 | 5 to 10 minutes                           |     459 |   473 | Duration                     |     0.152125 |
 | 23 | his                                       |     488 |   490 | Gender                       |     0.9987   |
 | 24 | respiratory congestion                    |     492 |   513 | VS_Finding                   |     0.6458   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_greedy_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_greedy_biobert_pipeline_en.md
index 29a4b89062..66bbe31e41 100644
--- a/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_greedy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_greedy_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.greedy_wip_biobert.pipeline").predict("""The patient is a 2
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.greedy_wip_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                      |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     1        |
@@ -123,9 +93,6 @@ Results
 | 22 | He                                             |     516 |   517 | Gender                       |     0.9998   |
 | 23 | tired                                          |     550 |   554 | Symptom                      |     0.8912   |
 | 24 | fussy                                          |     569 |   573 | Symptom                      |     0.9541   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_greedy_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_greedy_clinical_pipeline_en.md
index 98d66e121f..85a52de1d1 100644
--- a/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_greedy_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_greedy_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_greedy_clinical](ht
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.wip_greedy_clinical.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.wip_greedy_clinical.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                     |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     0.9817   |
@@ -123,9 +94,6 @@ Results
 | 22 | his                                            |     488 |   490 | Gender                       |     0.9904   |
 | 23 | respiratory congestion                         |     492 |   513 | Symptom                      |     0.5294   |
 | 24 | He                                             |     516 |   517 | Gender                       |     0.9989   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_modifier_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_modifier_clinical_pipeline_en.md
index b4aa34e8ff..b47dfc0b6b 100644
--- a/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_modifier_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-jsl_ner_wip_modifier_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_modifier_clinical](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.wip_modifier_clinical.pipeline").predict("""The patient is
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_modifier_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_modifier_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.wip_modifier_clinical.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                     |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     0.9817   |
@@ -123,9 +93,6 @@ Results
 | 22 | his                                            |     488 |   490 | Gender                       |     0.9904   |
 | 23 | respiratory congestion                         |     492 |   513 | Symptom                      |     0.5294   |
 | 24 | He                                             |     516 |   517 | Gender                       |     0.9989   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
index ea9ce1cf88..62e84eef3e 100644
--- a/docs/_posts/Cabir40/2023-06-16-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_rd_ner_wip_greedy_biobert](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.wip_greedy_biobert.pipeline").predict("""Bilateral breast u
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_rd_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_rd_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.wip_greedy_biobert.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Bilateral             |       0 |     8 | Direction                 |     0.9875   |
@@ -112,9 +82,6 @@ Results
 | 11 | internal color flow   |     294 |   312 | ImagingFindings           |     0.3726   |
 | 12 | benign fibrous tissue |     334 |   354 | ImagingFindings           |     0.484533 |
 | 13 | lipoma                |     361 |   366 | Disease_Syndrome_Disorder |     0.8955   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
index 5ba0ae06dc..db36b8b962 100644
--- a/docs/_posts/Cabir40/2023-06-16-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_rd_ner_wip_greedy_clinical]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.jsl_rd_wip_greedy.pipeline").predict("""The patient is a 21
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_rd_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_rd_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_rd_wip_greedy.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                  |   begin |   end | ner_label      |   confidence |
 |---:|:----------------------------|--------:|------:|:---------------|-------------:|
 |  0 | 21-day-old                  |      17 |    26 | Age            |     0.9913   |
@@ -123,9 +94,6 @@ Results
 | 22 | respiratory congestion      |     492 |   513 | Symptom        |     0.25015  |
 | 23 | He                          |     516 |   517 | Gender         |     0.9998   |
 | 24 | tired                       |     550 |   554 | Symptom        |     0.8179   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-mesh_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-mesh_umls_mapping_en.md
index 77878fc979..a8c645a7a6 100644
--- a/docs/_posts/Cabir40/2023-06-16-mesh_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-mesh_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `mesh_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,13 @@ nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | mesh_code                   | umls_code                      |
 |---:|:----------------------------|:-------------------------------|
 |  0 | C028491 | D019326 | C579867 | C0043904 | C0045010 | C3696376 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_abbreviation_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_abbreviation_clinical_pipeline_en.md
index 663d3113aa..7da77d9f74 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_abbreviation_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_abbreviation_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_abbreviation_clinical](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,15 @@ nlu.load("en.med_ner.clinical-abbreviation.pipeline").predict("""Gravid with est
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_abbreviation_clinical_pipeline", "en", "clinical/models")
-
-text = '''Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_abbreviation_clinical_pipeline", "en", "clinical/models")
-
-val text = "Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical-abbreviation.pipeline").predict("""Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | CBC          |     126 |   128 | ABBR        |            1 |
 |  1 | AB           |     159 |   160 | ABBR        |            1 |
 |  2 | VDRL         |     189 |   192 | ABBR        |            1 |
 |  3 | HIV          |     247 |   249 | ABBR        |            1 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_ade_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_ade_biobert_pipeline_en.md
index fa5f88dea1..04f4d404ba 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_ade_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_ade_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_biobert](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.biobert_ade.pipeline").predict("""Been taking Lipitor for 1
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_biobert_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_biobert_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biobert_ade.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |       0.9996 |
 |  1 | severe fatigue |      52 |    65 | ADE         |       0.7588 |
 |  2 | voltaren       |      97 |   104 | DRUG        |       0.998  |
 |  3 | cramps         |     152 |   157 | ADE         |       0.9258 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_ade_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_ade_clinical_pipeline_en.md
index 6a97ea00a7..3eb8f1a151 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_ade_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_ade_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_clinical](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,15 @@ nlu.load("en.med_ner.ade_clinical.pipeline").predict("""Been taking Lipitor for
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_clinical_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_clinical_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.ade_clinical.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |      0.9969  |
 |  1 | severe fatigue |      52 |    65 | ADE         |      0.48995 |
 |  2 | voltaren       |      97 |   104 | DRUG        |      0.9889  |
 |  3 | cramps         |     152 |   157 | ADE         |      0.7472  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_ade_clinicalbert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_ade_clinicalbert_pipeline_en.md
index cb4fd6b3ce..4180c939dd 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_ade_clinicalbert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_ade_clinicalbert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_clinicalbert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,16 @@ nlu.load("en.med_ner.clinical_bert_ade.pipeline").predict("""Been taking Lipitor
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_clinicalbert_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_clinicalbert_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_bert_ade.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |       0.9975 |
 |  1 | severe fatigue |      52 |    65 | ADE         |       0.7094 |
 |  2 | voltaren       |      97 |   104 | DRUG        |       0.9202 |
 |  3 | cramps         |     152 |   157 | ADE         |       0.5992 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_ade_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_ade_healthcare_pipeline_en.md
index c0d570ad51..f5f6316278 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_ade_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_ade_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_healthcare](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,16 @@ nlu.load("en.med_ner.healthcare_ade.pipeline").predict("""Been taking Lipitor fo
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_healthcare_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_healthcare_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.healthcare_ade.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |      0.998   |
 |  1 | severe fatigue |      52 |    65 | ADE         |      0.67055 |
 |  2 | voltaren       |      97 |   104 | DRUG        |      0.9255  |
 |  3 | cramps         |     152 |   157 | ADE         |      0.9392  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_anatomy_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_anatomy_biobert_pipeline_en.md
index e8defd69d3..be5a193e41 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_anatomy_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_anatomy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_biobert](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -80,58 +81,11 @@ Dermatologic: She has got redness along her right great toe, but no bleeding or
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_biobert_pipeline", "en", "clinical/models")
-
-text = '''This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_biobert_pipeline", "en", "clinical/models")
-
-val text = "This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatomy_biobert.pipeline").predict("""This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label              |   confidence |
 |---:|:-----------------|--------:|------:|:-----------------------|-------------:|
 |  0 | right            |     314 |   318 | Organism_subdivision   |      0.9948  |
@@ -154,9 +108,6 @@ Results
 | 17 | foot             |     999 |  1002 | Organism_subdivision   |      0.9831  |
 | 18 | toe              |    1023 |  1025 | Organism_subdivision   |      0.9653  |
 | 19 | toenails         |    1031 |  1038 | Organism_subdivision   |      0.9999  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_anatomy_coarse_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_anatomy_coarse_biobert_pipeline_en.md
index be9c5221e0..4f8275f19c 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_anatomy_coarse_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_anatomy_coarse_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_coarse_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,46 +63,13 @@ nlu.load("en.med_ner.anatomy_coarse_biobert.pipeline").predict("""content in the
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_coarse_biobert_pipeline", "en", "clinical/models")
-
-text = '''content in the lung tissue'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_coarse_biobert_pipeline", "en", "clinical/models")
-
-val text = "content in the lung tissue"
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatomy_coarse_biobert.pipeline").predict("""content in the lung tissue""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label   |   confidence |
 |---:|:------------|--------:|------:|:------------|-------------:|
 |  0 | lung tissue |      15 |    25 | Anatomy     |      0.99155 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_anatomy_coarse_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_anatomy_coarse_pipeline_en.md
index 175db8b321..6ab86061e3 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_anatomy_coarse_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_anatomy_coarse_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_coarse](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,46 +63,13 @@ nlu.load("en.med_ner.anatomy_coarse.pipeline").predict("""content in the lung ti
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_coarse_pipeline", "en", "clinical/models")
-
-text = '''content in the lung tissue'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_coarse_pipeline", "en", "clinical/models")
-
-val text = "content in the lung tissue"
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatomy_coarse.pipeline").predict("""content in the lung tissue""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | lung tissue  |      15 |    25 | Anatomy     |      0.99655 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_anatomy_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_anatomy_pipeline_en.md
index 0ffa123704..a87d0276b6 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_anatomy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_anatomy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy](https://nlp.johnsn
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -80,58 +81,11 @@ Dermatologic: She has got redness along the lateral portion of her right great t
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_pipeline", "en", "clinical/models")
-
-text = '''This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along the lateral portion of her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_pipeline", "en", "clinical/models")
-
-val text = "This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along the lateral portion of her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatom.pipeline").predict("""This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along the lateral portion of her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label              |   confidence |
 |---:|:--------------------|--------:|------:|:-----------------------|-------------:|
 |  0 | skin                |     374 |   377 | Organ                  |      1       |
@@ -140,9 +94,6 @@ Results
 |  3 | Mucous membranes    |     716 |   731 | Tissue                 |      0.90445 |
 |  4 | bowel               |     802 |   806 | Organ                  |      0.9648  |
 |  5 | skin                |     956 |   959 | Organ                  |      0.9999  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_bacterial_species_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_bacterial_species_pipeline_en.md
index 06c104a0ed..66c1d1b476 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_bacterial_species_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_bacterial_species_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_bacterial_species](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,14 @@ nlu.load("en.med_ner.bacterial_species.pipeline").predict("""Based on these gene
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_bacterial_species_pipeline", "en", "clinical/models")
-
-text = '''Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_bacterial_species_pipeline", "en", "clinical/models")
-
-val text = "Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T))."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.bacterial_species.pipeline").predict("""Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | SMSP (T)                |      73 |    80 | SPECIES     |     0.9725   |
 |  1 | Methanoregula formicica |     167 |   189 | SPECIES     |     0.97935  |
 |  2 | SMSP (T)                |     222 |   229 | SPECIES     |     0.991975 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_biomarker_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_biomarker_pipeline_en.md
index 4fd1409983..db67a5fd88 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_biomarker_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_biomarker_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_biomarker](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.biomarker.pipeline").predict("""Here , we report the first
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_biomarker_pipeline", "en", "clinical/models")
-
-text = '''Here , we report the first case of an intraductal tubulopapillary neoplasm of the pancreas with clear cell morphology . Immunohistochemistry revealed positivity for Pan-CK , CK7 , CK8/18 , MUC1 , MUC6 , carbonic anhydrase IX , CD10 , EMA , β-catenin and e-cadherin '''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_biomarker_pipeline", "en", "clinical/models")
-
-val text = "Here , we report the first case of an intraductal tubulopapillary neoplasm of the pancreas with clear cell morphology . Immunohistochemistry revealed positivity for Pan-CK , CK7 , CK8/18 , MUC1 , MUC6 , carbonic anhydrase IX , CD10 , EMA , β-catenin and e-cadherin "
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biomarker.pipeline").predict("""Here , we report the first case of an intraductal tubulopapillary neoplasm of the pancreas with clear cell morphology . Immunohistochemistry revealed positivity for Pan-CK , CK7 , CK8/18 , MUC1 , MUC6 , carbonic anhydrase IX , CD10 , EMA , β-catenin and e-cadherin """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks               |   begin |   end | ner_label             |   confidence |
 |---:|:-------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | intraductal              |      38 |    48 | CancerModifier        |     0.9998   |
@@ -114,9 +85,6 @@ Results
 | 13 | EMA                      |     234 |   236 | Biomarker             |     0.9985   |
 | 14 | β-catenin                |     240 |   248 | Biomarker             |     0.9948   |
 | 15 | e-cadherin               |     254 |   263 | Biomarker             |     0.9952   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_biomedical_bc2gm_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_biomedical_bc2gm_pipeline_en.md
index 090cc1c5a7..d5bfefb571 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_biomedical_bc2gm_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_biomedical_bc2gm_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_biomedical_bc2gm](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,16 @@ nlu.load("en.med_ner.biomedical_bc2gm.pipeline").predict("""Immunohistochemical
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_biomedical_bc2gm_pipeline", "en", "clinical/models")
-
-text = '''Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_biomedical_bc2gm_pipeline", "en", "clinical/models")
-
-val text = "Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biomedical_bc2gm.pipeline").predict("""Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label    |   confidence |
 |---:|:-------------|--------:|------:|:-------------|-------------:|
 |  0 | S-100        |      46 |    50 | GENE_PROTEIN |       0.9911 |
 |  1 | HMB-45       |      89 |    94 | GENE_PROTEIN |       0.9944 |
 |  2 | cytokeratin  |     131 |   141 | GENE_PROTEIN |       0.9951 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_bionlp_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_bionlp_biobert_pipeline_en.md
index fcb93cc8c1..af5ce66db1 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_bionlp_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_bionlp_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_bionlp_biobert](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.bionlp_biobert.pipeline").predict("""Both the erbA IRES and
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_bionlp_biobert_pipeline", "en", "clinical/models")
-
-text = '''Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_bionlp_biobert_pipeline", "en", "clinical/models")
-
-val text = "Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay"
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.bionlp_biobert.pipeline").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label              |   confidence |
 |---:|:--------------------|--------:|------:|:-----------------------|-------------:|
 |  0 | erbA                |       9 |    12 | Gene_or_gene_product   |      1       |
@@ -109,9 +80,6 @@ Results
 |  8 | erbA                |     236 |   239 | Gene_or_gene_product   |      0.9977  |
 |  9 | IRES virus          |     241 |   250 | Organism               |      0.9911  |
 | 10 | blastoderm          |     259 |   268 | Cell                   |      0.9941  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_bionlp_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_bionlp_pipeline_en.md
index 5d91cd77d2..f110749883 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_bionlp_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_bionlp_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_bionlp](https://nlp.johnsno
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.bionlp.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_bionlp_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_bionlp_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.bionlp.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label            |   confidence |
 |---:|:-----------------------|--------:|------:|:---------------------|-------------:|
 |  0 | human                  |       4 |     8 | Organism             |     0.9996   |
@@ -109,9 +80,6 @@ Results
 |  8 | fat andskeletal muscle |     749 |   770 | Tissue               |     0.955433 |
 |  9 | KCNJ9                  |     801 |   805 | Gene_or_gene_product |     0.9172   |
 | 10 | Type II                |     940 |   946 | Gene_or_gene_product |     0.98845  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_cancer_genetics_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_cancer_genetics_pipeline_en.md
index 133070e99c..119d21741e 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_cancer_genetics_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_cancer_genetics_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_cancer_genetics](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.cancer_genetics.pipeline").predict("""The human KCNJ9 (Kir
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_cancer_genetics_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_cancer_genetics_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.cancer_genetics.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | human KCNJ9                                                             |       4 |    14 | protein     |     0.674    |
@@ -110,9 +80,6 @@ Results
 |  9 | KCNJ9 gene                                                              |     801 |   810 | DNA         |     0.95605  |
 | 10 | KCNJ9 protein                                                           |     868 |   880 | protein     |     0.844    |
 | 11 | locus                                                                   |     931 |   935 | DNA         |     0.9685   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_cellular_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_cellular_biobert_pipeline_en.md
index 6cc8827da8..52f131c71a 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_cellular_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_cellular_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_cellular_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.cellular_biobert.pipeline").predict("""Detection of various
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_cellular_biobert_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_cellular_biobert_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.cellular_biobert.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                   |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | intracellular signaling proteins            |      27 |    58 | protein     |     0.673333 |
@@ -118,9 +88,6 @@ Results
 | 17 | GAD                                         |     791 |   793 | protein     |     0.6432   |
 | 18 | reporter gene                               |     848 |   860 | DNA         |     0.61005  |
 | 19 | Tax                                         |     863 |   865 | protein     |     0.99     |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_cellular_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_cellular_pipeline_en.md
index 709a3b541f..a05a579b30 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_cellular_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_cellular_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_cellular](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.cellular.pipeline").predict("""Detection of various other i
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_cellular_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_cellular_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.cellular.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                   |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | intracellular signaling proteins                            |      27 |    58 | protein     |     0.763367 |
@@ -118,9 +89,6 @@ Results
 | 17 | GAD                                                         |     791 |   793 | protein     |     0.9932   |
 | 18 | reporter gene                                               |     848 |   860 | DNA         |     0.78715  |
 | 19 | Tax                                                         |     863 |   865 | protein     |     0.9986   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_chemicals_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_chemicals_pipeline_en.md
index 526fca1f7c..7dbcd9c75d 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_chemicals_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_chemicals_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemicals](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.chemicals.pipeline").predict("""The results have shown that
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chemicals.pipeline").predict("""The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                  |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------|--------:|------:|:------------|-------------:|
 |  0 | p - choloroaniline          |      40 |    57 | CHEM        |     0.935767 |
@@ -103,9 +74,6 @@ Results
 |  2 | kanamycin                   |     168 |   176 | CHEM        |     0.9824   |
 |  3 | colistin                    |     180 |   187 | CHEM        |     0.9911   |
 |  4 | povidone - iodine           |     193 |   209 | CHEM        |     0.8111   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_chemprot_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_chemprot_biobert_pipeline_en.md
index ca5457f7b5..e1d7d3fe40 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_chemprot_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_chemprot_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemprot_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.chemprot_biobert.pipeline").predict("""Keratinocyte growth
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemprot_biobert_pipeline", "en", "clinical/models")
-
-text = '''Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemprot_biobert_pipeline", "en", "clinical/models")
-
-val text = "Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chemprot_biobert.pipeline").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk    |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | Keratinocyte |       0 |    11 | GENE-Y      |       0.894  |
@@ -105,9 +76,6 @@ Results
 |  4 | fibroblast   |      38 |    47 | GENE-Y      |       0.3905 |
 |  5 | growth       |      49 |    54 | GENE-Y      |       0.7109 |
 |  6 | factor       |      56 |    61 | GENE-Y      |       0.8693 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_chemprot_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_chemprot_clinical_pipeline_en.md
index c78c8823c5..5592f6fe64 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_chemprot_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_chemprot_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemprot_clinical](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.chemprot_clinical.pipeline").predict("""Keratinocyte growth
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemprot_clinical_pipeline", "en", "clinical/models")
-
-text = '''Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemprot_clinical_pipeline", "en", "clinical/models")
-
-val text = "Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chemprot_clinical.pipeline").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk    |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | Keratinocyte |       0 |    11 | GENE-Y      |       0.7433 |
@@ -105,9 +77,6 @@ Results
 |  4 | fibroblast   |      38 |    47 | GENE-Y      |       0.5111 |
 |  5 | growth       |      49 |    54 | GENE-Y      |       0.4559 |
 |  6 | factor       |      56 |    61 | GENE-Y      |       0.5213 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_chexpert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_chexpert_pipeline_en.md
index 221dedf1a8..8207eefa59 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_chexpert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_chexpert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chexpert](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.chexpert.pipeline").predict("""FINAL REPORT HISTORY : Chest
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chexpert_pipeline", "en", "clinical/models")
-
-text = '''FINAL REPORT HISTORY : Chest tube leak , to assess for pneumothorax. FINDINGS : In comparison with study of ___ , the endotracheal tube and Swan - Ganz catheter have been removed . The left chest tube remains in place and there is no evidence of pneumothorax. Mild atelectatic changes are seen at the left base.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chexpert_pipeline", "en", "clinical/models")
-
-val text = "FINAL REPORT HISTORY : Chest tube leak , to assess for pneumothorax. FINDINGS : In comparison with study of ___ , the endotracheal tube and Swan - Ganz catheter have been removed . The left chest tube remains in place and there is no evidence of pneumothorax. Mild atelectatic changes are seen at the left base."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chexpert.pipeline").predict("""FINAL REPORT HISTORY : Chest tube leak , to assess for pneumothorax. FINDINGS : In comparison with study of ___ , the endotracheal tube and Swan - Ganz catheter have been removed . The left chest tube remains in place and there is no evidence of pneumothorax. Mild atelectatic changes are seen at the left base.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | endotracheal |     118 |   129 | OBS         |       0.9881 |
@@ -112,9 +84,6 @@ Results
 | 11 | changes      |     277 |   283 | OBS         |       0.9984 |
 | 12 | left         |     301 |   304 | ANAT        |       0.9999 |
 | 13 | base         |     306 |   309 | ANAT        |       0.9999 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_clinical_bert_pipeline_ro.md b/docs/_posts/Cabir40/2023-06-16-ner_clinical_bert_pipeline_ro.md
index 2e0e6f13ee..17edf087b6 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_clinical_bert_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_clinical_bert_pipeline_ro.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_bert](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-text = '''Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-val text = "Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,48 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-text = '''Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-val text = "Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-text = '''Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
 
-result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-bass
 |    | ner_chunks                        |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Angio CT                          |      12 |    19 | Imaging_Test              |     0.96415  |
@@ -144,12 +86,6 @@ bass
 | 21 | cardiotoracica                    |     461 |   474 | Body_Part                 |     0.9344   |
 | 22 | achizitii secventiale prospective |     479 |   511 | Imaging_Technique         |     0.966833 |
 | 23 | 100/min                           |     546 |   552 | Pulse                     |     0.9128   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_clinical_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_clinical_biobert_pipeline_en.md
index e4939a57f3..bc193b88a2 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_clinical_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_clinical_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.clinical_biobert.pipeline").predict("""The patient is a 21-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                           |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | congestion                                          |      62 |    71 | PROBLEM     |     0.5069   |
@@ -112,9 +84,6 @@ Results
 | 11 | albuterol treatments                                |     637 |   656 | TREATMENT   |     0.8917   |
 | 12 | His urine output                                    |     675 |   690 | TEST        |     0.7114   |
 | 13 | any diarrhea                                        |     832 |   843 | PROBLEM     |     0.73595  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_clinical_large_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_clinical_large_pipeline_en.md
index e0d7926f21..c659858e71 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_clinical_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_clinical_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.clinical_large.pipeline").predict("""The human KCNJ9 (Kir 3
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_large_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_large_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_large.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                   |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | the G-protein-activated inwardly rectifying potassium (GIRK |      48 |   106 | TREATMENT   |     0.6926   |
@@ -118,9 +90,6 @@ Results
 | 17 | the standard therapy                                        |    1067 |  1086 | TREATMENT   |     0.757767 |
 | 18 | anthracyclines                                              |    1125 |  1138 | TREATMENT   |     0.9999   |
 | 19 | taxanes                                                     |    1144 |  1150 | TREATMENT   |     0.9999   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_clinical_pipeline_en.md
index ee70041dfc..85740f6129 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.clinical.pipeline").predict("""The human KCNJ9 (Kir 3.3, GI
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                   |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | the G-protein-activated inwardly rectifying potassium (GIRK |      48 |   106 | TREATMENT   |     0.6926   |
@@ -118,9 +88,6 @@ Results
 | 17 | the standard therapy                                        |    1067 |  1086 | TREATMENT   |     0.757767 |
 | 18 | anthracyclines                                              |    1125 |  1138 | TREATMENT   |     0.9999   |
 | 19 | taxanes                                                     |    1144 |  1150 | TREATMENT   |     0.9999   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_clinical_pipeline_ro.md b/docs/_posts/Cabir40/2023-06-16-ner_clinical_pipeline_ro.md
index 0ed3ecf557..da109425dc 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_clinical_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_clinical_pipeline_ro.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-text = ''' Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-val text = " Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -96,28 +75,12 @@ val text = " Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de val
 
 val result = pipeline.fullAnnotate(text)
 ```
-
-{:.nlu-block}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-text = ''' Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                        |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Angio CT                          |      13 |    20 | Imaging_Test              |     0.92675  |
@@ -145,12 +108,6 @@ Results
 | 22 | cardiotoracica                    |     455 |   468 | Body_Part                 |     0.9995   |
 | 23 | achizitii secventiale prospective |     473 |   505 | Imaging_Technique         |     0.8514   |
 | 24 | 100/min                           |     540 |   546 | Pulse                     |     0.8501   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_clinical_trials_abstracts_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_clinical_trials_abstracts_pipeline_en.md
index f845b8e9f3..20a41dc2b9 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_clinical_trials_abstracts_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_clinical_trials_abstracts_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_trials_abstracts](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.clinical_trials_abstracts.pipe").predict("""A one-year, ran
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-text = '''A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes. In a multicentre, open, randomised study, 570 patients with Type 2 diabetes, aged 34 - 80 years, were treated for 52 weeks with insulin glargine or NPH insulin given once daily at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-val text = "A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes. In a multicentre, open, randomised study, 570 patients with Type 2 diabetes, aged 34 - 80 years, were treated for 52 weeks with insulin glargine or NPH insulin given once daily at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_trials_abstracts.pipe").predict("""A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes. In a multicentre, open, randomised study, 570 patients with Type 2 diabetes, aged 34 - 80 years, were treated for 52 weeks with insulin glargine or NPH insulin given once daily at bedtime.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label          |   confidence |
 |---:|:-----------------|--------:|------:|:-------------------|-------------:|
 |  0 | randomised       |      12 |    21 | CTDesign           |     0.9996   |
@@ -115,9 +85,6 @@ Results
 | 14 | NPH insulin      |     300 |   310 | Drug               |     0.97955  |
 | 15 | once daily       |     318 |   327 | DrugTime           |     0.999    |
 | 16 | bedtime          |     332 |   338 | DrugTime           |     0.9937   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_clinical_trials_abstracts_pipeline_es.md b/docs/_posts/Cabir40/2023-06-16-ner_clinical_trials_abstracts_pipeline_es.md
index 5bafc0e86b..7d07a039f7 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_clinical_trials_abstracts_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_clinical_trials_abstracts_pipeline_es.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_clinical_trials_abstracts](
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | suplementación                |      13 |    26 | PROC        |     0.9987   |
@@ -132,12 +84,6 @@ Results
 |  9 | pp                            |     337 |   338 | CHEM        |     0.96     |
 | 10 | diálisis                      |     388 |   395 | PROC        |     0.9982   |
 | 11 | función  residual             |     398 |   414 | PROC        |     0.73045  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_covid_trials_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_covid_trials_pipeline_en.md
index 837f555e46..8cdb5ad613 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_covid_trials_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_covid_trials_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_covid_trials](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-text = '''In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-val text = "In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-text = '''In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-val text = "In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                          |   begin |   end | ner_label                 |   confidence |
 |---:|:------------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | December 2019                       |       3 |    15 | Date                      |     0.99655  |
@@ -140,12 +91,6 @@ Results
 | 17 | CDC                                 |     547 |   549 | Institution               |     0.8296   |
 | 18 | 2020                                |     848 |   851 | Date                      |     0.9997   |
 | 19 | COVID‑19 vaccine                    |     864 |   879 | Vaccine_Name              |     0.87505  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_augmented_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_augmented_pipeline_en.md
index 94643da259..74965b6d91 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_augmented_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_augmented](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.deid.ner_augmented.pipeline").predict("""HISTORY OF PRESENT ILLNESS
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_augmented_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_augmented_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.ner_augmented.pipeline").predict("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | Smith           |      32 |    36 | NAME        |     0.9998   |
@@ -110,9 +81,6 @@ Results
 |  9 | Hart            |    1221 |  1224 | NAME        |     0.9996   |
 | 10 | Smith           |    1231 |  1235 | NAME        |     0.9998   |
 | 11 | 02/07/2003      |    1329 |  1338 | DATE        |     0.9997   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_biobert_pipeline_en.md
index 5d5ab92852..70565e1bfa 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_biobert](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.deid.ner_biobert.pipeline").predict("""A. Record date : 2093-01-13,
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_biobert_pipeline", "en", "clinical/models")
-
-text = '''A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_biobert_pipeline", "en", "clinical/models")
-
-val text = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.ner_biobert.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      17 |    26 | DATE        |      0.981   |
@@ -107,9 +77,6 @@ Results
 |  6 | Keats Street                  |     150 |   161 | LOCATION    |      0.77305 |
 |  7 | Phone                         |     164 |   168 | LOCATION    |      0.7083  |
 |  8 | Brothers                      |     253 |   260 | LOCATION    |      0.9447  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_enriched_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_enriched_biobert_pipeline_en.md
index d7b86cb4c0..be84c067a3 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_enriched_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_enriched_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_enriched_biobert](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.deid.ner_enriched_biobert.pipeline").predict("""A. Record date : 20
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_enriched_biobert_pipeline", "en", "clinical/models")
-
-text = '''A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_enriched_biobert_pipeline", "en", "clinical/models")
-
-val text = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.ner_enriched_biobert.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label    |   confidence |
 |---:|:------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 2093-01-13                    |      17 |    26 | DATE         |     0.9267   |
@@ -106,9 +76,6 @@ Results
 |  5 | 0295 Keats Street             |     145 |   161 | STREET       |     0.592433 |
 |  6 | 302) 786-5227                 |     174 |   186 | PHONE        |     0.846833 |
 |  7 | Brothers Coal-Mine            |     253 |   270 | ORGANIZATION |     0.45085  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_enriched_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_enriched_pipeline_en.md
index c463ea18ba..e2c8600755 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_enriched_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_enriched_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_enriched](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.deid_enriched.pipeline").predict("""HISTORY OF PRESENT ILLN
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_enriched_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_enriched_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_enriched.pipeline").predict("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | Smith         |      32 |    36 | PATIENT     |      0.9997  |
@@ -108,9 +80,6 @@ Results
 |  7 | Hart          |    1221 |  1224 | DOCTOR      |      0.9985  |
 |  8 | Smith         |    1231 |  1235 | PATIENT     |      0.9992  |
 |  9 | 02/07/2003    |    1329 |  1338 | DATE        |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_augmented_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_augmented_pipeline_en.md
index b038c30fa2..686001f20e 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_augmented_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_augmented](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -56,59 +57,11 @@ nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-```scala
-val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
 
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-```scala
-val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +-------------------------------------------------+---------+
 |chunk                                            |ner_label|
 +-------------------------------------------------+---------+
@@ -124,12 +77,6 @@ Results
 |Cocke County Baptist Hospital. 0295 Keats Street.|LOCATION |
 |(302) 786-5227                                   |CONTACT  |
 +-------------------------------------------------+---------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_bert_pipeline_ro.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_bert_pipeline_ro.md
index fb9f271881..fabfb1b8e9 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_bert_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_bert_pipeline_ro.md
@@ -32,72 +32,11 @@ This pretrained pipeline is built on the top of [ner_deid_generic_bert](https://
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -147,12 +86,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | LOCATION    |     0.99352  |
@@ -167,12 +100,6 @@ Results
 |  9 | Agota Evelyn Tımar           |     191 |   210 | NAME        |     0.859975 |
 |    | C                            |         |       |             |              |
 | 10 | 2450502264401                |     218 |   230 | ID          |     1        |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_pipeline_de.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_pipeline_de.md
index ecdcc30c57..8afee1e0bd 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_pipeline_de.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_pipeline_de.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("de.med_ner.deid_generic.pipeline").predict("""Michael Berger wird am M
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "de", "clinical/models")
-
-text = '''Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "de", "clinical/models")
-
-val text = "Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.med_ner.deid_generic.pipeline").predict("""Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | Michael Berger            |       0 |    13 | NAME        |     0.99555  |
@@ -104,9 +74,6 @@ Results
 |  3 | Bad Kissingen             |      84 |    96 | LOCATION    |     0.90785  |
 |  4 | Berger                    |     117 |   122 | NAME        |     0.935    |
 |  5 | 76                        |     128 |   129 | AGE         |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_pipeline_ro.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_pipeline_ro.md
index dd19adeaae..b24428b399 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_generic_pipeline_ro.md
@@ -34,70 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -147,12 +84,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | LOCATION    |     0.88326  |
@@ -164,12 +95,6 @@ Results
 |  6 | 77                           |     179 |   180 | AGE         |     1        |
 |  7 | Agota Evelyn Tımar           |     190 |   207 | NAME        |     0.832933 |
 |  8 | 2450502264401                |     217 |   229 | ID          |     1        |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_large_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_large_pipeline_en.md
index a4ed4bbc13..e75aebcfa7 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_large](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.deid_large.pipeline").predict("""HISTORY OF PRESENT ILLNESS
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_large_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_large_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_large.pipeline").predict("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | Smith           |      32 |    36 | NAME        |     0.9998   |
@@ -110,9 +80,6 @@ Results
 |  9 | Hart            |    1221 |  1224 | NAME        |     0.9995   |
 | 10 | Smith           |    1231 |  1235 | NAME        |     0.9998   |
 | 11 | 02/07/2003      |    1329 |  1338 | DATE        |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_sd_large_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_sd_large_pipeline_en.md
index 3e3d33d6fa..90e364e697 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_sd_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_sd_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_sd_large](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.deid.med_ner_large.pipeline").predict("""Record date : 2093-01-13 ,
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_sd_large_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_sd_large_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.med_ner_large.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     0.9999   |
@@ -109,9 +79,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.795975 |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.741567 |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.984    |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_sd_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_sd_pipeline_en.md
index 55f0d458fe..b671ed316b 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_sd_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_sd_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_sd](https://nlp.johnsn
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.sd.pipeline").predict("""Record date : 2093-01-13 , David Hale
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_sd_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_sd_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.sd.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     0.9952   |
@@ -109,9 +81,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.84345  |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.775333 |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9492   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_augmented_i2b2_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_augmented_i2b2_pipeline_en.md
index 4107e28f65..3c5074ccaa 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_augmented_i2b2_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_augmented_i2b2_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_augmented_i2
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.deid.subentity_ner_augmented_i2b2.pipeline").predict("""Record date
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_augmented_i2b2_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_augmented_i2b2_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.subentity_ner_augmented_i2b2.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |     0.9997   |
@@ -109,9 +80,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |     0.863775 |
 |  9 | 0295 Keats Street             |     195 |   211 | STREET        |     0.754533 |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |     0.9697   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_augmented_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_augmented_pipeline_en.md
index b221a046ed..27ff595cec 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_augmented_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_augmented](h
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.subentity_ner_augmented.pipeline").predict("""Record date : 20
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_augmented_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_augmented_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.subentity_ner_augmented.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |      1       |
@@ -109,9 +81,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |      0.97485 |
 |  9 | 0295 Keats Street             |     195 |   211 | STREET        |      0.8209  |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |      0.9541  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_bert_pipeline_ro.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_bert_pipeline_ro.md
index 737c2a5183..74fed7e7c9 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_bert_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_bert_pipeline_ro.md
@@ -32,72 +32,11 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_bert](https:
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -147,12 +86,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | HOSPITAL    |     0.84306  |
@@ -165,12 +98,6 @@ Results
 |  7 | 77                           |     180 |   181 | AGE         |     1        |
 |  8 | Agota Evelyn Tımar           |     191 |   208 | DOCTOR      |     0.803667 |
 |  9 | 2450502264401                |     218 |   230 | IDNUM       |     0.9995   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_pipeline_de.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_pipeline_de.md
index 45727ea46c..891b2981cb 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_pipeline_de.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_pipeline_de.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("de.deid.ner_subentity.pipeline").predict("""Michael Berger wird am Mor
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "de", "clinical/models")
-
-text = '''Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "de", "clinical/models")
-
-val text = "Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.ner_subentity.pipeline").predict("""Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Michael Berger        |       0 |    13 | PATIENT     |      0.99685 |
@@ -104,9 +75,6 @@ Results
 |  3 | Bad Kissingen         |      84 |    96 | CITY        |      0.69685 |
 |  4 | Berger                |     117 |   122 | PATIENT     |      0.5764  |
 |  5 | 76                    |     128 |   129 | AGE         |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_pipeline_ro.md b/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_pipeline_ro.md
index bb6b85bad5..d0ac9c8fbc 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deid_subentity_pipeline_ro.md
@@ -32,72 +32,11 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -147,12 +86,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | HOSPITAL    |      0.5594  |
@@ -165,12 +98,6 @@ Results
 |  7 | 77                           |     180 |   181 | AGE         |      1       |
 |  8 | Agota Evelyn Tımar           |     191 |   208 | DOCTOR      |      0.8149  |
 |  9 | 2450502264401                |     218 |   230 | IDNUM       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_deidentify_dl_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_deidentify_dl_pipeline_en.md
index f3d5051583..e25672f50e 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_deidentify_dl_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_deidentify_dl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deidentify_dl](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.deidentify.pipeline").predict("""Record date : 2093-01-13 ,
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deidentify_dl_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deidentify_dl_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deidentify.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |      0.9999  |
@@ -109,9 +80,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |      0.9466  |
 |  9 | Keats Street                  |     200 |   211 | STREET        |      0.91485 |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |      0.7415  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_diseases_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_diseases_biobert_pipeline_en.md
index 45e5e019d1..a239a0bd4c 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_diseases_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_diseases_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_diseases_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.diseases_biobert.pipeline").predict("""Indomethacin resulte
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diseases_biobert_pipeline", "en", "clinical/models")
-
-text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diseases_biobert_pipeline", "en", "clinical/models")
-
-val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.diseases_biobert.pipeline").predict("""Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | interstitial cystitis |      61 |    81 | Disease     |      0.99655 |
 |  1 | mastocytosis          |     129 |   140 | Disease     |      0.8569  |
 |  2 | cystitis              |     209 |   216 | Disease     |      0.9717  |
 |  3 | prostate cancer       |     355 |   369 | Disease     |      0.85965 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_diseases_large_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_diseases_large_pipeline_en.md
index 90e195e7e4..fad8350dce 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_diseases_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_diseases_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_diseases_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.diseases_large.pipeline").predict("""Detection of various o
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diseases_large_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diseases_large_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.diseases_large.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | T-cell leukemia |     136 |   150 | Disease     |      0.93585 |
 |  1 | T-cell leukemia |     402 |   416 | Disease     |      0.9567  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_diseases_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_diseases_pipeline_en.md
index a06e121cd0..cd2bc92e56 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_diseases_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_diseases_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_diseases](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.diseases.pipeline").predict("""Detection of various other i
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diseases_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diseases_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.diseases.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | T-cell leukemia |     136 |   150 | Disease     |      0.92015 |
 |  1 | T-cell leukemia |     402 |   416 | Disease     |      0.94145 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_drugprot_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_drugprot_clinical_pipeline_en.md
index 98acdc4b00..fe554adaa4 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_drugprot_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_drugprot_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugprot_clinical](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.clinical_drugprot.pipeline").predict("""Anabolic effects of
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugprot_clinical_pipeline", "en", "clinical/models")
-
-text = '''Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugprot_clinical_pipeline", "en", "clinical/models")
-
-val text = "Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_drugprot.pipeline").predict("""Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | clenbuterol         |      20 |    30 | CHEMICAL    |      0.9691  |
 |  1 | beta 2-adrenoceptor |      67 |    85 | GENE        |      0.89855 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_drugs_greedy_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_drugs_greedy_pipeline_en.md
index e163341e87..7b44703cb9 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_drugs_greedy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_drugs_greedy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugs_greedy](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.drugs_greedy.pipeline").predict("""DOSAGE AND ADMINISTRATIO
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugs_greedy_pipeline", "en", "clinical/models")
-
-text = '''DOSAGE AND ADMINISTRATION The initial dosage of hydrocortisone tablets may vary from 20 mg to 240 mg of hydrocortisone per day depending on the specific disease entity being treated.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugs_greedy_pipeline", "en", "clinical/models")
-
-val text = "DOSAGE AND ADMINISTRATION The initial dosage of hydrocortisone tablets may vary from 20 mg to 240 mg of hydrocortisone per day depending on the specific disease entity being treated."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.drugs_greedy.pipeline").predict("""DOSAGE AND ADMINISTRATION The initial dosage of hydrocortisone tablets may vary from 20 mg to 240 mg of hydrocortisone per day depending on the specific disease entity being treated.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                         |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------------|--------:|------:|:------------|-------------:|
 |  0 | hydrocortisone tablets            |      48 |    69 | DRUG        |       0.9923 |
 |  1 | 20 mg to 240 mg of hydrocortisone |      85 |   117 | DRUG        |       0.7361 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_drugs_large_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_drugs_large_pipeline_en.md
index 43c73fd302..7aa2b2a6b8 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_drugs_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_drugs_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugs_large](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.drugs_large.pipeline").predict("""The patient is a 40-year-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugs_large_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. He has been advised Aspirin 81 milligrams QDay. Humulin N. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugs_large_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. He has been advised Aspirin 81 milligrams QDay. Humulin N. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain.."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.drugs_large.pipeline").predict("""The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. He has been advised Aspirin 81 milligrams QDay. Humulin N. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain..""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                        |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Aspirin 81 milligrams            |     306 |   326 | DRUG        |     0.8401   |
@@ -103,9 +74,6 @@ Results
 |  2 | insulin 50 units                 |     345 |   360 | DRUG        |     0.847067 |
 |  3 | HCTZ 50 mg                       |     370 |   379 | DRUG        |     0.875567 |
 |  4 | Nitroglycerin 1/150 sublingually |     387 |   418 | DRUG        |     0.845967 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_drugs_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_drugs_pipeline_en.md
index 805a2adfcf..9f1958eac6 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_drugs_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_drugs_pipeline_en.md
@@ -32,38 +32,10 @@ This pretrained pipeline is built on the top of [ner_drugs](https://nlp.johnsnow
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugs_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugs_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.drugs.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.""")
-```
-
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -93,9 +65,6 @@ nlu.load("en.med_ner.drugs.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | potassium      |      92 |   100 | DrugChem    |       0.5346 |
@@ -105,9 +74,6 @@ Results
 |  4 | vinorelbine    |    1343 |  1353 | DrugChem    |       0.9815 |
 |  5 | anthracyclines |    1390 |  1403 | DrugChem    |       0.9447 |
 |  6 | taxanes        |    1409 |  1415 | DrugChem    |       0.6213 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_en.md
index 480f5f33cb..e6979fa542 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_en.md
@@ -32,60 +32,10 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-val text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-val text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +76,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                         |   begin |   end | entities           |   confidence |
 |---:|:-------------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | A 3-year-old boy               |       1 |    16 | patient            |     0.733133 |
@@ -162,12 +106,6 @@ Results
 | 25 | revealed                       |     628 |   635 | clinical_event     |     0.9989   |
 | 26 | spindle cell proliferation     |     637 |   662 | clinical_condition |     0.4487   |
 | 27 | the submucosal layer           |     667 |   686 | bodypart           |     0.523    |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_es.md b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_es.md
index 2a128f5643..14aa6ab459 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_es.md
@@ -32,60 +32,10 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-val text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-val text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +76,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                       |   begin |   end | entities           |   confidence |
 |---:|:-----------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Un niño de 3 años            |       1 |    17 | patient            |     0.68856  |
@@ -170,12 +114,6 @@ Results
 | 33 | proliferación                |     711 |   723 | clinical_event     |     0.9996   |
 | 34 | células fusiformes           |     728 |   745 | bodypart           |     0.7001   |
 | 35 | la capa submucosa            |     750 |   766 | bodypart           |     0.641267 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_eu.md b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_eu.md
index 9dc2760333..647d909cf3 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_eu.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_eu.md
@@ -32,60 +32,11 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-val text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-val text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +77,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                   |   begin |   end | entities           |   confidence |
 |---:|:-------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | 3 urteko mutiko bat      |       1 |    19 | patient            |     0.813975 |
@@ -175,12 +120,6 @@ Results
 | 38 | utzi                     |     701 |   704 | clinical_event     |     0.925    |
 | 39 | mukosaren azpiko zelulen |     711 |   734 | bodypart           |     0.754933 |
 | 40 | ugaltzea                 |     736 |   743 | clinical_event     |     0.9989   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_fr.md b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_fr.md
index 22f80405fd..285430151a 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_fr.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_case_pipeline_fr.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-val text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-val text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                                                |   begin |   end | entities           |   confidence |
 |---:|:------------------------------------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Un garçon de 3 ans                                    |       1 |    18 | patient            |     0.58786  |
@@ -166,12 +109,6 @@ Results
 | 29 | prolifération                                         |     735 |   747 | clinical_event     |     0.6767   |
 | 30 | cellules fusiformes                                   |     752 |   770 | bodypart           |     0.5233   |
 | 31 | la couche sous-muqueuse                               |     777 |   799 | bodypart           |     0.6755   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_en.md
index 4c72182703..465594e972 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_en.md
@@ -34,32 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "en", "clinical/models")
-
-text = "
-Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "en", "clinical/models")
 
-val text = "
-Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -87,9 +62,6 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                  |   begin |   end | entities           |   confidence |
 |---:|:------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Hyperparathyroidism     |       1 |    19 | clinical_condition |     0.9375   |
@@ -100,9 +72,6 @@ Results
 |  5 | fractures               |     281 |   289 | clinical_condition |     0.9726   |
 |  6 | anesthesia              |     305 |   314 | clinical_condition |     0.991    |
 |  7 | mandibular fracture     |     330 |   348 | clinical_condition |     0.54925  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_es.md b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_es.md
index 02345a73df..7c26d064cc 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_es.md
@@ -32,60 +32,11 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-val text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-val text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +77,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks               |   begin |   end | entities           |   confidence |
 |---:|:---------------------|--------:|------:|:-------------------|-------------:|
 |  0 | cicatriz             |      37 |    44 | clinical_condition |      0.9883  |
@@ -139,12 +84,6 @@ Results
 |  2 | signos               |     170 |   175 | clinical_condition |      0.9862  |
 |  3 | irritación           |     180 |   189 | clinical_condition |      0.9975  |
 |  4 | hernias inguinales   |     214 |   231 | clinical_condition |      0.7543  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_eu.md b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_eu.md
index 9404b937c1..e628187e7f 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_eu.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_eu.md
@@ -32,60 +32,11 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-val text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-val text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +77,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks     |   begin |   end | entities           |   confidence |
 |---:|:-----------|--------:|------:|:-------------------|-------------:|
 |  0 | mina       |      98 |   101 | clinical_condition |       0.8754 |
@@ -141,12 +86,6 @@ Results
 |  4 | hantura    |     203 |   209 | clinical_condition |       0.8805 |
 |  5 | Polakiuria |     256 |   265 | clinical_condition |       0.6683 |
 |  6 | sintomarik |     345 |   354 | clinical_condition |       0.9632 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_fr.md b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_fr.md
index 0a642f5c88..d35490c409 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_fr.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_fr.md
@@ -32,64 +32,11 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-val text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-val text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,12 +80,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                   |   begin |   end | entities           |   confidence |
 |---:|:-------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | ulcérations              |      47 |    57 | clinical_condition |       0.9995 |
@@ -148,12 +89,6 @@ Results
 |  4 | apyrexie                 |     261 |   268 | clinical_condition |       0.9963 |
 |  5 | anasarque                |     353 |   361 | clinical_condition |       0.9973 |
 |  6 | décompensation cardiaque |     409 |   432 | clinical_condition |       0.8948 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_it.md b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_it.md
index 568cec33ff..beff005082 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_eu_clinical_condition_pipeline_it.md
@@ -32,64 +32,10 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-val text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-val text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,12 +79,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                 |   begin |   end | entities           |   confidence |
 |---:|:-----------------------|--------:|------:|:-------------------|-------------:|
 |  0 | dolore epigastrico     |      30 |    47 | clinical_condition |      0.90845 |
@@ -147,12 +87,6 @@ Results
 |  3 | edema                  |     188 |   192 | clinical_condition |      1       |
 |  4 | fistola transfinterica |     294 |   315 | clinical_condition |      0.97785 |
 |  5 | infiammazione          |     372 |   384 | clinical_condition |      0.9996  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_events_admission_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_events_admission_clinical_pipeline_en.md
index 9e07110712..e0b2cfa7b9 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_events_admission_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_events_admission_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_admission_clinical](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,15 @@ nlu.load("en.med_ner.events_admission_clinical.pipeline").predict("""The patient
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_admission_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_admission_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.events_admission_clinical.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk          |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | OCCURRENCE    |       0.6219 |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |       0.812  |
 |  2 | last evening       |      44 |    55 | TIME          |       0.9534 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_events_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_events_biobert_pipeline_en.md
index 28ed10414b..bbb2ca118d 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_events_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_events_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_biobert](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,15 @@ nlu.load("en.med_ner.biobert_events.pipeline").predict("""The patient presented
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biobert_events.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk          |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | OCCURRENCE    |     0.5019   |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |     0.695333 |
 |  2 | last evening       |      44 |    55 | DATE          |     0.7621   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_events_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_events_clinical_pipeline_en.md
index 461b335920..c16534a03a 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_events_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_events_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_clinical](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,15 @@ nlu.load("en.med_ner.events_clinical.pipeline").predict("""The patient presented
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.events_clinical.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk          |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | OCCURRENCE    |     0.7132   |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |     0.723267 |
 |  2 | last evening       |      44 |    55 | DATE          |     0.90555  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_events_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_events_healthcare_pipeline_en.md
index 493d9b8ac2..74efb946b9 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_events_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_events_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_healthcare](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,15 @@ nlu.load("en.med_ner.healthcare_events.pipeline").predict("""The patient present
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_healthcare_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_healthcare_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.healthcare_events.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks         |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | EVIDENTIAL    |     0.6769   |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |     0.835967 |
 |  2 | last evening       |      44 |    55 | DATE          |     0.59135  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_genetic_variants_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_genetic_variants_pipeline_en.md
index 0a34ae6341..436bc1c995 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_genetic_variants_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_genetic_variants_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_genetic_variants](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.genetic_variants.pipeline").predict("""The mutation pattern
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_genetic_variants_pipeline", "en", "clinical/models")
-
-text = '''The mutation pattern of mitochondrial DNA (mtDNA) in mainland Chinese patients with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) has been rarely reported, though previous data suggested that the mutation pattern of MELAS could be different among geographically localized populations. We presented the results of comprehensive mtDNA mutation analysis in 92 unrelated Chinese patients with MELAS (85 with classic MELAS and 7 with MELAS/Leigh syndrome (LS) overlap syndrome). The mtDNA A3243G mutation was the most common causal genotype in this patient group (79/92 and 85.9%). The second common gene mutation was G13513A (7/92 and 7.6%). Additionally, we identified T10191C (p.S45P) in ND3, A11470C (p. K237N) in ND4, T13046C (p.M237T) in ND5 and a large-scale deletion (13025-13033:14417-14425) involving partial ND5 and ND6 subunits of complex I in one patient each. Among them, A11470C, T13046C and the single deletion were novel mutations. In summary, patients with mutations affecting mitochondrially encoded complex I (MTND) reached 12.0% (11/92) in this group. It is noteworthy that all seven patients with MELAS/LS overlap syndrome were associated with MTND mutations. Our data emphasize the important role of MTND mutations in the pathogenicity of MELAS, especially MELAS/LS overlap syndrome.PURPOSE: Genes in the complement pathway, including complement factor H (CFH), C2/BF, and C3, have been reported to be associated with age-related macular degeneration (AMD). Genetic variants, single-nucleotide polymorphisms (SNPs), in these genes were geno-typed for a case-control association study in a mainland Han Chinese population. METHODS: One hundred and fifty-eight patients with wet AMD, 80 patients with soft drusen, and 220 matched control subjects were recruited among Han Chinese in mainland China. Seven SNPs in CFH and two SNPs in C2, CFB', and C3 were genotyped using the ABI SNaPshot method. A deletion of 84,682 base pairs covering the CFHR1 and CFHR3 genes was detected by direct polymerase chain reaction and gel electrophoresis. RESULTS: Four SNPs, including rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), in CFH showed a significant association with wet AMD in the cohort of this study. A haplotype containing these four SNPs (CATA) significantly increased protection of wet AMD with a P value of 0.0005 and an odds ratio of 0.29 (95% confidence interval: 0.15-0.60). Unlike in other populations, rs2274700 and rs1410996 did not show a significant association with AMD in the Chinese population of this study. None of the SNPs in CFH showed a significant association with drusen, and none of the SNPs in CFH, C2, CFB, and C3 showed a significant association with either wet AMD or drusen in the cohort of this study. The CFHR1 and CFHR3 deletion was not polymorphic in the Chinese population and was not associated with wet AMD or drusen. CONCLUSION: This study showed that SNPs rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), but not rs7535263, rs1410996, or rs2274700, in CFH were significantly associated with wet AMD in a mainland Han Chinese population. This study showed that CFH was more likely to be AMD susceptibility gene at Chr.1q31 based on the finding that the CFHR1 and CFHR3 deletion was not polymorphic in the cohort of this study, and none of the SNPs that were significantly associated with AMD in a white population in C2, CFB, and C3 genes showed a significant association with AMD.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_genetic_variants_pipeline", "en", "clinical/models")
-
-val text = "The mutation pattern of mitochondrial DNA (mtDNA) in mainland Chinese patients with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) has been rarely reported, though previous data suggested that the mutation pattern of MELAS could be different among geographically localized populations. We presented the results of comprehensive mtDNA mutation analysis in 92 unrelated Chinese patients with MELAS (85 with classic MELAS and 7 with MELAS/Leigh syndrome (LS) overlap syndrome). The mtDNA A3243G mutation was the most common causal genotype in this patient group (79/92 and 85.9%). The second common gene mutation was G13513A (7/92 and 7.6%). Additionally, we identified T10191C (p.S45P) in ND3, A11470C (p. K237N) in ND4, T13046C (p.M237T) in ND5 and a large-scale deletion (13025-13033:14417-14425) involving partial ND5 and ND6 subunits of complex I in one patient each. Among them, A11470C, T13046C and the single deletion were novel mutations. In summary, patients with mutations affecting mitochondrially encoded complex I (MTND) reached 12.0% (11/92) in this group. It is noteworthy that all seven patients with MELAS/LS overlap syndrome were associated with MTND mutations. Our data emphasize the important role of MTND mutations in the pathogenicity of MELAS, especially MELAS/LS overlap syndrome.PURPOSE: Genes in the complement pathway, including complement factor H (CFH), C2/BF, and C3, have been reported to be associated with age-related macular degeneration (AMD). Genetic variants, single-nucleotide polymorphisms (SNPs), in these genes were geno-typed for a case-control association study in a mainland Han Chinese population. METHODS: One hundred and fifty-eight patients with wet AMD, 80 patients with soft drusen, and 220 matched control subjects were recruited among Han Chinese in mainland China. Seven SNPs in CFH and two SNPs in C2, CFB', and C3 were genotyped using the ABI SNaPshot method. A deletion of 84,682 base pairs covering the CFHR1 and CFHR3 genes was detected by direct polymerase chain reaction and gel electrophoresis. RESULTS: Four SNPs, including rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), in CFH showed a significant association with wet AMD in the cohort of this study. A haplotype containing these four SNPs (CATA) significantly increased protection of wet AMD with a P value of 0.0005 and an odds ratio of 0.29 (95% confidence interval: 0.15-0.60). Unlike in other populations, rs2274700 and rs1410996 did not show a significant association with AMD in the Chinese population of this study. None of the SNPs in CFH showed a significant association with drusen, and none of the SNPs in CFH, C2, CFB, and C3 showed a significant association with either wet AMD or drusen in the cohort of this study. The CFHR1 and CFHR3 deletion was not polymorphic in the Chinese population and was not associated with wet AMD or drusen. CONCLUSION: This study showed that SNPs rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), but not rs7535263, rs1410996, or rs2274700, in CFH were significantly associated with wet AMD in a mainland Han Chinese population. This study showed that CFH was more likely to be AMD susceptibility gene at Chr.1q31 based on the finding that the CFHR1 and CFHR3 deletion was not polymorphic in the cohort of this study, and none of the SNPs that were significantly associated with AMD in a white population in C2, CFB, and C3 genes showed a significant association with AMD."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.genetic_variants.pipeline").predict("""The mutation pattern of mitochondrial DNA (mtDNA) in mainland Chinese patients with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) has been rarely reported, though previous data suggested that the mutation pattern of MELAS could be different among geographically localized populations. We presented the results of comprehensive mtDNA mutation analysis in 92 unrelated Chinese patients with MELAS (85 with classic MELAS and 7 with MELAS/Leigh syndrome (LS) overlap syndrome). The mtDNA A3243G mutation was the most common causal genotype in this patient group (79/92 and 85.9%). The second common gene mutation was G13513A (7/92 and 7.6%). Additionally, we identified T10191C (p.S45P) in ND3, A11470C (p. K237N) in ND4, T13046C (p.M237T) in ND5 and a large-scale deletion (13025-13033:14417-14425) involving partial ND5 and ND6 subunits of complex I in one patient each. Among them, A11470C, T13046C and the single deletion were novel mutations. In summary, patients with mutations affecting mitochondrially encoded complex I (MTND) reached 12.0% (11/92) in this group. It is noteworthy that all seven patients with MELAS/LS overlap syndrome were associated with MTND mutations. Our data emphasize the important role of MTND mutations in the pathogenicity of MELAS, especially MELAS/LS overlap syndrome.PURPOSE: Genes in the complement pathway, including complement factor H (CFH), C2/BF, and C3, have been reported to be associated with age-related macular degeneration (AMD). Genetic variants, single-nucleotide polymorphisms (SNPs), in these genes were geno-typed for a case-control association study in a mainland Han Chinese population. METHODS: One hundred and fifty-eight patients with wet AMD, 80 patients with soft drusen, and 220 matched control subjects were recruited among Han Chinese in mainland China. Seven SNPs in CFH and two SNPs in C2, CFB', and C3 were genotyped using the ABI SNaPshot method. A deletion of 84,682 base pairs covering the CFHR1 and CFHR3 genes was detected by direct polymerase chain reaction and gel electrophoresis. RESULTS: Four SNPs, including rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), in CFH showed a significant association with wet AMD in the cohort of this study. A haplotype containing these four SNPs (CATA) significantly increased protection of wet AMD with a P value of 0.0005 and an odds ratio of 0.29 (95% confidence interval: 0.15-0.60). Unlike in other populations, rs2274700 and rs1410996 did not show a significant association with AMD in the Chinese population of this study. None of the SNPs in CFH showed a significant association with drusen, and none of the SNPs in CFH, C2, CFB, and C3 showed a significant association with either wet AMD or drusen in the cohort of this study. The CFHR1 and CFHR3 deletion was not polymorphic in the Chinese population and was not associated with wet AMD or drusen. CONCLUSION: This study showed that SNPs rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), but not rs7535263, rs1410996, or rs2274700, in CFH were significantly associated with wet AMD in a mainland Han Chinese population. This study showed that CFH was more likely to be AMD susceptibility gene at Chr.1q31 based on the finding that the CFHR1 and CFHR3 deletion was not polymorphic in the cohort of this study, and none of the SNPs that were significantly associated with AMD in a white population in C2, CFB, and C3 genes showed a significant association with AMD.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label       |   confidence |
 |---:|:-------------|--------:|------:|:----------------|-------------:|
 |  0 | A3243G       |     527 |   532 | DNAMutation     |       1      |
@@ -121,9 +93,6 @@ Results
 | 20 | rs7535263    |    3108 |  3116 | SNP             |       1      |
 | 21 | rs1410996    |    3119 |  3127 | SNP             |       1      |
 | 22 | rs2274700    |    3133 |  3141 | SNP             |       1      |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_healthcare_pipeline_en.md
index ea05d0bdf0..a3cc72951a 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_healthcare](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.healthcare_pipeline").predict("""A 28-year-old female with
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_pipeline", "en", "clinical/models")
-
-text = '''A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "en", "clinical/models")
-
-val text = "A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG ."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.healthcare_pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG .""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | gestational diabetes mellitus |      39 |    67 | PROBLEM     |     0.938233 |
@@ -118,9 +90,6 @@ Results
 | 17 | atorvastatin                  |     625 |   636 | TREATMENT   |     0.9993   |
 | 18 | gemfibrozil                   |     642 |   652 | TREATMENT   |     0.9997   |
 | 19 | HTG                           |     658 |   660 | PROBLEM     |     0.9927   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_gene_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_gene_biobert_pipeline_en.md
index 054a1d36b1..8f92c00c19 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_gene_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_gene_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_gene_biober
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.human_phenotype_gene_biobert.pipeline").predict("""Here we
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_gene_biobert_pipeline", "en", "clinical/models")
-
-text = '''Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_gene_biobert_pipeline", "en", "clinical/models")
-
-val text = "Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3)."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.human_phenotype_gene_biobert.pipeline").predict("""Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | type             |      29 |    32 | GENE        |       0.9977 |
@@ -103,9 +75,6 @@ Results
 |  2 | polyuria         |      91 |    98 | HP          |       0.9955 |
 |  3 | nephrocalcinosis |     101 |   116 | HP          |       0.995  |
 |  4 | hypokalemia      |     122 |   132 | HP          |       0.9986 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_gene_clinical_pipeline_en.md
index a4f4943980..8ae6bab3ed 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_gene_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_gene_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_gene_clinic
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.human_phnotype_gene_clinical.pipeline").predict("""Here we
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-text = '''Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-val text = "Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3)."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.human_phnotype_gene_clinical.pipeline").predict("""Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | type             |      29 |    32 | GENE        |       0.9837 |
@@ -103,9 +74,6 @@ Results
 |  2 | polyuria         |      91 |    98 | HP          |       0.9964 |
 |  3 | nephrocalcinosis |     101 |   116 | HP          |       0.9979 |
 |  4 | hypokalemia      |     122 |   132 | HP          |       0.9952 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_go_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_go_biobert_pipeline_en.md
index 4345a8b77f..8246908590 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_go_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_go_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_go_biobert]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,15 @@ nlu.load("en.med_ner.phenotype_go_biobert.pipeline").predict("""Another disease
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_go_biobert_pipeline", "en", "clinical/models")
-
-text = '''Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_go_biobert_pipeline", "en", "clinical/models")
-
-val text = "Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.phenotype_go_biobert.pipeline").predict("""Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------|--------:|------:|:------------|-------------:|
 |  0 | tumor                    |      39 |    43 | HP          |     1        |
 |  1 | tricarboxylic acid cycle |      79 |   102 | GO          |     0.999867 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_go_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_go_clinical_pipeline_en.md
index f6d0228157..a01659ca80 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_go_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_human_phenotype_go_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_go_clinical
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,15 @@ nlu.load("en.med_ner.human_phenotype_clinical.pipeline").predict("""Another dise
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_go_clinical_pipeline", "en", "clinical/models")
-
-text = '''Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_go_clinical_pipeline", "en", "clinical/models")
-
-val text = "Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.human_phenotype_clinical.pipeline").predict("""Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------|--------:|------:|:------------|-------------:|
 |  0 | tumor                    |      39 |    43 | HP          |     0.9996   |
 |  1 | tricarboxylic acid cycle |      79 |   102 | GO          |     0.994633 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_jsl_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_jsl_biobert_pipeline_en.md
index 5bea5925ee..dba6fd7de6 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_jsl_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_jsl_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_biobert](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl_biobert.pipeline").predict("""The patient is a 21-day-o
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                 |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     1        |
@@ -123,9 +95,6 @@ Results
 | 22 | his                                       |     488 |   490 | Gender                       |     0.9573   |
 | 23 | respiratory congestion                    |     492 |   513 | Symptom                      |     0.5144   |
 | 24 | He                                        |     516 |   517 | Gender                       |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_jsl_enriched_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_jsl_enriched_biobert_pipeline_en.md
index 2aee0bafeb..d6ee407532 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_jsl_enriched_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_jsl_enriched_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_enriched_biobert](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.jsl_enriched_biobert.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_enriched_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_enriched_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_enriched_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                   |   begin |   end | ner_label    |   confidence |
 |---:|:----------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 21-day-old                  |      17 |    26 | Age          |     1        |
@@ -123,9 +94,6 @@ Results
 | 22 | denies                      |     825 |   830 | Negation     |     0.9841   |
 | 23 | diarrhea                    |     836 |   843 | Symptom_Name |     0.6033   |
 | 24 | His                         |     846 |   848 | Gender       |     0.8459   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_jsl_enriched_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_jsl_enriched_pipeline_en.md
index 061553d103..e01b00cb9b 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_jsl_enriched_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_jsl_enriched_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_enriched](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl_enriched.pipeline").predict("""The patient is a 21-day-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_enriched_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_enriched_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_enriched.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     0.9993   |
@@ -137,9 +109,6 @@ Results
 | 36 | diarrhea                                  |     836 |   843 | Symptom                      |     0.9995   |
 | 37 | His                                       |     846 |   848 | Gender                       |     0.9998   |
 | 38 | bowel                                     |     850 |   854 | Internal_organ_or_component  |     0.9675   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_jsl_greedy_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_jsl_greedy_biobert_pipeline_en.md
index 627e83c76a..1eebf49efe 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_jsl_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_jsl_greedy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_greedy_biobert](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.biobert_jsl_greedy.pipeline").predict("""The patient is a 2
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_greedy_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_greedy_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biobert_jsl_greedy.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                      |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     1        |
@@ -123,9 +95,6 @@ Results
 | 22 | He                                             |     516 |   517 | Gender                       |     0.9998   |
 | 23 | tired                                          |     550 |   554 | Symptom                      |     0.8912   |
 | 24 | fussy                                          |     569 |   573 | Symptom                      |     0.9541   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_jsl_greedy_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_jsl_greedy_pipeline_en.md
index 7468da5a9b..341b1bc2ed 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_jsl_greedy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_jsl_greedy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_greedy](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.jsl_greedy.pipeline").predict("""The patient is a 21-day-ol
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_greedy_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_greedy_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_greedy.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                     |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     0.9817   |
@@ -123,9 +93,6 @@ Results
 | 22 | his                                            |     488 |   490 | Gender                       |     0.9904   |
 | 23 | respiratory congestion                         |     492 |   513 | Symptom                      |     0.5294   |
 | 24 | He                                             |     516 |   517 | Gender                       |     0.9989   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_jsl_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_jsl_pipeline_en.md
index d6f3897637..01529240fb 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_jsl_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl](https://nlp.johnsnowla
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.jsl.pipeline").predict("""The patient is a 21-day-old Cauca
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     0.997    |
@@ -139,9 +109,6 @@ Results
 | 38 | diarrhea                                  |     908 |   915 | Symptom                      |     0.9956   |
 | 39 | His                                       |     918 |   920 | Gender                       |     0.9997   |
 | 40 | bowel                                     |     922 |   926 | Internal_organ_or_component  |     0.9218   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_jsl_slim_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_jsl_slim_pipeline_en.md
index f9fd4f98f9..c5b77888eb 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_jsl_slim_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_jsl_slim_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_slim](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -65,40 +66,11 @@ nlu.load("en.med_ner.jsl_slim.pipeline").predict("""Hyperparathyroidism was cons
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_slim_pipeline", "en", "clinical/models")
-
-text = "Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture."
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_slim_pipeline", "en", "clinical/models")
-
-val text = "Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_slim.pipeline").predict("""Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                               |   begin |   end | entities                  |   confidence |
 |---:|:-------------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Hyperparathyroidism                  |       0 |    18 | Disease_Syndrome_Disorder |     0.9977   |
@@ -112,9 +84,6 @@ Results
 |  8 | fractures under general anesthesia   |     280 |   313 | Drug                      |     0.79585  |
 |  9 | He                                   |     316 |   317 | Demographics              |     0.9992   |
 | 10 | sustained mandibular fracture        |     319 |   347 | Disease_Syndrome_Disorder |     0.662467 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_living_species_300_pipeline_es.md b/docs/_posts/Cabir40/2023-06-16-ner_living_species_300_pipeline_es.md
index ea6ce5b32c..42356acf2e 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_living_species_300_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_living_species_300_pipeline_es.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_living_species_300](https:/
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.92045 |
@@ -131,12 +84,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      0.9963  |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_measurements_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_measurements_clinical_pipeline_en.md
index 10f9f293d9..4b11ad2688 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_measurements_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_measurements_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_measurements_clinical](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,13 @@ nlu.load("en.med_ner.clinical_measurements.pipeline").predict("""Bilateral breas
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_measurements_clinical_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_measurements_clinical_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_measurements.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label    |   confidence |
 |---:|:----------------|--------:|------:|:-------------|-------------:|
 |  0 | 0.5 x 0.5 x 0.4 |     113 |   127 | Measurements |      0.98748 |
 |  1 | cm              |     129 |   130 | Units        |      0.9996  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_medication_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_medication_pipeline_en.md
index fc4163ec71..c9a40e321d 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_medication_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_medication_pipeline_en.md
@@ -34,39 +34,7 @@ A pretrained pipeline to detect medication entities. It was built on the top of
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."""
-
-result = ner_medication_pipeline.fullAnnotate([text])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
-```
 
-{:.nlu-block}
-```python
-| ner_chunk          | entity   |
-|:-------------------|:---------|
-| metformin 1000 MG  | DRUG     |
-| glipizide 2.5 MG   | DRUG     |
-| Fragmin 5000 units | DRUG     |
-| Xenaderm           | DRUG     |
-| OxyContin 30 mg    | DRUG     |
-```
-</div>
-
-{:.model-param}
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -83,42 +51,13 @@ val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline",
 
 val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
 ```
-
-{:.nlu-block}
-```python
-| ner_chunk          | entity   |
-|:-------------------|:---------|
-| metformin 1000 MG  | DRUG     |
-| glipizide 2.5 MG   | DRUG     |
-| Fragmin 5000 units | DRUG     |
-| Xenaderm           | DRUG     |
-| OxyContin 30 mg    | DRUG     |
-```
 </div>
 
-{:.model-param}
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
 
-text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."""
 
-result = ner_medication_pipeline.fullAnnotate([text])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+## Results
 
-val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
-```
-
-{:.nlu-block}
-```python
+```bash
 | ner_chunk          | entity   |
 |:-------------------|:---------|
 | metformin 1000 MG  | DRUG     |
@@ -127,7 +66,6 @@ val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed me
 | Xenaderm           | DRUG     |
 | OxyContin 30 mg    | DRUG     |
 ```
-</div>
 
 {:.model-param}
 ## Model Information
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_medmentions_coarse_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_medmentions_coarse_pipeline_en.md
index 73248358bb..e42ad126ce 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_medmentions_coarse_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_medmentions_coarse_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_medmentions_coarse](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.medmentions_coarse.pipeline").predict("""he patient is a 21
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_medmentions_coarse_pipeline", "en", "clinical/models")
-
-text = '''he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_medmentions_coarse_pipeline", "en", "clinical/models")
-
-val text = "he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.medmentions_coarse.pipeline").predict("""he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                  |   begin |   end | ner_label                            |   confidence |
 |---:|:----------------------------|--------:|------:|:-------------------------------------|-------------:|
 |  0 | Caucasian                   |      27 |    35 | Population_Group                     |     0.8439   |
@@ -123,9 +93,6 @@ Results
 | 22 | bowel movements             |     921 |   935 | Biologic_Function                    |     0.29385  |
 | 23 | yellow                      |     941 |   946 | Qualitative_Concept                  |     0.742    |
 | 24 | colored                     |     948 |   954 | Qualitative_Concept                  |     0.275    |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_negation_uncertainty_pipeline_es.md b/docs/_posts/Cabir40/2023-06-16-ner_negation_uncertainty_pipeline_es.md
index 6bc8a887d1..a7ba54e09e 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_negation_uncertainty_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_negation_uncertainty_pipeline_es.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_negation_uncertainty](https
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +------------------------------------------------------+---------+
 |chunk                                                 |ner_label|
 +------------------------------------------------------+---------+
@@ -130,12 +83,6 @@ Results
 |susceptible de                                        |UNC      |
 |ca basocelular perlado                                |USCO     |
 +------------------------------------------------------+---------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_nihss_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_nihss_pipeline_en.md
index e4ec1b4858..524ec1a1ab 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_nihss_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_nihss_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_nihss](https://nlp.johnsnow
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.nihss_pipeline").predict("""Abdomen , soft , nontender . NI
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_nihss_pipeline", "en", "clinical/models")
-
-text = '''Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_nihss_pipeline", "en", "clinical/models")
-
-val text = "Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently"
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.nihss_pipeline").predict("""Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label       |   confidence |
 |---:|:-----------------|--------:|------:|:----------------|-------------:|
 |  0 | NIH stroke scale |      29 |    44 | NIHSS           |     0.973533 |
@@ -120,9 +91,6 @@ Results
 | 19 | three            |     258 |   262 | Measurement     |     0.8896   |
 | 20 | best language    |     268 |   280 | 9_BestLanguage  |     0.89415  |
 | 21 | two              |     286 |   288 | Measurement     |     0.949    |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_anatomy_general_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_anatomy_general_healthcare_pipeline_en.md
index 553ab09970..697002c65e 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_anatomy_general_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_anatomy_general_healthcare_pipeline_en.md
@@ -32,60 +32,11 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_general_he
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,24 +77,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks   |   begin |   end | entities        |   confidence |
 |---:|:---------|--------:|------:|:----------------|-------------:|
 |  0 | left     |      37 |    40 | Direction       |       0.9948 |
 |  1 | breast   |      42 |    47 | Anatomical_Site |       0.5814 |
 |  2 | lungs    |      83 |    87 | Anatomical_Site |       0.9486 |
 |  3 | liver    |     100 |   104 | Anatomical_Site |       0.9646 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_anatomy_general_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_anatomy_general_pipeline_en.md
index 9ae878641e..de79012877 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_anatomy_general_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_anatomy_general_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_general](h
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +71,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label       |   confidence |
 |---:|:-------------|--------:|------:|:----------------|-------------:|
 |  0 | left         |      36 |    39 | Direction       |       0.9825 |
 |  1 | breast       |      41 |    46 | Anatomical_Site |       0.9005 |
 |  2 | lungs        |      82 |    86 | Anatomical_Site |       0.9735 |
 |  3 | liver        |      99 |   103 | Anatomical_Site |       0.9817 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_biomarker_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_biomarker_healthcare_pipeline_en.md
index 3b98a6d6ba..bf7755e700 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_biomarker_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_biomarker_healthcare_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_oncology_biomarker_healthca
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-text = '''he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-val text = "he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-text = '''he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-val text = "he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                                   |   begin |   end | entities         |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-----------------|-------------:|
 |  0 | negative                                 |      69 |    76 | Biomarker_Result |      1       |
@@ -138,12 +91,6 @@ Results
 | 15 | p53                                      |     244 |   246 | Biomarker        |      1       |
 | 16 | Ki-67 index                              |     253 |   263 | Biomarker        |      0.99865 |
 | 17 | 87%                                      |     275 |   277 | Biomarker_Result |      0.828   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_biomarker_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_biomarker_pipeline_en.md
index 6041d0c643..6e879a130f 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_biomarker_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_biomarker_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_oncology_biomarker](https:/
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                               |   begin |   end | ner_label        |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-----------------|-------------:|
 |  0 | negative                                 |      70 |    77 | Biomarker_Result |      0.9984  |
@@ -138,12 +91,6 @@ Results
 | 15 | p53                                      |     245 |   247 | Biomarker        |      1       |
 | 16 | Ki-67 index                              |     254 |   264 | Biomarker        |      0.99465 |
 | 17 | 87%                                      |     276 |   278 | Biomarker_Result |      0.9814  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_demographics_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_demographics_pipeline_en.md
index 3ae36d0609..957d831299 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_demographics_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_demographics_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_oncology_demographics](http
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old man with history of heavy smoking.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old man with history of heavy smoking."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old man with history of heavy smoking.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old man with history of heavy smoking."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,23 +70,11 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label      |   confidence |
 |---:|:--------------|--------:|------:|:---------------|-------------:|
 |  0 | 40-year-old   |      17 |    27 | Age            |       0.6743 |
 |  1 | man           |      29 |    31 | Gender         |       0.9365 |
 |  2 | heavy smoking |      49 |    61 | Smoking_Status |       0.7294 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_diagnosis_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_diagnosis_pipeline_en.md
index ce5affd6ff..68033a6dee 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_diagnosis_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_diagnosis_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_oncology_diagnosis](https:/
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-text = '''Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-val text = "Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-text = '''Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-val text = "Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label         |   confidence |
 |---:|:-------------|--------:|------:|:------------------|-------------:|
 |  0 | tumor        |      44 |    48 | Tumor_Finding     |       0.9958 |
@@ -126,12 +79,6 @@ Results
 |  3 | ductal       |     119 |   124 | Histological_Type |       0.9996 |
 |  4 | carcinoma    |     126 |   134 | Cancer_Dx         |       0.9988 |
 |  5 | metastasis   |     181 |   190 | Metastasis        |       0.9996 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_posology_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_posology_pipeline_en.md
index e285f5f8d8..6c2e522cd7 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_posology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_posology_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_oncology_posology](https://
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label      |   confidence |
 |---:|:-----------------|--------:|------:|:---------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy |      1       |
@@ -127,12 +80,6 @@ Results
 |  4 | six courses      |     106 |   116 | Cycle_Count    |      0.494   |
 |  5 | second cycle     |     150 |   161 | Cycle_Number   |      0.98675 |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_response_to_treatment_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_response_to_treatment_pipeline_en.md
index 08b9723f36..c96a627c0f 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_response_to_treatment_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_response_to_treatment_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_oncology_response_to_treatm
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-text = '''She completed her first-line therapy, but some months later there was recurrence of the breast cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-val text = "She completed her first-line therapy, but some months later there was recurrence of the breast cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-text = '''She completed her first-line therapy, but some months later there was recurrence of the breast cancer.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-val text = "She completed her first-line therapy, but some months later there was recurrence of the breast cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,21 +71,9 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label             |   confidence |
 |---:|:-------------|--------:|------:|:----------------------|-------------:|
 |  0 | recurrence   |      70 |    79 | Response_To_Treatment |       0.9767 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_test_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_test_pipeline_en.md
index 51b9fb8aab..e442da49f0 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_test_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_test_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_oncology_test](https://nlp.
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-text = ''' biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-val text = " biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-text = ''' biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-val text = " biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +71,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                |   begin |   end | ner_label      |   confidence |
 |---:|:--------------------------|--------:|------:|:---------------|-------------:|
 |  0 | biopsy                    |       1 |     6 | Pathology_Test |      0.9987  |
 |  1 | ultrasound guided         |      31 |    47 | Imaging_Test   |      0.87635 |
 |  2 | chest computed tomography |      67 |    91 | Imaging_Test   |      0.9176  |
 |  3 | CT                        |      94 |    95 | Imaging_Test   |      0.8294  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_therapy_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_therapy_pipeline_en.md
index 72fbcb7ae5..010b8d9a3a 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_therapy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_therapy_pipeline_en.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_therapy](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                     |   begin |   end | ner_label             |   confidence |
 |---:|:-------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | mastectomy                     |      36 |    45 | Cancer_Surgery        |     0.9817   |
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_tnm_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_tnm_pipeline_en.md
index b73f55bf82..5366b2f621 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_tnm_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_tnm_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_tnm](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label         |   confidence |
 |---:|:-----------------|--------:|------:|:------------------|-------------:|
 |  0 | metastatic       |      24 |    33 | Metastasis        |     0.9999   |
@@ -126,12 +77,6 @@ Results
 |  3 | 4 cm             |     126 |   129 | Tumor_Description |     0.85105  |
 |  4 | tumor            |     131 |   135 | Tumor             |     0.9926   |
 |  5 | grade 2          |     141 |   147 | Tumor_Description |     0.89705  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_oncology_unspecific_posology_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
index 8d4bdf6b4b..6c921fb70f 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_unspecific_posolog
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks           |   begin |   end | entities             |   confidence |
 |---:|:-----------------|--------:|------:|:---------------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy       |     0.9999   |
@@ -141,12 +84,6 @@ Results
 |  4 | over six courses |     101 |   116 | Posology_Information |     0.689833 |
 |  5 | second cycle     |     150 |   161 | Posology_Information |     0.9906   |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy       |     0.9997   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_pathogen_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_pathogen_pipeline_en.md
index f13533ef3e..2a4ad9e6a4 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_pathogen_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_pathogen_pipeline_en.md
@@ -34,36 +34,7 @@ This pretrained pipeline is built on the top of [ner_pathogen](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_pathogen_pipeline", "en", "clinical/models")
-
-text = '''Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin.  This can progress to loss of skin color, a fast heart rate as it becomes more severe.  While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_pathogen_pipeline", "en", "clinical/models")
-
-val text = "Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin.  This can progress to loss of skin color, a fast heart rate as it becomes more severe.  While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions."
-
-val result = pipeline.fullAnnotate(text)
-```
-
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.pathogen.pipeline").predict("""Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin.  This can progress to loss of skin color, a fast heart rate as it becomes more severe.  While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -93,9 +64,6 @@ nlu.load("en.med_ner.pathogen.pipeline").predict("""Racecadotril is an antisecre
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label        |   confidence |
 |---:|:----------------|--------:|------:|:-----------------|-------------:|
 |  0 | Racecadotril    |       0 |    11 | Medicine         |     0.9468   |
@@ -107,9 +75,6 @@ Results
 |  6 | rabies virus    |     383 |   394 | Pathogen         |     0.95685  |
 |  7 | Lyssavirus      |     397 |   406 | Pathogen         |     0.9694   |
 |  8 | Ephemerovirus   |     412 |   424 | Pathogen         |     0.6919   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_posology_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_posology_biobert_pipeline_en.md
index 394e6b33ee..47b2883773 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_posology_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_posology_biobert_pipeline_en.md
@@ -34,36 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."
-
-val result = pipeline.fullAnnotate(text)
-```
-
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_biobert.pipeline").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -93,9 +64,6 @@ nlu.load("en.med_ner.posology_biobert.pipeline").predict("""The patient was pres
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | 1                   |      27 |    27 | DOSAGE      |     0.9993   |
@@ -115,9 +83,6 @@ Results
 | 14 | metformin           |     261 |   269 | DRUG        |     0.9999   |
 | 15 | 1000 mg             |     271 |   277 | STRENGTH    |     0.91255  |
 | 16 | two times a day     |     279 |   293 | FREQUENCY   |     0.9969   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_posology_experimental_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_posology_experimental_pipeline_en.md
index b2ce7fdeee..c93028f2cc 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_posology_experimental_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_posology_experimental_pipeline_en.md
@@ -34,42 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_experimental](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_experimental_pipeline", "en", "clinical/models")
-
-text = '''Y-90 Humanized Anti-Tac: 10 mCi (if a bone marrow transplant was part of the patient's previous therapy) or 15 mCi of yttrium labeled anti-TAC; followed by calcium trisodium Inj (Ca DTPA)..
-
-Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear the radioactive agent from the body.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_experimental_pipeline", "en", "clinical/models")
-
-val text = "Y-90 Humanized Anti-Tac: 10 mCi (if a bone marrow transplant was part of the patient's previous therapy) or 15 mCi of yttrium labeled anti-TAC; followed by calcium trisodium Inj (Ca DTPA)..
-
-Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear the radioactive agent from the body."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_experimental.pipeline").predict("""Y-90 Humanized Anti-Tac: 10 mCi (if a bone marrow transplant was part of the patient's previous therapy) or 15 mCi of yttrium labeled anti-TAC; followed by calcium trisodium Inj (Ca DTPA)..
-
-Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear the radioactive agent from the body.""")
-```
-
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -105,9 +70,6 @@ Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear th
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------|--------:|------:|:------------|-------------:|
 |  0 | Anti-Tac                 |      15 |    22 | Drug        |     0.8797   |
@@ -119,9 +81,6 @@ Results
 |  6 | Ca-DTPA                  |     205 |   211 | Drug        |     0.9544   |
 |  7 | intravenously            |     234 |   246 | Route       |     0.9518   |
 |  8 | Days 1-3                 |     251 |   258 | Cycleday    |     0.83325  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_posology_greedy_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_posology_greedy_pipeline_en.md
index 7ed12b1d4a..88c7dae0a6 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_posology_greedy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_posology_greedy_pipeline_en.md
@@ -32,38 +32,10 @@ This pretrained pipeline is built on the top of [ner_posology_greedy](https://nl
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_greedy_pipeline", "en", "clinical/models")
-
-text = '''The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_greedy_pipeline", "en", "clinical/models")
-
-val text = "The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_greedy.pipeline").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
-```
-
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -93,9 +65,6 @@ nlu.load("en.med_ner.posology_greedy.pipeline").predict("""The patient was presc
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                   |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 1 capsule of Advil 10 mg                    |      27 |    50 | DRUG        |     0.638183 |
@@ -107,9 +76,6 @@ Results
 |  6 | with meals                                  |     245 |   254 | FREQUENCY   |     0.79235  |
 |  7 | metformin 1000 mg                           |     261 |   277 | DRUG        |     0.707133 |
 |  8 | two times a day                             |     279 |   293 | FREQUENCY   |     0.700825 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_posology_healthcare_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_posology_healthcare_pipeline_en.md
index 81c372d830..e33b841fb9 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_posology_healthcare_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_posology_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_healthcare](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.posology.healthcare_pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_healthcare_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_healthcare_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology.healthcare_pipeline").predict("""The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk     |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | Aspirin       |     267 |   273 | Drug        |      0.9983  |
@@ -110,9 +80,6 @@ Results
 |  9 | Nitroglycerin |     337 |   349 | Drug        |      0.9927  |
 | 10 | 1/150         |     351 |   355 | Strength    |      0.9565  |
 | 11 | sublingually. |     357 |   369 | Route       |      0.72065 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_posology_large_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_posology_large_biobert_pipeline_en.md
index 6dd27a86d7..425b133866 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_posology_large_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_posology_large_biobert_pipeline_en.md
@@ -32,38 +32,10 @@ This pretrained pipeline is built on the top of [ner_posology_large_biobert](htt
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_large_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_large_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_biobert_large.pipeline").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
-```
-
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -93,9 +65,6 @@ nlu.load("en.med_ner.posology_biobert_large.pipeline").predict("""The patient wa
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | 1                   |      27 |    27 | DOSAGE      |     0.9998   |
@@ -116,9 +85,6 @@ Results
 | 15 | metformin           |     261 |   269 | DRUG        |     1        |
 | 16 | 1000 mg             |     271 |   277 | STRENGTH    |     0.69955  |
 | 17 | two times a day     |     279 |   293 | FREQUENCY   |     0.758125 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_posology_large_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_posology_large_pipeline_en.md
index c9767a6c42..dde51fde33 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_posology_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_posology_large_pipeline_en.md
@@ -32,38 +32,10 @@ This pretrained pipeline is built on the top of [ner_posology_large](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_large_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_large_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posoloy_large.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.""")
-```
-
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -93,9 +65,6 @@ nlu.load("en.med_ner.posoloy_large.pipeline").predict("""The patient is a 30-yea
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | insulin        |      59 |    65 | DRUG        |     0.9752   |
@@ -123,9 +92,6 @@ Results
 | 22 | 0.1 mg         |    1113 |  1118 | STRENGTH    |     0.9325   |
 | 23 | p.o.           |    1120 |  1123 | ROUTE       |     0.6783   |
 | 24 | daily          |    1125 |  1129 | FREQUENCY   |     0.9925   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_posology_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_posology_pipeline_en.md
index 3f5d57c1bb..47676b82b8 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_posology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_posology_pipeline_en.md
@@ -34,36 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d."
-
-val result = pipeline.fullAnnotate(text)
-```
-
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -93,9 +64,6 @@ nlu.load("en.med_ner.posology_pipeline").predict("""The patient is a 30-year-old
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | insulin        |      59 |    65 | DRUG        |      0.9759  |
@@ -123,9 +91,6 @@ Results
 | 22 | 0.1 mg         |    1113 |  1118 | STRENGTH    |      0.7658  |
 | 23 | p.o            |    1120 |  1122 | ROUTE       |      0.9982  |
 | 24 | daily          |    1125 |  1129 | FREQUENCY   |      0.9983  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_posology_small_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_posology_small_pipeline_en.md
index 2957656bad..93ac0d1bdd 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_posology_small_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_posology_small_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_small](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.posology_small.pipeline").predict("""The patient is a 30-ye
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_small_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_small_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_small.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | insulin        |      59 |    65 | DRUG        |      0.9984  |
@@ -123,9 +94,6 @@ Results
 | 22 | 0.1 mg         |    1113 |  1118 | STRENGTH    |      0.99965 |
 | 23 | p.o            |    1120 |  1122 | ROUTE       |      0.999   |
 | 24 | daily          |    1125 |  1129 | FREQUENCY   |      0.9373  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_profiling_biobert_en.md b/docs/_posts/Cabir40/2023-06-16-ner_profiling_biobert_en.md
index 0de75ee537..f8e433e0fc 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_profiling_biobert_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_profiling_biobert_en.md
@@ -38,6 +38,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -67,66 +68,9 @@ nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 ******************** ner_diseases_biobert Model Results ******************** 
 
 [('gestational diabetes mellitus', 'Disease'), ('type two diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('HTG-induced pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('poor appetite', 'Disease'), ('vomiting', 'Disease')]
@@ -150,13 +94,6 @@ Results
 ******************** ner_risk_factors_biobert Model Results ******************** 
 
 [('diabetes mellitus', 'DIABETES'), ('subsequent type two diabetes mellitus', 'DIABETES'), ('obesity', 'OBESE')]
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_radiology_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_radiology_pipeline_en.md
index 0876da8eab..7f389ee27b 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_radiology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_radiology_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_radiology](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.radiology.pipeline").predict("""Bilateral breast ultrasound
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_radiology_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_radiology_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.radiology.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                |   begin |   end | ner_label                 |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Bilateral breast                         |       0 |    15 | BodyPart                  |     0.945    |
@@ -110,9 +80,6 @@ Results
 |  9 | internal color flow                      |     294 |   312 | ImagingFindings           |     0.477233 |
 | 10 | benign fibrous tissue                    |     334 |   354 | ImagingFindings           |     0.524067 |
 | 11 | lipoma                                   |     361 |   366 | Disease_Syndrome_Disorder |     0.6081   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_radiology_wip_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_radiology_wip_clinical_pipeline_en.md
index c686ecd260..7eb18c50d1 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_radiology_wip_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_radiology_wip_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_radiology_wip_clinical](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.radiology.clinical_wip.pipeline").predict("""Bilateral brea
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_radiology_wip_clinical_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_radiology_wip_clinical_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.radiology.clinical_wip.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Bilateral             |       0 |     8 | Direction                 |     0.9828   |
@@ -113,9 +83,6 @@ Results
 | 12 | internal color flow   |     294 |   312 | ImagingFindings           |     0.5153   |
 | 13 | benign fibrous tissue |     334 |   354 | ImagingFindings           |     0.394867 |
 | 14 | lipoma                |     361 |   366 | Disease_Syndrome_Disorder |     0.9142   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_risk_factors_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_risk_factors_biobert_pipeline_en.md
index 5f0eaf205c..6ce7b719f3 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_risk_factors_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_risk_factors_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_risk_factors_biobert](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -86,64 +87,10 @@ FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_risk_factors_biobert_pipeline", "en", "clinical/models")
-
-text = '''ISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_risk_factors_biobert_pipeline", "en", "clinical/models")
-
-val text = "ISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother)."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.risk_factors_biobert.pipeline").predict("""ISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                |   begin |   end | ner_label    |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | diabetic                                 |     135 |   142 | DIABETES     |     0.9689   |
@@ -153,9 +100,6 @@ Results
 |  4 | hypertension                             |    1341 |  1352 | HYPERTENSION |     0.956    |
 |  5 | coronary artery disease                  |    1355 |  1377 | CAD          |     0.7962   |
 |  6 | Smokes 2 packs of cigarettes per day     |    1480 |  1515 | SMOKER       |     0.461643 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_risk_factors_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_risk_factors_pipeline_en.md
index 2981093a3d..7dbb48ff63 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_risk_factors_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_risk_factors_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_risk_factors](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -86,64 +87,9 @@ FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_risk_factors_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_risk_factors_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother)."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.risk_factors.pipeline").predict("""HISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                            |   begin |   end | ner_label    |   confidence |
 |---:|:-------------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | diabetic                             |     136 |   143 | DIABETES     |     0.9992   |
@@ -155,9 +101,6 @@ Results
 |  6 | ABC                                  |    1434 |  1436 | PHI          |     0.9999   |
 |  7 | Smokes 2 packs of cigarettes per day |    1481 |  1516 | SMOKER       |     0.634257 |
 |  8 | banker                               |    1530 |  1535 | PHI          |     0.9779   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-ner_sdoh_mentions_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-ner_sdoh_mentions_pipeline_en.md
index 24ef29d4a2..cbafa505d8 100644
--- a/docs/_posts/Cabir40/2023-06-16-ner_sdoh_mentions_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-ner_sdoh_mentions_pipeline_en.md
@@ -55,12 +55,9 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-
-
 ## Results
 
 ```bash
-
 |    | chunks           |   begin |   end | entities         |   confidence |
 |---:|:-----------------|--------:|------:|:-----------------|-------------:|
 |  0 | married          |     123 |   129 | sdoh_community   |       0.9972 |
@@ -69,7 +66,6 @@ val result = pipeline.fullAnnotate(text)
 |  3 | alcohol          |     185 |   191 | behavior_alcohol |       0.9925 |
 |  4 | intravenous drug |     196 |   211 | behavior_drug    |       0.9803 |
 |  5 | smoking          |     230 |   236 | behavior_tobacco |       0.9997 |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-oncology_biomarker_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-oncology_biomarker_pipeline_en.md
index 59fbb4199a..f1aec46668 100644
--- a/docs/_posts/Cabir40/2023-06-16-oncology_biomarker_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-oncology_biomarker_pipeline_en.md
@@ -34,36 +34,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status and Relation E
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2."
-
-val result = pipeline.fullAnnotate(text)
-```
-
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -83,51 +54,18 @@ val text = "Immunohistochemistry was negative for thyroid transcription factor-1
 val result = pipeline.fullAnnotate(text)
 ```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2."
-
-val result = pipeline.fullAnnotate(text)
-```
 
 {:.nlu-block}
 ```python
 import nlu
 nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.""")
 ```
+
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk                          | ner_label        |
@@ -253,13 +191,6 @@ Results
 | ER                   | Biomarker        | negative                       | Biomarker_Result | O             |
 | PR                   | Biomarker        | negative                       | Biomarker_Result | O             |
 | negative             | Biomarker_Result | HER2                           | Oncogene         | is_finding_of |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-oncology_general_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-oncology_general_pipeline_en.md
index 172935e428..9c462f1c4f 100644
--- a/docs/_posts/Cabir40/2023-06-16-oncology_general_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-oncology_general_pipeline_en.md
@@ -67,9 +67,6 @@ The tumor is positive for ER and PR.""")
 </div>
 
 
-
-
-
 ## Results
 
 ```bash
diff --git a/docs/_posts/Cabir40/2023-06-16-oncology_therapy_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-oncology_therapy_pipeline_en.md
index 3e3bda7e6f..a9b55f39b9 100644
--- a/docs/_posts/Cabir40/2023-06-16-oncology_therapy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-oncology_therapy_pipeline_en.md
@@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition and Assertion Status models to e
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,9 @@ nlu.load("en.oncology_therpay.pipeline").predict("""The patient underwent a mast
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_therapy_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_therapy_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_therpay.pipeline").predict("""The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk            | ner_label      |
@@ -153,10 +122,6 @@ Results
 | mastectomy       | Cancer_Surgery | Present_Or_Past |
 | adriamycin       | Chemotherapy   | Present_Or_Past |
 | cyclophosphamide | Chemotherapy   | Present_Or_Past |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-re_bodypart_directions_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-re_bodypart_directions_pipeline_en.md
index 8a8f85c3c6..ce0aa7926c 100644
--- a/docs/_posts/Cabir40/2023-06-16-re_bodypart_directions_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-re_bodypart_directions_pipeline_en.md
@@ -59,64 +59,9 @@ nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations | entity1                     | entity1_begin | entity1_end | chunk1     | entity2                     | entity2_end | entity2_end | chunk2        | confidence |
 |-------|-----------|-----------------------------|---------------|-------------|------------|-----------------------------|-------------|-------------|---------------|------------|
 | 0     | 1         | Direction                   | 35            | 39          | upper      | Internal_organ_or_component | 41          | 50          | brain stem    | 0.9999989  |
@@ -128,13 +73,6 @@ Results
 | 6     | 0         | Direction                   | 54            | 57          | left       | Internal_organ_or_component | 81          | 93          | basil ganglia | 0.97616416 |
 | 7     | 0         | Internal_organ_or_component | 59            | 68          | cerebellum | Direction                   | 75          | 79          | right         | 0.953046   |
 | 8     | 1         | Direction                   | 75            | 79          | right      | Internal_organ_or_component | 81          | 93          | basil ganglia | 1.0        |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-re_bodypart_proceduretest_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-re_bodypart_proceduretest_pipeline_en.md
index cc4184b7a3..3849e1d30d 100644
--- a/docs/_posts/Cabir40/2023-06-16-re_bodypart_proceduretest_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-re_bodypart_proceduretest_pipeline_en.md
@@ -59,74 +59,12 @@ nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations | entity1                      | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2              | confidence |
 |-------|-----------|------------------------------|---------------|-------------|--------|---------|-------------|-------------|---------------------|------------|
 | 0     | 1         | External_body_part_or_region | 94            | 98          | chest  | Test    | 117         | 135         | portable ultrasound | 1.0        |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-re_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-re_human_phenotype_gene_clinical_pipeline_en.md
index 2ab6a08df8..838e6ed3ee 100644
--- a/docs/_posts/Cabir40/2023-06-16-re_human_phenotype_gene_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-re_human_phenotype_gene_clinical_pipeline_en.md
@@ -34,30 +34,7 @@ This pretrained pipeline is built on the top of [re_human_phenotype_gene_clinica
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""")
-```
-
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
 
@@ -71,44 +48,18 @@ val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline
 pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
 ```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
 
 {:.nlu-block}
 ```python
 import nlu
 nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""")
 ```
+
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    |   relation | entity1   |   entity1_begin |   entity1_end | chunk1              | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+===========+=================+===============+=====================+===========+=================+===============+=====================+==============+
@@ -116,12 +67,6 @@ Results
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 |          1 | HP        |              23 |            36 | microphthalmia      | GENE      |             110 |           114 | TENM3               |     0.999999 |
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-re_temporal_events_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-re_temporal_events_clinical_pipeline_en.md
index 110c60f85d..0d2ecbd2a8 100644
--- a/docs/_posts/Cabir40/2023-06-16-re_temporal_events_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-re_temporal_events_clinical_pipeline_en.md
@@ -34,30 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_clinical](ht
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
 
@@ -71,44 +48,18 @@ val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "e
 pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
 ```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
 
 {:.nlu-block}
 ```python
 import nlu
 nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
 ```
+
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    | relation   | entity1    |   entity1_begin |   entity1_end | chunk1                   | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+============+=================+===============+==========================+===========+=================+===============+=====================+==============+
@@ -116,12 +67,6 @@ Results
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 | OVERLAP    | DATE       |             171 |           179 | that time                | PROBLEM   |             201 |           219 | any specific injury |     0.956654 |
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-re_temporal_events_enriched_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-re_temporal_events_enriched_clinical_pipeline_en.md
index a96ce1d75e..71861b3d87 100644
--- a/docs/_posts/Cabir40/2023-06-16-re_temporal_events_enriched_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-re_temporal_events_enriched_clinical_pipeline_en.md
@@ -34,30 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_enriched_cli
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
 
@@ -71,44 +48,18 @@ val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipe
 pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
 ```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
 
 {:.nlu-block}
 ```python
 import nlu
 nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
 ```
+
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |    | relation   | entity1   |   entity1_begin |   entity1_end | chunk1                                        | entity2    |   entity2_begin |   entity2_end | chunk2                   |   confidence |
 +====+============+===========+=================+===============+===============================================+============+=================+===============+==========================+==============+
@@ -116,12 +67,6 @@ Results
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |  1 | AFTER      | DATE      |             171 |           179 | that time                                     | PROBLEM    |             201 |           219 | any specific injury      |     0.577288 |
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-re_test_problem_finding_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-re_test_problem_finding_pipeline_en.md
index e670e357c4..75bfe396b5 100644
--- a/docs/_posts/Cabir40/2023-06-16-re_test_problem_finding_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-re_test_problem_finding_pipeline_en.md
@@ -59,74 +59,13 @@ nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | 1            | PROCEDURE    | biopsy              | SYMPTOM      |  lesion | 
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-re_test_result_date_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-re_test_result_date_pipeline_en.md
index 01235d3d88..750db134d0 100644
--- a/docs/_posts/Cabir40/2023-06-16-re_test_result_date_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-re_test_result_date_pipeline_en.md
@@ -59,76 +59,14 @@ nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised ches
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | O            | TEST         | chest X-ray         | MEASUREMENTS |  93%    | 
 | 1     | O            | TEST         | CT scan             | MEASUREMENTS |  93%    |
 | 2     | is_result_of | TEST         | SpO2                | MEASUREMENTS |  93%    |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-recognize_entities_posology_en.md b/docs/_posts/Cabir40/2023-06-16-recognize_entities_posology_en.md
index 15a901ee1b..c293ee36f0 100644
--- a/docs/_posts/Cabir40/2023-06-16-recognize_entities_posology_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-recognize_entities_posology_en.md
@@ -34,35 +34,7 @@ A pipeline with `ner_posology`. It will only extract medication entities.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
-
-res = pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-```scala
-val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "clinical/models")
-
-val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")(0)
-
-```
-
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
 
@@ -76,33 +48,9 @@ val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "
 val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
 She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
 """)(0)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
 
-res = pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
 ```
-```scala
-val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "clinical/models")
 
-val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")(0)
-```
 
 {:.nlu-block}
 ```python
@@ -111,17 +59,12 @@ nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with
 She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
 """)
 ```
+
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunk            |   begin |   end | entity    |
 |---:|:-----------------|--------:|------:|:----------|
 |  0 | metformin        |      83 |    91 | DRUG      |
@@ -133,13 +76,6 @@ Results
 |  6 | 12 units         |     309 |   316 | DOSAGE    |
 |  7 | insulin lispro   |     321 |   334 | DRUG      |
 |  8 | with meals       |     336 |   345 | FREQUENCY |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-rxnorm_mesh_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-rxnorm_mesh_mapping_en.md
index c27b9ea7de..3dd0024bb4 100644
--- a/docs/_posts/Cabir40/2023-06-16-rxnorm_mesh_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-rxnorm_mesh_mapping_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline maps RxNorm codes to MeSH codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline 
-pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-pipeline.annotate("1191 6809 47613")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-val result = pipeline.annotate("1191 6809 47613")
-```
-
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
-```
 
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline 
 pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
@@ -67,42 +46,18 @@ val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/model
 val result = pipeline.annotate("1191 6809 47613")
 ```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline 
-pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-pipeline.annotate("1191 6809 47613")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-val result = pipeline.annotate("1191 6809 47613")
-```
 
 {:.nlu-block}
 ```python
 import nlu
 nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
 ```
+
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 {'rxnorm': ['1191', '6809', '47613'],
 'mesh': ['D001241', 'D008687', 'D019355']}
 
@@ -120,12 +75,6 @@ Note:
 | D001241    | Aspirin             |
 | D008687    | Metformin           |
 | D019355    | Calcium Citrate     |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-rxnorm_ndc_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-rxnorm_ndc_mapping_en.md
index a7ae8da187..773692f3c7 100644
--- a/docs/_posts/Cabir40/2023-06-16-rxnorm_ndc_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-rxnorm_ndc_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps RXNORM codes to NDC codes without using any text d
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,75 +59,15 @@ nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1652674 259934)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1652674 259934)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1652674 259934)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1652674 259934)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 {'document': ['1652674 259934'],
 'package_ndc': ['62135-0625-60', '13349-0010-39'],
 'product_ndc': ['46708-0499', '13349-0010'],
 'rxnorm_code': ['1652674', '259934']}
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-rxnorm_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-rxnorm_umls_mapping_en.md
index 593d8b30e4..310f748bfa 100644
--- a/docs/_posts/Cabir40/2023-06-16-rxnorm_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-rxnorm_umls_mapping_en.md
@@ -34,32 +34,7 @@ This pretrained pipeline is built on the top of `rxnorm_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1161611 315677)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1161611 315677)
-```
 
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -75,57 +50,21 @@ val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/mod
 val result = pipeline.fullAnnotate(1161611 315677)
 ```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1161611 315677)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1161611 315677)
-```
 
 {:.nlu-block}
 ```python
 import nlu
 nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
 ```
+
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | rxnorm_code      | umls_code           |
 |---:|:-----------------|:--------------------|
 |  0 | 1161611 | 315677 | C3215948 | C0984912 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-snomed_icd10cm_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-snomed_icd10cm_mapping_en.md
index ce8cca0a4d..d251a54776 100644
--- a/docs/_posts/Cabir40/2023-06-16-snomed_icd10cm_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-snomed_icd10cm_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icd10cm_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,13 @@ nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | snomed_code                             | icd10cm_code               |
 |---:|:----------------------------------------|:---------------------------|
 |  0 | 128041000119107 | 292278006 | 293072005 | K22.70 | T43.595 | T37.1X5 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-snomed_icdo_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-snomed_icdo_mapping_en.md
index 617f0bb594..5c910f3006 100644
--- a/docs/_posts/Cabir40/2023-06-16-snomed_icdo_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-snomed_icdo_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icdo_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,13 @@ nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | snomed_code                   | icdo_code                |
 |---:|:------------------------------|:-------------------------|
 |  0 | 10376009 | 2026006 | 26638004 | 8050/2 | 9014/0 | 8322/0 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-snomed_umls_mapping_en.md b/docs/_posts/Cabir40/2023-06-16-snomed_umls_mapping_en.md
index d5b70d43b9..1e27bfbe0e 100644
--- a/docs/_posts/Cabir40/2023-06-16-snomed_umls_mapping_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-snomed_umls_mapping_en.md
@@ -34,32 +34,7 @@ This pretrained pipeline is built on the top of `snomed_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
 
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -75,57 +50,21 @@ val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/mod
 val result = pipeline.fullAnnotate(733187009 449433008 51264003)
 ```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
 
 {:.nlu-block}
 ```python
 import nlu
 nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
 ```
+
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | snomed_code                      | umls_code                      |
 |---:|:---------------------------------|:-------------------------------|
 |  0 | 733187009 | 449433008 | 51264003 | C4546029 | C3164619 | C0271267 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-16-spellcheck_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-16-spellcheck_clinical_pipeline_en.md
index c5373a9251..c16b3655cd 100644
--- a/docs/_posts/Cabir40/2023-06-16-spellcheck_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-16-spellcheck_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained medical spellchecker pipeline is built on the top of `spellcheck
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -66,44 +67,9 @@ nlu.load("en.spell.clinical.pipeline").predict("""Witth the hell of phisical ter
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("spellcheck_clinical_pipeline", "en", "clinical/models")
-example = ["Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.",
-           "With paint wel controlled on orall pain medications, she was discharged too reihabilitation facilitay.",
-           "Abdomen is sort, nontender, and nonintended.",
-           "Patient not showing pain or any wealth problems.",
-           "No cute distress"]
-pipeline.fullAnnotate(example)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("spellcheck_clinical_pipeline", "en", "clinical/models")
-val example = Array("Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.",
-           "With paint wel controlled on orall pain medications, she was discharged too reihabilitation facilitay.",
-           "Abdomen is sort, nontender, and nonintended.",
-           "Patient not showing pain or any wealth problems.",
-           "No cute distress")
-pipeline.fullAnnotate(example)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.spell.clinical.pipeline").predict("""Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 [{'checked': ['With','the','cell','of','physical','therapy','the','patient','was','ambulated','and','on','postoperative',',','the','patient','tolerating','a','post','surgical','soft','diet','.'],
   'document': ['Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.'],
   'token': ['Witth','the','hell','of','phisical','terapy','the','patient','was','imbulated','and','on','postoperative',',','the','impatient','tolerating','a','post','curgical','soft','diet','.']},
@@ -123,9 +89,6 @@ Results
  {'checked': ['No', 'acute', 'distress'],
   'document': ['No cute distress'],
   'token': ['No', 'cute', 'distress']}]
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ade_tweet_binary_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ade_tweet_binary_pipeline_en.md
index 6d30defe77..b9e8874caa 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ade_tweet_binary_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ade_tweet_binary_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ade_tweet
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +70,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | depressed        |      44 |    52 | ADE         |     0.999755 |
 |  1 | angry            |      73 |    77 | ADE         |     0.999608 |
 |  2 | insulin blocking |      97 |   112 | ADE         |     0.738712 |
 |  3 | sugar crashes    |     147 |   159 | ADE         |     0.993742 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_disease_mentions_tweet_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
index 529a593d6a..c5d2184fee 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_disease_m
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-text = '''El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-val text = "El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-text = '''El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-val text = "El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Neumonía en el pulmón |      41 |    61 | ENFERMEDAD  |     0.999969 |
@@ -125,12 +78,6 @@ Results
 |  2 | Faringitis aguda      |      94 |   109 | ENFERMEDAD  |     0.999969 |
 |  3 | infección de orina    |     113 |   130 | ENFERMEDAD  |     0.999969 |
 |  4 | Gripe                 |     150 |   154 | ENFERMEDAD  |     0.999983 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_negation_uncertainty_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_negation_uncertainty_pipeline_es.md
index 34bb31b0dc..a1f505385a 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_negation_uncertainty_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_negation_uncertainty_pipeline_es.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_negation_
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                                              |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | probable                                               |      16 |    23 | UNC         |     0.999994 |
@@ -128,12 +81,6 @@ Results
 |  5 | se realizó paracentesis control por escasez de liquido |     178 |   231 | NSCO        |     0.999995 |
 |  6 | susceptible de                                         |     293 |   306 | UNC         |     0.999986 |
 |  7 | ca basocelular perlado                                 |     308 |   329 | USCO        |     0.99999  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_ade_binary_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_ade_binary_pipeline_en.md
index 18dc19edae..b776ec3f5f 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_ade_binary_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_ade_binary_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ade_b
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,23 +71,11 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk     |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | depressed     |      44 |    52 | ADE         |     0.990846 |
 |  1 | angry         |      73 |    77 | ADE         |     0.972025 |
 |  2 | sugar crashes |     147 |   159 | ADE         |     0.933623 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_anatem_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_anatem_pipeline_en.md
index 0be1f56636..2d2dda6042 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_anatem_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_anatem_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_anate
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-text = '''Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-val text = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-text = '''Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-val text = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------|--------:|------:|:------------|-------------:|
 |  0 | Malignant cells        |       0 |    14 | Anatomy     |     0.999951 |
@@ -126,12 +79,6 @@ Results
 |  3 | breast                 |     343 |   348 | Anatomy     |     0.999842 |
 |  4 | ovarian                |     351 |   357 | Anatomy     |     0.99998  |
 |  5 | prostate cancer        |     364 |   378 | Anatomy     |     0.999968 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
index 7b5dc2d779..0073712ff1 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc2gm
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-text = '''ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-val text = "ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-text = '''ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-val text = "ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label    |   confidence |
 |---:|:------------|--------:|------:|:-------------|-------------:|
 |  0 | ROCK-I      |       0 |     5 | GENE/PROTEIN |     0.999978 |
@@ -129,12 +81,6 @@ Results
 |  6 | Rho         |     225 |   227 | GENE/PROTEIN |     0.999976 |
 |  7 | boxA        |     247 |   250 | GENE/PROTEIN |     0.999837 |
 |  8 | rut sites   |     256 |   264 | GENE/PROTEIN |     0.99115  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
index 6319513763..0d3cdc32f0 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc4ch
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin).'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                       |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------|--------:|------:|:------------|-------------:|
 |  0 | triterpenes                     |      33 |    43 | CHEM        |     0.99999  |
@@ -133,12 +86,6 @@ Results
 | 10 | 4 - hydroxybenzoic acid         |     184 |   206 | CHEM        |     0.999973 |
 | 11 | gallic and protocatechuic acids |     209 |   239 | CHEM        |     0.999984 |
 | 12 | isocorilagin                    |     245 |   256 | CHEM        |     0.999985 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
index 91b21369cd..2c9fdb59ce 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc5cd
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                 |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | amphetamine               |     128 |   138 | CHEM        |     0.999973 |
@@ -127,12 +79,6 @@ Results
 |  4 | kanamycin                 |     350 |   358 | CHEM        |     0.999978 |
 |  5 | colistin                  |     362 |   369 | CHEM        |     0.999942 |
 |  6 | povidone-iodine           |     375 |   389 | CHEM        |     0.999977 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
index 775421398a..e6c4e88103 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc5cd
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +71,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | interstitial cystitis |      61 |    81 | DISEASE     |     0.999746 |
 |  1 | mastocytosis          |     129 |   140 | DISEASE     |     0.999132 |
 |  2 | cystitis              |     209 |   216 | DISEASE     |     0.999912 |
 |  3 | prostate cancer       |     355 |   369 | DISEASE     |     0.999781 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
index fd4537ebee..cf3460f09e 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-text = '''This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-val text = "This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-text = '''This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-val text = "This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label          |   confidence |
 |---:|:-----------------|--------:|------:|:-------------------|-------------:|
 |  0 | open-label       |       5 |    14 | CTDesign           |     0.742075 |
@@ -138,12 +90,6 @@ Results
 | 15 | GLA              |     356 |   358 | Drug               |     0.972978 |
 | 16 | NPH              |     363 |   365 | Drug               |     0.989424 |
 | 17 | bedtime          |     370 |   376 | DrugTime           |     0.936016 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
index dca56681a7..105cca2c3d 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | suplementación          |      13 |    26 | PROC        |     0.999993 |
@@ -132,12 +84,6 @@ Results
 |  9 | pp                      |     337 |   338 | CHEM        |     0.999889 |
 | 10 | diálisis                |     388 |   395 | PROC        |     0.999993 |
 | 11 | función  residual       |     398 |   414 | PROC        |     0.999948 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
index 0583666567..5f34c36ba7 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jnlpb
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-text = '''The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-val text = "The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-text = '''The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells..'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-val text = "The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                             |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | protein kinase C                      |      39 |    54 | protein     |     0.993263 |
@@ -135,12 +88,6 @@ Results
 | 12 | tyrosine kinases                      |     732 |   747 | protein     |     0.999636 |
 | 13 | p95vav                                |     834 |   839 | protein     |     0.999842 |
 | 14 | hematopoietic and trophoblast cells   |     876 |   910 | cell_type   |     0.999709 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_linnaeus_species_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
index 959b22b016..d5ebf9730d 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_linna
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-text = '''First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-val text = "First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-text = '''First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-val text = "First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                 |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | chicken                   |      20 |    26 | SPECIES     |     0.998697 |
@@ -125,12 +78,6 @@ Results
 |  2 | Xenopus laevis            |      82 |    95 | SPECIES     |     0.999918 |
 |  3 | Drosophila melanogaster   |     102 |   124 | SPECIES     |     0.999925 |
 |  4 | Schizosaccharomyces pombe |     134 |   158 | SPECIES     |     0.999881 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_en.md
index 9e0c99016c..2529d92775 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |     0.986743 |
@@ -127,12 +80,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |     0.962562 |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |     0.999028 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |     0.999852 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_es.md
index 3663dc69bb..f548e31186 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_es.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |     0.999294 |
@@ -131,12 +84,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |     0.999971 |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |     0.99997  |
 | 10 | padres         |     728 |   733 | HUMAN       |     0.999971 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_it.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_it.md
index 96f0876b11..c3f24aed80 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_it.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |     0.999699 |
@@ -130,12 +83,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |     0.999616 |
 |  8 | HIV              |     523 |   525 | SPECIES     |     0.999383 |
 |  9 | paziente         |     634 |   641 | HUMAN       |     0.99977  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_pt.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_pt.md
index 98cd253367..b46e5aa78c 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_pt.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_living_species_pipeline_pt.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |     0.999888 |
@@ -126,12 +79,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |     0.999365 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |     0.982236 |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |     0.996602 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_ncbi_disease_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
index eb6330b7d8..2252505174 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ncbi_
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-text = '''Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-val text = "Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-text = '''Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-val text = "Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------|--------:|------:|:------------|-------------:|
 |  0 | Kniest dysplasia       |       0 |    15 | Disease     |     0.999886 |
@@ -126,12 +78,6 @@ Results
 |  3 | midface hypoplasia     |     120 |   137 | Disease     |     0.999911 |
 |  4 | myopia                 |     147 |   152 | Disease     |     0.999894 |
 |  5 | hearing loss           |     159 |   170 | Disease     |     0.999351 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_pathogen_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_pathogen_pipeline_en.md
index e24070996c..c96999c375 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_pathogen_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_pathogen_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_patho
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-text = '''Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-val text = "Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-text = '''Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-val text = "Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label        |   confidence |
 |---:|:----------------|--------:|------:|:-----------------|-------------:|
 |  0 | Racecadotril    |       0 |    11 | Medicine         |     0.986453 |
@@ -135,12 +88,6 @@ Results
 | 12 | rabies virus    |     381 |   392 | Pathogen         |     0.738198 |
 | 13 | Lyssavirus      |     395 |   404 | Pathogen         |     0.979239 |
 | 14 | Ephemerovirus   |     410 |   422 | Pathogen         |     0.992292 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_species_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_species_pipeline_en.md
index fcfc6bab0a..12188cb993 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_species_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_ner_species_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_speci
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-text = '''As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-val text = "As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) ."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-text = '''As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) .'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-val text = "As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) ."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | 6C (T)                  |      57 |    62 | SPECIES     |     0.998955 |
@@ -126,12 +79,6 @@ Results
 |  3 | DSM 18155 (T)           |     188 |   200 | SPECIES     |     0.997657 |
 |  4 | Thiomonas perometabolis |     206 |   228 | SPECIES     |     0.999614 |
 |  5 | DSM 18570 (T)           |     230 |   242 | SPECIES     |     0.997146 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_pharmacology_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_pharmacology_pipeline_es.md
index 79baaf8c7f..3bf2f5f07e 100644
--- a/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_pharmacology_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-bert_token_classifier_pharmacology_pipeline_es.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_pharmacol
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label     |   confidence |
 |---:|:----------------|--------:|------:|:--------------|-------------:|
 |  0 | creatinkinasa   |      32 |    44 | PROTEINAS     |     0.999973 |
@@ -132,12 +84,6 @@ Results
 |  9 | Interleukina II |     232 |   246 | PROTEINAS     |     0.999965 |
 | 10 | Dacarbacina     |     249 |   259 | NORMALIZABLES |     0.999988 |
 | 11 | Interferon alfa |     263 |   277 | PROTEINAS     |     0.999961 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_chemd_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-ner_chemd_clinical_pipeline_en.md
index 80494df16a..6ddd66288e 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_chemd_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_chemd_clinical_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_chemd_clinical](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-text = '''Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-val text = "Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-text = '''Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-val text = "Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                         |   begin |   end | ner_label    |   confidence |
 |---:|:-----------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | Lystabactins                       |      65 |    76 | FAMILY       |     0.9841   |
@@ -134,12 +86,6 @@ Results
 | 11 | amino acid                         |     602 |   611 | FAMILY       |     0.4204   |
 | 12 | 4,8-diamino-3-hydroxyoctanoic acid |     614 |   647 | SYSTEMATIC   |     0.9124   |
 | 13 | LySta                              |     650 |   654 | ABBREVIATION |     0.9193   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_glove_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_glove_pipeline_en.md
index 36acc031ba..1bb0ee0bad 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_glove_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_glove_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_deid_generic_glove](https:/
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     1        |
@@ -131,12 +84,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.8586   |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.948667 |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9972   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_pipeline_ar.md b/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_pipeline_ar.md
index 02ca93c4b4..27cc69e5cc 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_pipeline_ar.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_pipeline_ar.md
@@ -32,26 +32,11 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ar", "clinical/models")
-text = '''ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-''
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "ar", "clinical/models")
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-"
-val result = pipeline.fullAnnotate(text)
-```
-</div>
+
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ar", "clinical/models")
@@ -71,9 +56,6 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
 +---------------+----------------------+
 |chunks         |entities          |
 +---------------+----------------------+
@@ -88,9 +70,6 @@ Results
 |أميرة أحمد         |NAME          |
 |ليلى                 |NAME          |
 +---------------+---------------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_pipeline_it.md b/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_pipeline_it.md
index 04ba46bdda..dfca0d42d2 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_deid_generic_pipeline_it.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +70,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   | confidence   |
 |---:|:----------------------|--------:|------:|:------------|:-------------|
 |  0 | Gastone Montanariello |       9 |    29 | NAME        |              |
 |  1 | 49                    |      32 |    33 | AGE         |              |
 |  2 | Ospedale San Camillo  |      55 |    74 | LOCATION    |              |
 |  3 | marzo 2015            |     128 |   137 | DATE        |              |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_glove_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_glove_pipeline_en.md
index 79b0dd94c9..b08f2c2be1 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_glove_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_glove_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_glove](https
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |     1        |
@@ -131,12 +84,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |     0.731325 |
 |  9 | 0295 Keats Street             |     195 |   211 | STREET        |     0.737067 |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |     0.9882   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_pipeline_ar.md b/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_pipeline_ar.md
index d7ebe73426..422106d1fd 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_pipeline_ar.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_pipeline_ar.md
@@ -32,58 +32,11 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-text= '''
-ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
 
-text= '''
-ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -123,13 +76,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 +---------------+--------+
 |chunks         |entities|
 +---------------+--------+
@@ -145,13 +91,6 @@ Results
 |ليلى           |PATIENT |
 |35             |AGE     |
 +---------------+--------+
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_pipeline_it.md b/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_pipeline_it.md
index 361904f022..0d6a0e6d56 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_deid_subentity_pipeline_it.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +71,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   | confidence   |
 |---:|:----------------------|--------:|------:|:------------|:-------------|
 |  0 | Gastone Montanariello |       9 |    29 | PATIENT     |              |
 |  1 | 49                    |      32 |    33 | AGE         |              |
 |  2 | Ospedale San Camillo  |      55 |    74 | HOSPITAL    |              |
 |  3 | marzo 2015            |     128 |   137 | DATE        |              |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_deid_synthetic_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-ner_deid_synthetic_pipeline_en.md
index de26a2e38b..199c31ce68 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_deid_synthetic_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_deid_synthetic_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_deid_synthetic](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     1        |
@@ -131,12 +83,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.968825 |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.7831   |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9985   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_diag_proc_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-ner_diag_proc_pipeline_es.md
index 9d545a525e..c8808c419d 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_diag_proc_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_diag_proc_pipeline_es.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_diag_proc](https://nlp.john
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                             |   begin |   end | ner_label     |   confidence |
 |---:|:--------------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | ENFERMEDAD                            |      12 |    21 | DIAGNOSTICO   |      0.9989  |
@@ -131,12 +84,6 @@ Results
 |  8 | enfermedad de las arterias coronarias |     934 |   970 | DIAGNOSTICO   |      0.75594 |
 |  9 | estenosada                            |    1010 |  1019 | DIAGNOSTICO   |      0.9288  |
 | 10 | LAD                                   |    1068 |  1070 | DIAGNOSTICO   |      0.9365  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_healthcare_pipeline_de.md b/docs/_posts/Cabir40/2023-06-17-ner_healthcare_pipeline_de.md
index 79c0e37564..59d88ffc37 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_healthcare_pipeline_de.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_healthcare_pipeline_de.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_healthcare](https://nlp.joh
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk         |   begin |   end | ner_label             |   confidence |
 |---:|:------------------|--------:|------:|:----------------------|-------------:|
 |  0 | Kleinzellige      |       4 |    15 | MEASUREMENT           |       0.6897 |
@@ -136,12 +88,6 @@ Results
 | 13 | Hauptbronchus     |     195 |   207 | BODY_PART             |       0.9864 |
 | 14 | mittlere          |     223 |   230 | MEASUREMENT           |       0.9651 |
 | 15 | Prävalenz         |     232 |   240 | MEDICAL_CONDITION     |       0.9833 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_healthcare_slim_pipeline_de.md b/docs/_posts/Cabir40/2023-06-17-ner_healthcare_slim_pipeline_de.md
index 4b7a78e439..b36f8f3409 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_healthcare_slim_pipeline_de.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_healthcare_slim_pipeline_de.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_healthcare_slim](https://nl
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                          |   begin |   end | ner_label         |   confidence |
 |---:|:-----------------------------------|--------:|------:|:------------------|-------------:|
 |  0 | Bronchialkarzinom                  |      17 |    33 | MEDICAL_CONDITION |       0.9988 |
@@ -130,12 +82,6 @@ Results
 |  7 | Lunge                              |     179 |   183 | BODY_PART         |       0.9729 |
 |  8 | Hauptbronchus                      |     195 |   207 | BODY_PART         |       0.9987 |
 |  9 | Prävalenz                          |     232 |   240 | MEDICAL_CONDITION |       0.9986 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_es.md
index eec4d02c9e..4e0b150def 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_es.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.98915 |
@@ -131,12 +84,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      1       |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_fr.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_fr.md
index f9b6d6efad..16f553672a 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_fr.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_fr.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                       |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Femme                            |       0 |     4 | HUMAN       |     1        |
@@ -134,12 +86,6 @@ Results
 | 11 | virus de la varicelle et du zona |     518 |   549 | SPECIES     |     0.985429 |
 | 12 | parvovirus B19                   |     554 |   567 | SPECIES     |     0.98595  |
 | 13 | Brucella                         |     636 |   643 | SPECIES     |     0.9995   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_it.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_it.md
index 15bb650d86..7b09a84fcf 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_it.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |      0.9997  |
@@ -130,12 +83,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |      0.9745  |
 |  8 | HIV              |     523 |   525 | SPECIES     |      0.9838  |
 |  9 | paziente         |     634 |   641 | HUMAN       |      0.9994  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_pt.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_pt.md
index 1af64981c6..ac95935ba0 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_pt.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_pt.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito..'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |       0.9849 |
@@ -126,12 +79,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |       0.9912 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |       0.9909 |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |       0.9778 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_ro.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_ro.md
index 482150249c..e19940e50b 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_ro.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_bert_pipeline_ro.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-text = '''O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-val text = "O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-text = '''O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-val text = "O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks           |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------|--------:|------:|:------------|-------------:|
 |  0 | femeie               |       2 |     7 | HUMAN       |     0.9998   |
@@ -129,12 +81,6 @@ Results
 |  6 | enterovirus          |     804 |   814 | SPECIES     |     0.9984   |
 |  7 | parvovirus B19       |     819 |   832 | SPECIES     |     0.99255  |
 |  8 | fetală               |     932 |   937 | HUMAN       |     0.9994   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_biobert_pipeline_en.md
index 2ee2d2459d..58bfc7ccc5 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_biobert_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_living_species_biobert](htt
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |       0.9999 |
@@ -127,12 +80,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |       0.9926 |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |       0.8422 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |       0.9929 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_ca.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_ca.md
index 57edf07e3b..46f25efeef 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_ca.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_ca.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-text = '''Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-val text = "Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-text = '''Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-val text = "Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | Dona                    |       0 |     3 | HUMAN       |     1        |
@@ -135,12 +87,6 @@ Results
 | 12 | virus varicel·la zoster |     717 |   739 | SPECIES     |     0.778333 |
 | 13 | parvovirus B19          |     743 |   756 | SPECIES     |     0.9138   |
 | 14 | Brucella                |     847 |   854 | SPECIES     |     0.9483   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_en.md
index d4bae9fa85..824940b954 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |     0.9993   |
@@ -127,12 +79,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |     0.8838   |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |     0.748667 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |     0.9847   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_es.md
index 407c43d382..959384dc2e 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |       0.9926 |
@@ -131,12 +82,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |       0.9997 |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |       0.9998 |
 | 10 | padres         |     728 |   733 | HUMAN       |       0.9992 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_fr.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_fr.md
index a74929fb91..f6cfc2c09c 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_fr.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_fr.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                       |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Femme                            |       0 |     4 | HUMAN       |     1        |
@@ -134,12 +87,6 @@ Results
 | 11 | virus de la varicelle et du zona |     518 |   549 | SPECIES     |     0.788543 |
 | 12 | parvovirus B19                   |     554 |   567 | SPECIES     |     0.9341   |
 | 13 | Brucella                         |     636 |   643 | SPECIES     |     0.9993   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_gl.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_gl.md
index 7c287a3ab7..66113a2538 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_gl.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_gl.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-text = '''Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-val text = "Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-text = '''Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-val text = "Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Muller                |       0 |     5 | HUMAN       |      0.9998  |
@@ -127,12 +79,6 @@ Results
 |  4 | herpética             |     437 |   445 | SPECIES     |      0.9592  |
 |  5 | púbico                |     551 |   556 | HUMAN       |      0.7293  |
 |  6 | Staphylococcus aureus |     644 |   664 | SPECIES     |      0.87005 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_it.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_it.md
index 68cc61db65..d94bc16a8a 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_it.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_it.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |       0.9992 |
@@ -130,12 +82,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |       0.991  |
 |  8 | HIV              |     523 |   525 | SPECIES     |       0.991  |
 |  9 | paziente         |     634 |   641 | HUMAN       |       0.9978 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_pt.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_pt.md
index 046419e41d..92686d77ad 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_pt.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_pipeline_pt.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |       0.9991 |
@@ -126,12 +79,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |       0.9847 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |       0.91   |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |       0.9996 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_roberta_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_roberta_pipeline_es.md
index 3daafec8ed..ed33d0a13a 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_roberta_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_roberta_pipeline_es.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_living_species_roberta](htt
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.93805 |
@@ -131,12 +83,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      0.9985  |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_living_species_roberta_pipeline_pt.md b/docs/_posts/Cabir40/2023-06-17-ner_living_species_roberta_pipeline_pt.md
index b90e4a0edf..fd4acc93b3 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_living_species_roberta_pipeline_pt.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_living_species_roberta_pipeline_pt.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_living_species_roberta](htt
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-text = '''Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-val text = "Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-text = '''Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-val text = "Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | Mulher        |       0 |     5 | HUMAN       |       0.9975 |
@@ -127,12 +80,6 @@ Results
 |  4 | HBV           |     360 |   362 | SPECIES     |       0.9911 |
 |  5 | HCV           |     365 |   367 | SPECIES     |       0.9858 |
 |  6 | sífilis       |     384 |   390 | SPECIES     |       0.8898 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_nature_nero_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-ner_nature_nero_clinical_pipeline_en.md
index 0310988377..2305a08990 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_nature_nero_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_nature_nero_clinical_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_nature_nero_clinical](https
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-text = '''he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-val text = "he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-text = '''he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-val text = "he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                                   |   begin |   end | ner_label             |   confidence |
 |---:|:---------------------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | perioral cyanosis                            |     236 |   252 | Medicalfinding        |     0.198    |
@@ -142,12 +95,6 @@ Results
 | 19 | diarrhea                                     |     835 |   842 | Medicalfinding        |     0.533    |
 | 20 | bowel movements                              |     849 |   863 | Biologicalprocess     |     0.2036   |
 | 21 | soft in nature                               |     888 |   901 | Biologicalprocess     |     0.170467 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_neoplasms_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-ner_neoplasms_pipeline_es.md
index 7ee6afddff..ff256428ff 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_neoplasms_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_neoplasms_pipeline_es.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_neoplasms](https://nlp.john
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,22 +71,10 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label            |   confidence |
 |---:|:------------|--------:|------:|:---------------------|-------------:|
 |  0 | cáncer      |     140 |   145 | MORFOLOGIA_NEOPLASIA |       0.9997 |
 |  1 | Multi-Link  |    1195 |  1204 | MORFOLOGIA_NEOPLASIA |       0.574  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_oncology_unspecific_posology_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-ner_oncology_unspecific_posology_pipeline_en.md
index 59714f7c28..0849a6c4db 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_oncology_unspecific_posology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_oncology_unspecific_posology_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_oncology_unspecific_posolog
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label            |   confidence |
 |---:|:-----------------|--------:|------:|:---------------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy       |      1       |
@@ -127,12 +80,6 @@ Results
 |  4 | over six courses |     101 |   116 | Posology_Information |      0.9078  |
 |  5 | second cycle     |     150 |   161 | Posology_Information |      0.9853  |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy       |      0.9998  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_pharmacology_pipeline_es.md b/docs/_posts/Cabir40/2023-06-17-ner_pharmacology_pipeline_es.md
index ee1568ccbc..6e4db5e08b 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_pharmacology_pipeline_es.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_pharmacology_pipeline_es.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_pharmacology](https://nlp.j
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +71,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label     |   confidence |
 |---:|:----------------|--------:|------:|:--------------|-------------:|
 |  0 | creatinkinasa   |      31 |    43 | PROTEINAS     |      0.9994  |
@@ -132,12 +85,6 @@ Results
 |  9 | Interleukina II |     231 |   245 | PROTEINAS     |      0.99955 |
 | 10 | Dacarbacina     |     248 |   258 | NORMALIZABLES |      0.9996  |
 | 11 | Interferon alfa |     262 |   276 | PROTEINAS     |      0.99935 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-ner_supplement_clinical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-ner_supplement_clinical_pipeline_en.md
index 1d07ca0454..ced047dbd1 100644
--- a/docs/_posts/Cabir40/2023-06-17-ner_supplement_clinical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-ner_supplement_clinical_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [ner_supplement_clinical](https:
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-text = '''Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-val text = "Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-text = '''Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-val text = "Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +71,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | nervousness  |      42 |    52 | CONDITION   |      0.9999  |
 |  1 | night sleep  |      70 |    80 | BENEFIT     |      0.80775 |
 |  2 | hair         |     109 |   112 | BENEFIT     |      0.9997  |
 |  3 | nail growth  |     118 |   128 | BENEFIT     |      0.9997  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-17-nerdl_tumour_demo_pipeline_en.md b/docs/_posts/Cabir40/2023-06-17-nerdl_tumour_demo_pipeline_en.md
index 97ea7dac3f..e7bfb6ee5a 100644
--- a/docs/_posts/Cabir40/2023-06-17-nerdl_tumour_demo_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-17-nerdl_tumour_demo_pipeline_en.md
@@ -32,52 +32,11 @@ This pretrained pipeline is built on the top of [nerdl_tumour_demo](https://nlp.
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,21 +71,9 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label    | confidence   |
 |---:|:-----------------|--------:|------:|:-------------|:-------------|
 |  0 | breast carcinoma |      35 |    50 | Localization |              |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-19-ner_profiling_clinical_en.md b/docs/_posts/Cabir40/2023-06-19-ner_profiling_clinical_en.md
index efd77832c1..5419abfc1e 100644
--- a/docs/_posts/Cabir40/2023-06-19-ner_profiling_clinical_en.md
+++ b/docs/_posts/Cabir40/2023-06-19-ner_profiling_clinical_en.md
@@ -38,6 +38,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -67,38 +68,10 @@ nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline("ner_profiling_clinical", "en", "clinical/models")
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_clinical", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 ******************** ner_jsl Model Results ******************** 
 
 [('28-year-old', 'Age'), ('female', 'Gender'), ('gestational diabetes mellitus', 'Diabetes'), ('eight years prior', 'RelativeDate'), ('subsequent', 'Modifier'), ('type two diabetes mellitus', 'Diabetes'), ('T2DM', 'Diabetes'), ('HTG-induced pancreatitis', 'Disease_Syndrome_Disorder'), ('three years prior', 'RelativeDate'), ('acute', 'Modifier'), ('hepatitis', 'Communicable_Disease'), ('obesity', 'Obesity'), ('body mass index', 'Symptom'), ('33.5 kg/m2', 'Weight'), ('one-week', 'Duration'), ('polyuria', 'Symptom'), ('polydipsia', 'Symptom'), ('poor appetite', 'Symptom'), ('vomiting', 'Symptom')]
@@ -124,10 +97,6 @@ Results
 [('female', 'Organism_Attribute'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('T2DM', 'Disease_or_Syndrome'), ('HTG-induced pancreatitis', 'Disease_or_Syndrome'), ('associated with', 'Qualitative_Concept'), ('acute hepatitis', 'Disease_or_Syndrome'), ('obesity', 'Disease_or_Syndrome'), ('body mass index', 'Clinical_Attribute'), ('BMI', 'Clinical_Attribute'), ('polyuria', 'Sign_or_Symptom'), ('polydipsia', 'Sign_or_Symptom'), ('poor appetite', 'Sign_or_Symptom'), ('vomiting', 'Sign_or_Symptom')]
 
 ...
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-21-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md b/docs/_posts/Cabir40/2023-06-21-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md
index 82c9f5afc7..fd77866bbf 100644
--- a/docs/_posts/Cabir40/2023-06-21-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-21-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md
@@ -55,11 +55,9 @@ val result = pipeline.annotate(My son has been to two doctors who gave him antib
 ## Results
 
 ```bash
-
 | text                                                                                                                   | prediction       |
 |:-----------------------------------------------------------------------------------------------------------------------|:-----------------|
 | My son has been to two doctors who gave him antibiotic drops but they also say the problem might related to allergies. | Consulted_By_HCP |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_drug_side_effect_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_drug_side_effect_pipeline_en.md
index 7ad99eace0..00d2c7a262 100644
--- a/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_drug_side_effect_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_drug_side_effect_pipeline_en.md
@@ -55,11 +55,9 @@ val result = pipeline.annotate(I felt kind of dizzy after taking that medication
 ## Results
 
 ```bash
-
 | text                                                           | prediction   |
 |:---------------------------------------------------------------|:-------------|
 | I felt kind of dizzy after taking that medication for a month. | Drug_AE      |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_self_report_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_self_report_pipeline_en.md
index a97c7e2785..ed38943952 100644
--- a/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_self_report_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_self_report_pipeline_en.md
@@ -55,12 +55,9 @@ val result = pipeline.annotate(My friend was treated for her skin cancer two yea
 ## Results
 
 ```bash
-
 | text                                                     | prediction   |
 |:---------------------------------------------------------|:-------------|
 | My friend was treated for her skin cancer two years ago. | 3rd_Person   |
-
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_side_effect_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_side_effect_pipeline_en.md
index 936c51df16..8097e908d0 100644
--- a/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_side_effect_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_side_effect_pipeline_en.md
@@ -57,11 +57,9 @@ val result = pipeline.annotate(I felt kind of dizzy after taking that medication
 ## Results
 
 ```bash
-
 | text                                                           | prediction   |
 |:---------------------------------------------------------------|:-------------|
 | I felt kind of dizzy after taking that medication for a month. | True         |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_sound_medical_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_sound_medical_pipeline_en.md
index 92f374b2cf..94ab8cb026 100644
--- a/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_sound_medical_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-bert_sequence_classifier_vop_sound_medical_pipeline_en.md
@@ -54,11 +54,9 @@ val result = pipeline.annotate(I had a lung surgery for emphyema and after surge
 ## Results
 
 ```bash
-
 | text                                                                               | prediction   |
 |:-----------------------------------------------------------------------------------|:-------------|
 | I had a lung surgery for emphyema and after surgery my xray showing some recovery. | True         |
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-clinical_deidentification_ar.md b/docs/_posts/Cabir40/2023-06-22-clinical_deidentification_ar.md
index 01109e7c4b..f9171a87a3 100644
--- a/docs/_posts/Cabir40/2023-06-22-clinical_deidentification_ar.md
+++ b/docs/_posts/Cabir40/2023-06-22-clinical_deidentification_ar.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify Arabic PHI information from medical text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -118,99 +119,11 @@ val result = deid_pipeline.annotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ar", "clinical/models")
-
-text = '''
-
-ملاحظات سريرية - مريض الربو:
-
-التاريخ: 30 مايو 2023
-اسم المريضة: ليلى حسن
-
-تم تسجيل المريض في النظام باستخدام رقم الضمان الاجتماعي 123456789012.
-
-العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة
-الرمز البريدي: 54321
-البلد: المملكة العربية السعودية
-اسم المستشفى: مستشفى النور
-اسم الطبيب: د. أميرة أحمد
-
-تفاصيل الحالة:
-المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة.
-
-الخطة:
-
-تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج.
-يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة.
-يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة.
-يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو.
-تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح
 
-'''
-result = deid_pipeline.annotate(text)
-
-print("\nMasked with entity labels")
-print("-"*30)
-print("\n".join(result['masked_with_entity']))
-print("\nMasked with chars")
-print("-"*30)
-print("\n".join(result['masked_with_chars']))
-print("\nMasked with fixed length chars")
-print("-"*30)
-print("\n".join(result['masked_fixed_length_chars']))
-print("\nObfuscated")
-print("-"*30)
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification","ar","clinical/models")
-
-val text = '''
-
-ملاحظات سريرية - مريض الربو:
-
-التاريخ: 30 مايو 2023
-اسم المريضة: ليلى حسن
-
-تم تسجيل المريض في النظام باستخدام رقم الضمان الاجتماعي 123456789012.
-
-العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة
-الرمز البريدي: 54321
-البلد: المملكة العربية السعودية
-اسم المستشفى: مستشفى النور
-اسم الطبيب: د. أميرة أحمد
-
-تفاصيل الحالة:
-المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة.
-
-الخطة:
-
-تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج.
-يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة.
-يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة.
-يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو.
-تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح
-
-'''
-
-val result = deid_pipeline.annotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 Masked with entity labels
 ------------------------------
 ملاحظات سريرية - مريض الربو:
@@ -306,9 +219,6 @@ Obfuscated
 يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة.
 يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو.
 تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-cvx_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-cvx_resolver_pipeline_en.md
index 24de5b760f..b8a70116ed 100644
--- a/docs/_posts/Cabir40/2023-06-22-cvx_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-cvx_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities with their corresponding CVX codes. You
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,38 +61,11 @@ nlu.load("en.resolve.cvx_pipeline").predict("""The patient has a history of infl
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-resolver_pipeline = PretrainedPipeline("cvx_resolver_pipeline", "en", "clinical/models")
-
-text= "The patient has a history of influenza vaccine, tetanus and DTaP"
-
-result = resolver_pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val resolver_pipeline = new PretrainedPipeline("cvx_resolver_pipeline", "en", "clinical/models")
 
-val result = resolver_pipeline.fullAnnotate("The patient has a history of influenza vaccine, tetanus and DTaP")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.cvx_pipeline").predict("""The patient has a history of influenza vaccine, tetanus and DTaP""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-----------------+---------+--------+
 |chunk            |ner_chunk|cvx_code|
 +-----------------+---------+--------+
@@ -99,9 +73,6 @@ Results
 |tetanus          |Vaccine  |35      |
 |DTaP             |Vaccine  |20      |
 +-----------------+---------+--------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-icd10cm_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-icd10cm_resolver_pipeline_en.md
index d1b7a2e5f0..862b7fd644 100644
--- a/docs/_posts/Cabir40/2023-06-22-icd10cm_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-icd10cm_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities with their corresponding ICD-10-CM codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,38 +61,10 @@ nlu.load("en.icd10cm_resolver.pipeline").predict("""A 28-year-old female with a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-resolver_pipeline = PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")
-
-text = """A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage"""
-
-result = resolver_pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val resolver_pipeline = new PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")
-
-val result = resolver_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm_resolver.pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-----------------------------+---------+------------+
 |chunk                        |ner_chunk|icd10cm_code|
 +-----------------------------+---------+------------+
@@ -99,9 +72,6 @@ Results
 |anisakiasis                  |PROBLEM  |B81.0       |
 |fetal and neonatal hemorrhage|PROBLEM  |P545        |
 +-----------------------------+---------+------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-icd9_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-icd9_resolver_pipeline_en.md
index e256b5e366..a5a8d36986 100644
--- a/docs/_posts/Cabir40/2023-06-22-icd9_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-icd9_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities with their corresponding ICD-9-CM codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,39 +61,10 @@ nlu.load("en.resolve.icd9.pipeline").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd9_resolver_pipeline", "en", "clinical/models")
-
-text= A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd9_resolver_pipeline", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.icd9.pipeline").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +-----------------------------+---------+---------+
 |chunk                        |ner_chunk|icd9_code|
 +-----------------------------+---------+---------+
@@ -100,10 +72,6 @@ Results
 |anisakiasis                  |PROBLEM  |127.1    |
 |fetal and neonatal hemorrhage|PROBLEM  |772      |
 +-----------------------------+---------+---------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-ner_vop_anatomy_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-ner_vop_anatomy_pipeline_en.md
index 3bcd3910a1..71339fd44e 100644
--- a/docs/_posts/Cabir40/2023-06-22-ner_vop_anatomy_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-ner_vop_anatomy_pipeline_en.md
@@ -58,15 +58,12 @@ Ugh, I pulled a muscle in my neck from sleeping weird last night. It's like a kn
 ## Results
 
 ```bash
-
 | chunk     | ner_label   |
 |:----------|:------------|
 | muscle    | BodyPart    |
 | neck      | BodyPart    |
 | trapezius | BodyPart    |
 | head      | BodyPart    |
-
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-ner_vop_clinical_dept_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-ner_vop_clinical_dept_pipeline_en.md
index d947450a80..ba92b22117 100644
--- a/docs/_posts/Cabir40/2023-06-22-ner_vop_clinical_dept_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-ner_vop_clinical_dept_pipeline_en.md
@@ -59,13 +59,10 @@ My little brother is having surgery tomorrow in the orthopedic department. He is
 ## Results
 
 ```bash
-
 | chunk                 | ner_label     |
 |:----------------------|:--------------|
 | orthopedic department | ClinicalDept  |
 | titanium plate        | MedicalDevice |
-
-
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md
index f649106259..e1c74744d6 100644
--- a/docs/_posts/Cabir40/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md
@@ -60,49 +60,16 @@ nlu.load("en.classify.rct_binary_biobert.pipeline").predict("""Abstract:Based on
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-
-pipeline = PretrainedPipeline("rct_binary_classifier_biobert_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rct_binary_classifier_biobert_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.rct_binary_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |true|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-rct_binary_classifier_use_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-rct_binary_classifier_use_pipeline_en.md
index 5d8cf0e405..57830000c8 100644
--- a/docs/_posts/Cabir40/2023-06-22-rct_binary_classifier_use_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-rct_binary_classifier_use_pipeline_en.md
@@ -59,48 +59,15 @@ nlu.load("en.classify.rct_binary_use.pipeline").predict("""Abstract:Based on the
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rct_binary_classifier_use_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rct_binary_classifier_use_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.rct_binary_use.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |true|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md
index 7e477a53ef..76fa51c4d6 100644
--- a/docs/_posts/Cabir40/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_biomedical_pubmed](h
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_biomedical_pubmed_pipeline", "en", "clinical/models")
-
-text = """Residual disease after initial surgery for ovarian cancer is the strongest prognostic factor for survival. However, the extent of surgical resection required to achieve optimal cytoreduction is controversial. Our goal was to estimate the effect of aggressive surgical resection on ovarian cancer patient survival.\n A retrospective cohort study of consecutive patients with International Federation of Gynecology and Obstetrics stage IIIC ovarian cancer undergoing primary surgery was conducted between January 1, 1994, and December 31, 1998. The main outcome measures were residual disease after cytoreduction, frequency of radical surgical resection, and 5-year disease-specific survival.\n The study comprised 194 patients, including 144 with carcinomatosis. The mean patient age and follow-up time were 64.4 and 3.5 years, respectively. After surgery, 131 (67.5%) of the 194 patients had less than 1 cm of residual disease (definition of optimal cytoreduction). Considering all patients, residual disease was the only independent predictor of survival; the need to perform radical procedures to achieve optimal cytoreduction was not associated with a decrease in survival. For the subgroup of patients with carcinomatosis, residual disease and the performance of radical surgical procedures were the only independent predictors. Disease-specific survival was markedly improved for patients with carcinomatosis operated on by surgeons who most frequently used radical procedures compared with those least likely to use radical procedures (44% versus 17%, P < .001).\n Overall, residual disease was the only independent predictor of survival. Minimizing residual disease through aggressive surgical resection was beneficial, especially in patients with carcinomatosis."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_biomedical_pubmed_pipeline", "en", "clinical/models")
-
-val text = """Residual disease after initial surgery for ovarian cancer is the strongest prognostic factor for survival. However, the extent of surgical resection required to achieve optimal cytoreduction is controversial. Our goal was to estimate the effect of aggressive surgical resection on ovarian cancer patient survival.\n A retrospective cohort study of consecutive patients with International Federation of Gynecology and Obstetrics stage IIIC ovarian cancer undergoing primary surgery was conducted between January 1, 1994, and December 31, 1998. The main outcome measures were residual disease after cytoreduction, frequency of radical surgical resection, and 5-year disease-specific survival.\n The study comprised 194 patients, including 144 with carcinomatosis. The mean patient age and follow-up time were 64.4 and 3.5 years, respectively. After surgery, 131 (67.5%) of the 194 patients had less than 1 cm of residual disease (definition of optimal cytoreduction). Considering all patients, residual disease was the only independent predictor of survival; the need to perform radical procedures to achieve optimal cytoreduction was not associated with a decrease in survival. For the subgroup of patients with carcinomatosis, residual disease and the performance of radical surgical procedures were the only independent predictors. Disease-specific survival was markedly improved for patients with carcinomatosis operated on by surgeons who most frequently used radical procedures compared with those least likely to use radical procedures (44% versus 17%, P < .001).\n Overall, residual disease was the only independent predictor of survival. Minimizing residual disease through aggressive surgical resection was beneficial, especially in patients with carcinomatosis."""
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,18 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
-
 The results of this review suggest that aggressive ovarian cancer surgery is associated with a significant reduction in the risk of recurrence and a reduction in the number of radical versus conservative surgical resections. However, the results of this review are based on only one small trial. Further research is needed to determine the role of aggressive ovarian cancer surgery in women with stage IIIC ovarian cancer.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md
index 0cd596b8c0..c8c82c4c17 100644
--- a/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md
@@ -34,84 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_guidelines_
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_guidelines_large_pipeline", "en", "clinical/models")
-
-text = """Clinical Guidelines for Breast Cancer:
-
-Breast cancer is the most common type of cancer among women. It occurs when the cells in the breast start growing abnormally, forming a lump or mass. This can result in the spread of cancerous cells to other parts of the body. Breast cancer may occur in both men and women but is more prevalent in women.
-
-The exact cause of breast cancer is unknown. However, several risk factors can increase your likelihood of developing breast cancer, such as:
-- A personal or family history of breast cancer
-- A genetic mutation, such as BRCA1 or BRCA2
-- Exposure to radiation
-- Age (most commonly occurring in women over 50)
-- Early onset of menstruation or late menopause
-- Obesity
-- Hormonal factors, such as taking hormone replacement therapy
-
-Breast cancer may not present symptoms during its early stages. Symptoms typically manifest as the disease progresses. Some notable symptoms include:
-- A lump or thickening in the breast or underarm area
-- Changes in the size or shape of the breast
-- Nipple discharge
-- Nipple changes in appearance, such as inversion or flattening
-- Redness or swelling in the breast
-
-Treatment for breast cancer depends on several factors, including the stage of the cancer, the location of the tumor, and the individual's overall health. Common treatment options include:
-- Surgery (such as lumpectomy or mastectomy)
-- Radiation therapy
-- Chemotherapy
-- Hormone therapy
-- Targeted therapy
-
-Early detection is crucial for the successful treatment of breast cancer. Women are advised to routinely perform self-examinations and undergo regular mammogram testing starting at age 40. If you notice any changes in your breast tissue, consult with your healthcare provider immediately.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_guidelines_large_pipeline", "en", "clinical/models")
-
-val text = """Clinical Guidelines for Breast Cancer:
 
-Breast cancer is the most common type of cancer among women. It occurs when the cells in the breast start growing abnormally, forming a lump or mass. This can result in the spread of cancerous cells to other parts of the body. Breast cancer may occur in both men and women but is more prevalent in women.
-
-The exact cause of breast cancer is unknown. However, several risk factors can increase your likelihood of developing breast cancer, such as:
-- A personal or family history of breast cancer
-- A genetic mutation, such as BRCA1 or BRCA2
-- Exposure to radiation
-- Age (most commonly occurring in women over 50)
-- Early onset of menstruation or late menopause
-- Obesity
-- Hormonal factors, such as taking hormone replacement therapy
-
-Breast cancer may not present symptoms during its early stages. Symptoms typically manifest as the disease progresses. Some notable symptoms include:
-- A lump or thickening in the breast or underarm area
-- Changes in the size or shape of the breast
-- Nipple discharge
-- Nipple changes in appearance, such as inversion or flattening
-- Redness or swelling in the breast
-
-Treatment for breast cancer depends on several factors, including the stage of the cancer, the location of the tumor, and the individual's overall health. Common treatment options include:
-- Surgery (such as lumpectomy or mastectomy)
-- Radiation therapy
-- Chemotherapy
-- Hormone therapy
-- Targeted therapy
-
-Early detection is crucial for the successful treatment of breast cancer. Women are advised to routinely perform self-examinations and undergo regular mammogram testing starting at age 40. If you notice any changes in your breast tissue, consult with your healthcare provider immediately.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -188,13 +111,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
-
 Overview of the disease: Breast cancer is the most common type of cancer among women, occurring when the cells in the breast start growing abnormally, forming a lump or mass. It can result in the spread of cancerous cells to other parts of the body. 
 
 Causes: The exact cause of breast cancer is unknown, but several risk factors can increase the likelihood of developing it, such as a personal or family history, a genetic mutation, exposure to radiation, age, early onset of menstruation or late menopause, obesity, and hormonal factors. 
@@ -202,10 +122,6 @@ Causes: The exact cause of breast cancer is unknown, but several risk factors ca
 Symptoms: Symptoms of breast cancer typically manifest as the disease progresses, including a lump or thickening in the breast or underarm area, changes in the size or shape of the breast, nipple discharge, nipple changes in appearance, and redness or swelling in the breast. 
 
 Treatment recommendations: Treatment for breast cancer depends on several factors, including the stage of the cancer, the location of the tumor, and the individual's overall health. Common treatment options include surgery, radiation therapy, chemotherapy, hormone therapy, and targeted therapy. Early detection is crucial for successful treatment of breast cancer. Women are advised to routinely perform self-examinations and undergo regular mammogram testing starting at age 40.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md
index 645dd206c6..c820ca4536 100644
--- a/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_jsl_augment
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -70,56 +71,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_jsl_augmented_pipeline", "en", "clinical/models")
-
-text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_jsl_augmented_pipeline", "en", "clinical/models")
-
-val text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 A 78-year-old female with hypertension, syncope, and spinal stenosis returns for a recheck. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema. Her medications include Atenolol, Premarin, calcium with vitamin D, multivitamin, aspirin, and TriViFlor. She also has Elocon cream and Synalar cream for rash.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_jsl_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_jsl_pipeline_en.md
index 82affdbbe2..6f1f99c88e 100644
--- a/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_jsl_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_jsl_pipeline_en.md
@@ -34,44 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_jsl](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_jsl_pipeline", "en", "clinical/models")
 
-text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_jsl_pipeline", "en", "clinical/models")
-
-val text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -108,18 +71,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
-
 A 78-year-old female with hypertension, syncope, and spinal stenosis returns for recheck. She denies chest pain, palpations, orthopnea, nocturnal dyspnea, or edema. She is on multiple medications and has Elocon cream and Synalar cream for rash.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_questions_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_questions_pipeline_en.md
index ba71bf4fa3..7d63afd891 100644
--- a/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_questions_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-summarizer_clinical_questions_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_questions](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_questions_pipeline", "en", "clinical/models")
-
-text = """
-Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_questions_pipeline", "en", "clinical/models")
-
-val text = """
-Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.
-"""
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 What are the treatments for hyperthyroidism?
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-summarizer_generic_jsl_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-summarizer_generic_jsl_pipeline_en.md
index 2bd7b0e434..92ca2c58f3 100644
--- a/docs/_posts/Cabir40/2023-06-22-summarizer_generic_jsl_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-summarizer_generic_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_generic_jsl](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -70,56 +71,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_generic_jsl_pipeline", "en", "clinical/models")
-
-text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_generic_jsl_pipeline", "en", "clinical/models")
-
-val text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 The patient is 78 years old and has hypertension. She has a history of chest pain, palpations, orthopedics, and spinal stenosis. She has a prescription of Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin, and TriViFlor 25 mg two pills daily.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-22-summarizer_radiology_pipeline_en.md b/docs/_posts/Cabir40/2023-06-22-summarizer_radiology_pipeline_en.md
index fc471a39f8..9ad3a94a5f 100644
--- a/docs/_posts/Cabir40/2023-06-22-summarizer_radiology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-22-summarizer_radiology_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_radiology](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -86,72 +87,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_radiology_pipeline", "en", "clinical/models")
-
-text = """INDICATIONS: Peripheral vascular disease with claudication.
-
-RIGHT:
-1. Normal arterial imaging of right lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic.
-4. Ankle brachial index is 0.96.
-
-LEFT:
-1. Normal arterial imaging of left lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic throughout except in posterior tibial artery where it is biphasic.
-4. Ankle brachial index is 1.06.
-
-IMPRESSION: 
-Normal arterial imaging of both lower lobes.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_radiology_pipeline", "en", "clinical/models")
-
-val text = """INDICATIONS: Peripheral vascular disease with claudication.
-
-RIGHT:
-1. Normal arterial imaging of right lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic.
-4. Ankle brachial index is 0.96.
-
-LEFT:
-1. Normal arterial imaging of left lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic throughout except in posterior tibial artery where it is biphasic.
-4. Ankle brachial index is 1.06.
-
-IMPRESSION: 
-Normal arterial imaging of both lower lobes.
-"""
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 The patient has peripheral vascular disease with claudication. The right lower extremity shows normal arterial imaging, but the peak systolic velocity is normal. The arterial waveform is triphasic throughout, except for the posterior tibial artery, which is biphasic. The ankle brachial index is 0.96. The impression is normal arterial imaging of both lower lobes.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md
index 7586dbea27..e845fc09ed 100644
--- a/docs/_posts/Cabir40/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities (Diseases and Syndromes) with their corre
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,34 +59,9 @@ nlu.load("en.map_entity.umls_disease_syndrome_resolver").predict("""A 34-year-ol
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline= PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
-pipeline.annotate("A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline= PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
-val pipeline.annotate("A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.umls_disease_syndrome_resolver").predict("""A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 +-----------------------------+---------+---------+
 |chunk                        |ner_label|umls_code|
 +-----------------------------+---------+---------+
@@ -94,10 +70,6 @@ Results
 |acyclovir allergy            |PROBLEM  |C0571297 |
 |polyuria                     |PROBLEM  |C0018965 |
 +-----------------------------+---------+---------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-23-umls_drug_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-06-23-umls_drug_resolver_pipeline_en.md
index 93ed53f075..851319ceab 100644
--- a/docs/_posts/Cabir40/2023-06-23-umls_drug_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-23-umls_drug_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities (Clinical Drugs) with their corresponding
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -56,43 +57,16 @@ nlu.load("en.map_entity.umls_drug_resolver").predict("""The patient was given Ad
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
-pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
-val pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.umls_drug_resolver").predict("""The patient was given Adapin 10 MG, coumadn 5 mg""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +------------+---------+---------+
 |chunk       |ner_label|umls_code|
 +------------+---------+---------+
 |Adapin 10 MG|DRUG     |C2930083 |
 |coumadn 5 mg|DRUG     |C2723075 |
 +------------+---------+---------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-23-umls_major_concepts_resolver_pipeline_en.md b/docs/_posts/Cabir40/2023-06-23-umls_major_concepts_resolver_pipeline_en.md
index cd9afadf21..9192cce06e 100644
--- a/docs/_posts/Cabir40/2023-06-23-umls_major_concepts_resolver_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-23-umls_major_concepts_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities (Clinical Major Concepts) with their corr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -56,34 +57,11 @@ nlu.load("en.map_entity.umls_major_concepts_resolver").predict("""The patient co
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline= PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
-pipeline.annotate("The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline= PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
-val pipeline.annotate("The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.umls_major_concepts_resolver").predict("""The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-----------+-----------------------------------+---------+
 |chunk      |ner_label                          |umls_code|
 +-----------+-----------------------------------+---------+
@@ -91,9 +69,6 @@ Results
 |stairs     |Daily_or_Recreational_Activity     |C4300351 |
 |Arthroscopy|Therapeutic_or_Preventive_Procedure|C0179144 |
 +-----------+-----------------------------------+---------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md b/docs/_posts/Cabir40/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md
index f144a093b5..0b7553e409 100644
--- a/docs/_posts/Cabir40/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_granular](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_granular_pipeline", "en", "clinical/models")
-
-text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_granular_pipeline", "en", "clinical/models")
 
-val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -79,18 +58,12 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | left         |      36 |    39 | Direction   |       0.9981 |
 |  1 | breast       |      41 |    46 | Site_Breast |       0.9969 |
 |  2 | lungs        |      82 |    86 | Site_Lung   |       0.9978 |
 |  3 | liver        |      99 |   103 | Site_Liver  |       0.9999 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-26-ner_oncology_pipeline_en.md b/docs/_posts/Cabir40/2023-06-26-ner_oncology_pipeline_en.md
index 5ebe0c9606..48f0e8789a 100644
--- a/docs/_posts/Cabir40/2023-06-26-ner_oncology_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-26-ner_oncology_pipeline_en.md
@@ -34,32 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_pipeline", "en", "clinical/models")
-
-text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_pipeline", "en", "clinical/models")
-
-val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -84,12 +59,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                     |   begin |   end | ner_label             |   confidence |
 |---:|:-------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | left                           |      31 |    34 | Direction             |     0.9913   |
@@ -116,9 +89,6 @@ Results
 | 21 | 600 mg/m2                      |     390 |   398 | Dosage                |     0.9647   |
 | 22 | six courses                    |     406 |   416 | Cycle_Count           |     0.6798   |
 | 23 | first line                     |     422 |   431 | Line_Of_Therapy       |     0.9792   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-06-26-oncology_diagnosis_pipeline_en.md b/docs/_posts/Cabir40/2023-06-26-oncology_diagnosis_pipeline_en.md
index 9272bf2711..4fb030a1a6 100644
--- a/docs/_posts/Cabir40/2023-06-26-oncology_diagnosis_pipeline_en.md
+++ b/docs/_posts/Cabir40/2023-06-26-oncology_diagnosis_pipeline_en.md
@@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status, Relation Extr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -65,44 +66,11 @@ According to her last CT, she has no lung metastases.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_diagnosis_pipeline", "en", "clinical/models")
-
-text = '''Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
-According to her last CT, she has no lung metastases.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_diagnosis_pipeline", "en", "clinical/models")
-
-val text = "Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
-According to her last CT, she has no lung metastases."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_diagnosis.pipeline").predict("""Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
-According to her last CT, she has no lung metastases.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk      | ner_label         |
@@ -199,10 +167,6 @@ Results
 | carcinoma  | Cancer_Dx         | 8010/3 | carcinoma         |
 | lung       | Site_Lung         | C34.9  | lung              |
 | metastases | Metastasis        | 8000/6 | tumor, metastatic |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/Cabir40/2023-08-17-clinical_notes_qa_large_onnx_en.md b/docs/_posts/Cabir40/2023-08-17-clinical_notes_qa_large_onnx_en.md
index 9b5820e452..9a3deaf7ad 100644
--- a/docs/_posts/Cabir40/2023-08-17-clinical_notes_qa_large_onnx_en.md
+++ b/docs/_posts/Cabir40/2023-08-17-clinical_notes_qa_large_onnx_en.md
@@ -37,6 +37,7 @@ This model is capable of open-book question answering on Medical Notes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 document_assembler = MultiDocumentAssembler()\
     .setInputCols("question", "context")\
diff --git a/docs/_posts/Cabir40/2023-10-03-multiclassifierdl_respiratory_disease_en.md b/docs/_posts/Cabir40/2023-10-03-multiclassifierdl_respiratory_disease_en.md
index 654e6680f8..97877b8826 100644
--- a/docs/_posts/Cabir40/2023-10-03-multiclassifierdl_respiratory_disease_en.md
+++ b/docs/_posts/Cabir40/2023-10-03-multiclassifierdl_respiratory_disease_en.md
@@ -188,6 +188,6 @@ COPD                 267   27	   52	   0.90816325	 0.8369906	 0.8711256
 No                   55	   8	   19	   0.8730159	 0.7432432	 0.8029197
 Chronic bronchitis   241   27	   25	   0.8992537	 0.90601504	 0.9026217
 Asthma               104   15	   25	   0.8739496	 0.8062016	 0.83870965
-Macro-average        823   110   193   0.83017427  0.7265169   0.7748944
-Micro-average        823   110   193   0.88210076  0.8100393   0.84453565
+Macro-average        823   110     193     0.83017427    0.7265169   0.7748944
+Micro-average        823   110     193     0.88210076    0.8100393   0.84453565
 ```
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-06-11-ner_profiling_biobert_en.md b/docs/_posts/ahmedlone127/2023-06-11-ner_profiling_biobert_en.md
index d41c2719ce..617f041e15 100644
--- a/docs/_posts/ahmedlone127/2023-06-11-ner_profiling_biobert_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-11-ner_profiling_biobert_en.md
@@ -38,6 +38,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -67,38 +68,10 @@ nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 ******************** ner_diseases_biobert Model Results ******************** 
 
 [('gestational diabetes mellitus', 'Disease'), ('type two diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('HTG-induced pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('poor appetite', 'Disease'), ('vomiting', 'Disease')]
@@ -122,10 +95,6 @@ Results
 ******************** ner_risk_factors_biobert Model Results ******************** 
 
 [('diabetes mellitus', 'DIABETES'), ('subsequent type two diabetes mellitus', 'DIABETES'), ('obesity', 'OBESE')]
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ade_tweet_binary_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ade_tweet_binary_pipeline_en.md
index db3e07520f..34cdb91fbe 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ade_tweet_binary_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ade_tweet_binary_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ade_tweet
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,21 +55,16 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | depressed        |      44 |    52 | ADE         |     0.999755 |
 |  1 | angry            |      73 |    77 | ADE         |     0.999608 |
 |  2 | insulin blocking |      97 |   112 | ADE         |     0.738712 |
 |  3 | sugar crashes    |     147 |   159 | ADE         |     0.993742 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_disease_mentions_tweet_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
index 9df7244b10..1d0316eb46 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_disease_m
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-text = '''El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-val text = "El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Neumonía en el pulmón |      41 |    61 | ENFERMEDAD  |     0.999969 |
@@ -89,9 +66,6 @@ Results
 |  2 | Faringitis aguda      |      94 |   109 | ENFERMEDAD  |     0.999969 |
 |  3 | infección de orina    |     113 |   130 | ENFERMEDAD  |     0.999969 |
 |  4 | Gripe                 |     150 |   154 | ENFERMEDAD  |     0.999983 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_negation_uncertainty_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_negation_uncertainty_pipeline_es.md
index 80ee5a428b..6a35ea6ffa 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_negation_uncertainty_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_negation_uncertainty_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_negation_
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
 
-val text = "Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                              |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | probable                                               |      16 |    23 | UNC         |     0.999994 |
@@ -92,9 +70,6 @@ Results
 |  5 | se realizó paracentesis control por escasez de liquido |     178 |   231 | NSCO        |     0.999995 |
 |  6 | susceptible de                                         |     293 |   306 | UNC         |     0.999986 |
 |  7 | ca basocelular perlado                                 |     308 |   329 | USCO        |     0.99999  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_ade_binary_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_ade_binary_pipeline_en.md
index bb74aa5ee9..3fe0edbd76 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_ade_binary_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_ade_binary_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ade_b
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,20 +55,15 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk     |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | depressed     |      44 |    52 | ADE         |     0.990846 |
 |  1 | angry         |      73 |    77 | ADE         |     0.972025 |
 |  2 | sugar crashes |     147 |   159 | ADE         |     0.933623 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_anatem_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_anatem_pipeline_en.md
index 60696b6c2a..f2ac5e3216 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_anatem_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_anatem_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_anate
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-text = '''Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
 
-val text = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------|--------:|------:|:------------|-------------:|
 |  0 | Malignant cells        |       0 |    14 | Anatomy     |     0.999951 |
@@ -90,9 +68,6 @@ Results
 |  3 | breast                 |     343 |   348 | Anatomy     |     0.999842 |
 |  4 | ovarian                |     351 |   357 | Anatomy     |     0.99998  |
 |  5 | prostate cancer        |     364 |   378 | Anatomy     |     0.999968 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
index 5e9daa8b83..d0e897982d 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc2gm
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-text = '''ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
 
-val text = "ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -79,9 +58,6 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label    |   confidence |
 |---:|:------------|--------:|------:|:-------------|-------------:|
 |  0 | ROCK-I      |       0 |     5 | GENE/PROTEIN |     0.999978 |
@@ -93,9 +69,6 @@ Results
 |  6 | Rho         |     225 |   227 | GENE/PROTEIN |     0.999976 |
 |  7 | boxA        |     247 |   250 | GENE/PROTEIN |     0.999837 |
 |  8 | rut sites   |     256 |   264 | GENE/PROTEIN |     0.99115  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
index e5f173a36d..793c44fc21 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc4ch
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                       |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------|--------:|------:|:------------|-------------:|
 |  0 | triterpenes                     |      33 |    43 | CHEM        |     0.99999  |
@@ -97,9 +74,6 @@ Results
 | 10 | 4 - hydroxybenzoic acid         |     184 |   206 | CHEM        |     0.999973 |
 | 11 | gallic and protocatechuic acids |     209 |   239 | CHEM        |     0.999984 |
 | 12 | isocorilagin                    |     245 |   256 | CHEM        |     0.999985 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
index fe87577c45..f9cee39860 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc5cd
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
 
-val text = "The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                 |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | amphetamine               |     128 |   138 | CHEM        |     0.999973 |
@@ -91,9 +69,6 @@ Results
 |  4 | kanamycin                 |     350 |   358 | CHEM        |     0.999978 |
 |  5 | colistin                  |     362 |   369 | CHEM        |     0.999942 |
 |  6 | povidone-iodine           |     375 |   389 | CHEM        |     0.999977 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
index 5e8291e7cd..95a9b9239a 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc5cd
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,21 +55,16 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | interstitial cystitis |      61 |    81 | DISEASE     |     0.999746 |
 |  1 | mastocytosis          |     129 |   140 | DISEASE     |     0.999132 |
 |  2 | cystitis              |     209 |   216 | DISEASE     |     0.999912 |
 |  3 | prostate cancer       |     355 |   369 | DISEASE     |     0.999781 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
index b727a6b84d..5e3aa54198 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-text = '''This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-val text = "This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label          |   confidence |
 |---:|:-----------------|--------:|------:|:-------------------|-------------:|
 |  0 | open-label       |       5 |    14 | CTDesign           |     0.742075 |
@@ -102,9 +79,6 @@ Results
 | 15 | GLA              |     356 |   358 | Drug               |     0.972978 |
 | 16 | NPH              |     363 |   365 | Drug               |     0.989424 |
 | 17 | bedtime          |     370 |   376 | DrugTime           |     0.936016 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
index ec82bf1925..426256bea5 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
 
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | suplementación          |      13 |    26 | PROC        |     0.999993 |
@@ -96,9 +74,6 @@ Results
 |  9 | pp                      |     337 |   338 | CHEM        |     0.999889 |
 | 10 | diálisis                |     388 |   395 | PROC        |     0.999993 |
 | 11 | función  residual       |     398 |   414 | PROC        |     0.999948 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
index a269bb411a..a52fbb8e3d 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jnlpb
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-text = '''The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-val text = "The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                             |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | protein kinase C                      |      39 |    54 | protein     |     0.993263 |
@@ -99,9 +76,6 @@ Results
 | 12 | tyrosine kinases                      |     732 |   747 | protein     |     0.999636 |
 | 13 | p95vav                                |     834 |   839 | protein     |     0.999842 |
 | 14 | hematopoietic and trophoblast cells   |     876 |   910 | cell_type   |     0.999709 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_linnaeus_species_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
index d26266b9de..759767bd92 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_linna
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-text = '''First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
 
-val text = "First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                 |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | chicken                   |      20 |    26 | SPECIES     |     0.998697 |
@@ -89,9 +67,6 @@ Results
 |  2 | Xenopus laevis            |      82 |    95 | SPECIES     |     0.999918 |
 |  3 | Drosophila melanogaster   |     102 |   124 | SPECIES     |     0.999925 |
 |  4 | Schizosaccharomyces pombe |     134 |   158 | SPECIES     |     0.999881 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_en.md
index e1259214c9..c1467285dd 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
 
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |     0.986743 |
@@ -91,9 +69,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |     0.962562 |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |     0.999028 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |     0.999852 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_es.md
index 9999f9d687..15c8f8d889 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
 
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |     0.999294 |
@@ -95,9 +73,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |     0.999971 |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |     0.99997  |
 | 10 | padres         |     728 |   733 | HUMAN       |     0.999971 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
@@ -119,4 +94,4 @@ Results
 - SentenceDetectorDLModel
 - TokenizerModel
 - MedicalBertForTokenClassifier
-- NerConverterInternalModel
\ No newline at end of file
+- NerConverterInternalModels
\ No newline at end of file
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_it.md
index 694ec8a2c7..e6ee8b3e65 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_it.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |     0.999699 |
@@ -94,9 +71,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |     0.999616 |
 |  8 | HIV              |     523 |   525 | SPECIES     |     0.999383 |
 |  9 | paziente         |     634 |   641 | HUMAN       |     0.99977  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_pt.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_pt.md
index 8db2a08e7f..afe62bae76 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_pt.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_living_species_pipeline_pt.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
 
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |     0.999888 |
@@ -90,9 +68,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |     0.999365 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |     0.982236 |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |     0.996602 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_ncbi_disease_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
index f174f46a9b..846b44e513 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ncbi_
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-text = '''Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-val text = "Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------|--------:|------:|:------------|-------------:|
 |  0 | Kniest dysplasia       |       0 |    15 | Disease     |     0.999886 |
@@ -90,9 +67,6 @@ Results
 |  3 | midface hypoplasia     |     120 |   137 | Disease     |     0.999911 |
 |  4 | myopia                 |     147 |   152 | Disease     |     0.999894 |
 |  5 | hearing loss           |     159 |   170 | Disease     |     0.999351 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_pathogen_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_pathogen_pipeline_en.md
index 64fdd6bb9f..4c71864f2b 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_pathogen_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_pathogen_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_patho
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-text = '''Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
 
-val text = "Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label        |   confidence |
 |---:|:----------------|--------:|------:|:-----------------|-------------:|
 |  0 | Racecadotril    |       0 |    11 | Medicine         |     0.986453 |
@@ -99,9 +77,6 @@ Results
 | 12 | rabies virus    |     381 |   392 | Pathogen         |     0.738198 |
 | 13 | Lyssavirus      |     395 |   404 | Pathogen         |     0.979239 |
 | 14 | Ephemerovirus   |     410 |   422 | Pathogen         |     0.992292 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_species_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_species_pipeline_en.md
index 5c42a35e1f..44f1a46e21 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_species_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_ner_species_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_speci
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-text = '''As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
 
-val text = "As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) ."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | 6C (T)                  |      57 |    62 | SPECIES     |     0.998955 |
@@ -90,9 +68,6 @@ Results
 |  3 | DSM 18155 (T)           |     188 |   200 | SPECIES     |     0.997657 |
 |  4 | Thiomonas perometabolis |     206 |   228 | SPECIES     |     0.999614 |
 |  5 | DSM 18570 (T)           |     230 |   242 | SPECIES     |     0.997146 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_pharmacology_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_pharmacology_pipeline_es.md
index 7eb0ad5b9a..6e56879cf3 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_pharmacology_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-bert_token_classifier_pharmacology_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_pharmacol
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
 
-val text = "Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label     |   confidence |
 |---:|:----------------|--------:|------:|:--------------|-------------:|
 |  0 | creatinkinasa   |      32 |    44 | PROTEINAS     |     0.999973 |
@@ -96,9 +74,6 @@ Results
 |  9 | Interleukina II |     232 |   246 | PROTEINAS     |     0.999965 |
 | 10 | Dacarbacina     |     249 |   259 | NORMALIZABLES |     0.999988 |
 | 11 | Interferon alfa |     263 |   277 | PROTEINAS     |     0.999961 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_carp_en.md b/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_carp_en.md
index 6b7341a149..4afc9eaacc 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_carp_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_carp_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_clinical`, `assertion_dl`, `re_clinical` and `ner_posology`
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,11 @@ nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history o
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-val text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
 
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |   | chunks                        | ner_clinical | assertion | posology_chunk   | ner_posology | relations |
 |---|-------------------------------|--------------|-----------|------------------|--------------|-----------|
 | 0 | gestational diabetes mellitus | PROBLEM      | present   | metformin        | Drug         | TrAP      |
@@ -106,10 +77,6 @@ Results
 | 4 | poor appetite                 | PROBLEM      | present   | insulin glargine | Drug         | TrCP      |
 | 5 | vomiting                      | PROBLEM      | present   | at night         | Frequency    | TrAP      |
 | 6 | insulin glargine              | TREATMENT    | present   | 12 units         | Dosage       | TrAP      |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_era_en.md b/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_era_en.md
index 7cd7aab93f..a47f084f51 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_era_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_era_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_clinical_events`, `assertion_dl` and `re_temporal_events_cl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,11 @@ nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Ho
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-val text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
 
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | relation   | entity1       |   entity1_begin |   entity1_end | chunk1                    | entity2       |   entity2_begin |   entity2_end | chunk2                        |   confidence |
 |---:|:-----------|:--------------|----------------:|--------------:|:--------------------------|:--------------|----------------:|--------------:|:------------------------------|-------------:|
 |  0 | AFTER      | OCCURRENCE    |               7 |            14 | admitted                  | CLINICAL_DEPT |              19 |            43 | The John Hopkins Hospital     |     0.963836 |
@@ -107,10 +78,6 @@ Results
 |  5 | OVERLAP    | DATE          |              45 |            54 | 2 days ago                | PROBLEM       |              74 |           102 | gestational diabetes mellitus |     0.996954 |
 |  6 | BEFORE     | EVIDENTIAL    |             119 |           124 | denied                    | PROBLEM       |             126 |           129 | pain                          |     1        |
 |  7 | BEFORE     | EVIDENTIAL    |             119 |           124 | denied                    | PROBLEM       |             135 |           146 | any headache                  |     1        |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_medication_en.md b/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_medication_en.md
index cd8704c1fa..d122de2bf4 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_medication_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-explain_clinical_doc_medication_en.md
@@ -34,6 +34,7 @@ A pipeline for detecting posology entities with the `ner_posology_large` NER mod
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,11 @@ nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient i
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime."
 
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +----+----------------+------------+
 |    | chunks         | entities   |
 |---:|:---------------|:-----------|
@@ -133,10 +104,6 @@ Results
 | DRUG-ROUTE     | DRUG      | Lantus     | ROUTE     | subcutaneously |
 | DRUG-FREQUENCY | DRUG      | Lantus     | FREQUENCY | at bedtime     |
 +----------------+-----------+------------+-----------+----------------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-icd10_icd9_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-13-icd10_icd9_mapping_en.md
index 95b141b1df..8948417997 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-icd10_icd9_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-icd10_icd9_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10_icd9_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,14 @@ nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | icd10_code          | icd9_code          |
 |---:|:--------------------|:-------------------|
 |  0 | Z833 | A0100 | A000 | V180 | 0020 | 0010 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-icd10cm_umls_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-13-icd10cm_umls_mapping_en.md
index 5212f7b85a..1bae168d5b 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-icd10cm_umls_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-icd10cm_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps ICD10CM codes to UMLS codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,43 +59,13 @@ nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 {'icd10cm': ['M89.50', 'R82.2', 'R09.01'],
 'umls': ['C4721411', 'C0159076', 'C0004044']}
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-icdo_snomed_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-13-icdo_snomed_mapping_en.md
index c3c9838ac9..7e6ed12b72 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-icdo_snomed_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-icdo_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icdo_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | icdo_code                | snomed_code                    |
 |---:|:-------------------------|:-------------------------------|
 |  0 | 8120/1 | 8170/3 | 8380/3 | 45083001 | 25370001 | 30289006 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-mesh_umls_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-13-mesh_umls_mapping_en.md
index 6935071cc4..fbecac0945 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-mesh_umls_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-mesh_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `mesh_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,14 @@ nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | mesh_code                   | umls_code                      |
 |---:|:----------------------------|:-------------------------------|
 |  0 | C028491 | D019326 | C579867 | C0043904 | C0045010 | C3696376 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_chemd_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_chemd_clinical_pipeline_en.md
index 5e254ccbfa..5133c0c2d3 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_chemd_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_chemd_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemd_clinical](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-text = '''Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
 
-val text = "Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                         |   begin |   end | ner_label    |   confidence |
 |---:|:-----------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | Lystabactins                       |      65 |    76 | FAMILY       |     0.9841   |
@@ -98,9 +76,6 @@ Results
 | 11 | amino acid                         |     602 |   611 | FAMILY       |     0.4204   |
 | 12 | 4,8-diamino-3-hydroxyoctanoic acid |     614 |   647 | SYSTEMATIC   |     0.9124   |
 | 13 | LySta                              |     650 |   654 | ABBREVIATION |     0.9193   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_bert_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_bert_pipeline_ro.md
index 3daf2301af..37e46ed22b 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_bert_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_bert_pipeline_ro.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_bert](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
 
-text = '''Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-val text = "Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-bass
 |    | ner_chunks                        |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Angio CT                          |      12 |    19 | Imaging_Test              |     0.96415  |
@@ -108,9 +86,6 @@ bass
 | 21 | cardiotoracica                    |     461 |   474 | Body_Part                 |     0.9344   |
 | 22 | achizitii secventiale prospective |     479 |   511 | Imaging_Technique         |     0.966833 |
 | 23 | 100/min                           |     546 |   552 | Pulse                     |     0.9128   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_pipeline_ro.md
index 393cdc0fba..0c62583463 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_pipeline_ro.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-text = ''' Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
 
-val text = " Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                        |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Angio CT                          |      13 |    20 | Imaging_Test              |     0.92675  |
@@ -109,9 +87,6 @@ Results
 | 22 | cardiotoracica                    |     455 |   468 | Body_Part                 |     0.9995   |
 | 23 | achizitii secventiale prospective |     473 |   505 | Imaging_Technique         |     0.8514   |
 | 24 | 100/min                           |     540 |   546 | Pulse                     |     0.8501   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_trials_abstracts_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_trials_abstracts_pipeline_es.md
index 8fbab6bfa8..29f556c09a 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_trials_abstracts_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_clinical_trials_abstracts_pipeline_es.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_trials_abstracts](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | suplementación                |      13 |    26 | PROC        |     0.9987   |
@@ -96,9 +73,6 @@ Results
 |  9 | pp                            |     337 |   338 | CHEM        |     0.96     |
 | 10 | diálisis                      |     388 |   395 | PROC        |     0.9982   |
 | 11 | función  residual             |     398 |   414 | PROC        |     0.73045  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_covid_trials_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_covid_trials_pipeline_en.md
index 48521cc40e..e258069efe 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_covid_trials_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_covid_trials_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_covid_trials](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-text = '''In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
 
-val text = "In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                          |   begin |   end | ner_label                 |   confidence |
 |---:|:------------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | December 2019                       |       3 |    15 | Date                      |     0.99655  |
@@ -104,9 +82,6 @@ Results
 | 17 | CDC                                 |     547 |   549 | Institution               |     0.8296   |
 | 18 | 2020                                |     848 |   851 | Date                      |     0.9997   |
 | 19 | COVID‑19 vaccine                    |     864 |   879 | Vaccine_Name              |     0.87505  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_bert_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_bert_pipeline_ro.md
index 1c3aa6c171..1190b9eb71 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_bert_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_bert_pipeline_ro.md
@@ -34,38 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_bert](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -96,12 +65,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | LOCATION    |     0.99352  |
@@ -116,9 +83,6 @@ Results
 |  9 | Agota Evelyn Tımar           |     191 |   210 | NAME        |     0.859975 |
 |    | C                            |         |       |             |              |
 | 10 | 2450502264401                |     218 |   230 | ID          |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_glove_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_glove_pipeline_en.md
index 050cdd6e83..94b08c06a6 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_glove_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_glove_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_glove](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
 
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     1        |
@@ -95,9 +73,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.8586   |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.948667 |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9972   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_pipeline_it.md
index 41a76613d1..16587ca231 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_pipeline_it.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,43 +55,17 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
 
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   | confidence   |
 |---:|:----------------------|--------:|------:|:------------|:-------------|
 |  0 | Gastone Montanariello |       9 |    29 | NAME        |              |
 |  1 | 49                    |      32 |    33 | AGE         |              |
 |  2 | Ospedale San Camillo  |      55 |    74 | LOCATION    |              |
 |  3 | marzo 2015            |     128 |   137 | DATE        |              |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_pipeline_ro.md
index bbc2a50251..3b081f9e9d 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_generic_pipeline_ro.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -64,44 +65,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | LOCATION    |     0.88326  |
@@ -113,9 +81,6 @@ Results
 |  6 | 77                           |     179 |   180 | AGE         |     1        |
 |  7 | Agota Evelyn Tımar           |     190 |   207 | NAME        |     0.832933 |
 |  8 | 2450502264401                |     217 |   229 | ID          |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_bert_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_bert_pipeline_ro.md
index 8f2074bffd..55a7e1393c 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_bert_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_bert_pipeline_ro.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -64,44 +65,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | HOSPITAL    |     0.84306  |
@@ -114,9 +82,6 @@ Results
 |  7 | 77                           |     180 |   181 | AGE         |     1        |
 |  8 | Agota Evelyn Tımar           |     191 |   208 | DOCTOR      |     0.803667 |
 |  9 | 2450502264401                |     218 |   230 | IDNUM       |     0.9995   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_glove_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_glove_pipeline_en.md
index ba705fe880..35f76089e5 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_glove_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_glove_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_glove](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
 
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |     1        |
@@ -95,9 +73,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |     0.731325 |
 |  9 | 0295 Keats Street             |     195 |   211 | STREET        |     0.737067 |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |     0.9882   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_pipeline_it.md
index 066e17b080..cb76e75713 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_pipeline_it.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,43 +55,17 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
 
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   | confidence   |
 |---:|:----------------------|--------:|------:|:------------|:-------------|
 |  0 | Gastone Montanariello |       9 |    29 | PATIENT     |              |
 |  1 | 49                    |      32 |    33 | AGE         |              |
 |  2 | Ospedale San Camillo  |      55 |    74 | HOSPITAL    |              |
 |  3 | marzo 2015            |     128 |   137 | DATE        |              |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_pipeline_ro.md
index 0288354bae..cd29b3583d 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_subentity_pipeline_ro.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -64,44 +65,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | HOSPITAL    |      0.5594  |
@@ -114,9 +82,6 @@ Results
 |  7 | 77                           |     180 |   181 | AGE         |      1       |
 |  8 | Agota Evelyn Tımar           |     191 |   208 | DOCTOR      |      0.8149  |
 |  9 | 2450502264401                |     218 |   230 | IDNUM       |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_synthetic_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_synthetic_pipeline_en.md
index b6aec1c228..a20c2d9c57 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_deid_synthetic_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_deid_synthetic_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_synthetic](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
 
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     1        |
@@ -95,9 +73,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.968825 |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.7831   |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9985   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_diag_proc_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_diag_proc_pipeline_es.md
index bcfd4e26d5..16ef6a0427 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_diag_proc_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_diag_proc_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_diag_proc](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
 
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                             |   begin |   end | ner_label     |   confidence |
 |---:|:--------------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | ENFERMEDAD                            |      12 |    21 | DIAGNOSTICO   |      0.9989  |
@@ -95,9 +73,6 @@ Results
 |  8 | enfermedad de las arterias coronarias |     934 |   970 | DIAGNOSTICO   |      0.75594 |
 |  9 | estenosada                            |    1010 |  1019 | DIAGNOSTICO   |      0.9288  |
 | 10 | LAD                                   |    1068 |  1070 | DIAGNOSTICO   |      0.9365  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_en.md
index c24acac06f..599a19c69c 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,38 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
 
-val text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                         |   begin |   end | entities           |   confidence |
 |---:|:-------------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | A 3-year-old boy               |       1 |    16 | patient            |     0.733133 |
@@ -120,9 +94,6 @@ Results
 | 25 | revealed                       |     628 |   635 | clinical_event     |     0.9989   |
 | 26 | spindle cell proliferation     |     637 |   662 | clinical_condition |     0.4487   |
 | 27 | the submucosal layer           |     667 |   686 | bodypart           |     0.523    |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_es.md
index a785230f55..6863d3408b 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_es.md
@@ -34,32 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-val text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -84,12 +59,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                       |   begin |   end | entities           |   confidence |
 |---:|:-----------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Un niño de 3 años            |       1 |    17 | patient            |     0.68856  |
@@ -128,9 +101,6 @@ Results
 | 33 | proliferación                |     711 |   723 | clinical_event     |     0.9996   |
 | 34 | células fusiformes           |     728 |   745 | bodypart           |     0.7001   |
 | 35 | la capa submucosa            |     750 |   766 | bodypart           |     0.641267 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_eu.md b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_eu.md
index 1eea6776c7..63a0c5e17f 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_eu.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_eu.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,38 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
 
-val text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                   |   begin |   end | entities           |   confidence |
 |---:|:-------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | 3 urteko mutiko bat      |       1 |    19 | patient            |     0.813975 |
@@ -133,9 +107,6 @@ Results
 | 38 | utzi                     |     701 |   704 | clinical_event     |     0.925    |
 | 39 | mukosaren azpiko zelulen |     711 |   734 | bodypart           |     0.754933 |
 | 40 | ugaltzea                 |     736 |   743 | clinical_event     |     0.9989   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_fr.md b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_fr.md
index 6faedc241f..cb792ba16d 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_fr.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_case_pipeline_fr.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,38 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
 
-val text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                                                |   begin |   end | entities           |   confidence |
 |---:|:------------------------------------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Un garçon de 3 ans                                    |       1 |    18 | patient            |     0.58786  |
@@ -124,9 +98,6 @@ Results
 | 29 | prolifération                                         |     735 |   747 | clinical_event     |     0.6767   |
 | 30 | cellules fusiformes                                   |     752 |   770 | bodypart           |     0.5233   |
 | 31 | la couche sous-muqueuse                               |     777 |   799 | bodypart           |     0.6755   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_es.md
index 46481b9931..a49c3fd046 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_es.md
@@ -34,32 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-val text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -84,12 +59,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks               |   begin |   end | entities           |   confidence |
 |---:|:---------------------|--------:|------:|:-------------------|-------------:|
 |  0 | cicatriz             |      37 |    44 | clinical_condition |      0.9883  |
@@ -97,9 +70,6 @@ Results
 |  2 | signos               |     170 |   175 | clinical_condition |      0.9862  |
 |  3 | irritación           |     180 |   189 | clinical_condition |      0.9975  |
 |  4 | hernias inguinales   |     214 |   231 | clinical_condition |      0.7543  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_eu.md b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_eu.md
index c206689764..0e6060eb71 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_eu.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_eu.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,38 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
 
-val text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks     |   begin |   end | entities           |   confidence |
 |---:|:-----------|--------:|------:|:-------------------|-------------:|
 |  0 | mina       |      98 |   101 | clinical_condition |       0.8754 |
@@ -99,9 +73,6 @@ Results
 |  4 | hantura    |     203 |   209 | clinical_condition |       0.8805 |
 |  5 | Polakiuria |     256 |   265 | clinical_condition |       0.6683 |
 |  6 | sintomarik |     345 |   354 | clinical_condition |       0.9632 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_fr.md b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_fr.md
index e61002d2ce..d29b55ff9b 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_fr.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_fr.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,40 +61,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-val text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                   |   begin |   end | entities           |   confidence |
 |---:|:-------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | ulcérations              |      47 |    57 | clinical_condition |       0.9995 |
@@ -103,9 +75,6 @@ Results
 |  4 | apyrexie                 |     261 |   268 | clinical_condition |       0.9963 |
 |  5 | anasarque                |     353 |   361 | clinical_condition |       0.9973 |
 |  6 | décompensation cardiaque |     409 |   432 | clinical_condition |       0.8948 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_it.md
index 5af1da7c3f..9515916fa5 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_eu_clinical_condition_pipeline_it.md
@@ -34,34 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-val text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -88,12 +61,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                 |   begin |   end | entities           |   confidence |
 |---:|:-----------------------|--------:|------:|:-------------------|-------------:|
 |  0 | dolore epigastrico     |      30 |    47 | clinical_condition |      0.90845 |
@@ -102,9 +73,6 @@ Results
 |  3 | edema                  |     188 |   192 | clinical_condition |      1       |
 |  4 | fistola transfinterica |     294 |   315 | clinical_condition |      0.97785 |
 |  5 | infiammazione          |     372 |   384 | clinical_condition |      0.9996  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_healthcare_pipeline_de.md b/docs/_posts/ahmedlone127/2023-06-13-ner_healthcare_pipeline_de.md
index bd9742d5c1..1aca568e5e 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_healthcare_pipeline_de.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_healthcare_pipeline_de.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_healthcare](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
 
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk         |   begin |   end | ner_label             |   confidence |
 |---:|:------------------|--------:|------:|:----------------------|-------------:|
 |  0 | Kleinzellige      |       4 |    15 | MEASUREMENT           |       0.6897 |
@@ -100,9 +78,6 @@ Results
 | 13 | Hauptbronchus     |     195 |   207 | BODY_PART             |       0.9864 |
 | 14 | mittlere          |     223 |   230 | MEASUREMENT           |       0.9651 |
 | 15 | Prävalenz         |     232 |   240 | MEDICAL_CONDITION     |       0.9833 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_healthcare_slim_pipeline_de.md b/docs/_posts/ahmedlone127/2023-06-13-ner_healthcare_slim_pipeline_de.md
index 51381dfe61..f690a69107 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_healthcare_slim_pipeline_de.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_healthcare_slim_pipeline_de.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_healthcare_slim](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
 
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -79,9 +58,6 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                          |   begin |   end | ner_label         |   confidence |
 |---:|:-----------------------------------|--------:|------:|:------------------|-------------:|
 |  0 | Bronchialkarzinom                  |      17 |    33 | MEDICAL_CONDITION |       0.9988 |
@@ -94,9 +70,6 @@ Results
 |  7 | Lunge                              |     179 |   183 | BODY_PART         |       0.9729 |
 |  8 | Hauptbronchus                      |     195 |   207 | BODY_PART         |       0.9987 |
 |  9 | Prävalenz                          |     232 |   240 | MEDICAL_CONDITION |       0.9986 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_300_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_300_pipeline_es.md
index 4d9738282c..25adf57200 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_300_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_300_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_300](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
 
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.92045 |
@@ -95,9 +73,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      0.9963  |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_es.md
index d415e16bad..cf991c51ac 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
 
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.98915 |
@@ -95,9 +73,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      1       |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_fr.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_fr.md
index c97c7c7774..4808667e8b 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_fr.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_fr.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
 
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                       |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Femme                            |       0 |     4 | HUMAN       |     1        |
@@ -98,9 +76,6 @@ Results
 | 11 | virus de la varicelle et du zona |     518 |   549 | SPECIES     |     0.985429 |
 | 12 | parvovirus B19                   |     554 |   567 | SPECIES     |     0.98595  |
 | 13 | Brucella                         |     636 |   643 | SPECIES     |     0.9995   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_it.md
index 43b65546d4..ebbdf4513e 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_it.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
 
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |      0.9997  |
@@ -94,9 +72,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |      0.9745  |
 |  8 | HIV              |     523 |   525 | SPECIES     |      0.9838  |
 |  9 | paziente         |     634 |   641 | HUMAN       |      0.9994  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_pt.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_pt.md
index 0df0eb9536..36a529365e 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_pt.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_pt.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |       0.9849 |
@@ -90,9 +67,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |       0.9912 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |       0.9909 |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |       0.9778 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_ro.md
index 5aada0e0ac..f8a916d764 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_bert_pipeline_ro.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-text = '''O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-val text = "O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks           |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------|--------:|------:|:------------|-------------:|
 |  0 | femeie               |       2 |     7 | HUMAN       |     0.9998   |
@@ -93,9 +70,6 @@ Results
 |  6 | enterovirus          |     804 |   814 | SPECIES     |     0.9984   |
 |  7 | parvovirus B19       |     819 |   832 | SPECIES     |     0.99255  |
 |  8 | fetală               |     932 |   937 | HUMAN       |     0.9994   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_biobert_pipeline_en.md
index 0eacb71c61..69e48bbc35 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
 
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |       0.9999 |
@@ -91,9 +69,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |       0.9926 |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |       0.8422 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |       0.9929 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_ca.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_ca.md
index 80346e6035..8c7c31e220 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_ca.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_ca.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,35 +55,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-text = '''Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
 
-val text = "Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-|    | ner_chunks              |   begin |   end | ner_label   |   confidence |
+    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | Dona                    |       0 |     3 | HUMAN       |     1        |
 |  1 | marit                   |     192 |   196 | HUMAN       |     0.9867   |
@@ -99,9 +77,6 @@ Results
 | 12 | virus varicel·la zoster |     717 |   739 | SPECIES     |     0.778333 |
 | 13 | parvovirus B19          |     743 |   756 | SPECIES     |     0.9138   |
 | 14 | Brucella                |     847 |   854 | SPECIES     |     0.9483   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_en.md
index 897d82a5e8..2b0cdd253f 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
 
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |     0.9993   |
@@ -91,9 +69,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |     0.8838   |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |     0.748667 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |     0.9847   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_es.md
index 3d1816f7af..a53bfee2a8 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
 
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |       0.9926 |
@@ -95,9 +73,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |       0.9997 |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |       0.9998 |
 | 10 | padres         |     728 |   733 | HUMAN       |       0.9992 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_fr.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_fr.md
index 9192655723..9611382b31 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_fr.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_fr.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
 
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                       |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Femme                            |       0 |     4 | HUMAN       |     1        |
@@ -98,9 +76,6 @@ Results
 | 11 | virus de la varicelle et du zona |     518 |   549 | SPECIES     |     0.788543 |
 | 12 | parvovirus B19                   |     554 |   567 | SPECIES     |     0.9341   |
 | 13 | Brucella                         |     636 |   643 | SPECIES     |     0.9993   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_gl.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_gl.md
index dbd5b459b4..370b93e140 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_gl.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_gl.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-text = '''Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
 
-val text = "Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Muller                |       0 |     5 | HUMAN       |      0.9998  |
@@ -91,9 +69,6 @@ Results
 |  4 | herpética             |     437 |   445 | SPECIES     |      0.9592  |
 |  5 | púbico                |     551 |   556 | HUMAN       |      0.7293  |
 |  6 | Staphylococcus aureus |     644 |   664 | SPECIES     |      0.87005 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_it.md
index 8af01b65fc..57768089bd 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_it.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
 
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |       0.9992 |
@@ -94,9 +72,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |       0.991  |
 |  8 | HIV              |     523 |   525 | SPECIES     |       0.991  |
 |  9 | paziente         |     634 |   641 | HUMAN       |       0.9978 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_pt.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_pt.md
index 4968010210..b7470ed06b 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_pt.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_pipeline_pt.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
 
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |       0.9991 |
@@ -90,9 +68,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |       0.9847 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |       0.91   |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |       0.9996 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_roberta_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_roberta_pipeline_es.md
index a8499ef997..77cb48f477 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_roberta_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_roberta_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_roberta](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
 
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.93805 |
@@ -95,9 +73,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      0.9985  |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_roberta_pipeline_pt.md b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_roberta_pipeline_pt.md
index 9c9c713040..499ccbfcbb 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_roberta_pipeline_pt.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_living_species_roberta_pipeline_pt.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_roberta](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-text = '''Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-val text = "Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | Mulher        |       0 |     5 | HUMAN       |       0.9975 |
@@ -91,9 +68,6 @@ Results
 |  4 | HBV           |     360 |   362 | SPECIES     |       0.9911 |
 |  5 | HCV           |     365 |   367 | SPECIES     |       0.9858 |
 |  6 | sífilis       |     384 |   390 | SPECIES     |       0.8898 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_nature_nero_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_nature_nero_clinical_pipeline_en.md
index 2fa6b5e012..c669531cec 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_nature_nero_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_nature_nero_clinical_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_nature_nero_clinical](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-text = '''he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-val text = "he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                   |   begin |   end | ner_label             |   confidence |
 |---:|:---------------------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | perioral cyanosis                            |     236 |   252 | Medicalfinding        |     0.198    |
@@ -106,9 +83,6 @@ Results
 | 19 | diarrhea                                     |     835 |   842 | Medicalfinding        |     0.533    |
 | 20 | bowel movements                              |     849 |   863 | Biologicalprocess     |     0.2036   |
 | 21 | soft in nature                               |     888 |   901 | Biologicalprocess     |     0.170467 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_negation_uncertainty_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_negation_uncertainty_pipeline_es.md
index 39b7780a91..3ee2eddeab 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_negation_uncertainty_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_negation_uncertainty_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_negation_uncertainty](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,35 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-+------------------------------------------------------+---------+
 |chunk                                                 |ner_label|
 +------------------------------------------------------+---------+
 |probable de                                           |UNC      |
@@ -93,10 +70,6 @@ Results
 |se realizó paracentesis control por escasez de liquido|NSCO     |
 |susceptible de                                        |UNC      |
 |ca basocelular perlado                                |USCO     |
-+------------------------------------------------------+---------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_neoplasms_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_neoplasms_pipeline_es.md
index f537ad54ce..1a515c7d7a 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_neoplasms_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_neoplasms_pipeline_es.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_neoplasms](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,41 +55,15 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
 
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label            |   confidence |
 |---:|:------------|--------:|------:|:---------------------|-------------:|
 |  0 | cáncer      |     140 |   145 | MORFOLOGIA_NEOPLASIA |       0.9997 |
 |  1 | Multi-Link  |    1195 |  1204 | MORFOLOGIA_NEOPLASIA |       0.574  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_anatomy_general_healthcare_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_anatomy_general_healthcare_pipeline_en.md
index 211bf821f4..4c3f05be3f 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_anatomy_general_healthcare_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_anatomy_general_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_general_he
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,47 +59,17 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
 
-val text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks   |   begin |   end | entities        |   confidence |
 |---:|:---------|--------:|------:|:----------------|-------------:|
 |  0 | left     |      37 |    40 | Direction       |       0.9948 |
 |  1 | breast   |      42 |    47 | Anatomical_Site |       0.5814 |
 |  2 | lungs    |      83 |    87 | Anatomical_Site |       0.9486 |
 |  3 | liver    |     100 |   104 | Anatomical_Site |       0.9646 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_anatomy_general_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_anatomy_general_pipeline_en.md
index e974e34c47..ef94070cdb 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_anatomy_general_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_anatomy_general_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_general](h
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
 
-val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -79,18 +58,12 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label       |   confidence |
 |---:|:-------------|--------:|------:|:----------------|-------------:|
 |  0 | left         |      36 |    39 | Direction       |       0.9825 |
 |  1 | breast       |      41 |    46 | Anatomical_Site |       0.9005 |
 |  2 | lungs        |      82 |    86 | Anatomical_Site |       0.9735 |
 |  3 | liver        |      99 |   103 | Anatomical_Site |       0.9817 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_biomarker_healthcare_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_biomarker_healthcare_pipeline_en.md
index 6aef3c2d34..130eea49b0 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_biomarker_healthcare_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_biomarker_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_biomarker_healthca
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-text = '''he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
 
-val text = "he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                                   |   begin |   end | entities         |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-----------------|-------------:|
 |  0 | negative                                 |      69 |    76 | Biomarker_Result |      1       |
@@ -102,9 +80,6 @@ Results
 | 15 | p53                                      |     244 |   246 | Biomarker        |      1       |
 | 16 | Ki-67 index                              |     253 |   263 | Biomarker        |      0.99865 |
 | 17 | 87%                                      |     275 |   277 | Biomarker_Result |      0.828   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_biomarker_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_biomarker_pipeline_en.md
index 7a363114a9..c45aed795d 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_biomarker_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_biomarker_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_biomarker](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,34 +55,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
 
-val text = "The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                               |   begin |   end | ner_label        |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-----------------|-------------:|
 |  0 | negative                                 |      70 |    77 | Biomarker_Result |      0.9984  |
@@ -102,9 +80,6 @@ Results
 | 15 | p53                                      |     245 |   247 | Biomarker        |      1       |
 | 16 | Ki-67 index                              |     254 |   264 | Biomarker        |      0.99465 |
 | 17 | 87%                                      |     276 |   278 | Biomarker_Result |      0.9814  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_demographics_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_demographics_pipeline_en.md
index 216a977af7..a7cb8fefd1 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_demographics_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_demographics_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_demographics](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,42 +55,16 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old man with history of heavy smoking.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
 
-val text = "The patient is a 40-year-old man with history of heavy smoking."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label      |   confidence |
 |---:|:--------------|--------:|------:|:---------------|-------------:|
 |  0 | 40-year-old   |      17 |    27 | Age            |       0.6743 |
 |  1 | man           |      29 |    31 | Gender         |       0.9365 |
 |  2 | heavy smoking |      49 |    61 | Smoking_Status |       0.7294 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_diagnosis_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_diagnosis_pipeline_en.md
index 98134d36ac..eea52e1510 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_diagnosis_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_diagnosis_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_diagnosis](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-text = '''Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-val text = "Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label         |   confidence |
 |---:|:-------------|--------:|------:|:------------------|-------------:|
 |  0 | tumor        |      44 |    48 | Tumor_Finding     |       0.9958 |
@@ -90,9 +67,6 @@ Results
 |  3 | ductal       |     119 |   124 | Histological_Type |       0.9996 |
 |  4 | carcinoma    |     126 |   134 | Cancer_Dx         |       0.9988 |
 |  5 | metastasis   |     181 |   190 | Metastasis        |       0.9996 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_posology_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_posology_pipeline_en.md
index cb66da2057..848bfed7ad 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_posology_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_posology_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_posology](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label      |   confidence |
 |---:|:-----------------|--------:|------:|:---------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy |      1       |
@@ -91,9 +68,6 @@ Results
 |  4 | six courses      |     106 |   116 | Cycle_Count    |      0.494   |
 |  5 | second cycle     |     150 |   161 | Cycle_Number   |      0.98675 |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_response_to_treatment_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_response_to_treatment_pipeline_en.md
index b35e8b0ad9..d2b81cb784 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_response_to_treatment_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_response_to_treatment_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_response_to_treatm
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,40 +55,15 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
 
-text = '''She completed her first-line therapy, but some months later there was recurrence of the breast cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-val text = "She completed her first-line therapy, but some months later there was recurrence of the breast cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label             |   confidence |
 |---:|:-------------|--------:|------:|:----------------------|-------------:|
 |  0 | recurrence   |      70 |    79 | Response_To_Treatment |       0.9767 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_test_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_test_pipeline_en.md
index 03d0e14af8..1939bd8397 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_test_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_test_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_test](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-text = ''' biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-val text = " biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,21 +55,16 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                |   begin |   end | ner_label      |   confidence |
 |---:|:--------------------------|--------:|------:|:---------------|-------------:|
 |  0 | biopsy                    |       1 |     6 | Pathology_Test |      0.9987  |
 |  1 | ultrasound guided         |      31 |    47 | Imaging_Test   |      0.87635 |
 |  2 | chest computed tomography |      67 |    91 | Imaging_Test   |      0.9176  |
 |  3 | CT                        |      94 |    95 | Imaging_Test   |      0.8294  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_therapy_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_therapy_pipeline_en.md
index ccd987ab10..66eed704a8 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_therapy_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_therapy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_therapy](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,38 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
 
-val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                     |   begin |   end | ner_label             |   confidence |
 |---:|:-------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | mastectomy                     |      36 |    45 | Cancer_Surgery        |     0.9817   |
@@ -101,10 +75,7 @@ Results
 |  6 | cyclophosphamide               |     363 |   378 | Chemotherapy          |     0.9976   |
 |  7 | 600 mg/m2                      |     381 |   389 | Dosage                |     0.64205  |
 |  8 | six courses                    |     397 |   407 | Cycle_Count           |     0.46815  |
-|  9 | first line                     |     413 |   422 | Line_Of_Therapy       |     0.95015  |
-
-
-{:.model-param}
+|  9 | first line                     |     413 |   422 | Line_Of_Therapy       |     0.95015  |s
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_tnm_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_tnm_pipeline_en.md
index 31fff0b6e1..17b56264d5 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_tnm_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_tnm_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_tnm](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
 
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -79,9 +58,6 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label         |   confidence |
 |---:|:-----------------|--------:|------:|:------------------|-------------:|
 |  0 | metastatic       |      24 |    33 | Metastasis        |     0.9999   |
@@ -90,9 +66,6 @@ Results
 |  3 | 4 cm             |     126 |   129 | Tumor_Description |     0.85105  |
 |  4 | tumor            |     131 |   135 | Tumor             |     0.9926   |
 |  5 | grade 2          |     141 |   147 | Tumor_Description |     0.89705  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_unspecific_posology_healthcare_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
index 604538a2af..0128823c81 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_unspecific_posolog
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,38 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
 
-val text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks           |   begin |   end | entities             |   confidence |
 |---:|:-----------------|--------:|------:|:---------------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy       |     0.9999   |
@@ -99,9 +73,6 @@ Results
 |  4 | over six courses |     101 |   116 | Posology_Information |     0.689833 |
 |  5 | second cycle     |     150 |   161 | Posology_Information |     0.9906   |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy       |     0.9997   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_unspecific_posology_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_unspecific_posology_pipeline_en.md
index 7e37f0a8b6..4c10264f91 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_unspecific_posology_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_oncology_unspecific_posology_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_unspecific_posolog
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label            |   confidence |
 |---:|:-----------------|--------:|------:|:---------------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy       |      1       |
@@ -91,9 +68,6 @@ Results
 |  4 | over six courses |     101 |   116 | Posology_Information |      0.9078  |
 |  5 | second cycle     |     150 |   161 | Posology_Information |      0.9853  |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy       |      0.9998  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_pharmacology_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-13-ner_pharmacology_pipeline_es.md
index eebd0e02f9..9c4d6b253b 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_pharmacology_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_pharmacology_pipeline_es.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_pharmacology](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,12 +55,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label     |   confidence |
 |---:|:----------------|--------:|------:|:--------------|-------------:|
 |  0 | creatinkinasa   |      31 |    43 | PROTEINAS     |      0.9994  |
@@ -96,9 +73,6 @@ Results
 |  9 | Interleukina II |     231 |   245 | PROTEINAS     |      0.99955 |
 | 10 | Dacarbacina     |     248 |   258 | NORMALIZABLES |      0.9996  |
 | 11 | Interferon alfa |     262 |   276 | PROTEINAS     |      0.99935 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_profiling_biobert_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_profiling_biobert_en.md
index a3c3b4020f..beb8148ded 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_profiling_biobert_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_profiling_biobert_en.md
@@ -38,6 +38,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -67,38 +68,11 @@ nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 ******************** ner_diseases_biobert Model Results ******************** 
 
 [('gestational diabetes mellitus', 'Disease'), ('type two diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('HTG-induced pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('poor appetite', 'Disease'), ('vomiting', 'Disease')]
@@ -122,10 +96,6 @@ Results
 ******************** ner_risk_factors_biobert Model Results ******************** 
 
 [('diabetes mellitus', 'DIABETES'), ('subsequent type two diabetes mellitus', 'DIABETES'), ('obesity', 'OBESE')]
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-ner_supplement_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-ner_supplement_clinical_pipeline_en.md
index 3c07aed5f2..3498fdff3c 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-ner_supplement_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-ner_supplement_clinical_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_supplement_clinical](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-text = '''Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-val text = "Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,21 +55,16 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | nervousness  |      42 |    52 | CONDITION   |      0.9999  |
 |  1 | night sleep  |      70 |    80 | BENEFIT     |      0.80775 |
 |  2 | hair         |     109 |   112 | BENEFIT     |      0.9997  |
 |  3 | nail growth  |     118 |   128 | BENEFIT     |      0.9997  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-nerdl_tumour_demo_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-nerdl_tumour_demo_pipeline_en.md
index 97544cb8b3..913c5e5f0f 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-nerdl_tumour_demo_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-nerdl_tumour_demo_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [nerdl_tumour_demo](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,18 +55,13 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label    | confidence   |
 |---:|:-----------------|--------:|------:|:-------------|:-------------|
 |  0 | breast carcinoma |      35 |    50 | Localization |              |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-oncology_biomarker_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-oncology_biomarker_pipeline_en.md
index c62a08ce7b..3f75088552 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-oncology_biomarker_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-oncology_biomarker_pipeline_en.md
@@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status and Relation E
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,11 @@ nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was n
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk                          | ner_label        |
@@ -222,10 +193,6 @@ Results
 | ER                   | Biomarker        | negative                       | Biomarker_Result | O             |
 | PR                   | Biomarker        | negative                       | Biomarker_Result | O             |
 | negative             | Biomarker_Result | HER2                           | Oncogene         | is_finding_of |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-oncology_general_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-oncology_general_pipeline_en.md
index be47583b99..4d4008825a 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-oncology_general_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-oncology_general_pipeline_en.md
@@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status and Relation E
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -65,44 +66,11 @@ The tumor is positive for ER and PR.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a left mastectomy for a left breast cancer two months ago.
-The tumor is positive for ER and PR.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_general_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a left mastectomy for a left breast cancer two months ago.
-The tumor is positive for ER and PR."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_general.pipeline").predict("""The patient underwent a left mastectomy for a left breast cancer two months ago.
-The tumor is positive for ER and PR.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk          | ner_label        |
@@ -183,11 +151,6 @@ Results
 | tumor         | Tumor_Finding    | PR             | Biomarker     | O             |
 | positive      | Biomarker_Result | ER             | Biomarker     | is_finding_of |
 | positive      | Biomarker_Result | PR             | Biomarker     | is_finding_of |
-
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-re_bodypart_directions_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-re_bodypart_directions_pipeline_en.md
index 2510e17770..77b36da034 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-re_bodypart_directions_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-re_bodypart_directions_pipeline_en.md
@@ -59,37 +59,11 @@ nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations | entity1                     | entity1_begin | entity1_end | chunk1     | entity2                     | entity2_end | entity2_end | chunk2        | confidence |
 |-------|-----------|-----------------------------|---------------|-------------|------------|-----------------------------|-------------|-------------|---------------|------------|
 | 0     | 1         | Direction                   | 35            | 39          | upper      | Internal_organ_or_component | 41          | 50          | brain stem    | 0.9999989  |
@@ -101,10 +75,6 @@ Results
 | 6     | 0         | Direction                   | 54            | 57          | left       | Internal_organ_or_component | 81          | 93          | basil ganglia | 0.97616416 |
 | 7     | 0         | Internal_organ_or_component | 59            | 68          | cerebellum | Direction                   | 75          | 79          | right         | 0.953046   |
 | 8     | 1         | Direction                   | 75            | 79          | right      | Internal_organ_or_component | 81          | 93          | basil ganglia | 1.0        |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-re_bodypart_proceduretest_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-re_bodypart_proceduretest_pipeline_en.md
index 840b33fd0f..4f3e84d1ed 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-re_bodypart_proceduretest_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-re_bodypart_proceduretest_pipeline_en.md
@@ -59,44 +59,14 @@ nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations | entity1                      | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2              | confidence |
 |-------|-----------|------------------------------|---------------|-------------|--------|---------|-------------|-------------|---------------------|------------|
 | 0     | 1         | External_body_part_or_region | 94            | 98          | chest  | Test    | 117         | 135         | portable ultrasound | 1.0        |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md
index 310b831ac3..52398ddbec 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-re_human_phenotype_gene_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_human_phenotype_gene_clinica
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
 
@@ -56,34 +57,9 @@ nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobo
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    |   relation | entity1   |   entity1_begin |   entity1_end | chunk1              | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+===========+=================+===============+=====================+===========+=================+===============+=====================+==============+
@@ -91,9 +67,6 @@ Results
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 |          1 | HP        |              23 |            36 | microphthalmia      | GENE      |             110 |           114 | TENM3               |     0.999999 |
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-re_temporal_events_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-re_temporal_events_clinical_pipeline_en.md
index 91f32224b1..c7455a049d 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-re_temporal_events_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-re_temporal_events_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_clinical](ht
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
 
@@ -56,34 +57,10 @@ nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    | relation   | entity1    |   entity1_begin |   entity1_end | chunk1                   | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+============+=================+===============+==========================+===========+=================+===============+=====================+==============+
@@ -91,9 +68,6 @@ Results
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 | OVERLAP    | DATE       |             171 |           179 | that time                | PROBLEM   |             201 |           219 | any specific injury |     0.956654 |
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md
index 221d3b1c8d..d8d41df71a 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-re_temporal_events_enriched_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_enriched_cli
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
 
@@ -56,34 +57,9 @@ nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 5
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |    | relation   | entity1   |   entity1_begin |   entity1_end | chunk1                                        | entity2    |   entity2_begin |   entity2_end | chunk2                   |   confidence |
 +====+============+===========+=================+===============+===============================================+============+=================+===============+==========================+==============+
@@ -91,9 +67,6 @@ Results
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |  1 | AFTER      | DATE      |             171 |           179 | that time                                     | PROBLEM    |             201 |           219 | any specific injury      |     0.577288 |
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-re_test_problem_finding_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-re_test_problem_finding_pipeline_en.md
index 1d21df784c..e15304ba07 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-re_test_problem_finding_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-re_test_problem_finding_pipeline_en.md
@@ -50,32 +50,6 @@ val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en",
 pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
 ```
 
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-
 {:.nlu-block}
 ```python
 import nlu
@@ -86,17 +60,9 @@ nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | 1            | PROCEDURE    | biopsy              | SYMPTOM      |  lesion | 
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-re_test_result_date_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-13-re_test_result_date_pipeline_en.md
index e1e07a48b2..faf1223fc0 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-re_test_result_date_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-re_test_result_date_pipeline_en.md
@@ -59,46 +59,15 @@ nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised ches
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | O            | TEST         | chest X-ray         | MEASUREMENTS |  93%    | 
 | 1     | O            | TEST         | CT scan             | MEASUREMENTS |  93%    |
 | 2     | is_result_of | TEST         | SpO2                | MEASUREMENTS |  93%    |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-rxnorm_ndc_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-13-rxnorm_ndc_mapping_en.md
index 6c64d3e810..bde10a3b39 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-rxnorm_ndc_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-rxnorm_ndc_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps RXNORM codes to NDC codes without using any text d
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,45 +59,15 @@ nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1652674 259934)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1652674 259934)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 {'document': ['1652674 259934'],
 'package_ndc': ['62135-0625-60', '13349-0010-39'],
 'product_ndc': ['46708-0499', '13349-0010'],
 'rxnorm_code': ['1652674', '259934']}
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-snomed_icd10cm_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-13-snomed_icd10cm_mapping_en.md
index 342e47911a..074a56292d 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-snomed_icd10cm_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-snomed_icd10cm_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icd10cm_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | snomed_code                             | icd10cm_code               |
 |---:|:----------------------------------------|:---------------------------|
 |  0 | 128041000119107 | 292278006 | 293072005 | K22.70 | T43.595 | T37.1X5 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-snomed_icdo_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-13-snomed_icdo_mapping_en.md
index 73e7a09ecb..30cfb9827d 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-snomed_icdo_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-snomed_icdo_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icdo_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,13 @@ nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 |    | snomed_code                   | icdo_code                |
 |---:|:------------------------------|:-------------------------|
 |  0 | 10376009 | 2026006 | 26638004 | 8050/2 | 9014/0 | 8322/0 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-13-snomed_umls_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-13-snomed_umls_mapping_en.md
index 1efd8e428f..e4425c3a66 100644
--- a/docs/_posts/ahmedlone127/2023-06-13-snomed_umls_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-13-snomed_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,44 +59,12 @@ nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-
 |    | snomed_code                      | umls_code                      |
 |---:|:---------------------------------|:-------------------------------|
 |  0 | 733187009 | 449433008 | 51264003 | C4546029 | C3164619 | C0271267 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-15-ner_profiling_biobert_en.md b/docs/_posts/ahmedlone127/2023-06-15-ner_profiling_biobert_en.md
index cd88978b3b..08cc640739 100644
--- a/docs/_posts/ahmedlone127/2023-06-15-ner_profiling_biobert_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-15-ner_profiling_biobert_en.md
@@ -67,66 +67,10 @@ nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 ******************** ner_diseases_biobert Model Results ******************** 
 
 [('gestational diabetes mellitus', 'Disease'), ('type two diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('HTG-induced pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('poor appetite', 'Disease'), ('vomiting', 'Disease')]
@@ -150,13 +94,6 @@ Results
 ******************** ner_risk_factors_biobert Model Results ******************** 
 
 [('diabetes mellitus', 'DIABETES'), ('subsequent type two diabetes mellitus', 'DIABETES'), ('obesity', 'OBESE')]
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
index 8a042312ff..9f8c04ddb0 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_sequence_classifier_binary_rct_biobert_pipeline_en.md
@@ -59,75 +59,15 @@ nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abs
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_sequence_classifier_binary_rct_biobert_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_sequence.binary_rct_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |True|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ade_tweet_binary_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ade_tweet_binary_pipeline_en.md
index ff774b75d0..18afddb83e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ade_tweet_binary_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ade_tweet_binary_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ade_tweet
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +70,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | depressed        |      44 |    52 | ADE         |     0.999755 |
 |  1 | angry            |      73 |    77 | ADE         |     0.999608 |
 |  2 | insulin blocking |      97 |   112 | ADE         |     0.738712 |
 |  3 | sugar crashes    |     147 |   159 | ADE         |     0.993742 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_disease_mentions_tweet_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
index 7c71ddc716..3136eb2d90 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_disease_mentions_tweet_pipeline_es.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_disease_m
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-text = '''El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-val text = "El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-text = '''El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_disease_mentions_tweet_pipeline", "es", "clinical/models")
-
-val text = "El diagnóstico fueron varios. Principal: Neumonía en el pulmón derecho. Sinusitis de caballo, Faringitis aguda e infección de orina, también elevada. Gripe No. Estuvo hablando conmigo, sin exagerar, mas de media hora, dándome ánimo y fuerza y que sabe, porque ha visto."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Neumonía en el pulmón |      41 |    61 | ENFERMEDAD  |     0.999969 |
@@ -125,12 +77,6 @@ Results
 |  2 | Faringitis aguda      |      94 |   109 | ENFERMEDAD  |     0.999969 |
 |  3 | infección de orina    |     113 |   130 | ENFERMEDAD  |     0.999969 |
 |  4 | Gripe                 |     150 |   154 | ENFERMEDAD  |     0.999983 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_drug_development_trials_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_drug_development_trials_pipeline_en.md
index ca64e8e544..b6e9b1bf57 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_drug_development_trials_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_drug_development_trials_pipeline_en.md
@@ -34,36 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_drug_deve
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_drug_development_trials_pipeline", "en", "clinical/models")
-
-text = '''In June 2003, the median overall survival  with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response  ( CR ) , partial response  ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with  topotecan,  and 11, 61, 66 and 32 patients, respectively, without topotecan.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_drug_development_trials_pipeline", "en", "clinical/models")
-
-val text = "In June 2003, the median overall survival  with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response  ( CR ) , partial response  ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with  topotecan,  and 11, 61, 66 and 32 patients, respectively, without topotecan."
-
-val result = pipeline.fullAnnotate(text)
-```
-
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.druge_developement.pipeline").predict("""In June 2003, the median overall survival  with and without topotecan were 4.0 and 3.6 months, respectively. The best complete response  ( CR ) , partial response  ( PR ) , stable disease and progressive disease were observed in 23, 63, 55 and 33 patients, respectively, with  topotecan,  and 11, 61, 66 and 32 patients, respectively, without topotecan.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -93,9 +64,6 @@ nlu.load("en.classify.token_bert.druge_developement.pipeline").predict("""In Jun
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------|--------:|------:|:--------------|-------------:|
 |  0 | June 2003               |       3 |    11 | DATE          |     0.996034 |
@@ -118,9 +86,6 @@ Results
 | 17 | 66                      |     301 |   302 | Patient_Count |     0.998066 |
 | 18 | 32 patients             |     308 |   318 | Patient_Count |     0.996285 |
 | 19 | without topotecan       |     335 |   351 | Trial_Group   |     0.971218 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_negation_uncertainty_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_negation_uncertainty_pipeline_es.md
index 271c891421..f008113e4a 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_negation_uncertainty_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_negation_uncertainty_pipeline_es.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_negation_
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "Con diagnóstico probable de cirrosis hepática (no conocida previamente) y peritonitis espontanea primaria con tratamiento durante 8 dias con ceftriaxona en el primer ingreso (no se realizó paracentesis control por escasez de liquido). Lesión tumoral en hélix izquierdo de 0,5 cms. de diámetro susceptible de ca basocelular perlado."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                                              |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | probable                                               |      16 |    23 | UNC         |     0.999994 |
@@ -128,12 +80,6 @@ Results
 |  5 | se realizó paracentesis control por escasez de liquido |     178 |   231 | NSCO        |     0.999995 |
 |  6 | susceptible de                                         |     293 |   306 | UNC         |     0.999986 |
 |  7 | ca basocelular perlado                                 |     308 |   329 | USCO        |     0.99999  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ade_binary_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ade_binary_pipeline_en.md
index b9ceeea619..6a6abca60d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ade_binary_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ade_binary_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ade_b
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-text = '''I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_binary_pipeline", "en", "clinical/models")
-
-val text = "I used to be on paxil but that made me more depressed and prozac made me angry, Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,23 +70,11 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk     |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | depressed     |      44 |    52 | ADE         |     0.990846 |
 |  1 | angry         |      73 |    77 | ADE         |     0.972025 |
 |  2 | sugar crashes |     147 |   159 | ADE         |     0.933623 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ade_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ade_pipeline_en.md
index da18ad7584..a32681e2ac 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ade_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ade_pipeline_en.md
@@ -34,42 +34,13 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ade](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_pipeline", "en", "clinical/models")
-
-text = '''Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_pipeline", "en", "clinical/models")
-
-val text = "Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.ade_pipeline").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
 pipeline = PretrainedPipeline("bert_token_classifier_ner_ade_pipeline", "en", "clinical/models")
 
-text = '''Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.'''
+text = '''I have an allergic reaction to vancomycin so I have itchy skin, sore throat/burning/itching, numbness of tongue and gums. I would not recommend this drug to anyone, especially since I have never had such an adverse reaction to any other medication.'''
 
 result = pipeline.fullAnnotate(text)
 ```
@@ -78,7 +49,7 @@ import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
 val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ade_pipeline", "en", "clinical/models")
 
-val text = "Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay."
+val text = "I have an allergic reaction to vancomycin so I have itchy skin, sore throat/burning/itching, numbness of tongue and gums. I would not recommend this drug to anyone, especially since I have never had such an adverse reaction to any other medication."
 
 val result = pipeline.fullAnnotate(text)
 ```
@@ -86,21 +57,22 @@ val result = pipeline.fullAnnotate(text)
 {:.nlu-block}
 ```python
 import nlu
-nlu.load("en.classify.token_bert.ade_pipeline").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.""")
+nlu.load("en.classify.token_bert.ade_pipeline").predict("""I have an allergic reaction to vancomycin so I have itchy skin, sore throat/burning/itching, numbness of tongue and gums. I would not recommend this drug to anyone, especially since I have never had such an adverse reaction to any other medication.""")
 ```
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-| ner_chunk   | begin   | end   | ner_label   | confidence   |
-|-------------|---------|-------|-------------|--------------|
-
-
-{:.model-param}
+|sentence_id|chunk                      |begin|end|ner_label|
++-----------+---------------------------+-----+---+---------+
+|0          |allergic reaction          |10   |26 |ADE      |
+|0          |vancomycin                 |31   |40 |DRUG     |
+|0          |itchy skin                 |52   |61 |ADE      |
+|0          |sore throat/burning/itching|64   |90 |ADE      |
+|0          |numbness of tongue and gums|93   |119|ADE      |
+|1          |other                      |231  |235|DRUG     |
+|1          |medication                 |237  |246|DRUG     |
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_anatem_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_anatem_pipeline_en.md
index 448acf9564..715bf873e9 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_anatem_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_anatem_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_anate
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-text = '''Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-val text = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-text = '''Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
-
-val text = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------|--------:|------:|:------------|-------------:|
 |  0 | Malignant cells        |       0 |    14 | Anatomy     |     0.999951 |
@@ -126,12 +77,6 @@ Results
 |  3 | breast                 |     343 |   348 | Anatomy     |     0.999842 |
 |  4 | ovarian                |     351 |   357 | Anatomy     |     0.99998  |
 |  5 | prostate cancer        |     364 |   378 | Anatomy     |     0.999968 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_anatomy_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_anatomy_pipeline_en.md
index bff0373578..e16bfe6320 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_anatomy_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_anatomy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_anato
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -80,58 +81,9 @@ Dermatologic: She has got redness along her right great toe, but no bleeding or
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_anatomy_pipeline", "en", "clinical/models")
-
-text = '''This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatomy_pipeline", "en", "clinical/models")
-
-val text = "This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.anatomy_pipeline").predict("""This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label              |   confidence |
 |---:|:-----------------|--------:|------:|:-----------------------|-------------:|
 |  0 | great            |     320 |   324 | Multi-tissue_structure |     0.693343 |
@@ -154,9 +106,6 @@ Results
 | 17 | great            |    1017 |  1021 | Multi-tissue_structure |     0.818323 |
 | 18 | toe              |    1023 |  1025 | Organism_subdivision   |     0.341098 |
 | 19 | toenails         |    1031 |  1038 | Organism_subdivision   |     0.75016  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bacteria_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bacteria_pipeline_en.md
index 891d1b3bdd..70d5b9e708 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bacteria_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bacteria_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bacte
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,15 @@ nlu.load("en.classify.token_bert.bacteria_ner.pipeline").predict("""Based on the
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bacteria_pipeline", "en", "clinical/models")
-
-text = '''Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bacteria_pipeline", "en", "clinical/models")
-
-val text = "Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T))."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.bacteria_ner.pipeline").predict("""Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | SMSP (T)                |      73 |    80 | SPECIES     |     0.99985  |
 |  1 | Methanoregula formicica |     167 |   189 | SPECIES     |     0.999787 |
 |  2 | SMSP (T)                |     222 |   229 | SPECIES     |     0.999871 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
index f3115f2038..b550199d3d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc2gm_gene_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc2gm
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-text = '''ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-val text = "ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-text = '''ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc2gm_gene_pipeline", "en", "clinical/models")
-
-val text = "ROCK-I, Kinectin, and mDia2 can bind the wild type forms of both RhoA and Cdc42 in a GTP-dependent manner in vitro. These results support the hypothesis that in the presence of tryptophan the ribosome translating tnaC blocks Rho ' s access to the boxA and rut sites, thereby preventing transcription termination."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label    |   confidence |
 |---:|:------------|--------:|------:|:-------------|-------------:|
 |  0 | ROCK-I      |       0 |     5 | GENE/PROTEIN |     0.999978 |
@@ -129,12 +80,6 @@ Results
 |  6 | Rho         |     225 |   227 | GENE/PROTEIN |     0.999976 |
 |  7 | boxA        |     247 |   250 | GENE/PROTEIN |     0.999837 |
 |  8 | rut sites   |     256 |   264 | GENE/PROTEIN |     0.99115  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
index 8c4487ff7d..594acef865 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc4chemd_chemicals_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc4ch
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc4chemd_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The main isolated compounds were triterpenes (alpha - amyrin, beta - amyrin, lupeol, betulin, betulinic acid, uvaol, erythrodiol and oleanolic acid) and phenolic acid derivatives from 4 - hydroxybenzoic acid (gallic and protocatechuic acids and isocorilagin)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                       |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------|--------:|------:|:------------|-------------:|
 |  0 | triterpenes                     |      33 |    43 | CHEM        |     0.99999  |
@@ -133,12 +84,6 @@ Results
 | 10 | 4 - hydroxybenzoic acid         |     184 |   206 | CHEM        |     0.999973 |
 | 11 | gallic and protocatechuic acids |     209 |   239 | CHEM        |     0.999984 |
 | 12 | isocorilagin                    |     245 |   256 | CHEM        |     0.999985 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
index 8a57433c76..f8620c1d0e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc5cdr_chemicals_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc5cd
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The possibilities that these cardiovascular findings might be the result of non-selective inhibition of monoamine oxidase or of amphetamine and metamphetamine are discussed. The results have shown that the degradation product p-choloroaniline is not a significant factor in chlorhexidine-digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone-iodine irrigations were associated with erosive cystitis and suggested a possible complication with human usage."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                 |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | amphetamine               |     128 |   138 | CHEM        |     0.999973 |
@@ -127,12 +78,6 @@ Results
 |  4 | kanamycin                 |     350 |   358 | CHEM        |     0.999978 |
 |  5 | colistin                  |     362 |   369 | CHEM        |     0.999942 |
 |  6 | povidone-iodine           |     375 |   389 | CHEM        |     0.999977 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
index a79e591014..3b48e7501f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bc5cdr_disease_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bc5cd
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bc5cdr_disease_pipeline", "en", "clinical/models")
-
-val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +69,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | interstitial cystitis |      61 |    81 | DISEASE     |     0.999746 |
 |  1 | mastocytosis          |     129 |   140 | DISEASE     |     0.999132 |
 |  2 | cystitis              |     209 |   216 | DISEASE     |     0.999912 |
 |  3 | prostate cancer       |     355 |   369 | DISEASE     |     0.999781 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bionlp_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bionlp_pipeline_en.md
index c8b5ebf636..b8fc945ad9 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bionlp_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_bionlp_pipeline_en.md
@@ -34,36 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_bionl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_bionlp_pipeline", "en", "clinical/models")
-
-text = '''Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bionlp_pipeline", "en", "clinical/models")
-
-val text = "Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay."
-
-val result = pipeline.fullAnnotate(text)
-```
-
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.biolp.pipeline").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.""")
-```
-
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -93,9 +64,6 @@ nlu.load("en.classify.token_bert.biolp.pipeline").predict("""Both the erbA IRES
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label              |   confidence |
 |---:|:--------------------|--------:|------:|:-----------------------|-------------:|
 |  0 | erbA IRES           |       9 |    17 | Organism               |     0.999188 |
@@ -107,9 +75,6 @@ Results
 |  6 | erbA/myb IRES virus |     140 |   158 | Organism               |     0.999751 |
 |  7 | erbA IRES virus     |     236 |   250 | Organism               |     0.999749 |
 |  8 | blastoderm          |     259 |   268 | Cell                   |     0.999897 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_cellular_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_cellular_pipeline_en.md
index e7e4afbfaf..15bba41a9a 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_cellular_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_cellular_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_cellu
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.classify.token_bert.cellular_pipeline").predict("""Detection of var
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_cellular_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_cellular_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.cellular_pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                   |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | intracellular signaling proteins            |      27 |    58 | protein     |     0.999477 |
@@ -119,9 +90,6 @@ Results
 | 18 | GAD                                         |     791 |   793 | protein     |     0.999684 |
 | 19 | reporter gene                               |     848 |   860 | DNA         |     0.998856 |
 | 20 | Tax                                         |     863 |   865 | protein     |     0.999717 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_chemicals_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_chemicals_pipeline_en.md
index bf674523d3..19babd7413 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_chemicals_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_chemicals_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_chemi
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.classify.token_bert.chemicals_pipeline").predict("""The results hav
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.chemicals_pipeline").predict("""The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. "A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                   |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------|--------:|------:|:------------|-------------:|
 |  0 | p - choloroaniline          |      40 |    57 | CHEM        |     0.999986 |
@@ -103,9 +73,6 @@ Results
 |  2 | kanamycin                   |     169 |   177 | CHEM        |     0.999985 |
 |  3 | colistin                    |     181 |   188 | CHEM        |     0.999982 |
 |  4 | povidone - iodine           |     194 |   210 | CHEM        |     0.99998  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_chemprot_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_chemprot_pipeline_en.md
index 850834859a..34fafb13e8 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_chemprot_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_chemprot_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_chemp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.chemprot_pipeline").predict("""Keratinocyte gro
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_chemprot_pipeline", "en", "clinical/models")
-
-text = '''Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_chemprot_pipeline", "en", "clinical/models")
-
-val text = "Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.chemprot_pipeline").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk    |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | Keratinocyte |       0 |    11 | GENE-Y      |     0.999147 |
@@ -105,9 +77,6 @@ Results
 |  4 | fibroblast   |      38 |    47 | GENE-Y      |     0.999753 |
 |  5 | growth       |      49 |    54 | GENE-Y      |     0.999771 |
 |  6 | factor       |      56 |    61 | GENE-Y      |     0.999742 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_pipeline_en.md
index 97994eca71..145add4047 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.classify.token_bert.clinical_pipeline").predict("""A 28-year-old fe
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_pipeline", "en", "clinical/models")
-
-text = '''A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG . She had been on dapagliflozin for six months at the time of presentation . Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness , guarding , or rigidity . Pertinent laboratory findings on admission were : serum glucose 111 mg/dl , bicarbonate 18 mmol/l , anion gap 20 , creatinine 0.4 mg/dL , triglycerides 508 mg/dL , total cholesterol 122 mg/dL , glycated hemoglobin ( HbA1c ) 10% , and venous pH 7.27 . Serum lipase was normal at 43 U/L . Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia . The patient was initially admitted for starvation ketosis , as she reported poor oral intake for three days prior to admission . However , serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL , the anion gap was still elevated at 21 , serum bicarbonate was 16 mmol/L , triglyceride level peaked at 2050 mg/dL , and lipase was 52 U/L . The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again . The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL , within 24 hours . Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely . She had close follow-up with endocrinology post discharge .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_pipeline", "en", "clinical/models")
-
-val text = "A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG . She had been on dapagliflozin for six months at the time of presentation . Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness , guarding , or rigidity . Pertinent laboratory findings on admission were : serum glucose 111 mg/dl , bicarbonate 18 mmol/l , anion gap 20 , creatinine 0.4 mg/dL , triglycerides 508 mg/dL , total cholesterol 122 mg/dL , glycated hemoglobin ( HbA1c ) 10% , and venous pH 7.27 . Serum lipase was normal at 43 U/L . Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia . The patient was initially admitted for starvation ketosis , as she reported poor oral intake for three days prior to admission . However , serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL , the anion gap was still elevated at 21 , serum bicarbonate was 16 mmol/L , triglyceride level peaked at 2050 mg/dL , and lipase was 52 U/L . The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again . The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL , within 24 hours . Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely . She had close follow-up with endocrinology post discharge ."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.clinical_pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG . She had been on dapagliflozin for six months at the time of presentation . Physical examination on presentation was significant for dry oral mucosa ; significantly , her abdominal examination was benign with no tenderness , guarding , or rigidity . Pertinent laboratory findings on admission were : serum glucose 111 mg/dl , bicarbonate 18 mmol/l , anion gap 20 , creatinine 0.4 mg/dL , triglycerides 508 mg/dL , total cholesterol 122 mg/dL , glycated hemoglobin ( HbA1c ) 10% , and venous pH 7.27 . Serum lipase was normal at 43 U/L . Serum acetone levels could not be assessed as blood samples kept hemolyzing due to significant lipemia . The patient was initially admitted for starvation ketosis , as she reported poor oral intake for three days prior to admission . However , serum chemistry obtained six hours after presentation revealed her glucose was 186 mg/dL , the anion gap was still elevated at 21 , serum bicarbonate was 16 mmol/L , triglyceride level peaked at 2050 mg/dL , and lipase was 52 U/L . The β-hydroxybutyrate level was obtained and found to be elevated at 5.29 mmol/L - the original sample was centrifuged and the chylomicron layer removed prior to analysis due to interference from turbidity caused by lipemia again . The patient was treated with an insulin drip for euDKA and HTG with a reduction in the anion gap to 13 and triglycerides to 1400 mg/dL , within 24 hours . Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . The patient was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night , 12 units of insulin lispro with meals , and metformin 1000 mg two times a day . It was determined that all SGLT2 inhibitors should be discontinued indefinitely . She had close follow-up with endocrinology post discharge .""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | gestational diabetes mellitus |      39 |    67 | PROBLEM     |     0.999895 |
@@ -123,9 +94,6 @@ Results
 | 22 | Physical examination          |     739 |   758 | TEST        |     0.985332 |
 | 23 | dry oral mucosa               |     796 |   810 | PROBLEM     |     0.991374 |
 | 24 | her abdominal examination     |     830 |   854 | TEST        |     0.999292 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
index b2ecf04bae..0e5e970862 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-text = '''This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-val text = "This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-text = '''This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-val text = "This open-label, parallel-group, two-arm, pilot study compared the beta-cell protective effect of adding insulin glargine (GLA) vs. NPH insulin to ongoing metformin. Overall, 28 insulin-naive type 2 diabetes subjects (mean +/- SD age, 61.5 +/- 6.7 years; BMI, 30.7 +/- 4.3 kg/m(2)) treated with metformin and sulfonylurea were randomized to add once-daily GLA or NPH at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label          |   confidence |
 |---:|:-----------------|--------:|------:|:-------------------|-------------:|
 |  0 | open-label       |       5 |    14 | CTDesign           |     0.742075 |
@@ -138,12 +89,6 @@ Results
 | 15 | GLA              |     356 |   358 | Drug               |     0.972978 |
 | 16 | NPH              |     363 |   365 | Drug               |     0.989424 |
 | 17 | bedtime          |     370 |   376 | DrugTime           |     0.936016 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
index a33ed19440..ddc660d13d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_clinical_trials_abstracts_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_clini
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | suplementación          |      13 |    26 | PROC        |     0.999993 |
@@ -132,12 +83,6 @@ Results
 |  9 | pp                      |     337 |   338 | CHEM        |     0.999889 |
 | 10 | diálisis                |     388 |   395 | PROC        |     0.999993 |
 | 11 | función  residual       |     398 |   414 | PROC        |     0.999948 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_deid_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_deid_pipeline_en.md
index f6c5af64ad..a67a89defd 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_deid_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_deid_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_deid]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.ner_deid.pipeline").predict("""A. Record date :
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_deid_pipeline", "en", "clinical/models")
-
-text = '''A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_deid_pipeline", "en", "clinical/models")
-
-val text = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.ner_deid.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      17 |    26 | DATE          |     0.957256 |
@@ -107,9 +79,6 @@ Results
 |  6 | 0295 Keats Street             |     145 |   161 | STREET        |     0.997889 |
 |  7 | 302) 786-5227                 |     174 |   186 | PHONE         |     0.970114 |
 |  8 | Brothers Coal-Mine            |     253 |   270 | ORGANIZATION  |     0.998911 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_drugs_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_drugs_pipeline_en.md
index c40be5a230..0929a43eed 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_drugs_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_drugs_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_drugs
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.ner_ade.pipeline").predict("""The human KCNJ9 (
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_drugs_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_drugs_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.ner_ade.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | potassium      |      92 |   100 | DrugChem    |     0.990254 |
@@ -106,9 +78,6 @@ Results
 |  5 | vinorelbine    |    1343 |  1353 | DrugChem    |     0.999991 |
 |  6 | anthracyclines |    1390 |  1403 | DrugChem    |     0.99999  |
 |  7 | taxanes        |    1409 |  1415 | DrugChem    |     0.999946 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
index b79a1dc165..656353e9da 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jnlpba_cellular_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jnlpb
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-text = '''The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-val text = "The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-text = '''The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jnlpba_cellular_pipeline", "en", "clinical/models")
-
-val text = "The results suggest that activation of protein kinase C, but not new protein synthesis, is required for IL-2 induction of IFN-gamma and GM-CSF cytoplasmic mRNA. It also was observed that suppression of cytokine gene expression by these agents was independent of the inhibition of proliferation. These data indicate that IL-2 and IL-12 may have distinct signaling pathways leading to the induction of IFN-gammaand GM-CSFgene expression, andthatthe NK3.3 cell line may serve as a novel model for dissecting the biochemical and molecular events involved in these pathways. A functional T-cell receptor signaling pathway is required for p95vav activity. Stimulation of the T-cell antigen receptor ( TCR ) induces activation of multiple tyrosine kinases, resulting in phosphorylation of numerous intracellular substrates. One substrate is p95vav, which is expressed exclusively in hematopoietic and trophoblast cells.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                             |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | protein kinase C                      |      39 |    54 | protein     |     0.993263 |
@@ -135,12 +86,6 @@ Results
 | 12 | tyrosine kinases                      |     732 |   747 | protein     |     0.999636 |
 | 13 | p95vav                                |     834 |   839 | protein     |     0.999842 |
 | 14 | hematopoietic and trophoblast cells   |     876 |   910 | cell_type   |     0.999709 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jsl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jsl_pipeline_en.md
index 3a0004346e..3795a574bb 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jsl_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,71 +63,10 @@ nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.bert_token_ner_jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby-girl also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                        |   begin |   end | ner_label    |   confidence |
 |---:|:---------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 21-day-old                       |      17 |    26 | Age          |     0.999456 |
@@ -154,12 +94,6 @@ Results
 | 22 | fussy                            |     574 |   578 | Symptom      |     0.997592 |
 | 23 | over the past 2 days             |     580 |   599 | Date_Time    |     0.994786 |
 | 24 | albuterol                        |     642 |   650 | Drug         |     0.999735 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jsl_slim_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jsl_slim_pipeline_en.md
index 93da8cec7f..d335f11109 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jsl_slim_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_jsl_slim_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_jsl_s
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.classify.token_bert.jsl_slim.pipeline").predict("""HISTORY: 30-year
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_jsl_slim_pipeline", "en", "clinical/models")
-
-text = '''HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_jsl_slim_pipeline", "en", "clinical/models")
-
-val text = "HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.token_bert.jsl_slim.pipeline").predict("""HISTORY: 30-year-old female presents for digital bilateral mammography secondary to a soft tissue lump palpated by the patient in the upper right shoulder. The patient has a family history of breast cancer within her mother at age 58. Patient denies personal history of breast cancer.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label    |   confidence |
 |---:|:-----------------|--------:|------:|:-------------|-------------:|
 |  0 | HISTORY:         |       0 |     7 | Header       |     0.994786 |
@@ -108,9 +80,6 @@ Results
 |  7 | her mother       |     213 |   222 | Demographics |     0.997765 |
 |  8 | age 58           |     227 |   232 | Age          |     0.997636 |
 |  9 | breast cancer    |     270 |   282 | Oncological  |     0.999452 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_linnaeus_species_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
index fe25dd27fe..32bc3598f8 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_linnaeus_species_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_linna
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-text = '''First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-val text = "First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-text = '''First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_linnaeus_species_pipeline", "en", "clinical/models")
-
-val text = "First identified in chicken, vigilin homologues have now been found in human (6), Xenopus laevis (7), Drosophila melanogaster (8) and Schizosaccharomyces pombe."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                 |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | chicken                   |      20 |    26 | SPECIES     |     0.998697 |
@@ -125,12 +76,6 @@ Results
 |  2 | Xenopus laevis            |      82 |    95 | SPECIES     |     0.999918 |
 |  3 | Drosophila melanogaster   |     102 |   124 | SPECIES     |     0.999925 |
 |  4 | Schizosaccharomyces pombe |     134 |   158 | SPECIES     |     0.999881 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_en.md
index 9faf23bf54..383e118a59 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |     0.986743 |
@@ -127,12 +78,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |     0.962562 |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |     0.999028 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |     0.999852 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_es.md
index 51f195d463..88b98312df 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |     0.999294 |
@@ -131,12 +82,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |     0.999971 |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |     0.99997  |
 | 10 | padres         |     728 |   733 | HUMAN       |     0.999971 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_it.md
index b055975ef2..b291c3d234 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_it.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
 
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,11 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
 
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
@@ -130,12 +82,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |     0.999616 |
 |  8 | HIV              |     523 |   525 | SPECIES     |     0.999383 |
 |  9 | paziente         |     634 |   641 | HUMAN       |     0.99977  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_pt.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_pt.md
index a9fcc5c9f0..1b3d9d0bff 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_pt.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_living_species_pipeline_pt.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_livin
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |     0.999888 |
@@ -126,12 +77,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |     0.999365 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |     0.982236 |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |     0.996602 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ncbi_disease_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
index 748469a6bd..39c14a6feb 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_ncbi_disease_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_ncbi_
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-text = '''Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-val text = "Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-text = '''Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
-
-val text = "Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------|--------:|------:|:------------|-------------:|
 |  0 | Kniest dysplasia       |       0 |    15 | Disease     |     0.999886 |
@@ -126,12 +77,6 @@ Results
 |  3 | midface hypoplasia     |     120 |   137 | Disease     |     0.999911 |
 |  4 | myopia                 |     147 |   152 | Disease     |     0.999894 |
 |  5 | hearing loss           |     159 |   170 | Disease     |     0.999351 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_pathogen_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_pathogen_pipeline_en.md
index 51e3e3dcb1..e7d89b48b3 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_pathogen_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_pathogen_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_patho
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-text = '''Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-val text = "Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-text = '''Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_pathogen_pipeline", "en", "clinical/models")
-
-val text = "Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin. This can progress to loss of skin color, a fast heart rate as it becomes more severe; while it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label        |   confidence |
 |---:|:----------------|--------:|------:|:-----------------|-------------:|
 |  0 | Racecadotril    |       0 |    11 | Medicine         |     0.986453 |
@@ -135,12 +86,6 @@ Results
 | 12 | rabies virus    |     381 |   392 | Pathogen         |     0.738198 |
 | 13 | Lyssavirus      |     395 |   404 | Pathogen         |     0.979239 |
 | 14 | Ephemerovirus   |     410 |   422 | Pathogen         |     0.992292 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_species_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_species_pipeline_en.md
index 61aa8b92fc..ea4bec4493 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_species_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_ner_species_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_ner_speci
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-text = '''As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-val text = "As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) ."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-text = '''As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_ner_species_pipeline", "en", "clinical/models")
-
-val text = "As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) ."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | 6C (T)                  |      57 |    62 | SPECIES     |     0.998955 |
@@ -126,12 +77,6 @@ Results
 |  3 | DSM 18155 (T)           |     188 |   200 | SPECIES     |     0.997657 |
 |  4 | Thiomonas perometabolis |     206 |   228 | SPECIES     |     0.999614 |
 |  5 | DSM 18570 (T)           |     230 |   242 | SPECIES     |     0.997146 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_pharmacology_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_pharmacology_pipeline_es.md
index c2efba1a1a..445a53abd9 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_pharmacology_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-bert_token_classifier_pharmacology_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [bert_token_classifier_pharmacol
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("bert_token_classifier_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "Se realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label     |   confidence |
 |---:|:----------------|--------:|------:|:--------------|-------------:|
 |  0 | creatinkinasa   |      32 |    44 | PROTEINAS     |     0.999973 |
@@ -132,12 +83,6 @@ Results
 |  9 | Interleukina II |     232 |   246 | PROTEINAS     |     0.999965 |
 | 10 | Dacarbacina     |     249 |   259 | NORMALIZABLES |     0.999988 |
 | 11 | Interferon alfa |     263 |   277 | PROTEINAS     |     0.999961 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_augmented_es.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_augmented_es.md
index 1c895b0d3c..137608b8a1 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_augmented_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_augmented_es.md
@@ -36,6 +36,7 @@ The PHI information will be masked and obfuscated in the resulting text. The pip
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from johnsnowlabs import *
 
@@ -121,185 +122,11 @@ Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servic
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical_augmented").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_augmented", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical_augmented").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Datos <SEX>.
@@ -471,12 +298,6 @@ Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura trata
 F. 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra.
 Francisco José Roca Bermúdez Hospital 12 de Octubre Servicio de Endocrinología y Nutrición Calle Ramón y Cajal s/n 03129 Zaragoza - Alicante (Portugal) Correo electrónico: richard@company.it
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_de.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_de.md
index 775b2b95de..f6a60df702 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_de.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_de.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from **German** medical
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -95,137 +96,10 @@ Adresse : St.Johann-Straße 13 19300
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "de", "clinical/models")
-
-sample = """Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-"""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification","de","clinical/models")
-
-val sample = "Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.clinical").predict("""Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "de", "clinical/models")
-
-sample = """Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-"""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification","de","clinical/models")
-
-val sample = "Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.clinical").predict("""Zusammenfassung : Michael Berger wird am Morgen des 12 Dezember 2018 ins St.Elisabeth Krankenhaus eingeliefert. 
-Herr Michael Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.
-
-Persönliche Daten :
-ID-Nummer: T0110053F
-Platte A-BC124
-Kontonummer: DE89370400440532013000
-SSN : 13110587M565
-Lizenznummer: B072RRE2I55
-Adresse : St.Johann-Straße 13 19300
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Zusammenfassung : <PATIENT> wird am Morgen des <DATE> ins <HOSPITAL> eingeliefert.
@@ -273,12 +147,6 @@ Kontonummer: 192837465738
 SSN : 1310011981M454
 Lizenznummer: XX123456
 Adresse : Klingelhöferring 31206
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_en.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_en.md
index fd25a5aa30..31a6b8e911 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_en.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts. The
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,56 +79,10 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -159,9 +114,6 @@ Dr. Dr Felice Lacer, IDXO:4884578, IP 444.444.444.444.
 He is a 75 male was admitted to the MADISON VALLEY MEDICAL CENTER for cystectomy on 07-01-1972.
 Patient's VIN : 2BBBB11BBBB222999, SSN SSN-814-86-1962, Driver's license P055567317431.
 Phone 0381-6762484, Budaörsi út 14., New brunswick, E-MAIL: Reba@google.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_es.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_es.md
index 90b86e61b6..c8145b83d2 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_es.md
@@ -34,6 +34,7 @@ This pipeline is trained with sciwiki_300d embeddings and can be used to deident
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from johnsnowlabs import *
 
@@ -121,189 +122,11 @@ Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servic
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from johnsnowlabs import *
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = """Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "es", "clinical/models")
-
-sample = "Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-"
-
-val result = deid_pipeline.annotate(sample)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("es.deid.clinical").predict("""Datos del paciente.
-Nombre:  Jose .
-Apellidos: Aranda Martinez.
-NHC: 2748903.
-NASS: 26 37482910 04.
-Domicilio: Calle Losada Martí 23, 5 B..
-Localidad/ Provincia: Madrid.
-CP: 28016.
-Datos asistenciales.
-Fecha de nacimiento: 15/04/1977.
-País: España.
-Edad: 37 años Sexo: F.
-Fecha de Ingreso: 05/06/2018.
-Médico: María Merino Viveros  NºCol: 28 28 35489.
-Informe clínico del paciente: varón de 37 años con vida previa activa que refiere dolores osteoarticulares de localización variable en el último mes y fiebre en la última semana con picos (matutino y vespertino) de 40 C las últimas 24-48 horas, por lo que acude al Servicio de Urgencias. Antes de comenzar el cuadro estuvo en Extremadura en una región endémica de brucella, ingiriendo leche de cabra sin pasteurizar y queso de dicho ganado. Entre los comensales aparecieron varios casos de brucelosis. Durante el ingreso para estudio del síndrome febril con antecedentes epidemiológicos de posible exposición a Brucella presenta un cuadro de orquiepididimitis derecha.
-La exploración física revela: Tª 40,2 C; T.A: 109/68 mmHg; Fc: 105 lpm. Se encuentra consciente, orientado, sudoroso, eupneico, con buen estado de nutrición e hidratación. En cabeza y cuello no se palpan adenopatías, ni bocio ni ingurgitación de vena yugular, con pulsos carotídeos simétricos. Auscultación cardíaca rítmica, sin soplos, roces ni extratonos. Auscultación pulmonar con conservación del murmullo vesicular. Abdomen blando, depresible, sin masas ni megalias. En la exploración neurológica no se detectan signos meníngeos ni datos de focalidad. Extremidades sin varices ni edemas. Pulsos periféricos presentes y simétricos. En la exploración urológica se aprecia el teste derecho aumentado de tamaño, no adherido a piel, con zonas de fluctuación e intensamente doloroso a la palpación, con pérdida del límite epidídimo-testicular y transiluminación positiva.
-Los datos analíticos muestran los siguentes resultados: Hemograma: Hb 13,7 g/dl; leucocitos 14.610/mm3 (neutrófilos 77%); plaquetas 206.000/ mm3. VSG: 40 mm 1ª hora. Coagulación: TQ 87%; TTPA 25,8 seg. Bioquímica: Glucosa 117 mg/dl; urea 29 mg/dl; creatinina 0,9 mg/dl; sodio 136 mEq/l; potasio 3,6 mEq/l; GOT 11 U/l; GPT 24 U/l; GGT 34 U/l; fosfatasa alcalina 136 U/l; calcio 8,3 mg/dl. Orina: sedimento normal.
-Durante el ingreso se solicitan Hemocultivos: positivo para Brucella y Serologías específicas para Brucella: Rosa de Bengala +++; Test de Coombs > 1/1280; Brucellacapt > 1/5120. Las pruebas de imagen solicitadas ( Rx tórax, Ecografía abdominal, TAC craneal, Ecocardiograma transtorácico) no evidencian patología significativa, excepto la Ecografía testicular, que muestra engrosamiento de la bolsa escrotal con pequeña cantidad de líquido con septos y testículo aumentado de tamaño con pequeñas zonas hipoecoicas en su interior que pueden representar microabscesos.
-Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura tratamiento sintomático (antitérmicos, antiinflamatorios, reposo y elevación testicular) así como tratamiento antibiótico específico: Doxiciclina 100 mg vía oral cada 12 horas (durante 6 semanas) y Estreptomicina 1 gramo intramuscular cada 24 horas (durante 3 semanas). El paciente mejora significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
-Remitido por: Dra. María Merino Viveros Hospital Universitario de Getafe Servicio de Endocrinología y Nutrición Carretera de Toledo km 12,500 28905 Getafe - Madrid (España) Correo electrónico: marietta84@hotmail.com
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Datos del paciente.
@@ -475,13 +298,6 @@ Con el diagnóstico de orquiepididimitis secundaria a Brucella se instaura trata
 El paciente 041010000011 significativamente de su cuadro tras una semana de ingreso, decidiéndose el alta a su domicilio donde completó la pauta de tratamiento antibiótico. En revisiones sucesivas en consultas se constató la completa remisión del cuadro.
 Remitido por: Dra.
 Reinaldo Manjón Malo Barcelona Servicio de Endocrinología y Nutrición Valencia km 12,500 28905 Bilbao - Madrid (Barcelona) Correo electrónico: quintanasalome@example.net
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_fr.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_fr.md
index cdb0a9bf45..54db7d3ab8 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_fr.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_fr.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts in Fr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -120,187 +121,11 @@ COURRIEL : mariebreton@chb.fr
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = """COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"""
 
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = "COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("fr.deid_obfuscated").predict("""COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = """COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "fr", "clinical/models")
-
-sample = "COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("fr.deid_obfuscated").predict("""COMPTE-RENDU D'HOSPITALISATION
-PRENOM : Jean
-NOM : Dubois
-NUMÉRO DE SÉCURITÉ SOCIALE : 1780160471058
-ADRESSE : 18 Avenue Matabiau
-VILLE : Grenoble
-CODE POSTAL : 38000
-DATE DE NAISSANCE : 03/03/1946
-Âge : 70 ans 
-Sexe : H
-COURRIEL : jdubois@hotmail.fr
-DATE D'ADMISSION : 12/12/2016
-MÉDÉCIN : Dr Michel Renaud
-RAPPORT CLINIQUE : 70 ans, retraité, sans allergie médicamenteuse connue, qui présente comme antécédents : ancien accident du travail avec fractures vertébrales et des côtes ; opéré de la maladie de Dupuytren à la main droite et d'un pontage ilio-fémoral gauche ; diabète de type II, hypercholestérolémie et hyperuricémie ; alcoolisme actif, fume 20 cigarettes / jour.
-Il nous a été adressé car il présentait une hématurie macroscopique postmictionnelle à une occasion et une microhématurie persistante par la suite, avec une miction normale.
-L'examen physique a montré un bon état général, avec un abdomen et des organes génitaux normaux ; le toucher rectal était compatible avec un adénome de la prostate de grade I/IV.
-L'analyse d'urine a montré 4 globules rouges/champ et 0-5 leucocytes/champ ; le reste du sédiment était normal.
-Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des triglycérides de 456 mg/dl ; les fonctions hépatiques et rénales étaient normales. PSA de 1,16 ng/ml.
-ADDRESSÉ À : Dre Marie Breton - Centre Hospitalier de Bellevue Service D'Endocrinologie et de Nutrition - Rue Paulin Bussières, 38000 Grenoble
-COURRIEL : mariebreton@chb.fr
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 COMPTE-RENDU D'HOSPITALISATION
@@ -399,12 +224,6 @@ Hémogramme normal ; la biochimie a montré une glycémie de 169 mg/dl et des tr
 PSA de 1,16 ng/ml.
 ADDRESSÉ À : Dr Tristan-Gilbert Poulain - CENTRE HOSPITALIER D'ORTHEZ Service D'Endocrinologie et de Nutrition - 6, avenue Pages, 37443 Sainte Antoine
 COURRIEL : massecatherine@bouygtel.fr
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_glove_augmented_en.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_glove_augmented_en.md
index 0cda45c3d2..c6d4fb43c4 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_glove_augmented_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_glove_augmented_en.md
@@ -36,6 +36,7 @@ It's different to `clinical_deidentification_glove` in the way it manages PHONE
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,36 +61,11 @@ nlu.load("en.deid.glove_augmented.pipeline").predict("""Record date : 2093-01-13
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_glove_augmented", "en", "clinical/models")
-
-deid_pipeline.annotate("""Record date : 2093-01-13, David Hale, M.D. IP: 203.120.223.13. The driver's license no:A334455B. the SSN: 324598674 and e-mail: hale@gmail.com. Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93. PCP : Oliveira, 25 years old. Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = PretrainedPipeline("clinical_deidentification_glove_augmented", "en", "clinical/models")
-
-val result = pipeline.annotate("""Record date : 2093-01-13, David Hale, M.D. IP: 203.120.223.13. The driver's license no:A334455B. the SSN: 324598674 and e-mail: hale@gmail.com. Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93. PCP : Oliveira, 25 years old. Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286.""")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.glove_augmented.pipeline").predict("""Record date : 2093-01-13, David Hale, M.D. IP: 203.120.223.13. The driver's license no:A334455B. the SSN: 324598674 and e-mail: hale@gmail.com. Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93. PCP : Oliveira, 25 years old. Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 {'masked': ['Record date : <DATE>, <DOCTOR>, M.D.',
     'IP: <IPADDR>.',
     "The driver's license no: <LICENSE>.",
@@ -138,9 +114,6 @@ Results
     'Name : Hendrickson, Ora MR. # 719435 Date : 01/13/93.',
     'PCP : Oliveira, 25 years old.',
     "Record date : 2079-11-09, Patient's VIN : 1HGBH41JXMN109286."]}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_glove_en.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_glove_en.md
index 5d91f333ba..a9668cdd77 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_glove_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_glove_en.md
@@ -34,6 +34,7 @@ This pipeline is trained with lightweight `glove_100d` embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -74,95 +75,11 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_glove", "en", "clinical/models")
-
 
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_glove","en","clinical/models")
-
-val result = deid_pipeline.annotate("Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. Dr. John Green, ID: 1231511863, IP 203.120.223.13. He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.glove_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_glove", "en", "clinical/models")
-
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_glove","en","clinical/models")
-
-val result = deid_pipeline.annotate("Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435. Dr. John Green, ID: 1231511863, IP 203.120.223.13. He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93. Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B. Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.glove_pipeline").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -194,12 +111,6 @@ Dr. Dr Worley Colonel, ID: ZJ:9570208, IP 005.005.005.005.
 He is a 67 male was admitted to the ST. LUKE'S HOSPITAL AT THE VINTAGE for cystectomy on 06-02-1981.
 Patient's VIN : 3CCCC22DDDD333888, SSN SSN-618-77-1042, Driver's license W693817528998.
 Phone 0496 46 46 70, 3100 weston rd, Shattuck, E-MAIL: Freddi@hotmail.com.
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_it.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_it.md
index 17e9fb22ef..bddc8afde5 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_it.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts in It
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -117,181 +118,11 @@ EMAIL: bferrabosco@poste.it""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = """RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = "RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("it.deid.clinical").predict("""RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it""")
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = """RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"""
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "it", "clinical/models")
-
-sample = "RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("it.deid.clinical").predict("""RAPPORTO DI RICOVERO
-NOME: Lodovico Fibonacci
-CODICE FISCALE: MVANSK92F09W408A
-INDIRIZZO: Viale Burcardo 7
-CITTÀ : Napoli
-CODICE POSTALE: 80139
-DATA DI NASCITA: 03/03/1946
-ETÀ: 70 anni 
-SESSO: M
-EMAIL: lpizzo@tim.it
-DATA DI AMMISSIONE: 12/12/2016
-DOTTORE: Eva Viviani
-RAPPORTO CLINICO: 70 anni, pensionato, senza allergie farmacologiche note, che presenta la seguente storia: ex incidente sul lavoro con fratture vertebrali e costali; operato per la malattia di Dupuytren alla mano destra e un bypass ileo-femorale sinistro; diabete di tipo II, ipercolesterolemia e iperuricemia; alcolismo attivo, fuma 20 sigarette/giorno.
-È stato indirizzato a noi perché ha presentato un'ematuria macroscopica post-evacuazione in un'occasione e una microematuria persistente in seguito, con un'evacuazione normale.
-L'esame fisico ha mostrato buone condizioni generali, con addome e genitali normali; l'esame digitale rettale era coerente con un adenoma prostatico di grado I/IV.
-L'analisi delle urine ha mostrato 4 globuli rossi/campo e 0-5 leucociti/campo; il resto del sedimento era normale.
-L'emocromo è normale; la biochimica ha mostrato una glicemia di 169 mg/dl e trigliceridi 456 mg/dl; la funzione epatica e renale sono normali. PSA di 1,16 ng/ml.
-
-INDIRIZZATO A: Dott. Bruno Ferrabosco - ASL Napoli 1 Centro, Dipartimento di Endocrinologia e Nutrizione - Stretto Scamarcio 320, 80138 Napoli
-EMAIL: bferrabosco@poste.it""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 RAPPORTO DI RICOVERO
@@ -387,12 +218,6 @@ PSA di 1,16 ng/ml.
 INDIRIZZATO A: Dott.
 Antonio Rusticucci - ASL 7 DI CARBONIA AZIENDA U.S.L. N. 7, Dipartimento di Endocrinologia e Nutrizione - Via Giorgio 0 Appartamento 26, 03461 Sesto Raimondo
 EMAIL: murat.g@jsl.com
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_pt.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_pt.md
index 05264704b5..75ba3d2671 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_pt.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_pt.md
@@ -34,6 +34,7 @@ This pipeline is trained with `w2v_cc_300d` portuguese embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,213 +134,11 @@ Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@ho
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = """Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = "Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("pt.deid.clinical").predict("""Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-""")
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = """Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-"""
-
-result = deid_pipeline .annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "pt", "clinical/models")
-
-sample = "Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com"
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("pt.deid.clinical").predict("""Dados do paciente.
-Nome: Mauro.
-Apelido: Gonçalves.
-NIF: 368503.
-NISS: 26 63514095.
-Endereço: Calle Miguel Benitez 90.
-CÓDIGO POSTAL: 28016.
-Dados de cuidados.
-Data de nascimento: 03/03/1946.
-País: Portugal.
-Idade: 70 anos Sexo: M.
-Data de admissão: 12/12/2016.
-Doutor: Ignacio Navarro Cuéllar NºCol: 28 28 70973.
-Relatório clínico do paciente: Paciente de 70 anos, mineiro reformado, sem alergias medicamentosas conhecidas, que apresenta como história pessoal: acidente de trabalho antigo com fracturas vertebrais e das costelas; operado por doença de Dupuytren na mão direita e iliofemoral esquerda; Diabetes Mellitus tipo II, hipercolesterolemia e hiperuricemia; alcoolismo activo, fumador de 20 cigarros / dia.
-Foi encaminhado dos cuidados primários porque apresentou uma vez hematúria macroscópica pós-morte e depois microhaematúria persistente, com micturição normal.
-O exame físico mostrou um bom estado geral, com abdómen e genitália normais; o exame rectal foi compatível com adenoma de próstata de grau I/IV.
-A urinálise mostrou 4 glóbulos vermelhos/campo e 0-5 leucócitos/campo; o resto do sedimento estava normal.
-Hemograma normal; a bioquímica mostrou glicemia de 169 mg/dl e triglicéridos de 456 mg/dl; função hepática e renal normal. PSA de 1,16 ng/ml.
-A citologia da urina era repetidamente desconfiada por malignidade.
-A radiografia simples abdominal mostra alterações degenerativas na coluna lombar e calcificações vasculares tanto no hipocôndrio como na pélvis.
-A ecografia urológica revelou cistos corticais simples no rim direito, uma bexiga inalterada com boa capacidade e uma próstata com 30g de peso.
-O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta renal direita e ureteres artrosados com imagens de adição no terço superior de ambos os ureteres, relacionadas com pseudodiverticulose ureteral. O cistograma mostra uma bexiga com boa capacidade, mas com paredes trabeculadas em relação à bexiga de stress. A tomografia computorizada abdominal é normal.
-A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
-Referido por: Miguel Santos - Avenida dos Aliados, 22 Portugal E-mail: nnavcu@hotmail.com.
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Dados do <SEX>.
@@ -455,12 +254,6 @@ O IVUS mostrou normofuncionalismo renal bilateral, calcificações na silhueta r
 A tomografia computorizada abdominal é normal.
 A cistoscopia revelou a existência de pequenos tumores na bexiga, e a ressecção transuretral foi realizada com o resultado anatomopatológico do carcinoma urotelial superficial da bexiga.
 Referido por: Carlos Melo - Avenida Dos Aliados, 56, 22 Espanha E-mail: maria.prado@jacob.com.
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_ro.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_ro.md
index 032a735e87..279ce5fc42 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_ro.md
@@ -75,95 +75,11 @@ Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-val sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("ro.deid.clinical").predict("""Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-result = deid_pipeline.annotate(sample)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification", "ro", "clinical/models")
-
-val sample = """Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("ro.deid.clinical").predict("""Medic : Dr. Agota EVELYN, C.N.P : 2450502264401, Data setului de analize: 25 May 2022 
-Varsta : 77, Nume si Prenume : BUREAN MARIA 
-Tel: +40(235)413773, E-mail : hale@gmail.com,
-Licență : B004256985M, Înmatriculare : CD205113, Cont : FXHZ7170951927104999, 
-Spitalul Pentru Ochi de Deal Drumul Oprea Nr. 972 Vaslui, 737405 """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Medic : Dr. <DOCTOR>, C.N.P : <IDNUM>, Data setului de analize: <DATE>
@@ -195,12 +111,6 @@ Varsta : 91, Nume si Prenume : Dragomir Emilia
 Tel: 0248 551 376, E-mail : tudorsmaranda@kappa.ro,
 Licență : T003485962M, Înmatriculare : AR-65-UPQ, Cont : KHHO5029180812813651, 
 Centrul Medical de Evaluare si Recuperare pentru Copii si Tineri Cristian Serban Buzias Aleea Voinea Curcani, 328479
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_slim_en.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_slim_en.md
index 802fa56b8c..b5104bc69f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_slim_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_slim_en.md
@@ -34,6 +34,7 @@ This pipeline is trained with lightweight `glove_100d` embeddings and can be use
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,103 +79,10 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_slim", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_slim","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_slim").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_slim", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_slim","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.de_identify.clinical_slim").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -206,12 +114,6 @@ Dr. Dr Rosalba Hill, ID: JY:3489547, IP 005.005.005.005.
 He is a 79 male was admitted to the JOHN MUIR MEDICAL CENTER-CONCORD CAMPUS for cystectomy on 01-25-1997.
 Patient's VIN : 3CCCC22DDDD333888, SSN SSN-289-37-4495, Driver's license S99983662.
 Phone 04.32.52.27.90, North Adrienne, Colorado Springs, E-MAIL: Rawland@google.com.
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_wip_en.md b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_wip_en.md
index 21ec236697..79f7a588f0 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_wip_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-clinical_deidentification_wip_en.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify PHI information from medical texts. The
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -78,56 +79,10 @@ Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification_wip", "en", "clinical/models")
-
-sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-result = deid_pipeline.annotate(sample)
-print("\n".join(result['masked']))
-print("\n".join(result['masked_with_chars']))
-print("\n".join(result['masked_fixed_length_chars']))
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification_wip","en","clinical/models")
-
-val sample = """Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com."""
-
-val result = deid_pipeline.annotate(sample)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.clinical_wip").predict("""Name : Hendrickson, Ora, Record date: 2093-01-13, # 719435.
-Dr. John Green, ID: 1231511863, IP 203.120.223.13.
-He is a 60-year-old male was admitted to the Day Hospital for cystectomy on 01/13/93.
-Patient's VIN : 1HGBH41JXMN109286, SSN #333-44-6666, Driver's license no:A334455B.
-Phone (302) 786-5227, 0295 Keats Street, San Francisco, E-MAIL: smith@gmail.com.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 Masked with entity labels
 ------------------------------
 Name : <PATIENT>, Record date: <DATE>, # <MEDICALRECORD>.
@@ -159,9 +114,6 @@ Dr. Dr Felice Lacer, IDXO:4884578, IP 444.444.444.444.
 He is a 75 male was admitted to the MADISON VALLEY MEDICAL CENTER for cystectomy on 07-01-1972.
 Patient's VIN : 2BBBB11BBBB222999, SSN SSN-814-86-1962, Driver's license P055567317431.
 Phone 0381-6762484, Budaörsi út 14., New brunswick, E-MAIL: Reba@google.com.
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_ade_en.md b/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_ade_en.md
index 782f3c3672..5db8297080 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_ade_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_ade_en.md
@@ -34,6 +34,7 @@ A pipeline for Adverse Drug Events (ADE) with `ner_ade_biobert`, `assertion_dl_b
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,10 @@ nlu.load("en.explain_doc.clinical_ade").predict("""Been taking Lipitor for 15 ye
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_ade", "en", "clinical/models")
-
-text = """Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_ade", "en", "clinical/models")
-
-val text = """Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"""
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.clinical_ade").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 Class: True
 
 NER_Assertion:
@@ -114,10 +84,6 @@ Relations:
 | 1  | cramps                        | ADE        | Lipitor     | DRUG    |        0 |
 | 2  | severe fatigue                | ADE        | voltaren    | DRUG    |        0 |
 | 3  | cramps                        | ADE        | voltaren    | DRUG    |        1 |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_carp_en.md b/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_carp_en.md
index 6752018d2c..ecb87ee3dd 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_carp_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_carp_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_clinical`, `assertion_dl`, `re_clinical` and `ner_posology`
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,72 +63,11 @@ nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history o
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-val text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_carp", "en", "clinical/models")
-
-val text = """A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals."""
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.carp").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting. She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |   | chunks                        | ner_clinical | assertion | posology_chunk   | ner_posology | relations |
 |---|-------------------------------|--------------|-----------|------------------|--------------|-----------|
 | 0 | gestational diabetes mellitus | PROBLEM      | present   | metformin        | Drug         | TrAP      |
@@ -137,13 +77,6 @@ Results
 | 4 | poor appetite                 | PROBLEM      | present   | insulin glargine | Drug         | TrCP      |
 | 5 | vomiting                      | PROBLEM      | present   | at night         | Frequency    | TrAP      |
 | 6 | insulin glargine              | TREATMENT    | present   | 12 units         | Dosage       | TrAP      |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_era_en.md b/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_era_en.md
index 3c803c209f..fac25d1847 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_era_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_era_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_clinical_events`, `assertion_dl` and `re_temporal_events_cl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,71 +63,11 @@ nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Ho
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-val text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
 
-text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_era", "en", "clinical/models")
-
-val text = """She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.era").predict("""She is admitted to The John Hopkins Hospital 2 days ago with a history of gestational diabetes mellitus diagnosed. She denied pain and any headache. She was seen by the endocrinology service and she was discharged on 03/02/2018 on 40 units of insulin glargine, 12 units of insulin lispro, and metformin 1000 mg two times a day. She had close follow-up with endocrinology post discharge. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 
 |    | relation   | entity1       |   entity1_begin |   entity1_end | chunk1                    | entity2       |   entity2_begin |   entity2_end | chunk2                        |   confidence |
 |---:|:-----------|:--------------|----------------:|--------------:|:--------------------------|:--------------|----------------:|--------------:|:------------------------------|-------------:|
@@ -138,13 +79,6 @@ Results
 |  5 | OVERLAP    | DATE          |              45 |            54 | 2 days ago                | PROBLEM       |              74 |           102 | gestational diabetes mellitus |     0.996954 |
 |  6 | BEFORE     | EVIDENTIAL    |             119 |           124 | denied                    | PROBLEM       |             126 |           129 | pain                          |     1        |
 |  7 | BEFORE     | EVIDENTIAL    |             119 |           124 | denied                    | PROBLEM       |             135 |           146 | any headache                  |     1        |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_medication_en.md b/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_medication_en.md
index 6bfd2770b4..bafba2ec86 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_medication_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_medication_en.md
@@ -34,6 +34,7 @@ A pipeline for detecting posology entities with the `ner_posology_large` NER mod
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,72 +63,10 @@ nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient i
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI.  She was prescribed 5000 units of Fragmin  subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 +----+----------------+------------+
 |    | chunks         | entities   |
 |---:|:---------------|:-----------|
@@ -164,13 +103,6 @@ Results
 | DRUG-ROUTE     | DRUG      | Lantus     | ROUTE     | subcutaneously |
 | DRUG-FREQUENCY | DRUG      | Lantus     | FREQUENCY | at bedtime     |
 +----------------+-----------+------------+-----------+----------------+
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_radiology_en.md b/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_radiology_en.md
index fa51983aa1..e118e63ad3 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_radiology_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-explain_clinical_doc_radiology_en.md
@@ -34,6 +34,7 @@ A pipeline for detecting posology entities with the `ner_radiology` NER model, a
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,11 @@ nlu.load("en.explain_doc.clinical_radiology.pipeline").predict("""Bilateral brea
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("explain_clinical_doc_radiology", "en", "clinical/models")
-
-text = """Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("explain_clinical_doc_radiology", "en", "clinical/models")
-
-val text = """Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."""
 
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.explain_doc.clinical_radiology.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +----+------------------------------------------+---------------------------+
 |    | chunks                                   | entities                  |
 |---:|:-----------------------------------------|:--------------------------|
@@ -132,10 +103,6 @@ Results
 |       1 | ImagingTest     | ultrasound            | ImagingFindings           | ovoid mass |
 |       0 | ImagingFindings | benign fibrous tissue | Disease_Syndrome_Disorder | lipoma     |
 +---------+-----------------+-----------------------+---------------------------+------------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-icd10_icd9_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-icd10_icd9_mapping_en.md
index 002d4f2675..1ba35c9ab7 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-icd10_icd9_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-icd10_icd9_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10_icd9_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,13 @@ nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(Z833 A0100 A000)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10_icd9.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | icd10_code          | icd9_code          |
 |---:|:--------------------|:-------------------|
 |  0 | Z833 | A0100 | A000 | V180 | 0020 | 0010 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-icd10cm_snomed_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-icd10cm_snomed_mapping_en.md
index 9c7eff306b..12559c0797 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-icd10cm_snomed_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-icd10cm_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icd10cm_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,13 @@ nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(R079 N4289 M62830)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icd10cm_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | icd10cm_code          | snomed_code                              |
 |---:|:----------------------|:-----------------------------------------|
 |  0 | R079 | N4289 | M62830 | 161972006 | 22035000 | 16410651000119105 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-icd10cm_umls_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-icd10cm_umls_mapping_en.md
index 3fb41f0588..ca1ae883e8 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-icd10cm_umls_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-icd10cm_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps ICD10CM codes to UMLS codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,73 +59,13 @@ nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd10cm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(['M8950', 'R822', 'R0901'])
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 {'icd10cm': ['M89.50', 'R82.2', 'R09.01'],
 'umls': ['C4721411', 'C0159076', 'C0004044']}
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-icdo_snomed_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-icdo_snomed_mapping_en.md
index a106205e06..20d7f411d5 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-icdo_snomed_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-icdo_snomed_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `icdo_snomed_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icdo_snomed_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(8120/1 8170/3 8380/3)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.icdo_to_snomed.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 
 |    | icdo_code                | snomed_code                    |
 |---:|:-------------------------|:-------------------------------|
 |  0 | 8120/1 | 8170/3 | 8380/3 | 45083001 | 25370001 | 30289006 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_clinical_pipeline_en.md
index 9023b57c86..d24d3c4559 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_clinical](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl_wip_clinical.pipeline").predict("""The patient is a 21-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_wip_clinical.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                 |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     0.9984   |
@@ -123,9 +95,6 @@ Results
 | 22 | 5 to 10 minutes                           |     459 |   473 | Duration                     |     0.152125 |
 | 23 | his                                       |     488 |   490 | Gender                       |     0.9987   |
 | 24 | respiratory congestion                    |     492 |   513 | VS_Finding                   |     0.6458   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_greedy_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_greedy_biobert_pipeline_en.md
index 9426820d6d..25aa55aaf2 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_greedy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_greedy_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.greedy_wip_biobert.pipeline").predict("""The patient is a 2
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.greedy_wip_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                      |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     1        |
@@ -123,9 +95,6 @@ Results
 | 22 | He                                             |     516 |   517 | Gender                       |     0.9998   |
 | 23 | tired                                          |     550 |   554 | Symptom                      |     0.8912   |
 | 24 | fussy                                          |     569 |   573 | Symptom                      |     0.9541   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_greedy_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_greedy_clinical_pipeline_en.md
index 31c7e748e0..2e2496390d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_greedy_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_greedy_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_greedy_clinical](ht
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.wip_greedy_clinical.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.wip_greedy_clinical.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                     |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     0.9817   |
@@ -123,9 +95,6 @@ Results
 | 22 | his                                            |     488 |   490 | Gender                       |     0.9904   |
 | 23 | respiratory congestion                         |     492 |   513 | Symptom                      |     0.5294   |
 | 24 | He                                             |     516 |   517 | Gender                       |     0.9989   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_modifier_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_modifier_clinical_pipeline_en.md
index 52e5a92557..c7ef928bde 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_modifier_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-jsl_ner_wip_modifier_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_ner_wip_modifier_clinical](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.wip_modifier_clinical.pipeline").predict("""The patient is
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_ner_wip_modifier_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_ner_wip_modifier_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.wip_modifier_clinical.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                     |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     0.9817   |
@@ -123,9 +95,6 @@ Results
 | 22 | his                                            |     488 |   490 | Gender                       |     0.9904   |
 | 23 | respiratory congestion                         |     492 |   513 | Symptom                      |     0.5294   |
 | 24 | He                                             |     516 |   517 | Gender                       |     0.9989   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
index 8f5e59ae8f..a18e83a27f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-jsl_rd_ner_wip_greedy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_rd_ner_wip_greedy_biobert](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.wip_greedy_biobert.pipeline").predict("""Bilateral breast u
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_rd_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_rd_ner_wip_greedy_biobert_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.wip_greedy_biobert.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Bilateral             |       0 |     8 | Direction                 |     0.9875   |
@@ -112,9 +84,6 @@ Results
 | 11 | internal color flow   |     294 |   312 | ImagingFindings           |     0.3726   |
 | 12 | benign fibrous tissue |     334 |   354 | ImagingFindings           |     0.484533 |
 | 13 | lipoma                |     361 |   366 | Disease_Syndrome_Disorder |     0.8955   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
index 644ce4a28f..13dc36ccc2 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-jsl_rd_ner_wip_greedy_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [jsl_rd_ner_wip_greedy_clinical]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl_rd_wip_greedy.pipeline").predict("""The patient is a 21
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("jsl_rd_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("jsl_rd_ner_wip_greedy_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_rd_wip_greedy.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature..""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                  |   begin |   end | ner_label      |   confidence |
 |---:|:----------------------------|--------:|------:|:---------------|-------------:|
 |  0 | 21-day-old                  |      17 |    26 | Age            |     0.9913   |
@@ -123,9 +95,6 @@ Results
 | 22 | respiratory congestion      |     492 |   513 | Symptom        |     0.25015  |
 | 23 | He                          |     516 |   517 | Gender         |     0.9998   |
 | 24 | tired                       |     550 |   554 | Symptom        |     0.8179   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-mesh_umls_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-mesh_umls_mapping_en.md
index 86214112b7..ac228ee59e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-mesh_umls_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-mesh_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `mesh_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,13 @@ nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(C028491 D019326 C579867)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | mesh_code                   | umls_code                      |
 |---:|:----------------------------|:-------------------------------|
 |  0 | C028491 | D019326 | C579867 | C0043904 | C0045010 | C3696376 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_abbreviation_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_abbreviation_clinical_pipeline_en.md
index 63dce0f3d4..6e94ab88d3 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_abbreviation_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_abbreviation_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_abbreviation_clinical](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.clinical-abbreviation.pipeline").predict("""Gravid with est
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_abbreviation_clinical_pipeline", "en", "clinical/models")
-
-text = '''Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_abbreviation_clinical_pipeline", "en", "clinical/models")
-
-val text = "Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical-abbreviation.pipeline").predict("""Gravid with estimated fetal weight of 6-6/12 pounds. LOWER EXTREMITIES: No edema. LABORATORY DATA: Laboratory tests include a CBC which is normal. Blood Type: AB positive. Rubella: Immune. VDRL: Nonreactive. Hepatitis C surface antigen: Negative. HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | CBC          |     126 |   128 | ABBR        |            1 |
 |  1 | AB           |     159 |   160 | ABBR        |            1 |
 |  2 | VDRL         |     189 |   192 | ABBR        |            1 |
 |  3 | HIV          |     247 |   249 | ABBR        |            1 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_ade_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_ade_biobert_pipeline_en.md
index 01a1ecdb96..5b44eb4340 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_ade_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_ade_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_biobert](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.biobert_ade.pipeline").predict("""Been taking Lipitor for 1
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_biobert_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_biobert_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps"
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biobert_ade.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |       0.9996 |
 |  1 | severe fatigue |      52 |    65 | ADE         |       0.7588 |
 |  2 | voltaren       |      97 |   104 | DRUG        |       0.998  |
 |  3 | cramps         |     152 |   157 | ADE         |       0.9258 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_ade_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_ade_clinical_pipeline_en.md
index 217374937a..b701f8a3ba 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_ade_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_ade_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_clinical](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.ade_clinical.pipeline").predict("""Been taking Lipitor for
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_clinical_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_clinical_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.ade_clinical.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |      0.9969  |
 |  1 | severe fatigue |      52 |    65 | ADE         |      0.48995 |
 |  2 | voltaren       |      97 |   104 | DRUG        |      0.9889  |
 |  3 | cramps         |     152 |   157 | ADE         |      0.7472  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_ade_clinicalbert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_ade_clinicalbert_pipeline_en.md
index 393ea1beb1..efd58b31af 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_ade_clinicalbert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_ade_clinicalbert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_clinicalbert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,16 @@ nlu.load("en.med_ner.clinical_bert_ade.pipeline").predict("""Been taking Lipitor
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_clinicalbert_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_clinicalbert_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_bert_ade.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |       0.9975 |
 |  1 | severe fatigue |      52 |    65 | ADE         |       0.7094 |
 |  2 | voltaren       |      97 |   104 | DRUG        |       0.9202 |
 |  3 | cramps         |     152 |   157 | ADE         |       0.5992 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_ade_healthcare_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_ade_healthcare_pipeline_en.md
index 466fdde470..b718ad4fb0 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_ade_healthcare_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_ade_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_ade_healthcare](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.healthcare_ade.pipeline").predict("""Been taking Lipitor fo
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_ade_healthcare_pipeline", "en", "clinical/models")
-
-text = '''Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_ade_healthcare_pipeline", "en", "clinical/models")
-
-val text = "Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.healthcare_ade.pipeline").predict("""Been taking Lipitor for 15 years , have experienced severe fatigue a lot!!! . Doctor moved me to voltaren 2 months ago , so far , have only experienced cramps.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lipitor        |      12 |    18 | DRUG        |      0.998   |
 |  1 | severe fatigue |      52 |    65 | ADE         |      0.67055 |
 |  2 | voltaren       |      97 |   104 | DRUG        |      0.9255  |
 |  3 | cramps         |     152 |   157 | ADE         |      0.9392  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_biobert_pipeline_en.md
index 6c585ac870..3b0a13432e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_biobert](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -80,58 +81,11 @@ Dermatologic: She has got redness along her right great toe, but no bleeding or
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_biobert_pipeline", "en", "clinical/models")
-
-text = '''This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_biobert_pipeline", "en", "clinical/models")
-
-val text = "This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatomy_biobert.pipeline").predict("""This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label              |   confidence |
 |---:|:-----------------|--------:|------:|:-----------------------|-------------:|
 |  0 | right            |     314 |   318 | Organism_subdivision   |      0.9948  |
@@ -154,9 +108,6 @@ Results
 | 17 | foot             |     999 |  1002 | Organism_subdivision   |      0.9831  |
 | 18 | toe              |    1023 |  1025 | Organism_subdivision   |      0.9653  |
 | 19 | toenails         |    1031 |  1038 | Organism_subdivision   |      0.9999  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_coarse_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_coarse_biobert_pipeline_en.md
index fb86b4ae42..c76f80f680 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_coarse_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_coarse_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_coarse_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,46 +63,14 @@ nlu.load("en.med_ner.anatomy_coarse_biobert.pipeline").predict("""content in the
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_coarse_biobert_pipeline", "en", "clinical/models")
-
-text = '''content in the lung tissue'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_coarse_biobert_pipeline", "en", "clinical/models")
-
-val text = "content in the lung tissue"
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatomy_coarse_biobert.pipeline").predict("""content in the lung tissue""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label   |   confidence |
 |---:|:------------|--------:|------:|:------------|-------------:|
 |  0 | lung tissue |      15 |    25 | Anatomy     |      0.99155 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_coarse_pipeline_en.md
index 8427c646fe..783f8f8ffd 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_coarse_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_coarse_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy_coarse](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,46 +63,14 @@ nlu.load("en.med_ner.anatomy_coarse.pipeline").predict("""content in the lung ti
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_coarse_pipeline", "en", "clinical/models")
-
-text = '''content in the lung tissue'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_coarse_pipeline", "en", "clinical/models")
-
-val text = "content in the lung tissue"
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatomy_coarse.pipeline").predict("""content in the lung tissue""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | lung tissue  |      15 |    25 | Anatomy     |      0.99655 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_pipeline_en.md
index 4f2f424afc..69d5a46de4 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_anatomy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_anatomy](https://nlp.johnsn
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -80,58 +81,11 @@ Dermatologic: She has got redness along the lateral portion of her right great t
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_anatomy_pipeline", "en", "clinical/models")
-
-text = '''This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along the lateral portion of her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_anatomy_pipeline", "en", "clinical/models")
-
-val text = "This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along the lateral portion of her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.anatom.pipeline").predict("""This is an 11-year-old female who comes in for two different things. 1. She was seen by the allergist. No allergies present, so she stopped her Allegra, but she is still real congested and does a lot of snorting. They do not notice a lot of snoring at night though, but she seems to be always like that. 2. On her right great toe, she has got some redness and erythema. Her skin is kind of peeling a little bit, but it has been like that for about a week and a half now.
-General: Well-developed female, in no acute distress, afebrile.
-HEENT: Sclerae and conjunctivae clear. Extraocular muscles intact. TMs clear. Nares patent. A little bit of swelling of the turbinates on the left. Oropharynx is essentially clear. Mucous membranes are moist.
-Neck: No lymphadenopathy.
-Chest: Clear.
-Abdomen: Positive bowel sounds and soft.
-Dermatologic: She has got redness along the lateral portion of her right great toe, but no bleeding or oozing. Some dryness of her skin. Her toenails themselves are very short and even on her left foot and her left great toe the toenails are very short.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label              |   confidence |
 |---:|:--------------------|--------:|------:|:-----------------------|-------------:|
 |  0 | skin                |     374 |   377 | Organ                  |      1       |
@@ -140,9 +94,6 @@ Results
 |  3 | Mucous membranes    |     716 |   731 | Tissue                 |      0.90445 |
 |  4 | bowel               |     802 |   806 | Organ                  |      0.9648  |
 |  5 | skin                |     956 |   959 | Organ                  |      0.9999  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_bacterial_species_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_bacterial_species_pipeline_en.md
index 39880e0735..4f9c8d847f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_bacterial_species_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_bacterial_species_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_bacterial_species](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,16 @@ nlu.load("en.med_ner.bacterial_species.pipeline").predict("""Based on these gene
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_bacterial_species_pipeline", "en", "clinical/models")
-
-text = '''Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_bacterial_species_pipeline", "en", "clinical/models")
-
-val text = "Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T))."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.bacterial_species.pipeline").predict("""Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | SMSP (T)                |      73 |    80 | SPECIES     |     0.9725   |
 |  1 | Methanoregula formicica |     167 |   189 | SPECIES     |     0.97935  |
 |  2 | SMSP (T)                |     222 |   229 | SPECIES     |     0.991975 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_biomarker_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_biomarker_pipeline_en.md
index 313c1ae10a..8382b6c6f9 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_biomarker_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_biomarker_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_biomarker](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.biomarker.pipeline").predict("""Here , we report the first
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_biomarker_pipeline", "en", "clinical/models")
-
-text = '''Here , we report the first case of an intraductal tubulopapillary neoplasm of the pancreas with clear cell morphology . Immunohistochemistry revealed positivity for Pan-CK , CK7 , CK8/18 , MUC1 , MUC6 , carbonic anhydrase IX , CD10 , EMA , β-catenin and e-cadherin '''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_biomarker_pipeline", "en", "clinical/models")
-
-val text = "Here , we report the first case of an intraductal tubulopapillary neoplasm of the pancreas with clear cell morphology . Immunohistochemistry revealed positivity for Pan-CK , CK7 , CK8/18 , MUC1 , MUC6 , carbonic anhydrase IX , CD10 , EMA , β-catenin and e-cadherin "
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biomarker.pipeline").predict("""Here , we report the first case of an intraductal tubulopapillary neoplasm of the pancreas with clear cell morphology . Immunohistochemistry revealed positivity for Pan-CK , CK7 , CK8/18 , MUC1 , MUC6 , carbonic anhydrase IX , CD10 , EMA , β-catenin and e-cadherin """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks               |   begin |   end | ner_label             |   confidence |
 |---:|:-------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | intraductal              |      38 |    48 | CancerModifier        |     0.9998   |
@@ -114,9 +86,6 @@ Results
 | 13 | EMA                      |     234 |   236 | Biomarker             |     0.9985   |
 | 14 | β-catenin                |     240 |   248 | Biomarker             |     0.9948   |
 | 15 | e-cadherin               |     254 |   263 | Biomarker             |     0.9952   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_biomedical_bc2gm_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_biomedical_bc2gm_pipeline_en.md
index 7b07fcb595..f243f2ba19 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_biomedical_bc2gm_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_biomedical_bc2gm_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_biomedical_bc2gm](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,15 @@ nlu.load("en.med_ner.biomedical_bc2gm.pipeline").predict("""Immunohistochemical
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_biomedical_bc2gm_pipeline", "en", "clinical/models")
-
-text = '''Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_biomedical_bc2gm_pipeline", "en", "clinical/models")
-
-val text = "Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biomedical_bc2gm.pipeline").predict("""Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label    |   confidence |
 |---:|:-------------|--------:|------:|:-------------|-------------:|
 |  0 | S-100        |      46 |    50 | GENE_PROTEIN |       0.9911 |
 |  1 | HMB-45       |      89 |    94 | GENE_PROTEIN |       0.9944 |
 |  2 | cytokeratin  |     131 |   141 | GENE_PROTEIN |       0.9951 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_bionlp_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_bionlp_biobert_pipeline_en.md
index 49a22b21db..f58631d798 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_bionlp_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_bionlp_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_bionlp_biobert](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.bionlp_biobert.pipeline").predict("""Both the erbA IRES and
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_bionlp_biobert_pipeline", "en", "clinical/models")
-
-text = '''Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_bionlp_biobert_pipeline", "en", "clinical/models")
-
-val text = "Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay"
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.bionlp_biobert.pipeline").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label              |   confidence |
 |---:|:--------------------|--------:|------:|:-----------------------|-------------:|
 |  0 | erbA                |       9 |    12 | Gene_or_gene_product   |      1       |
@@ -109,9 +80,6 @@ Results
 |  8 | erbA                |     236 |   239 | Gene_or_gene_product   |      0.9977  |
 |  9 | IRES virus          |     241 |   250 | Organism               |      0.9911  |
 | 10 | blastoderm          |     259 |   268 | Cell                   |      0.9941  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_bionlp_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_bionlp_pipeline_en.md
index a3b4f6e8a6..7c02473021 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_bionlp_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_bionlp_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_bionlp](https://nlp.johnsno
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.bionlp.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_bionlp_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_bionlp_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.bionlp.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk              |   begin |   end | ner_label            |   confidence |
 |---:|:-----------------------|--------:|------:|:---------------------|-------------:|
 |  0 | human                  |       4 |     8 | Organism             |     0.9996   |
@@ -109,9 +81,6 @@ Results
 |  8 | fat andskeletal muscle |     749 |   770 | Tissue               |     0.955433 |
 |  9 | KCNJ9                  |     801 |   805 | Gene_or_gene_product |     0.9172   |
 | 10 | Type II                |     940 |   946 | Gene_or_gene_product |     0.98845  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_cancer_genetics_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_cancer_genetics_pipeline_en.md
index af6e01c692..75b58ead5b 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_cancer_genetics_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_cancer_genetics_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_cancer_genetics](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.cancer_genetics.pipeline").predict("""The human KCNJ9 (Kir
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_cancer_genetics_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_cancer_genetics_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.cancer_genetics.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | human KCNJ9                                                             |       4 |    14 | protein     |     0.674    |
@@ -110,9 +82,6 @@ Results
 |  9 | KCNJ9 gene                                                              |     801 |   810 | DNA         |     0.95605  |
 | 10 | KCNJ9 protein                                                           |     868 |   880 | protein     |     0.844    |
 | 11 | locus                                                                   |     931 |   935 | DNA         |     0.9685   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_cellular_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_cellular_biobert_pipeline_en.md
index d85c1bbc4c..5bd345c8e9 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_cellular_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_cellular_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_cellular_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.cellular_biobert.pipeline").predict("""Detection of various
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_cellular_biobert_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_cellular_biobert_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.cellular_biobert.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                   |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | intracellular signaling proteins            |      27 |    58 | protein     |     0.673333 |
@@ -118,9 +90,6 @@ Results
 | 17 | GAD                                         |     791 |   793 | protein     |     0.6432   |
 | 18 | reporter gene                               |     848 |   860 | DNA         |     0.61005  |
 | 19 | Tax                                         |     863 |   865 | protein     |     0.99     |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_cellular_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_cellular_pipeline_en.md
index 088cac03a0..fa6a8724dc 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_cellular_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_cellular_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_cellular](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.cellular.pipeline").predict("""Detection of various other i
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_cellular_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_cellular_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.cellular.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                   |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | intracellular signaling proteins                            |      27 |    58 | protein     |     0.763367 |
@@ -118,9 +90,6 @@ Results
 | 17 | GAD                                                         |     791 |   793 | protein     |     0.9932   |
 | 18 | reporter gene                                               |     848 |   860 | DNA         |     0.78715  |
 | 19 | Tax                                                         |     863 |   865 | protein     |     0.9986   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_chemd_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_chemd_clinical_pipeline_en.md
index d6216f0366..31182ce95a 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_chemd_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_chemd_clinical_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemd_clinical](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-text = '''Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-val text = "Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-text = '''Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemd_clinical_pipeline", "en", "clinical/models")
-
-val text = "Isolation, Structure Elucidation, and Iron-Binding Properties of Lystabactins, Siderophores Isolated from a Marine Pseudoalteromonas sp. The marine bacterium Pseudoalteromonas sp. S2B, isolated from the Gulf of Mexico after the Deepwater Horizon oil spill, was found to produce lystabactins A, B, and C (1-3), three new siderophores. The structures were elucidated through mass spectrometry, amino acid analysis, and NMR. The lystabactins are composed of serine (Ser), asparagine (Asn), two formylated/hydroxylated ornithines (FOHOrn), dihydroxy benzoic acid (Dhb), and a very unusual nonproteinogenic amino acid, 4,8-diamino-3-hydroxyoctanoic acid (LySta). The iron-binding properties of the compounds were investigated through a spectrophotometric competition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                         |   begin |   end | ner_label    |   confidence |
 |---:|:-----------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | Lystabactins                       |      65 |    76 | FAMILY       |     0.9841   |
@@ -134,12 +85,6 @@ Results
 | 11 | amino acid                         |     602 |   611 | FAMILY       |     0.4204   |
 | 12 | 4,8-diamino-3-hydroxyoctanoic acid |     614 |   647 | SYSTEMATIC   |     0.9124   |
 | 13 | LySta                              |     650 |   654 | ABBREVIATION |     0.9193   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_chemicals_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_chemicals_pipeline_en.md
index f29345241c..e3f58f564e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_chemicals_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_chemicals_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemicals](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.chemicals.pipeline").predict("""The results have shown that
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemicals_pipeline", "en", "clinical/models")
-
-text = '''The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemicals_pipeline", "en", "clinical/models")
-
-val text = "The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chemicals.pipeline").predict("""The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                  |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------|--------:|------:|:------------|-------------:|
 |  0 | p - choloroaniline          |      40 |    57 | CHEM        |     0.935767 |
@@ -103,9 +74,6 @@ Results
 |  2 | kanamycin                   |     168 |   176 | CHEM        |     0.9824   |
 |  3 | colistin                    |     180 |   187 | CHEM        |     0.9911   |
 |  4 | povidone - iodine           |     193 |   209 | CHEM        |     0.8111   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_chemprot_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_chemprot_biobert_pipeline_en.md
index e7d083a08a..52c45d959a 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_chemprot_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_chemprot_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemprot_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.chemprot_biobert.pipeline").predict("""Keratinocyte growth
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemprot_biobert_pipeline", "en", "clinical/models")
-
-text = '''Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemprot_biobert_pipeline", "en", "clinical/models")
-
-val text = "Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chemprot_biobert.pipeline").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk    |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | Keratinocyte |       0 |    11 | GENE-Y      |       0.894  |
@@ -105,9 +77,6 @@ Results
 |  4 | fibroblast   |      38 |    47 | GENE-Y      |       0.3905 |
 |  5 | growth       |      49 |    54 | GENE-Y      |       0.7109 |
 |  6 | factor       |      56 |    61 | GENE-Y      |       0.8693 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_chemprot_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_chemprot_clinical_pipeline_en.md
index b4a5f28ee3..ca2d185504 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_chemprot_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_chemprot_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chemprot_clinical](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.chemprot_clinical.pipeline").predict("""Keratinocyte growth
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chemprot_clinical_pipeline", "en", "clinical/models")
-
-text = '''Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chemprot_clinical_pipeline", "en", "clinical/models")
-
-val text = "Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chemprot_clinical.pipeline").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk    |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | Keratinocyte |       0 |    11 | GENE-Y      |       0.7433 |
@@ -105,9 +77,6 @@ Results
 |  4 | fibroblast   |      38 |    47 | GENE-Y      |       0.5111 |
 |  5 | growth       |      49 |    54 | GENE-Y      |       0.4559 |
 |  6 | factor       |      56 |    61 | GENE-Y      |       0.5213 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_chexpert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_chexpert_pipeline_en.md
index 149458646c..8dac9f3b0c 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_chexpert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_chexpert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_chexpert](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.chexpert.pipeline").predict("""FINAL REPORT HISTORY : Chest
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_chexpert_pipeline", "en", "clinical/models")
-
-text = '''FINAL REPORT HISTORY : Chest tube leak , to assess for pneumothorax. FINDINGS : In comparison with study of ___ , the endotracheal tube and Swan - Ganz catheter have been removed . The left chest tube remains in place and there is no evidence of pneumothorax. Mild atelectatic changes are seen at the left base.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_chexpert_pipeline", "en", "clinical/models")
-
-val text = "FINAL REPORT HISTORY : Chest tube leak , to assess for pneumothorax. FINDINGS : In comparison with study of ___ , the endotracheal tube and Swan - Ganz catheter have been removed . The left chest tube remains in place and there is no evidence of pneumothorax. Mild atelectatic changes are seen at the left base."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.chexpert.pipeline").predict("""FINAL REPORT HISTORY : Chest tube leak , to assess for pneumothorax. FINDINGS : In comparison with study of ___ , the endotracheal tube and Swan - Ganz catheter have been removed . The left chest tube remains in place and there is no evidence of pneumothorax. Mild atelectatic changes are seen at the left base.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | endotracheal |     118 |   129 | OBS         |       0.9881 |
@@ -112,9 +84,6 @@ Results
 | 11 | changes      |     277 |   283 | OBS         |       0.9984 |
 | 12 | left         |     301 |   304 | ANAT        |       0.9999 |
 | 13 | base         |     306 |   309 | ANAT        |       0.9999 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_bert_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_bert_pipeline_ro.md
index 6438bf0403..6e6501c296 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_bert_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_bert_pipeline_ro.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_bert](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-text = '''Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-val text = "Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
 
-text = '''Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_bert_pipeline", "ro", "clinical/models")
-
-val text = "Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Scout. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-bass
 |    | ner_chunks                        |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Angio CT                          |      12 |    19 | Imaging_Test              |     0.96415  |
@@ -144,12 +95,6 @@ bass
 | 21 | cardiotoracica                    |     461 |   474 | Body_Part                 |     0.9344   |
 | 22 | achizitii secventiale prospective |     479 |   511 | Imaging_Technique         |     0.966833 |
 | 23 | 100/min                           |     546 |   552 | Pulse                     |     0.9128   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_biobert_pipeline_en.md
index a53773ad68..2d6b4edea1 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.clinical_biobert.pipeline").predict("""The patient is a 21-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                           |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | congestion                                          |      62 |    71 | PROBLEM     |     0.5069   |
@@ -112,9 +83,6 @@ Results
 | 11 | albuterol treatments                                |     637 |   656 | TREATMENT   |     0.8917   |
 | 12 | His urine output                                    |     675 |   690 | TEST        |     0.7114   |
 | 13 | any diarrhea                                        |     832 |   843 | PROBLEM     |     0.73595  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_large_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_large_pipeline_en.md
index 8ded3c10b6..e1a92bf388 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_large_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.clinical_large.pipeline").predict("""The human KCNJ9 (Kir 3
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_large_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_large_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_large.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                   |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | the G-protein-activated inwardly rectifying potassium (GIRK |      48 |   106 | TREATMENT   |     0.6926   |
@@ -118,9 +90,6 @@ Results
 | 17 | the standard therapy                                        |    1067 |  1086 | TREATMENT   |     0.757767 |
 | 18 | anthracyclines                                              |    1125 |  1138 | TREATMENT   |     0.9999   |
 | 19 | taxanes                                                     |    1144 |  1150 | TREATMENT   |     0.9999   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_pipeline_en.md
index 0d874a3e89..cb91e2e55c 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.clinical.pipeline").predict("""The human KCNJ9 (Kir 3.3, GI
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes. BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                                   |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | the G-protein-activated inwardly rectifying potassium (GIRK |      48 |   106 | TREATMENT   |     0.6926   |
@@ -118,9 +89,6 @@ Results
 | 17 | the standard therapy                                        |    1067 |  1086 | TREATMENT   |     0.757767 |
 | 18 | anthracyclines                                              |    1125 |  1138 | TREATMENT   |     0.9999   |
 | 19 | taxanes                                                     |    1144 |  1150 | TREATMENT   |     0.9999   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_pipeline_ro.md
index fd44a93594..eece28c92d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_pipeline_ro.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-text = ''' Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-val text = " Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-text = ''' Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_pipeline", "ro", "clinical/models")
-
-val text = " Solicitare: Angio CT cardio-toracic Dg. de trimitere Atrezie de valva pulmonara. Hipoplazie VS. Atrezie VAV stang. Anastomoza Glenn. Sp. Tromboza la nivelul anastomozei. Trimis de: Sectia Clinica Cardiologie (dr. Sue T.) Procedura Aparat GE Revolution HD. Branula albastra montata la nivelul membrului superior drept. Se administreaza 30 ml Iomeron 350 cu flux 2.2 ml/s, urmate de 20 ml ser fiziologic cu acelasi flux. Se efectueaza o examinare angio-CT cardiotoracica cu achizitii secventiale prospective la o frecventa cardiaca medie de 100/min."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                        |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Angio CT                          |      13 |    20 | Imaging_Test              |     0.92675  |
@@ -145,12 +96,6 @@ Results
 | 22 | cardiotoracica                    |     455 |   468 | Body_Part                 |     0.9995   |
 | 23 | achizitii secventiale prospective |     473 |   505 | Imaging_Technique         |     0.8514   |
 | 24 | 100/min                           |     540 |   546 | Pulse                     |     0.8501   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_trials_abstracts_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_trials_abstracts_pipeline_en.md
index afce484ff9..bc4a052c0d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_trials_abstracts_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_trials_abstracts_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_trials_abstracts](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.clinical_trials_abstracts.pipe").predict("""A one-year, ran
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-text = '''A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes. In a multicentre, open, randomised study, 570 patients with Type 2 diabetes, aged 34 - 80 years, were treated for 52 weeks with insulin glargine or NPH insulin given once daily at bedtime.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "en", "clinical/models")
-
-val text = "A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes. In a multicentre, open, randomised study, 570 patients with Type 2 diabetes, aged 34 - 80 years, were treated for 52 weeks with insulin glargine or NPH insulin given once daily at bedtime."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_trials_abstracts.pipe").predict("""A one-year, randomised, multicentre trial comparing insulin glargine with NPH insulin in combination with oral agents in patients with type 2 diabetes. In a multicentre, open, randomised study, 570 patients with Type 2 diabetes, aged 34 - 80 years, were treated for 52 weeks with insulin glargine or NPH insulin given once daily at bedtime.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label          |   confidence |
 |---:|:-----------------|--------:|------:|:-------------------|-------------:|
 |  0 | randomised       |      12 |    21 | CTDesign           |     0.9996   |
@@ -115,9 +87,6 @@ Results
 | 14 | NPH insulin      |     300 |   310 | Drug               |     0.97955  |
 | 15 | once daily       |     318 |   327 | DrugTime           |     0.999    |
 | 16 | bedtime          |     332 |   338 | DrugTime           |     0.9937   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_trials_abstracts_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_trials_abstracts_pipeline_es.md
index 43bbc0ccb0..b8b67a7f21 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_trials_abstracts_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_clinical_trials_abstracts_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_clinical_trials_abstracts](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-text = '''Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_clinical_trials_abstracts_pipeline", "es", "clinical/models")
-
-val text = "Efecto de la suplementación con ácido fólico sobre los niveles de homocisteína total en pacientes en hemodiálisis. La hiperhomocisteinemia es un marcador de riesgo independiente de morbimortalidad cardiovascular. Hemos prospectivamente reducir los niveles de homocisteína total (tHcy) mediante suplemento con ácido fólico y vitamina B6 (pp), valorando su posible correlación con dosis de diálisis, función  residual y parámetros nutricionales."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | suplementación                |      13 |    26 | PROC        |     0.9987   |
@@ -132,12 +83,6 @@ Results
 |  9 | pp                            |     337 |   338 | CHEM        |     0.96     |
 | 10 | diálisis                      |     388 |   395 | PROC        |     0.9982   |
 | 11 | función  residual             |     398 |   414 | PROC        |     0.73045  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_covid_trials_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_covid_trials_pipeline_en.md
index 091c6456bf..9e2963b7b0 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_covid_trials_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_covid_trials_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_covid_trials](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-text = '''In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-val text = "In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-text = '''In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_covid_trials_pipeline", "en", "clinical/models")
-
-val text = "In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tractsuch as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).In 2020, the first COVID‑19 vaccine was developed and made available to the public through emergency authorizations and conditional approvals."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                          |   begin |   end | ner_label                 |   confidence |
 |---:|:------------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | December 2019                       |       3 |    15 | Date                      |     0.99655  |
@@ -140,12 +91,6 @@ Results
 | 17 | CDC                                 |     547 |   549 | Institution               |     0.8296   |
 | 18 | 2020                                |     848 |   851 | Date                      |     0.9997   |
 | 19 | COVID‑19 vaccine                    |     864 |   879 | Vaccine_Name              |     0.87505  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_augmented_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_augmented_pipeline_en.md
index 6b44cf963c..218d7be74b 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_augmented_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_augmented](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.ner_augmented.pipeline").predict("""HISTORY OF PRESENT ILLNESS
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_augmented_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_augmented_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.ner_augmented.pipeline").predict("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | Smith           |      32 |    36 | NAME        |     0.9998   |
@@ -110,9 +82,6 @@ Results
 |  9 | Hart            |    1221 |  1224 | NAME        |     0.9996   |
 | 10 | Smith           |    1231 |  1235 | NAME        |     0.9998   |
 | 11 | 02/07/2003      |    1329 |  1338 | DATE        |     0.9997   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_biobert_pipeline_en.md
index 97fb55f20f..18f56cc651 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_biobert](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.ner_biobert.pipeline").predict("""A. Record date : 2093-01-13,
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_biobert_pipeline", "en", "clinical/models")
-
-text = '''A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_biobert_pipeline", "en", "clinical/models")
-
-val text = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.ner_biobert.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      17 |    26 | DATE        |      0.981   |
@@ -107,9 +79,6 @@ Results
 |  6 | Keats Street                  |     150 |   161 | LOCATION    |      0.77305 |
 |  7 | Phone                         |     164 |   168 | LOCATION    |      0.7083  |
 |  8 | Brothers                      |     253 |   260 | LOCATION    |      0.9447  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_enriched_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_enriched_biobert_pipeline_en.md
index c92cc48df6..a775ad1841 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_enriched_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_enriched_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_enriched_biobert](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.deid.ner_enriched_biobert.pipeline").predict("""A. Record date : 20
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_enriched_biobert_pipeline", "en", "clinical/models")
-
-text = '''A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_enriched_biobert_pipeline", "en", "clinical/models")
-
-val text = "A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.ner_enriched_biobert.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D. Name : Hendrickson, Ora MR. # 7194334. PCP : Oliveira, non-smoking. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227. Patient's complaints first surfaced when he started working for Brothers Coal-Mine.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                     |   begin |   end | ner_label    |   confidence |
 |---:|:------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 2093-01-13                    |      17 |    26 | DATE         |     0.9267   |
@@ -106,9 +77,6 @@ Results
 |  5 | 0295 Keats Street             |     145 |   161 | STREET       |     0.592433 |
 |  6 | 302) 786-5227                 |     174 |   186 | PHONE        |     0.846833 |
 |  7 | Brothers Coal-Mine            |     253 |   270 | ORGANIZATION |     0.45085  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_enriched_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_enriched_pipeline_en.md
index 3770e4ab6a..6608fdca9f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_enriched_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_enriched_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_enriched](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.deid_enriched.pipeline").predict("""HISTORY OF PRESENT ILLN
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_enriched_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_enriched_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_enriched.pipeline").predict("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | Smith         |      32 |    36 | PATIENT     |      0.9997  |
@@ -108,9 +80,6 @@ Results
 |  7 | Hart          |    1221 |  1224 | DOCTOR      |      0.9985  |
 |  8 | Smith         |    1231 |  1235 | PATIENT     |      0.9992  |
 |  9 | 02/07/2003    |    1329 |  1338 | DATE        |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_augmented_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_augmented_pipeline_en.md
index c6d045eef2..ce99c18ffa 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_augmented_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_augmented_pipeline_en.md
@@ -34,30 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_augmented](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-```scala
-val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""")
-```
-
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -71,44 +48,18 @@ val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en
 pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
 ```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
-```scala
-val pipeline = new PretrainedPipeline("ner_deid_generic_augmented_pipeline", "en", "clinical/models")
-
-pipeline.annotate("A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.")
-```
 
 {:.nlu-block}
 ```python
 import nlu
 nlu.load("en.med_ner.deid_generic_augmented.pipeline").predict("""A. Record date : 2093-01-13, David Hale, M.D., Name : Hendrickson, Ora MR. # 7194334 Date : 01/13/93 PCP : Oliveira, 25 -year-old, Record date : 1-11-2000. Cocke County Baptist Hospital. 0295 Keats Street. Phone +1 (302) 786-5227.""")
 ```
+
 </div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +-------------------------------------------------+---------+
 |chunk                                            |ner_label|
 +-------------------------------------------------+---------+
@@ -124,12 +75,6 @@ Results
 |Cocke County Baptist Hospital. 0295 Keats Street.|LOCATION |
 |(302) 786-5227                                   |CONTACT  |
 +-------------------------------------------------+---------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_bert_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_bert_pipeline_ro.md
index a27367684d..1736336d4a 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_bert_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_bert_pipeline_ro.md
@@ -60,70 +60,6 @@ Nume si Prenume : BUREAN MARIA, Varsta: 77
 Medic : Agota Evelyn Tımar
 C.N.P : 2450502264401"
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
 val result = pipeline.fullAnnotate(text)
 ```
 
@@ -147,12 +83,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | LOCATION    |     0.99352  |
@@ -167,12 +97,6 @@ Results
 |  9 | Agota Evelyn Tımar           |     191 |   210 | NAME        |     0.859975 |
 |    | C                            |         |       |             |              |
 | 10 | 2450502264401                |     218 |   230 | ID          |     1        |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_glove_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_glove_pipeline_en.md
index cb05059ea8..8c582bfaf7 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_glove_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_glove_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic_glove](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     1        |
@@ -131,12 +82,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.8586   |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.948667 |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9972   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_ar.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_ar.md
index 04cba58ff2..8062047882 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_ar.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_ar.md
@@ -34,24 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ar", "clinical/models")
-text = '''ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-''
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "ar", "clinical/models")
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-"
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ar", "clinical/models")
@@ -71,9 +54,6 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
 +---------------+----------------------+
 |chunks         |entities          |
 +---------------+----------------------+
@@ -88,9 +68,6 @@ Results
 |أميرة أحمد         |NAME          |
 |ليلى                 |NAME          |
 +---------------+---------------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_de.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_de.md
index 39c8df7a77..2c4db9febe 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_de.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_de.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("de.med_ner.deid_generic.pipeline").predict("""Michael Berger wird am M
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "de", "clinical/models")
-
-text = '''Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "de", "clinical/models")
-
-val text = "Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.med_ner.deid_generic.pipeline").predict("""Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------|--------:|------:|:------------|-------------:|
 |  0 | Michael Berger            |       0 |    13 | NAME        |     0.99555  |
@@ -104,9 +75,6 @@ Results
 |  3 | Bad Kissingen             |      84 |    96 | LOCATION    |     0.90785  |
 |  4 | Berger                    |     117 |   122 | NAME        |     0.935    |
 |  5 | 76                        |     128 |   129 | AGE         |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_it.md
index 361e18a68d..7017c292bb 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_it.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_deid_generic](https://nlp.j
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +70,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   | confidence   |
 |---:|:----------------------|--------:|------:|:------------|:-------------|
 |  0 | Gastone Montanariello |       9 |    29 | NAME        |              |
 |  1 | 49                    |      32 |    33 | AGE         |              |
 |  2 | Ospedale San Camillo  |      55 |    74 | LOCATION    |              |
 |  3 | marzo 2015            |     128 |   137 | DATE        |              |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_ro.md
index 55b509ec07..c58b7fc06f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_generic_pipeline_ro.md
@@ -60,70 +60,6 @@ Nume si Prenume : BUREAN MARIA, Varsta: 77
 Medic : Agota Evelyn Tımar
 C.N.P : 2450502264401"
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_generic_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui,737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
 val result = pipeline.fullAnnotate(text)
 ```
 
@@ -147,12 +83,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | LOCATION    |     0.88326  |
@@ -164,12 +94,6 @@ Results
 |  6 | 77                           |     179 |   180 | AGE         |     1        |
 |  7 | Agota Evelyn Tımar           |     190 |   207 | NAME        |     0.832933 |
 |  8 | 2450502264401                |     217 |   229 | ID          |     1        |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_large_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_large_pipeline_en.md
index d09b5d0eb2..258357e9c4 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_large_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_large](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.deid_large.pipeline").predict("""HISTORY OF PRESENT ILLNESS
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_large_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_large_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deid_large.pipeline").predict("""HISTORY OF PRESENT ILLNESS: Mr. Smith is a 60-year-old white male veteran with multiple comorbidities, who has a history of bladder cancer diagnosed approximately two years ago by the VA Hospital. He underwent a resection there. He was to be admitted to the Day Hospital for cystectomy. He was seen in Urology Clinic and Radiology Clinic on 02/04/2003.	HOSPITAL COURSE: Mr. Smith presented to the Day Hospital in anticipation for Urology surgery. On evaluation, EKG, echocardiogram was abnormal, a Cardiology consult was obtained. A cardiac adenosine stress MRI was then proceeded, same was positive for inducible ischemia, mild-to-moderate inferolateral subendocardial infarction with peri-infarct ischemia. In addition, inducible ischemia seen in the inferior lateral septum. Mr. Smith underwent a left heart catheterization, which revealed two vessel coronary artery disease. The RCA, proximal was 95% stenosed and the distal 80% stenosed. The mid LAD was 85% stenosed and the distal LAD was 85% stenosed. There was four Multi-Link Vision bare metal stents placed to decrease all four lesions to 0%. Following intervention, Mr. Smith was admitted to 7 Ardmore Tower under Cardiology Service under the direction of Dr. Hart. Mr. Smith had a noncomplicated post-intervention hospital course. He was stable for discharge home on 02/07/2003 with instructions to take Plavix daily for one month and Urology is aware of the same.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | Smith           |      32 |    36 | NAME        |     0.9998   |
@@ -110,9 +82,6 @@ Results
 |  9 | Hart            |    1221 |  1224 | NAME        |     0.9995   |
 | 10 | Smith           |    1231 |  1235 | NAME        |     0.9998   |
 | 11 | 02/07/2003      |    1329 |  1338 | DATE        |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_sd_large_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_sd_large_pipeline_en.md
index 0d16aa8ecf..d4e4d87951 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_sd_large_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_sd_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_sd_large](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.deid.med_ner_large.pipeline").predict("""Record date : 2093-01-13 ,
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_sd_large_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_sd_large_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.med_ner_large.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     0.9999   |
@@ -109,9 +80,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.795975 |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.741567 |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.984    |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_sd_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_sd_pipeline_en.md
index 5efe2b143b..f078f3800c 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_sd_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_sd_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_sd](https://nlp.johnsn
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.sd.pipeline").predict("""Record date : 2093-01-13 , David Hale
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_sd_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_sd_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.sd.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     0.9952   |
@@ -109,9 +81,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.84345  |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.775333 |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9492   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_augmented_i2b2_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_augmented_i2b2_pipeline_en.md
index e18999885b..67d876d9f3 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_augmented_i2b2_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_augmented_i2b2_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_augmented_i2
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.deid.subentity_ner_augmented_i2b2.pipeline").predict("""Record date
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_augmented_i2b2_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_augmented_i2b2_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.subentity_ner_augmented_i2b2.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |     0.9997   |
@@ -109,9 +80,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |     0.863775 |
 |  9 | 0295 Keats Street             |     195 |   211 | STREET        |     0.754533 |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |     0.9697   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_augmented_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_augmented_pipeline_en.md
index 1d10235bec..086ab5ae4d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_augmented_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_augmented](h
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.deid.subentity_ner_augmented.pipeline").predict("""Record date : 20
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_augmented_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_augmented_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.deid.subentity_ner_augmented.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |      1       |
@@ -109,9 +81,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |      0.97485 |
 |  9 | 0295 Keats Street             |     195 |   211 | STREET        |      0.8209  |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |      0.9541  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_bert_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_bert_pipeline_ro.md
index e7922e4995..d731fb7e6a 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_bert_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_bert_pipeline_ro.md
@@ -34,70 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -147,12 +84,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | HOSPITAL    |     0.84306  |
@@ -165,12 +96,6 @@ Results
 |  7 | 77                           |     180 |   181 | AGE         |     1        |
 |  8 | Agota Evelyn Tımar           |     191 |   208 | DOCTOR      |     0.803667 |
 |  9 | 2450502264401                |     218 |   230 | IDNUM       |     0.9995   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_glove_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_glove_pipeline_en.md
index 2f41a56043..34eb1ff207 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_glove_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_glove_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity_glove](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_glove_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |     1        |
@@ -131,12 +82,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |     0.731325 |
 |  9 | 0295 Keats Street             |     195 |   211 | STREET        |     0.737067 |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |     0.9882   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_ar.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_ar.md
index 072273c1e6..f8b86a803c 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_ar.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_ar.md
@@ -51,56 +51,6 @@ import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
 
 
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-text= '''
-ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-
-val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-text= '''
-ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح.
-'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ar", "clinical/models")
-
-
 val text = "ملاحظات سريرية - مريض الربو. التاريخ: 16 أبريل 2000. اسم المريضة: ليلى حسن. العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة. الرمز البريدي: 54321. البلد: المملكة العربية السعودية. اسم المستشفى: مستشفى النور. اسم الطبيب: د. أميرة أحمد. تفاصيل الحالة: المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة. الخطة: تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج. يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة. يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة. يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو. تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح."
 
 val result = pipeline.fullAnnotate(text)
@@ -123,13 +73,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 +---------------+--------+
 |chunks         |entities|
 +---------------+--------+
@@ -145,13 +88,6 @@ Results
 |ليلى           |PATIENT |
 |35             |AGE     |
 +---------------+--------+
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_de.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_de.md
index c5848f82ee..b56208a609 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_de.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_de.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("de.deid.ner_subentity.pipeline").predict("""Michael Berger wird am Mor
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "de", "clinical/models")
-
-text = '''Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "de", "clinical/models")
-
-val text = "Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("de.deid.ner_subentity.pipeline").predict("""Michael Berger wird am Morgen des 12 Dezember 2018 ins St. Elisabeth-Krankenhaus in Bad Kissingen eingeliefert. Herr Berger ist 76 Jahre alt und hat zu viel Wasser in den Beinen.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Michael Berger        |       0 |    13 | PATIENT     |      0.99685 |
@@ -104,9 +74,6 @@ Results
 |  3 | Bad Kissingen         |      84 |    96 | CITY        |      0.69685 |
 |  4 | Berger                |     117 |   122 | PATIENT     |      0.5764  |
 |  5 | 76                    |     128 |   129 | AGE         |      1       |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_it.md
index 64f34e0537..6d50eccbb7 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_it.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-text = '''Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "it", "clinical/models")
-
-val text = "Ho visto Gastone Montanariello (49 anni) riferito all' Ospedale San Camillo per diabete mal controllato con sintomi risalenti a marzo 2015."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +69,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   | confidence   |
 |---:|:----------------------|--------:|------:|:------------|:-------------|
 |  0 | Gastone Montanariello |       9 |    29 | PATIENT     |              |
 |  1 | 49                    |      32 |    33 | AGE         |              |
 |  2 | Ospedale San Camillo  |      55 |    74 | HOSPITAL    |              |
 |  3 | marzo 2015            |     128 |   137 | DATE        |              |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_ro.md
index e7ac59cb47..2d28b1d038 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_subentity_pipeline_ro.md
@@ -34,70 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_subentity](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_subentity_pipeline", "ro", "clinical/models")
-
-val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
-Tel: +40(235)413773
-Data setului de analize: 25 May 2022 15:36:00
-Nume si Prenume : BUREAN MARIA, Varsta: 77
-Medic : Agota Evelyn Tımar
-C.N.P : 2450502264401"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -147,12 +84,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                   |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------------------|--------:|------:|:------------|-------------:|
 |  0 | Spitalul Pentru Ochi de Deal |       0 |    27 | HOSPITAL    |      0.5594  |
@@ -165,12 +96,6 @@ Results
 |  7 | 77                           |     180 |   181 | AGE         |      1       |
 |  8 | Agota Evelyn Tımar           |     191 |   208 | DOCTOR      |      0.8149  |
 |  9 | 2450502264401                |     218 |   230 | IDNUM       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_synthetic_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_synthetic_pipeline_en.md
index a142fa6239..5051d2172e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deid_synthetic_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deid_synthetic_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_deid_synthetic](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deid_synthetic_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE        |     1        |
@@ -131,12 +82,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | LOCATION    |     0.968825 |
 |  9 | 0295 Keats Street             |     195 |   211 | LOCATION    |     0.7831   |
 | 10 | 302-786-5227                  |     221 |   232 | CONTACT     |     0.9985   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_deidentify_dl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_deidentify_dl_pipeline_en.md
index 7b79b95163..cdeed1dfff 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_deidentify_dl_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_deidentify_dl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_deidentify_dl](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.deidentify.pipeline").predict("""Record date : 2093-01-13 ,
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_deidentify_dl_pipeline", "en", "clinical/models")
-
-text = '''Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_deidentify_dl_pipeline", "en", "clinical/models")
-
-val text = "Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.deidentify.pipeline").predict("""Record date : 2093-01-13 , David Hale , M.D . , Name : Hendrickson Ora , MR # 7194334 Date : 01/13/93 . PCP : Oliveira , 25 years old , Record date : 2079-11-09 . Cocke County Baptist Hospital , 0295 Keats Street , Phone 302-786-5227.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label     |   confidence |
 |---:|:------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | 2093-01-13                    |      14 |    23 | DATE          |      0.9999  |
@@ -109,9 +79,6 @@ Results
 |  8 | Cocke County Baptist Hospital |     163 |   191 | HOSPITAL      |      0.9466  |
 |  9 | Keats Street                  |     200 |   211 | STREET        |      0.91485 |
 | 10 | 302-786-5227                  |     221 |   232 | PHONE         |      0.7415  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_diag_proc_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_diag_proc_pipeline_es.md
index fce332bef1..48b6750bc2 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_diag_proc_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_diag_proc_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_diag_proc](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diag_proc_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                             |   begin |   end | ner_label     |   confidence |
 |---:|:--------------------------------------|--------:|------:|:--------------|-------------:|
 |  0 | ENFERMEDAD                            |      12 |    21 | DIAGNOSTICO   |      0.9989  |
@@ -131,12 +82,6 @@ Results
 |  8 | enfermedad de las arterias coronarias |     934 |   970 | DIAGNOSTICO   |      0.75594 |
 |  9 | estenosada                            |    1010 |  1019 | DIAGNOSTICO   |      0.9288  |
 | 10 | LAD                                   |    1068 |  1070 | DIAGNOSTICO   |      0.9365  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_biobert_pipeline_en.md
index de7fb5e7a2..c7da552533 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_diseases_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,49 +63,17 @@ nlu.load("en.med_ner.diseases_biobert.pipeline").predict("""Indomethacin resulte
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diseases_biobert_pipeline", "en", "clinical/models")
-
-text = '''Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diseases_biobert_pipeline", "en", "clinical/models")
-
-val text = "Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.diseases_biobert.pipeline").predict("""Indomethacin resulted in histopathologic findings typical of interstitial cystitis, such as leaky bladder epithelium and mucosal mastocytosis. The true incidence of nonsteroidal anti-inflammatory drug-induced cystitis in humans must be clarified by prospective clinical trials. An open-label phase II study of low-dose thalidomide in androgen-independent prostate cancer.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk             |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | interstitial cystitis |      61 |    81 | Disease     |      0.99655 |
 |  1 | mastocytosis          |     129 |   140 | Disease     |      0.8569  |
 |  2 | cystitis              |     209 |   216 | Disease     |      0.9717  |
 |  3 | prostate cancer       |     355 |   369 | Disease     |      0.85965 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_large_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_large_pipeline_en.md
index f80c70ade5..59bd090dab 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_large_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_diseases_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,13 @@ nlu.load("en.med_ner.diseases_large.pipeline").predict("""Detection of various o
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diseases_large_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diseases_large_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.diseases_large.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | T-cell leukemia |     136 |   150 | Disease     |      0.93585 |
 |  1 | T-cell leukemia |     402 |   416 | Disease     |      0.9567  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_pipeline_en.md
index 0e720aebf1..5a4f807568 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_diseases_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_diseases](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,15 @@ nlu.load("en.med_ner.diseases.pipeline").predict("""Detection of various other i
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_diseases_pipeline", "en", "clinical/models")
-
-text = '''Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_diseases_pipeline", "en", "clinical/models")
-
-val text = "Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.diseases.pipeline").predict("""Detection of various other intracellular signaling proteins is also described. Genetic characterization of transactivation of the human T-cell leukemia virus type 1 promoter: Binding of Tax to Tax-responsive element 1 is mediated by the cyclic AMP-responsive members of the CREB/ATF family of transcription factors. To achieve a better understanding of the mechanism of transactivation by Tax of human T-cell leukemia virus type 1 Tax-responsive element 1 (TRE-1), we developed a genetic approach with Saccharomyces cerevisiae. We constructed a yeast reporter strain containing the lacZ gene under the control of the CYC1 promoter associated with three copies of TRE-1. Expression of either the cyclic AMP response element-binding protein (CREB) or CREB fused to the GAL4 activation domain (GAD) in this strain did not modify the expression of the reporter gene. Tax alone was also inactive.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk       |   begin |   end | ner_label   |   confidence |
 |---:|:----------------|--------:|------:|:------------|-------------:|
 |  0 | T-cell leukemia |     136 |   150 | Disease     |      0.92015 |
 |  1 | T-cell leukemia |     402 |   416 | Disease     |      0.94145 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_drugprot_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_drugprot_clinical_pipeline_en.md
index 2bf5edeb4d..74fa04c474 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_drugprot_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_drugprot_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugprot_clinical](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.clinical_drugprot.pipeline").predict("""Anabolic effects of
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugprot_clinical_pipeline", "en", "clinical/models")
-
-text = '''Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugprot_clinical_pipeline", "en", "clinical/models")
-
-val text = "Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_drugprot.pipeline").predict("""Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | clenbuterol         |      20 |    30 | CHEMICAL    |      0.9691  |
 |  1 | beta 2-adrenoceptor |      67 |    85 | GENE        |      0.89855 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_greedy_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_greedy_pipeline_en.md
index e752725be2..5421a7eb9a 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_greedy_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_greedy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugs_greedy](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,15 @@ nlu.load("en.med_ner.drugs_greedy.pipeline").predict("""DOSAGE AND ADMINISTRATIO
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugs_greedy_pipeline", "en", "clinical/models")
-
-text = '''DOSAGE AND ADMINISTRATION The initial dosage of hydrocortisone tablets may vary from 20 mg to 240 mg of hydrocortisone per day depending on the specific disease entity being treated.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugs_greedy_pipeline", "en", "clinical/models")
-
-val text = "DOSAGE AND ADMINISTRATION The initial dosage of hydrocortisone tablets may vary from 20 mg to 240 mg of hydrocortisone per day depending on the specific disease entity being treated."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.drugs_greedy.pipeline").predict("""DOSAGE AND ADMINISTRATION The initial dosage of hydrocortisone tablets may vary from 20 mg to 240 mg of hydrocortisone per day depending on the specific disease entity being treated.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                         |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------------------|--------:|------:|:------------|-------------:|
 |  0 | hydrocortisone tablets            |      48 |    69 | DRUG        |       0.9923 |
 |  1 | 20 mg to 240 mg of hydrocortisone |      85 |   117 | DRUG        |       0.7361 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_large_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_large_pipeline_en.md
index 6c47cd8897..fb6ef36e97 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_large_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugs_large](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.drugs_large.pipeline").predict("""The patient is a 40-year-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugs_large_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. He has been advised Aspirin 81 milligrams QDay. Humulin N. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugs_large_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. He has been advised Aspirin 81 milligrams QDay. Humulin N. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain.."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.drugs_large.pipeline").predict("""The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. He has been advised Aspirin 81 milligrams QDay. Humulin N. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually PRN chest pain..""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                        |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Aspirin 81 milligrams            |     306 |   326 | DRUG        |     0.8401   |
@@ -103,9 +75,6 @@ Results
 |  2 | insulin 50 units                 |     345 |   360 | DRUG        |     0.847067 |
 |  3 | HCTZ 50 mg                       |     370 |   379 | DRUG        |     0.875567 |
 |  4 | Nitroglycerin 1/150 sublingually |     387 |   418 | DRUG        |     0.845967 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_pipeline_en.md
index ee04f98ce1..358f186644 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_drugs_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_drugs](https://nlp.johnsnow
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.drugs.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_drugs_pipeline", "en", "clinical/models")
-
-text = '''The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_drugs_pipeline", "en", "clinical/models")
-
-val text = "The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.drugs.pipeline").predict("""The human KCNJ9 (Kir 3.3, GIRK3) is a member of the G-protein-activated inwardly rectifying potassium (GIRK) channel family. Here we describe the genomicorganization of the KCNJ9 locus on chromosome 1q21-23 as a candidate gene forType II diabetes mellitus in the Pima Indian population. The gene spansapproximately 7.6 kb and contains one noncoding and two coding exons separated byapproximately 2.2 and approximately 2.6 kb introns, respectively. We identified14 single nucleotide polymorphisms (SNPs), including one that predicts aVal366Ala substitution, and an 8 base-pair (bp) insertion/deletion. Ourexpression studies revealed the presence of the transcript in various humantissues including pancreas, and two major insulin-responsive tissues: fat andskeletal muscle. The characterization of the KCNJ9 gene should facilitate furtherstudies on the function of the KCNJ9 protein and allow evaluation of thepotential role of the locus in Type II diabetes.BACKGROUND: At present, it is one of the most important issues for the treatment of breast cancer to develop the standard therapy for patients previously treated with anthracyclines and taxanes. With the objective of determining the usefulnessof vinorelbine monotherapy in patients with advanced or recurrent breast cancerafter standard therapy, we evaluated the efficacy and safety of vinorelbine inpatients previously treated with anthracyclines and taxanes.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | potassium      |      92 |   100 | DrugChem    |       0.5346 |
@@ -105,9 +77,6 @@ Results
 |  4 | vinorelbine    |    1343 |  1353 | DrugChem    |       0.9815 |
 |  5 | anthracyclines |    1390 |  1403 | DrugChem    |       0.9447 |
 |  6 | taxanes        |    1409 |  1415 | DrugChem    |       0.6213 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_en.md
index e3a5b1183f..8e5898233b 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_en.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-val text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "en", "clinical/models")
-
-val text = "
-A 3-year-old boy with autistic disorder on hospital of pediatric ward A at university hospital. He has no family history of illness or autistic spectrum disorder. The child was diagnosed with a severe communication disorder, with social interaction difficulties and sensory processing delay. Blood work was normal (thyroid-stimulating hormone (TSH), hemoglobin, mean corpuscular volume (MCV), and ferritin). Upper endoscopy also showed a submucosal tumor causing subtotal obstruction of the gastric outlet. Because a gastrointestinal stromal tumor was suspected, distal gastrectomy was performed. Histopathological examination revealed spindle cell proliferation in the submucosal layer.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                         |   begin |   end | entities           |   confidence |
 |---:|:-------------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | A 3-year-old boy               |       1 |    16 | patient            |     0.733133 |
@@ -162,12 +105,6 @@ Results
 | 25 | revealed                       |     628 |   635 | clinical_event     |     0.9989   |
 | 26 | spindle cell proliferation     |     637 |   662 | clinical_condition |     0.4487   |
 | 27 | the submucosal layer           |     667 |   686 | bodypart           |     0.523    |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_es.md
index 67c9071ce2..4384b152e7 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_es.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-val text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "es", "clinical/models")
-
-val text = "
-Un niño de 3 años con trastorno autista en el hospital de la sala pediátrica A del hospital universitario. No tiene antecedentes familiares de enfermedad o trastorno del espectro autista. El niño fue diagnosticado con un trastorno de comunicación severo, con dificultades de interacción social y retraso en el procesamiento sensorial. Los análisis de sangre fueron normales (hormona estimulante de la tiroides (TSH), hemoglobina, volumen corpuscular medio (MCV) y ferritina). La endoscopia alta también mostró un tumor submucoso que causaba una obstrucción subtotal de la salida gástrica. Ante la sospecha de tumor del estroma gastrointestinal, se realizó gastrectomía distal. El examen histopatológico reveló proliferación de células fusiformes en la capa submucosa.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                       |   begin |   end | entities           |   confidence |
 |---:|:-----------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Un niño de 3 años            |       1 |    17 | patient            |     0.68856  |
@@ -170,12 +113,6 @@ Results
 | 33 | proliferación                |     711 |   723 | clinical_event     |     0.9996   |
 | 34 | células fusiformes           |     728 |   745 | bodypart           |     0.7001   |
 | 35 | la capa submucosa            |     750 |   766 | bodypart           |     0.641267 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_eu.md b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_eu.md
index b2f101b0ae..d6760c5e65 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_eu.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_eu.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-val text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "eu", "clinical/models")
-
-val text = "
-3 urteko mutiko bat nahasmendu autistarekin unibertsitateko ospitaleko A pediatriako ospitalean. Ez du autismoaren espektroaren nahaste edo gaixotasun familiaren aurrekaririk. Mutilari komunikazio-nahaste larria diagnostikatu zioten, elkarrekintza sozialeko zailtasunak eta prozesamendu sentsorial atzeratua. Odol-analisiak normalak izan ziren (tiroidearen hormona estimulatzailea (TSH), hemoglobina, batez besteko bolumen corpuskularra (MCV) eta ferritina). Goiko endoskopiak mukosaren azpiko tumore bat ere erakutsi zuen, urdail-irteeren guztizko oztopoa eragiten zuena. Estroma gastrointestinalaren tumore baten susmoa ikusita, distaleko gastrektomia egin zen. Azterketa histopatologikoak agerian utzi zuen mukosaren azpiko zelulen ugaltzea.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                   |   begin |   end | entities           |   confidence |
 |---:|:-------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | 3 urteko mutiko bat      |       1 |    19 | patient            |     0.813975 |
@@ -175,12 +118,6 @@ Results
 | 38 | utzi                     |     701 |   704 | clinical_event     |     0.925    |
 | 39 | mukosaren azpiko zelulen |     711 |   734 | bodypart           |     0.754933 |
 | 40 | ugaltzea                 |     736 |   743 | clinical_event     |     0.9989   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_fr.md b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_fr.md
index c72095c74a..0bea8d8996 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_fr.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_case_pipeline_fr.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_case](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-val text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_case_pipeline", "fr", "clinical/models")
-
-val text = "
-Un garçon de 3 ans atteint d'un trouble autistique à l'hôpital du service pédiatrique A de l'hôpital universitaire. Il n'a pas d'antécédents familiaux de troubles ou de maladies du spectre autistique. Le garçon a été diagnostiqué avec un trouble de communication sévère, avec des difficultés d'interaction sociale et un traitement sensoriel retardé. Les tests sanguins étaient normaux (thyréostimuline (TSH), hémoglobine, volume globulaire moyen (MCV) et ferritine). L'endoscopie haute a également montré une tumeur sous-muqueuse provoquant une obstruction subtotale de la sortie gastrique. Devant la suspicion d'une tumeur stromale gastro-intestinale, une gastrectomie distale a été réalisée. L'examen histopathologique a révélé une prolifération de cellules fusiformes dans la couche sous-muqueuse.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                                                |   begin |   end | entities           |   confidence |
 |---:|:------------------------------------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Un garçon de 3 ans                                    |       1 |    18 | patient            |     0.58786  |
@@ -166,12 +109,6 @@ Results
 | 29 | prolifération                                         |     735 |   747 | clinical_event     |     0.6767   |
 | 30 | cellules fusiformes                                   |     752 |   770 | bodypart           |     0.5233   |
 | 31 | la couche sous-muqueuse                               |     777 |   799 | bodypart           |     0.6755   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_en.md
index 5f00ca50fe..f4ce6fc88e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,38 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "en", "clinical/models")
-
-text = "
-Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "en", "clinical/models")
 
-val text = "
-Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                  |   begin |   end | entities           |   confidence |
 |---:|:------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | Hyperparathyroidism     |       1 |    19 | clinical_condition |     0.9375   |
@@ -100,9 +74,6 @@ Results
 |  5 | fractures               |     281 |   289 | clinical_condition |     0.9726   |
 |  6 | anesthesia              |     305 |   314 | clinical_condition |     0.991    |
 |  7 | mandibular fracture     |     330 |   348 | clinical_condition |     0.54925  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_es.md
index 0751292854..737863f82a 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_es.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-val text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "es", "clinical/models")
-
-val text = "
-La exploración abdominal revela una cicatriz de laparotomía media infraumbilical, la presencia de ruidos disminuidos, y dolor a la palpación de manera difusa sin claros signos de irritación peritoneal. No existen hernias inguinales o crurales.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks               |   begin |   end | entities           |   confidence |
 |---:|:---------------------|--------:|------:|:-------------------|-------------:|
 |  0 | cicatriz             |      37 |    44 | clinical_condition |      0.9883  |
@@ -139,12 +82,6 @@ Results
 |  2 | signos               |     170 |   175 | clinical_condition |      0.9862  |
 |  3 | irritación           |     180 |   189 | clinical_condition |      0.9975  |
 |  4 | hernias inguinales   |     214 |   231 | clinical_condition |      0.7543  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_eu.md b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_eu.md
index 3a5a55184c..a8edfa3955 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_eu.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_eu.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-val text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "eu", "clinical/models")
-
-val text = "
-Gertaera honetatik bi hilabetetara, umea Larrialdietako Zerbitzura dator 4 egunetan zehar buruko mina eta bekokiko hantura azaltzeagatik, sukarrik izan gabe. Miaketan, haztapen mingarria duen bekokiko  hantura bigunaz gain, ez da beste zeinurik azaltzen. Polakiuria eta tenesmo arina ere izan zuen egun horretan hematuriarekin batera. Geroztik sintomarik gabe dago.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks     |   begin |   end | entities           |   confidence |
 |---:|:-----------|--------:|------:|:-------------------|-------------:|
 |  0 | mina       |      98 |   101 | clinical_condition |       0.8754 |
@@ -141,12 +84,6 @@ Results
 |  4 | hantura    |     203 |   209 | clinical_condition |       0.8805 |
 |  5 | Polakiuria |     256 |   265 | clinical_condition |       0.6683 |
 |  6 | sintomarik |     345 |   354 | clinical_condition |       0.9632 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_fr.md b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_fr.md
index 9d5fe3752c..23ae4b87ec 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_fr.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_fr.md
@@ -34,62 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-val text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "fr", "clinical/models")
-
-val text = "
-Il aurait présenté il y’ a environ 30 ans des ulcérations génitales non traitées spontanément guéries. L’interrogatoire retrouvait une toux sèche depuis trois mois, des douleurs rétro-sternales constrictives, une dyspnée stade III de la NYHA et un contexte d’ apyrexie. Sur ce tableau s’ est greffé des œdèmes des membres inférieurs puis un tableau d’ anasarque d’ où son hospitalisation en cardiologie pour décompensation cardiaque globale.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,12 +78,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                   |   begin |   end | entities           |   confidence |
 |---:|:-------------------------|--------:|------:|:-------------------|-------------:|
 |  0 | ulcérations              |      47 |    57 | clinical_condition |       0.9995 |
@@ -148,12 +87,6 @@ Results
 |  4 | apyrexie                 |     261 |   268 | clinical_condition |       0.9963 |
 |  5 | anasarque                |     353 |   361 | clinical_condition |       0.9973 |
 |  6 | décompensation cardiaque |     409 |   432 | clinical_condition |       0.8948 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_it.md
index 7d62ac54f6..4d35076e55 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_eu_clinical_condition_pipeline_it.md
@@ -34,62 +34,7 @@ This pretrained pipeline is built on the top of [ner_eu_clinical_condition](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-val text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_eu_clinical_condition_pipeline", "it", "clinical/models")
-
-val text = "
-Donna, 64 anni, ricovero per dolore epigastrico persistente, irradiato a barra e posteriormente, associato a dispesia e anoressia. Poche settimane dopo compaiono, però, iperemia, intenso edema vulvare ed una esione ulcerativa sul lato sinistro della parete rettale che la RM mostra essere una fistola transfinterica. Questi trattamenti determinano un miglioramento dell’ infiammazione ed una riduzione dell’ ulcera, ma i condilomi permangono inalterati.
-
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -133,12 +78,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                 |   begin |   end | entities           |   confidence |
 |---:|:-----------------------|--------:|------:|:-------------------|-------------:|
 |  0 | dolore epigastrico     |      30 |    47 | clinical_condition |      0.90845 |
@@ -147,12 +86,6 @@ Results
 |  3 | edema                  |     188 |   192 | clinical_condition |      1       |
 |  4 | fistola transfinterica |     294 |   315 | clinical_condition |      0.97785 |
 |  5 | infiammazione          |     372 |   384 | clinical_condition |      0.9996  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_events_admission_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_events_admission_clinical_pipeline_en.md
index f54d7ba81f..a725ca89af 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_events_admission_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_events_admission_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_admission_clinical](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,15 @@ nlu.load("en.med_ner.events_admission_clinical.pipeline").predict("""The patient
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_admission_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_admission_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.events_admission_clinical.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk          |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | OCCURRENCE    |       0.6219 |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |       0.812  |
 |  2 | last evening       |      44 |    55 | TIME          |       0.9534 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_events_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_events_biobert_pipeline_en.md
index ce9ff112ac..0641ea326e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_events_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_events_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_biobert](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,14 @@ nlu.load("en.med_ner.biobert_events.pipeline").predict("""The patient presented
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biobert_events.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk          |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | OCCURRENCE    |     0.5019   |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |     0.695333 |
 |  2 | last evening       |      44 |    55 | DATE          |     0.7621   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_events_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_events_clinical_pipeline_en.md
index f77f1209ca..3cc11246f4 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_events_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_events_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_clinical](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,16 @@ nlu.load("en.med_ner.events_clinical.pipeline").predict("""The patient presented
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_clinical_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_clinical_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.events_clinical.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk          |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | OCCURRENCE    |     0.7132   |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |     0.723267 |
 |  2 | last evening       |      44 |    55 | DATE          |     0.90555  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_events_healthcare_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_events_healthcare_pipeline_en.md
index 269dbcc1dc..d064151e43 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_events_healthcare_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_events_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_events_healthcare](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,48 +63,15 @@ nlu.load("en.med_ner.healthcare_events.pipeline").predict("""The patient present
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_events_healthcare_pipeline", "en", "clinical/models")
-
-text = '''The patient presented to the emergency room last evening.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_events_healthcare_pipeline", "en", "clinical/models")
-
-val text = "The patient presented to the emergency room last evening."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.healthcare_events.pipeline").predict("""The patient presented to the emergency room last evening.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks         |   begin |   end | ner_label     |   confidence |
 |---:|:-------------------|--------:|------:|:--------------|-------------:|
 |  0 | presented          |      12 |    20 | EVIDENTIAL    |     0.6769   |
 |  1 | the emergency room |      25 |    42 | CLINICAL_DEPT |     0.835967 |
 |  2 | last evening       |      44 |    55 | DATE          |     0.59135  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_genetic_variants_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_genetic_variants_pipeline_en.md
index 28079d931d..db0e3e20b3 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_genetic_variants_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_genetic_variants_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_genetic_variants](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.genetic_variants.pipeline").predict("""The mutation pattern
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_genetic_variants_pipeline", "en", "clinical/models")
-
-text = '''The mutation pattern of mitochondrial DNA (mtDNA) in mainland Chinese patients with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) has been rarely reported, though previous data suggested that the mutation pattern of MELAS could be different among geographically localized populations. We presented the results of comprehensive mtDNA mutation analysis in 92 unrelated Chinese patients with MELAS (85 with classic MELAS and 7 with MELAS/Leigh syndrome (LS) overlap syndrome). The mtDNA A3243G mutation was the most common causal genotype in this patient group (79/92 and 85.9%). The second common gene mutation was G13513A (7/92 and 7.6%). Additionally, we identified T10191C (p.S45P) in ND3, A11470C (p. K237N) in ND4, T13046C (p.M237T) in ND5 and a large-scale deletion (13025-13033:14417-14425) involving partial ND5 and ND6 subunits of complex I in one patient each. Among them, A11470C, T13046C and the single deletion were novel mutations. In summary, patients with mutations affecting mitochondrially encoded complex I (MTND) reached 12.0% (11/92) in this group. It is noteworthy that all seven patients with MELAS/LS overlap syndrome were associated with MTND mutations. Our data emphasize the important role of MTND mutations in the pathogenicity of MELAS, especially MELAS/LS overlap syndrome.PURPOSE: Genes in the complement pathway, including complement factor H (CFH), C2/BF, and C3, have been reported to be associated with age-related macular degeneration (AMD). Genetic variants, single-nucleotide polymorphisms (SNPs), in these genes were geno-typed for a case-control association study in a mainland Han Chinese population. METHODS: One hundred and fifty-eight patients with wet AMD, 80 patients with soft drusen, and 220 matched control subjects were recruited among Han Chinese in mainland China. Seven SNPs in CFH and two SNPs in C2, CFB', and C3 were genotyped using the ABI SNaPshot method. A deletion of 84,682 base pairs covering the CFHR1 and CFHR3 genes was detected by direct polymerase chain reaction and gel electrophoresis. RESULTS: Four SNPs, including rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), in CFH showed a significant association with wet AMD in the cohort of this study. A haplotype containing these four SNPs (CATA) significantly increased protection of wet AMD with a P value of 0.0005 and an odds ratio of 0.29 (95% confidence interval: 0.15-0.60). Unlike in other populations, rs2274700 and rs1410996 did not show a significant association with AMD in the Chinese population of this study. None of the SNPs in CFH showed a significant association with drusen, and none of the SNPs in CFH, C2, CFB, and C3 showed a significant association with either wet AMD or drusen in the cohort of this study. The CFHR1 and CFHR3 deletion was not polymorphic in the Chinese population and was not associated with wet AMD or drusen. CONCLUSION: This study showed that SNPs rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), but not rs7535263, rs1410996, or rs2274700, in CFH were significantly associated with wet AMD in a mainland Han Chinese population. This study showed that CFH was more likely to be AMD susceptibility gene at Chr.1q31 based on the finding that the CFHR1 and CFHR3 deletion was not polymorphic in the cohort of this study, and none of the SNPs that were significantly associated with AMD in a white population in C2, CFB, and C3 genes showed a significant association with AMD.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_genetic_variants_pipeline", "en", "clinical/models")
-
-val text = "The mutation pattern of mitochondrial DNA (mtDNA) in mainland Chinese patients with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) has been rarely reported, though previous data suggested that the mutation pattern of MELAS could be different among geographically localized populations. We presented the results of comprehensive mtDNA mutation analysis in 92 unrelated Chinese patients with MELAS (85 with classic MELAS and 7 with MELAS/Leigh syndrome (LS) overlap syndrome). The mtDNA A3243G mutation was the most common causal genotype in this patient group (79/92 and 85.9%). The second common gene mutation was G13513A (7/92 and 7.6%). Additionally, we identified T10191C (p.S45P) in ND3, A11470C (p. K237N) in ND4, T13046C (p.M237T) in ND5 and a large-scale deletion (13025-13033:14417-14425) involving partial ND5 and ND6 subunits of complex I in one patient each. Among them, A11470C, T13046C and the single deletion were novel mutations. In summary, patients with mutations affecting mitochondrially encoded complex I (MTND) reached 12.0% (11/92) in this group. It is noteworthy that all seven patients with MELAS/LS overlap syndrome were associated with MTND mutations. Our data emphasize the important role of MTND mutations in the pathogenicity of MELAS, especially MELAS/LS overlap syndrome.PURPOSE: Genes in the complement pathway, including complement factor H (CFH), C2/BF, and C3, have been reported to be associated with age-related macular degeneration (AMD). Genetic variants, single-nucleotide polymorphisms (SNPs), in these genes were geno-typed for a case-control association study in a mainland Han Chinese population. METHODS: One hundred and fifty-eight patients with wet AMD, 80 patients with soft drusen, and 220 matched control subjects were recruited among Han Chinese in mainland China. Seven SNPs in CFH and two SNPs in C2, CFB', and C3 were genotyped using the ABI SNaPshot method. A deletion of 84,682 base pairs covering the CFHR1 and CFHR3 genes was detected by direct polymerase chain reaction and gel electrophoresis. RESULTS: Four SNPs, including rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), in CFH showed a significant association with wet AMD in the cohort of this study. A haplotype containing these four SNPs (CATA) significantly increased protection of wet AMD with a P value of 0.0005 and an odds ratio of 0.29 (95% confidence interval: 0.15-0.60). Unlike in other populations, rs2274700 and rs1410996 did not show a significant association with AMD in the Chinese population of this study. None of the SNPs in CFH showed a significant association with drusen, and none of the SNPs in CFH, C2, CFB, and C3 showed a significant association with either wet AMD or drusen in the cohort of this study. The CFHR1 and CFHR3 deletion was not polymorphic in the Chinese population and was not associated with wet AMD or drusen. CONCLUSION: This study showed that SNPs rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), but not rs7535263, rs1410996, or rs2274700, in CFH were significantly associated with wet AMD in a mainland Han Chinese population. This study showed that CFH was more likely to be AMD susceptibility gene at Chr.1q31 based on the finding that the CFHR1 and CFHR3 deletion was not polymorphic in the cohort of this study, and none of the SNPs that were significantly associated with AMD in a white population in C2, CFB, and C3 genes showed a significant association with AMD."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.genetic_variants.pipeline").predict("""The mutation pattern of mitochondrial DNA (mtDNA) in mainland Chinese patients with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) has been rarely reported, though previous data suggested that the mutation pattern of MELAS could be different among geographically localized populations. We presented the results of comprehensive mtDNA mutation analysis in 92 unrelated Chinese patients with MELAS (85 with classic MELAS and 7 with MELAS/Leigh syndrome (LS) overlap syndrome). The mtDNA A3243G mutation was the most common causal genotype in this patient group (79/92 and 85.9%). The second common gene mutation was G13513A (7/92 and 7.6%). Additionally, we identified T10191C (p.S45P) in ND3, A11470C (p. K237N) in ND4, T13046C (p.M237T) in ND5 and a large-scale deletion (13025-13033:14417-14425) involving partial ND5 and ND6 subunits of complex I in one patient each. Among them, A11470C, T13046C and the single deletion were novel mutations. In summary, patients with mutations affecting mitochondrially encoded complex I (MTND) reached 12.0% (11/92) in this group. It is noteworthy that all seven patients with MELAS/LS overlap syndrome were associated with MTND mutations. Our data emphasize the important role of MTND mutations in the pathogenicity of MELAS, especially MELAS/LS overlap syndrome.PURPOSE: Genes in the complement pathway, including complement factor H (CFH), C2/BF, and C3, have been reported to be associated with age-related macular degeneration (AMD). Genetic variants, single-nucleotide polymorphisms (SNPs), in these genes were geno-typed for a case-control association study in a mainland Han Chinese population. METHODS: One hundred and fifty-eight patients with wet AMD, 80 patients with soft drusen, and 220 matched control subjects were recruited among Han Chinese in mainland China. Seven SNPs in CFH and two SNPs in C2, CFB', and C3 were genotyped using the ABI SNaPshot method. A deletion of 84,682 base pairs covering the CFHR1 and CFHR3 genes was detected by direct polymerase chain reaction and gel electrophoresis. RESULTS: Four SNPs, including rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), in CFH showed a significant association with wet AMD in the cohort of this study. A haplotype containing these four SNPs (CATA) significantly increased protection of wet AMD with a P value of 0.0005 and an odds ratio of 0.29 (95% confidence interval: 0.15-0.60). Unlike in other populations, rs2274700 and rs1410996 did not show a significant association with AMD in the Chinese population of this study. None of the SNPs in CFH showed a significant association with drusen, and none of the SNPs in CFH, C2, CFB, and C3 showed a significant association with either wet AMD or drusen in the cohort of this study. The CFHR1 and CFHR3 deletion was not polymorphic in the Chinese population and was not associated with wet AMD or drusen. CONCLUSION: This study showed that SNPs rs3753394 (P = 0.0276), rs800292 (P = 0.0266), rs1061170 (P = 0.00514), and rs1329428 (P = 0.0089), but not rs7535263, rs1410996, or rs2274700, in CFH were significantly associated with wet AMD in a mainland Han Chinese population. This study showed that CFH was more likely to be AMD susceptibility gene at Chr.1q31 based on the finding that the CFHR1 and CFHR3 deletion was not polymorphic in the cohort of this study, and none of the SNPs that were significantly associated with AMD in a white population in C2, CFB, and C3 genes showed a significant association with AMD.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label       |   confidence |
 |---:|:-------------|--------:|------:|:----------------|-------------:|
 |  0 | A3243G       |     527 |   532 | DNAMutation     |       1      |
@@ -121,9 +92,6 @@ Results
 | 20 | rs7535263    |    3108 |  3116 | SNP             |       1      |
 | 21 | rs1410996    |    3119 |  3127 | SNP             |       1      |
 | 22 | rs2274700    |    3133 |  3141 | SNP             |       1      |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_pipeline_de.md b/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_pipeline_de.md
index 50481fee74..ec34f1793f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_pipeline_de.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_pipeline_de.md
@@ -50,50 +50,6 @@ val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical
 
 val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
 val result = pipeline.fullAnnotate(text)
 ```
 
@@ -112,12 +68,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk         |   begin |   end | ner_label             |   confidence |
 |---:|:------------------|--------:|------:|:----------------------|-------------:|
 |  0 | Kleinzellige      |       4 |    15 | MEASUREMENT           |       0.6897 |
@@ -136,12 +86,6 @@ Results
 | 13 | Hauptbronchus     |     195 |   207 | BODY_PART             |       0.9864 |
 | 14 | mittlere          |     223 |   230 | MEASUREMENT           |       0.9651 |
 | 15 | Prävalenz         |     232 |   240 | MEDICAL_CONDITION     |       0.9833 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_pipeline_en.md
index 37288610f9..8d27dda6e4 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_healthcare](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.healthcare_pipeline").predict("""A 28-year-old female with
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_pipeline", "en", "clinical/models")
-
-text = '''A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG .'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_pipeline", "en", "clinical/models")
-
-val text = "A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG ."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.healthcare_pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting . Two weeks prior to presentation , she was treated with a five-day course of amoxicillin for a respiratory tract infection . She was on metformin , glipizide , and dapagliflozin for T2DM and atorvastatin and gemfibrozil for HTG .""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                    |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------------|--------:|------:|:------------|-------------:|
 |  0 | gestational diabetes mellitus |      39 |    67 | PROBLEM     |     0.938233 |
@@ -118,9 +90,6 @@ Results
 | 17 | atorvastatin                  |     625 |   636 | TREATMENT   |     0.9993   |
 | 18 | gemfibrozil                   |     642 |   652 | TREATMENT   |     0.9997   |
 | 19 | HTG                           |     658 |   660 | PROBLEM     |     0.9927   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_slim_pipeline_de.md b/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_slim_pipeline_de.md
index 35f1506b02..4af6c14fa7 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_slim_pipeline_de.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_healthcare_slim_pipeline_de.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_healthcare_slim](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-text = '''Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_healthcare_slim_pipeline", "de", "clinical/models")
-
-val text = "Das Kleinzellige Bronchialkarzinom (Kleinzelliger Lungenkrebs, SCLC) ist Hernia femoralis, Akne, einseitig, ein hochmalignes bronchogenes Karzinom, das überwiegend im Zentrum der Lunge, in einem Hauptbronchus entsteht. Die mittlere Prävalenz wird auf 1/20.000 geschätzt."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk                          |   begin |   end | ner_label         |   confidence |
 |---:|:-----------------------------------|--------:|------:|:------------------|-------------:|
 |  0 | Bronchialkarzinom                  |      17 |    33 | MEDICAL_CONDITION |       0.9988 |
@@ -130,12 +81,6 @@ Results
 |  7 | Lunge                              |     179 |   183 | BODY_PART         |       0.9729 |
 |  8 | Hauptbronchus                      |     195 |   207 | BODY_PART         |       0.9987 |
 |  9 | Prävalenz                          |     232 |   240 | MEDICAL_CONDITION |       0.9986 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_gene_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_gene_biobert_pipeline_en.md
index a9250eed2c..55a570b7b1 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_gene_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_gene_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_gene_biober
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.human_phenotype_gene_biobert.pipeline").predict("""Here we
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_gene_biobert_pipeline", "en", "clinical/models")
-
-text = '''Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_gene_biobert_pipeline", "en", "clinical/models")
-
-val text = "Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3)."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.human_phenotype_gene_biobert.pipeline").predict("""Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | type             |      29 |    32 | GENE        |       0.9977 |
@@ -103,9 +74,6 @@ Results
 |  2 | polyuria         |      91 |    98 | HP          |       0.9955 |
 |  3 | nephrocalcinosis |     101 |   116 | HP          |       0.995  |
 |  4 | hypokalemia      |     122 |   132 | HP          |       0.9986 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_gene_clinical_pipeline_en.md
index 97cfbd809c..af6d6f6d6d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_gene_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_gene_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_gene_clinic
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.human_phnotype_gene_clinical.pipeline").predict("""Here we
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-text = '''Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-val text = "Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3)."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.human_phnotype_gene_clinical.pipeline").predict("""Here we presented a case (BS type) of a 17 years old female presented with polyhydramnios, polyuria, nephrocalcinosis and hypokalemia, which was alleviated after treatment with celecoxib and vitamin D(3).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk        |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | type             |      29 |    32 | GENE        |       0.9837 |
@@ -103,9 +74,6 @@ Results
 |  2 | polyuria         |      91 |    98 | HP          |       0.9964 |
 |  3 | nephrocalcinosis |     101 |   116 | HP          |       0.9979 |
 |  4 | hypokalemia      |     122 |   132 | HP          |       0.9952 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_go_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_go_biobert_pipeline_en.md
index adb9bcdb88..b74ad4e50f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_go_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_go_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_go_biobert]
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.phenotype_go_biobert.pipeline").predict("""Another disease
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_go_biobert_pipeline", "en", "clinical/models")
-
-text = '''Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_go_biobert_pipeline", "en", "clinical/models")
-
-val text = "Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.phenotype_go_biobert.pipeline").predict("""Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------|--------:|------:|:------------|-------------:|
 |  0 | tumor                    |      39 |    43 | HP          |     1        |
 |  1 | tricarboxylic acid cycle |      79 |   102 | GO          |     0.999867 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_go_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_go_clinical_pipeline_en.md
index e5117357aa..493947c092 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_go_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_human_phenotype_go_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_human_phenotype_go_clinical
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,15 @@ nlu.load("en.med_ner.human_phenotype_clinical.pipeline").predict("""Another dise
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_human_phenotype_go_clinical_pipeline", "en", "clinical/models")
-
-text = '''Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_human_phenotype_go_clinical_pipeline", "en", "clinical/models")
-
-val text = "Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.human_phenotype_clinical.pipeline").predict("""Another disease that shares two of the tumor components of CT, namely GIST and tricarboxylic acid cycle is the Carney-Stratakis syndrome (CSS) or dyad.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------|--------:|------:|:------------|-------------:|
 |  0 | tumor                    |      39 |    43 | HP          |     0.9996   |
 |  1 | tricarboxylic acid cycle |      79 |   102 | GO          |     0.994633 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_biobert_pipeline_en.md
index d06396a8c0..b2c842538a 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_biobert](https://nlp.jo
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl_biobert.pipeline").predict("""The patient is a 21-day-o
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                 |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     1        |
@@ -123,9 +95,6 @@ Results
 | 22 | his                                       |     488 |   490 | Gender                       |     0.9573   |
 | 23 | respiratory congestion                    |     492 |   513 | Symptom                      |     0.5144   |
 | 24 | He                                        |     516 |   517 | Gender                       |     1        |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_enriched_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_enriched_biobert_pipeline_en.md
index b521dfba00..ba69615c8b 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_enriched_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_enriched_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_enriched_biobert](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.jsl_enriched_biobert.pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_enriched_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_enriched_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_enriched_biobert.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                   |   begin |   end | ner_label    |   confidence |
 |---:|:----------------------------|--------:|------:|:-------------|-------------:|
 |  0 | 21-day-old                  |      17 |    26 | Age          |     1        |
@@ -123,9 +94,6 @@ Results
 | 22 | denies                      |     825 |   830 | Negation     |     0.9841   |
 | 23 | diarrhea                    |     836 |   843 | Symptom_Name |     0.6033   |
 | 24 | His                         |     846 |   848 | Gender       |     0.8459   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_enriched_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_enriched_pipeline_en.md
index fa382b39fa..30e247c27d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_enriched_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_enriched_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_enriched](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.jsl_enriched.pipeline").predict("""The patient is a 21-day-
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_enriched_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_enriched_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_enriched.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     0.9993   |
@@ -137,9 +108,6 @@ Results
 | 36 | diarrhea                                  |     836 |   843 | Symptom                      |     0.9995   |
 | 37 | His                                       |     846 |   848 | Gender                       |     0.9998   |
 | 38 | bowel                                     |     850 |   854 | Internal_organ_or_component  |     0.9675   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_greedy_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_greedy_biobert_pipeline_en.md
index dfa1e4f7e2..973eb7247d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_greedy_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_greedy_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_greedy_biobert](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.biobert_jsl_greedy.pipeline").predict("""The patient is a 2
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_greedy_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_greedy_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.biobert_jsl_greedy.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                      |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     1        |
@@ -123,9 +95,6 @@ Results
 | 22 | He                                             |     516 |   517 | Gender                       |     0.9998   |
 | 23 | tired                                          |     550 |   554 | Symptom                      |     0.8912   |
 | 24 | fussy                                          |     569 |   573 | Symptom                      |     0.9541   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_greedy_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_greedy_pipeline_en.md
index 08cb834470..258aec20c8 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_greedy_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_greedy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_greedy](https://nlp.joh
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl_greedy.pipeline").predict("""The patient is a 21-day-ol
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_greedy_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_greedy_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_greedy.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                     |   begin |   end | ner_label                    |   confidence |
 |---:|:-----------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                     |      17 |    26 | Age                          |     0.9817   |
@@ -123,9 +95,6 @@ Results
 | 22 | his                                            |     488 |   490 | Gender                       |     0.9904   |
 | 23 | respiratory congestion                         |     492 |   513 | Symptom                      |     0.5294   |
 | 24 | He                                             |     516 |   517 | Gender                       |     0.9989   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_pipeline_en.md
index 6f2bd33580..f01c0633ec 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl](https://nlp.johnsnowla
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.jsl.pipeline").predict("""The patient is a 21-day-old Cauca
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl.pipeline").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                                |   begin |   end | ner_label                    |   confidence |
 |---:|:------------------------------------------|--------:|------:|:-----------------------------|-------------:|
 |  0 | 21-day-old                                |      17 |    26 | Age                          |     0.997    |
@@ -139,9 +111,6 @@ Results
 | 38 | diarrhea                                  |     908 |   915 | Symptom                      |     0.9956   |
 | 39 | His                                       |     918 |   920 | Gender                       |     0.9997   |
 | 40 | bowel                                     |     922 |   926 | Internal_organ_or_component  |     0.9218   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_slim_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_slim_pipeline_en.md
index 21d1433cda..c197b64224 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_slim_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_jsl_slim_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_jsl_slim](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -65,40 +66,11 @@ nlu.load("en.med_ner.jsl_slim.pipeline").predict("""Hyperparathyroidism was cons
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_jsl_slim_pipeline", "en", "clinical/models")
-
-text = "Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture."
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_jsl_slim_pipeline", "en", "clinical/models")
-
-val text = "Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.jsl_slim.pipeline").predict("""Hyperparathyroidism was considered upon the fourth occasion. The history of weakness and generalized joint pains were present. He also had history of epigastric pain diagnosed informally as gastritis. He had previously had open reduction and internal fixation for the initial two fractures under general anesthesia. He sustained mandibular fracture.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | chunks                               |   begin |   end | entities                  |   confidence |
 |---:|:-------------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Hyperparathyroidism                  |       0 |    18 | Disease_Syndrome_Disorder |     0.9977   |
@@ -112,9 +84,6 @@ Results
 |  8 | fractures under general anesthesia   |     280 |   313 | Drug                      |     0.79585  |
 |  9 | He                                   |     316 |   317 | Demographics              |     0.9992   |
 | 10 | sustained mandibular fracture        |     319 |   347 | Disease_Syndrome_Disorder |     0.662467 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_300_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_300_pipeline_es.md
index 73a26f1762..929b7686cb 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_300_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_300_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_300](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_300_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.92045 |
@@ -131,12 +82,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      0.9963  |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_es.md
index a5d9c9de0c..765471e97d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.98915 |
@@ -131,12 +82,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      1       |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_fr.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_fr.md
index 5db7a3b3b1..c30d778577 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_fr.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_fr.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                       |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Femme                            |       0 |     4 | HUMAN       |     1        |
@@ -134,12 +85,6 @@ Results
 | 11 | virus de la varicelle et du zona |     518 |   549 | SPECIES     |     0.985429 |
 | 12 | parvovirus B19                   |     554 |   567 | SPECIES     |     0.98595  |
 | 13 | Brucella                         |     636 |   643 | SPECIES     |     0.9995   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_it.md
index 07e084dea9..b5b14b2f37 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_it.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |      0.9997  |
@@ -130,12 +81,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |      0.9745  |
 |  8 | HIV              |     523 |   525 | SPECIES     |      0.9838  |
 |  9 | paziente         |     634 |   641 | HUMAN       |      0.9994  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_pt.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_pt.md
index 39ca6027b0..4e5383ff9b 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_pt.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_pt.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito..'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |       0.9849 |
@@ -126,12 +77,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |       0.9912 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |       0.9909 |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |       0.9778 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_ro.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_ro.md
index b8904dedd4..622729219e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_ro.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_bert_pipeline_ro.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_bert](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-text = '''O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-val text = "O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-text = '''O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_bert_pipeline", "ro", "clinical/models")
-
-val text = "O femeie în vârstă de 26 de ani, însărcinată în 11 săptămâni, a consultat serviciul de urgențe dermatologice pentru că prezenta, de 4 zile, leziuni punctiforme dureroase de debut brusc pe vârful degetelor. Pacientul raportează că leziunile au început pe degete și ulterior s-au extins la degetele de la picioare. Markerii de imunitate, ANA și crioagglutininele, au fost negativi, iar serologia VHB a indicat doar vaccinarea. Pe baza acestor rezultate, diagnosticul de vasculită a fost exclus și, având în vedere diagnosticul suspectat de erupție cutanată cu mănuși și șosete, s-a efectuat serologia pentru virusul Ebstein Barr. Exantemă la mănuși și șosete datorat parvovirozei B19. Având în vedere suspiciunea unei afecțiuni infecțioase cu aceste caracteristici, a fost solicitată serologia pentru EBV, enterovirus și parvovirus B19, cu IgM pozitiv pentru acesta din urmă în două ocazii. De asemenea, nu au existat semne de anemie fetală sau complicații ale acesteia."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks           |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------|--------:|------:|:------------|-------------:|
 |  0 | femeie               |       2 |     7 | HUMAN       |     0.9998   |
@@ -129,12 +80,6 @@ Results
 |  6 | enterovirus          |     804 |   814 | SPECIES     |     0.9984   |
 |  7 | parvovirus B19       |     819 |   832 | SPECIES     |     0.99255  |
 |  8 | fetală               |     932 |   937 | HUMAN       |     0.9994   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_biobert_pipeline_en.md
index 412d9aab56..face995d65 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_biobert_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_biobert_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk               |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |       0.9999 |
@@ -127,12 +78,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |       0.9926 |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |       0.8422 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |       0.9929 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_ca.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_ca.md
index 92f6ba8266..409e8792be 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_ca.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_ca.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-text = '''Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-val text = "Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-text = '''Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "ca", "clinical/models")
-
-val text = "Dona de 47 anys al·lèrgica al iode, fumadora social, intervinguda de varices, dues cesàries i un abscés gluti. Sense altres antecedents mèdics d'interès ni tractament habitual. Viu amb el seu marit i tres fills, treballa com a professora. En el moment de la nostra valoració en la planta de Cirurgia General, la pacient presenta TA 69/40 mm Hg, freqüència cardíaca 120 lpm, taquipnea en repòs, pal·lidesa mucocutánea, mala perfusió distal i afligeix nàusees. L'abdomen és tou, no presenta peritonismo i el dèbit del drenatge abdominal roman sense canvis. Les serologies de Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, citomegalovirus, virus de Epstein Barr, virus varicel·la zoster i parvovirus B19 van ser negatives. No obstant això, es va detectar test de rosa de Bengala positiu per a Brucella, el test de Coombs i les aglutinacions també van ser positives amb un títol 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | Dona                    |       0 |     3 | HUMAN       |     1        |
@@ -135,12 +86,6 @@ Results
 | 12 | virus varicel·la zoster |     717 |   739 | SPECIES     |     0.778333 |
 | 13 | parvovirus B19          |     743 |   756 | SPECIES     |     0.9138   |
 | 14 | Brucella                |     847 |   854 | SPECIES     |     0.9483   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_en.md
index b9c1d94cd1..3dd7b22aea 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-text = '''42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "en", "clinical/models")
-
-val text = "42-year-old woman with end-stage chronic kidney disease, secondary to lupus nephropathy, and on peritoneal dialysis. History of four episodes of bacterial peritonitis and change of Tenckhoff catheter six months prior to admission due to catheter dysfunction. Three peritoneal fluid samples during her hospitalisation tested positive for Fusarium spp. The patient responded favourably and continued outpatient treatment with voriconazole (4mg/kg every 12 hours orally). All three isolates were identified as species of the Fusarium solani complex. In vitro susceptibility to itraconazole, voriconazole and posaconazole, according to Clinical and Laboratory Standards Institute - CLSI (M38-A) methodology, showed a minimum inhibitory concentration (MIC) in all three isolates and for all three antifungals of >16 μg/mL."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks              |   begin |   end | ner_label   |   confidence |
 |---:|:------------------------|--------:|------:|:------------|-------------:|
 |  0 | woman                   |      12 |    16 | HUMAN       |     0.9993   |
@@ -127,12 +78,6 @@ Results
 |  4 | species                 |     507 |   513 | SPECIES     |     0.8838   |
 |  5 | Fusarium solani complex |     522 |   544 | SPECIES     |     0.748667 |
 |  6 | antifungals             |     792 |   802 | SPECIES     |     0.9847   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_es.md
index 65c4a7f35c..6a36b76388 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |       0.9926 |
@@ -131,12 +82,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |       0.9997 |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |       0.9998 |
 | 10 | padres         |     728 |   733 | HUMAN       |       0.9992 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_fr.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_fr.md
index 6812fa8e00..040b19f994 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_fr.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_fr.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-text = '''Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "fr", "clinical/models")
-
-val text = "Femme de 47 ans allergique à l'iode, fumeuse sociale, opérée pour des varices, deux césariennes et un abcès fessier. Vit avec son mari et ses trois enfants, travaille comme enseignante. Initialement, le patient a eu une bonne évolution, mais au 2ème jour postopératoire, il a commencé à montrer une instabilité hémodynamique. Les sérologies pour Coxiella burnetii, Bartonella henselae, Borrelia burgdorferi, Entamoeba histolytica, Toxoplasma gondii, herpès simplex virus 1 et 2, cytomégalovirus, virus d'Epstein Barr, virus de la varicelle et du zona et parvovirus B19 étaient négatives. Cependant, un test au rose Bengale positif pour Brucella, le test de Coombs et les agglutinations étaient également positifs avec un titre de 1/40."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                       |   begin |   end | ner_label   |   confidence |
 |---:|:---------------------------------|--------:|------:|:------------|-------------:|
 |  0 | Femme                            |       0 |     4 | HUMAN       |     1        |
@@ -134,12 +85,6 @@ Results
 | 11 | virus de la varicelle et du zona |     518 |   549 | SPECIES     |     0.788543 |
 | 12 | parvovirus B19                   |     554 |   567 | SPECIES     |     0.9341   |
 | 13 | Brucella                         |     636 |   643 | SPECIES     |     0.9993   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_gl.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_gl.md
index 368ae08b8a..23dfd8a329 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_gl.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_gl.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-text = '''Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-val text = "Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-text = '''Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "gl", "clinical/models")
-
-val text = "Muller de 45 anos, sen antecedentes médicos de interese, que foi remitida á consulta de dermatoloxía de urxencias por lesións faciales de tres semanas de evolución. A paciente non presentaba lesións noutras localizaciones nin outra clínica de interese. No seu centro de saúde prescribíronlle corticoides tópicos ante a sospeita de picaduras de artrópodos e unha semana despois, antivirales orais baixo o diagnóstico de posible infección herpética. As lesións interferían de forma notable na súa vida persoal e profesional xa que traballaba de face ao púbico. Unha semana máis tarde o diagnóstico foi confirmado ao resultar o cultivo positivo a Staphylococcus aureus."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label   |   confidence |
 |---:|:----------------------|--------:|------:|:------------|-------------:|
 |  0 | Muller                |       0 |     5 | HUMAN       |      0.9998  |
@@ -127,12 +78,6 @@ Results
 |  4 | herpética             |     437 |   445 | SPECIES     |      0.9592  |
 |  5 | púbico                |     551 |   556 | HUMAN       |      0.7293  |
 |  6 | Staphylococcus aureus |     644 |   664 | SPECIES     |      0.87005 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_it.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_it.md
index 5d97f89d10..bedc4e8fb0 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_it.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_it.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-text = '''Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "it", "clinical/models")
-
-val text = "Una donna di 74 anni è stata ricoverata con dolore addominale diffuso, ipossia e astenia di 2 settimane di evoluzione. La sua storia personale includeva ipertensione in trattamento con amiloride/idroclorotiazide e dislipidemia controllata con lovastatina. La sua storia familiare era: madre morta di cancro gastrico, fratello con cirrosi epatica di eziologia sconosciuta e sorella con carcinoma epatocellulare. Lo studio eziologico delle diverse cause di malattia epatica cronica comprendeva: virus epatotropi (HBV, HCV) e HIV, studio dell'autoimmunità, ceruloplasmina, ferritina e porfirine nelle urine, tutti risultati negativi. Il paziente è stato messo in trattamento anticoagulante con acenocumarolo e diuretici a tempo indeterminato."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label   |   confidence |
 |---:|:-----------------|--------:|------:|:------------|-------------:|
 |  0 | donna            |       4 |     8 | HUMAN       |       0.9992 |
@@ -130,12 +81,6 @@ Results
 |  7 | HCV              |     516 |   518 | SPECIES     |       0.991  |
 |  8 | HIV              |     523 |   525 | SPECIES     |       0.991  |
 |  9 | paziente         |     634 |   641 | HUMAN       |       0.9978 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_pt.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_pt.md
index 1cd0d29673..a723f6721b 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_pt.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_pipeline_pt.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-text = '''Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_pipeline", "pt", "clinical/models")
-
-val text = "Uma rapariga de 16 anos com um historial pessoal de asma apresentou ao departamento de dermatologia com lesões cutâneas assintomáticas que tinham estado presentes durante 2 meses. A paciente tinha sido tratada com creme corticosteróide devido a uma suspeita inicial de eczema atópico, apesar do qual apresentava um crescimento progressivo marcado das lesões. Tinha um gato doméstico que ela nunca tinha levado ao veterinário. O exame físico revelou placas em forma de anel com uma borda periférica activa na parte superior das costas e nos aspectos laterais do pescoço e da face. Cultura local obtida por raspagem de tapete isolado Trichophyton rubrum. Com base em dados clínicos e cultura, foi estabelecido o diagnóstico de tinea incognito."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks          |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | rapariga            |       4 |    11 | HUMAN       |       0.9991 |
@@ -126,12 +77,6 @@ Results
 |  3 | gato                |     368 |   371 | SPECIES     |       0.9847 |
 |  4 | veterinário         |     413 |   423 | HUMAN       |       0.91   |
 |  5 | Trichophyton rubrum |     632 |   650 | SPECIES     |       0.9996 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_roberta_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_roberta_pipeline_es.md
index e811ddfa8b..72b778dbc5 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_roberta_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_roberta_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_roberta](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-text = '''Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "es", "clinical/models")
-
-val text = "Lactante varón de dos años. Antecedentes familiares sin interés. Antecedentes personales: Embarazo, parto y periodo neonatal normal. En seguimiento por alergia a legumbres, diagnosticado con diez meses por reacción urticarial generalizada con lentejas y garbanzos, con dieta de exclusión a legumbres desde entonces. En ésta visita la madre describe episodios de eritema en zona maxilar derecha con afectación ocular ipsilateral que se resuelve en horas tras la administración de corticoides. Le ha ocurrido en 5-6 ocasiones, en relación con la ingesta de alimentos previamente tolerados. Exploración complementaria: Cacahuete, ac(ige)19.2 Ku.arb/l. Resultados: Ante la sospecha clínica de Síndrome de Frey, se tranquiliza a los padres, explicándoles la naturaleza del cuadro y se cita para revisión anual."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks     |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | Lactante varón |       0 |    13 | HUMAN       |      0.93805 |
@@ -131,12 +82,6 @@ Results
 |  8 | madre          |     334 |   338 | HUMAN       |      1       |
 |  9 | Cacahuete      |     616 |   624 | SPECIES     |      0.9985  |
 | 10 | padres         |     728 |   733 | HUMAN       |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_roberta_pipeline_pt.md b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_roberta_pipeline_pt.md
index 48e8c2c2af..b556859f82 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_roberta_pipeline_pt.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_living_species_roberta_pipeline_pt.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_living_species_roberta](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-text = '''Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-val text = "Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-text = '''Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_living_species_roberta_pipeline", "pt", "clinical/models")
-
-val text = "Mulher de 23 anos, de Capinota, Cochabamba, Bolívia. Ela está no nosso país há quatro anos. Frequentou o departamento de emergência obstétrica onde foi encontrada grávida de 37 semanas, com um colo dilatado de 5 cm e membranas rompidas. O obstetra de emergência realizou um teste de estreptococos negativo e solicitou um hemograma, glucose, bioquímica básica, HBV, HCV e serologia da sífilis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | Mulher        |       0 |     5 | HUMAN       |       0.9975 |
@@ -127,12 +78,6 @@ Results
 |  4 | HBV           |     360 |   362 | SPECIES     |       0.9911 |
 |  5 | HCV           |     365 |   367 | SPECIES     |       0.9858 |
 |  6 | sífilis       |     384 |   390 | SPECIES     |       0.8898 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_measurements_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_measurements_clinical_pipeline_en.md
index 8b2d3b88fc..f68e780770 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_measurements_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_measurements_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_measurements_clinical](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,47 +63,14 @@ nlu.load("en.med_ner.clinical_measurements.pipeline").predict("""Bilateral breas
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_measurements_clinical_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_measurements_clinical_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.clinical_measurements.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label    |   confidence |
 |---:|:----------------|--------:|------:|:-------------|-------------:|
 |  0 | 0.5 x 0.5 x 0.4 |     113 |   127 | Measurements |      0.98748 |
 |  1 | cm              |     129 |   130 | Units        |      0.9996  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_medication_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_medication_pipeline_en.md
index 4eb46b33da..ef214bd1e9 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_medication_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_medication_pipeline_en.md
@@ -34,39 +34,7 @@ A pretrained pipeline to detect medication entities. It was built on the top of
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."""
-
-result = ner_medication_pipeline.fullAnnotate([text])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
-```
 
-{:.nlu-block}
-```python
-| ner_chunk          | entity   |
-|:-------------------|:---------|
-| metformin 1000 MG  | DRUG     |
-| glipizide 2.5 MG   | DRUG     |
-| Fragmin 5000 units | DRUG     |
-| Xenaderm           | DRUG     |
-| OxyContin 30 mg    | DRUG     |
-```
-</div>
-
-{:.model-param}
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -84,41 +52,11 @@ val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline",
 val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
 ```
 
-{:.nlu-block}
-```python
-| ner_chunk          | entity   |
-|:-------------------|:---------|
-| metformin 1000 MG  | DRUG     |
-| glipizide 2.5 MG   | DRUG     |
-| Fragmin 5000 units | DRUG     |
-| Xenaderm           | DRUG     |
-| OxyContin 30 mg    | DRUG     |
-```
 </div>
 
-{:.model-param}
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
+## Results
 
-ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."""
-
-result = ner_medication_pipeline.fullAnnotate([text])
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")
-
-val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")
-```
-
-{:.nlu-block}
-```python
+```bash
 | ner_chunk          | entity   |
 |:-------------------|:---------|
 | metformin 1000 MG  | DRUG     |
@@ -127,7 +65,6 @@ val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed me
 | Xenaderm           | DRUG     |
 | OxyContin 30 mg    | DRUG     |
 ```
-</div>
 
 {:.model-param}
 ## Model Information
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_medmentions_coarse_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_medmentions_coarse_pipeline_en.md
index 857a1e29bb..94b35d0dfc 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_medmentions_coarse_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_medmentions_coarse_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_medmentions_coarse](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.medmentions_coarse.pipeline").predict("""he patient is a 21
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_medmentions_coarse_pipeline", "en", "clinical/models")
-
-text = '''he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_medmentions_coarse_pipeline", "en", "clinical/models")
-
-val text = "he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.medmentions_coarse.pipeline").predict("""he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). Additionally, there is no side effect observed after Influenza vaccine. One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                  |   begin |   end | ner_label                            |   confidence |
 |---:|:----------------------------|--------:|------:|:-------------------------------------|-------------:|
 |  0 | Caucasian                   |      27 |    35 | Population_Group                     |     0.8439   |
@@ -123,9 +94,6 @@ Results
 | 22 | bowel movements             |     921 |   935 | Biologic_Function                    |     0.29385  |
 | 23 | yellow                      |     941 |   946 | Qualitative_Concept                  |     0.742    |
 | 24 | colored                     |     948 |   954 | Qualitative_Concept                  |     0.275    |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_nature_nero_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_nature_nero_clinical_pipeline_en.md
index 132889c8d9..a936a0231a 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_nature_nero_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_nature_nero_clinical_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_nature_nero_clinical](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-text = '''he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-val text = "he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-text = '''he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_nature_nero_clinical_pipeline", "en", "clinical/models")
-
-val text = "he patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                                   |   begin |   end | ner_label             |   confidence |
 |---:|:---------------------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | perioral cyanosis                            |     236 |   252 | Medicalfinding        |     0.198    |
@@ -142,12 +93,6 @@ Results
 | 19 | diarrhea                                     |     835 |   842 | Medicalfinding        |     0.533    |
 | 20 | bowel movements                              |     849 |   863 | Biologicalprocess     |     0.2036   |
 | 21 | soft in nature                               |     888 |   901 | Biologicalprocess     |     0.170467 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_negation_uncertainty_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_negation_uncertainty_pipeline_es.md
index af42fece1c..90438a24ab 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_negation_uncertainty_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_negation_uncertainty_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_negation_uncertainty](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_negation_uncertainty_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +------------------------------------------------------+---------+
 |chunk                                                 |ner_label|
 +------------------------------------------------------+---------+
@@ -130,12 +81,6 @@ Results
 |susceptible de                                        |UNC      |
 |ca basocelular perlado                                |USCO     |
 +------------------------------------------------------+---------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_neoplasms_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_neoplasms_pipeline_es.md
index b3b154754e..f3225e9c4e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_neoplasms_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_neoplasms_pipeline_es.md
@@ -50,50 +50,6 @@ val pipeline = new PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/
 
 val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-text = '''HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_neoplasms_pipeline", "es", "clinical/models")
-
-val text = "HISTORIA DE ENFERMEDAD ACTUAL: El Sr. Smith es un hombre blanco veterano de 60 años con múltiples comorbilidades, que tiene antecedentes de cáncer de vejiga diagnosticado hace aproximadamente dos años por el Hospital VA. Allí se sometió a una resección. Debía ser ingresado en el Hospital de Día para una cistectomía. Fue visto en la Clínica de Urología y Clínica de Radiología el 02/04/2003. CURSO DE HOSPITAL: El Sr. Smith se presentó en el Hospital de Día antes de la cirugía de Urología. En evaluación, EKG, ecocardiograma fue anormal, se obtuvo una consulta de Cardiología. Luego se procedió a una resonancia magnética de estrés con adenosina cardíaca, la misma fue positiva para isquemia inducible, infarto subendocárdico inferolateral leve a moderado con isquemia peri-infarto. Además, se observa isquemia inducible en el tabique lateral inferior. El Sr. Smith se sometió a un cateterismo del corazón izquierdo, que reveló una enfermedad de las arterias coronarias de dos vasos. La RCA, proximal estaba estenosada en un 95% y la distal en un 80% estenosada. La LAD media estaba estenosada en un 85% y la LAD distal estaba estenosada en un 85%. Se colocaron cuatro stents de metal desnudo Multi-Link Vision para disminuir las cuatro lesiones al 0%. Después de la intervención, el Sr. Smith fue admitido en 7 Ardmore Tower bajo el Servicio de Cardiología bajo la dirección del Dr. Hart. El Sr. Smith tuvo un curso hospitalario post-intervención sin complicaciones. Se mantuvo estable para el alta hospitalaria el 07/02/2003 con instrucciones de tomar Plavix diariamente durante un mes y Urología está al tanto de lo mismo."
-
 val result = pipeline.fullAnnotate(text)
 ```
 
@@ -112,22 +68,10 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunk   |   begin |   end | ner_label            |   confidence |
 |---:|:------------|--------:|------:|:---------------------|-------------:|
 |  0 | cáncer      |     140 |   145 | MORFOLOGIA_NEOPLASIA |       0.9997 |
 |  1 | Multi-Link  |    1195 |  1204 | MORFOLOGIA_NEOPLASIA |       0.574  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_nihss_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_nihss_pipeline_en.md
index 8e5e5d2b63..91840215b2 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_nihss_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_nihss_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_nihss](https://nlp.johnsnow
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.nihss_pipeline").predict("""Abdomen , soft , nontender . NI
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_nihss_pipeline", "en", "clinical/models")
-
-text = '''Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_nihss_pipeline", "en", "clinical/models")
-
-val text = "Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently"
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.nihss_pipeline").predict("""Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label       |   confidence |
 |---:|:-----------------|--------:|------:|:----------------|-------------:|
 |  0 | NIH stroke scale |      29 |    44 | NIHSS           |     0.973533 |
@@ -120,9 +91,6 @@ Results
 | 19 | three            |     258 |   262 | Measurement     |     0.8896   |
 | 20 | best language    |     268 |   280 | 9_BestLanguage  |     0.89415  |
 | 21 | two              |     286 |   288 | Measurement     |     0.949    |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_anatomy_general_healthcare_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_anatomy_general_healthcare_pipeline_en.md
index 459ad6aabb..11e13c7ea2 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_anatomy_general_healthcare_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_anatomy_general_healthcare_pipeline_en.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_general_he
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,24 +75,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks   |   begin |   end | entities        |   confidence |
 |---:|:---------|--------:|------:|:----------------|-------------:|
 |  0 | left     |      37 |    40 | Direction       |       0.9948 |
 |  1 | breast   |      42 |    47 | Anatomical_Site |       0.5814 |
 |  2 | lungs    |      83 |    87 | Anatomical_Site |       0.9486 |
 |  3 | liver    |     100 |   104 | Anatomical_Site |       0.9646 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_anatomy_general_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_anatomy_general_pipeline_en.md
index fe50e26ad2..df602f66a4 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_anatomy_general_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_anatomy_general_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_general](h
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_general_pipeline", "en", "clinical/models")
-
-val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +69,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label       |   confidence |
 |---:|:-------------|--------:|------:|:----------------|-------------:|
 |  0 | left         |      36 |    39 | Direction       |       0.9825 |
 |  1 | breast       |      41 |    46 | Anatomical_Site |       0.9005 |
 |  2 | lungs        |      82 |    86 | Anatomical_Site |       0.9735 |
 |  3 | liver        |      99 |   103 | Anatomical_Site |       0.9817 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_biomarker_healthcare_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_biomarker_healthcare_pipeline_en.md
index a0a88e7b68..97ca8a66a9 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_biomarker_healthcare_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_biomarker_healthcare_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_biomarker_healthca
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-text = '''he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-val text = "he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-text = '''he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_healthcare_pipeline", "en", "clinical/models")
-
-val text = "he results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks                                   |   begin |   end | entities         |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-----------------|-------------:|
 |  0 | negative                                 |      69 |    76 | Biomarker_Result |      1       |
@@ -138,12 +89,6 @@ Results
 | 15 | p53                                      |     244 |   246 | Biomarker        |      1       |
 | 16 | Ki-67 index                              |     253 |   263 | Biomarker        |      0.99865 |
 | 17 | 87%                                      |     275 |   277 | Biomarker_Result |      0.828   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_biomarker_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_biomarker_pipeline_en.md
index ef7e28eb8f..019861bf03 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_biomarker_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_biomarker_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_biomarker](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                               |   begin |   end | ner_label        |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-----------------|-------------:|
 |  0 | negative                                 |      70 |    77 | Biomarker_Result |      0.9984  |
@@ -138,12 +89,6 @@ Results
 | 15 | p53                                      |     245 |   247 | Biomarker        |      1       |
 | 16 | Ki-67 index                              |     254 |   264 | Biomarker        |      0.99465 |
 | 17 | 87%                                      |     276 |   278 | Biomarker_Result |      0.9814  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_demographics_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_demographics_pipeline_en.md
index 400059860d..9dfff01f5b 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_demographics_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_demographics_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_demographics](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old man with history of heavy smoking.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old man with history of heavy smoking."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old man with history of heavy smoking.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_demographics_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old man with history of heavy smoking."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,23 +69,11 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks    |   begin |   end | ner_label      |   confidence |
 |---:|:--------------|--------:|------:|:---------------|-------------:|
 |  0 | 40-year-old   |      17 |    27 | Age            |       0.6743 |
 |  1 | man           |      29 |    31 | Gender         |       0.9365 |
 |  2 | heavy smoking |      49 |    61 | Smoking_Status |       0.7294 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_diagnosis_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_diagnosis_pipeline_en.md
index fe68ec62dc..79c4a5d18f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_diagnosis_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_diagnosis_pipeline_en.md
@@ -32,52 +32,10 @@ This pretrained pipeline is built on the top of [ner_oncology_diagnosis](https:/
 
 ## How to use
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-text = '''Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-val text = "Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-text = '''Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_diagnosis_pipeline", "en", "clinical/models")
-
-val text = "Two years ago, the patient presented with a tumor in her left breast and adenopathies. She was diagnosed with invasive ductal carcinoma. Last week she was also found to have a lung metastasis."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +70,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label         |   confidence |
 |---:|:-------------|--------:|------:|:------------------|-------------:|
 |  0 | tumor        |      44 |    48 | Tumor_Finding     |       0.9958 |
@@ -126,12 +78,6 @@ Results
 |  3 | ductal       |     119 |   124 | Histological_Type |       0.9996 |
 |  4 | carcinoma    |     126 |   134 | Cancer_Dx         |       0.9988 |
 |  5 | metastasis   |     181 |   190 | Metastasis        |       0.9996 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_posology_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_posology_pipeline_en.md
index 4a6bfdf241..23628513db 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_posology_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_posology_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_posology](https://
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label      |   confidence |
 |---:|:-----------------|--------:|------:|:---------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy |      1       |
@@ -127,12 +78,6 @@ Results
 |  4 | six courses      |     106 |   116 | Cycle_Count    |      0.494   |
 |  5 | second cycle     |     150 |   161 | Cycle_Number   |      0.98675 |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy |      1       |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_response_to_treatment_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_response_to_treatment_pipeline_en.md
index 7184205db4..2d624f2039 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_response_to_treatment_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_response_to_treatment_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_response_to_treatm
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-text = '''She completed her first-line therapy, but some months later there was recurrence of the breast cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-val text = "She completed her first-line therapy, but some months later there was recurrence of the breast cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-text = '''She completed her first-line therapy, but some months later there was recurrence of the breast cancer.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_response_to_treatment_pipeline", "en", "clinical/models")
-
-val text = "She completed her first-line therapy, but some months later there was recurrence of the breast cancer."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,21 +69,9 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label             |   confidence |
 |---:|:-------------|--------:|------:|:----------------------|-------------:|
 |  0 | recurrence   |      70 |    79 | Response_To_Treatment |       0.9767 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_test_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_test_pipeline_en.md
index 8348312315..48099edfe3 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_test_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_test_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_test](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-text = ''' biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-val text = " biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-text = ''' biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_test_pipeline", "en", "clinical/models")
-
-val text = " biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +69,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                |   begin |   end | ner_label      |   confidence |
 |---:|:--------------------------|--------:|------:|:---------------|-------------:|
 |  0 | biopsy                    |       1 |     6 | Pathology_Test |      0.9987  |
 |  1 | ultrasound guided         |      31 |    47 | Imaging_Test   |      0.87635 |
 |  2 | chest computed tomography |      67 |    91 | Imaging_Test   |      0.9176  |
 |  3 | CT                        |      94 |    95 | Imaging_Test   |      0.8294  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_therapy_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_therapy_pipeline_en.md
index dd56b2d0f3..95eb94714d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_therapy_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_therapy_pipeline_en.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_therapy](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_therapy_pipeline", "en", "clinical/models")
-
-val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to her breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks                     |   begin |   end | ner_label             |   confidence |
 |---:|:-------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | mastectomy                     |      36 |    45 | Cancer_Surgery        |     0.9817   |
@@ -144,12 +87,6 @@ Results
 |  7 | 600 mg/m2                      |     381 |   389 | Dosage                |     0.64205  |
 |  8 | six courses                    |     397 |   407 | Cycle_Count           |     0.46815  |
 |  9 | first line                     |     413 |   422 | Line_Of_Therapy       |     0.95015  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_tnm_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_tnm_pipeline_en.md
index ac63009f80..0caf9ce4e5 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_tnm_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_tnm_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_tnm](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_tnm_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label         |   confidence |
 |---:|:-----------------|--------:|------:|:------------------|-------------:|
 |  0 | metastatic       |      24 |    33 | Metastasis        |     0.9999   |
@@ -126,12 +77,6 @@ Results
 |  3 | 4 cm             |     126 |   129 | Tumor_Description |     0.85105  |
 |  4 | tumor            |     131 |   135 | Tumor             |     0.9926   |
 |  5 | grade 2          |     141 |   147 | Tumor_Description |     0.89705  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_unspecific_posology_healthcare_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
index fd65c04fcc..df639523bd 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_unspecific_posology_healthcare_pipeline_en.md
@@ -34,58 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_unspecific_posolog
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_healthcare_pipeline", "en", "clinical/models")
-
-val text = "
-he patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.
-"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -126,12 +75,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunks           |   begin |   end | entities             |   confidence |
 |---:|:-----------------|--------:|------:|:---------------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy       |     0.9999   |
@@ -141,12 +84,6 @@ Results
 |  4 | over six courses |     101 |   116 | Posology_Information |     0.689833 |
 |  5 | second cycle     |     150 |   161 | Posology_Information |     0.9906   |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy       |     0.9997   |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_unspecific_posology_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_unspecific_posology_pipeline_en.md
index 30fb6d8d49..19e4cd5a49 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_unspecific_posology_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_oncology_unspecific_posology_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_unspecific_posolog
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_unspecific_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label            |   confidence |
 |---:|:-----------------|--------:|------:|:---------------------|-------------:|
 |  0 | adriamycin       |      46 |    55 | Cancer_Therapy       |      1       |
@@ -127,12 +78,6 @@ Results
 |  4 | over six courses |     101 |   116 | Posology_Information |      0.9078  |
 |  5 | second cycle     |     150 |   161 | Posology_Information |      0.9853  |
 |  6 | chemotherapy     |     166 |   177 | Cancer_Therapy       |      0.9998  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_pathogen_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_pathogen_pipeline_en.md
index 4d4e61a5d3..140bd7f841 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_pathogen_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_pathogen_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_pathogen](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.pathogen.pipeline").predict("""Racecadotril is an antisecre
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_pathogen_pipeline", "en", "clinical/models")
-
-text = '''Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin.  This can progress to loss of skin color, a fast heart rate as it becomes more severe.  While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_pathogen_pipeline", "en", "clinical/models")
-
-val text = "Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin.  This can progress to loss of skin color, a fast heart rate as it becomes more severe.  While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.pathogen.pipeline").predict("""Racecadotril is an antisecretory medication and it has better tolerability than loperamide. Diarrhea is the condition of having loose, liquid or watery bowel movements each day. Signs of dehydration often begin with loss of the normal stretchiness of the skin.  This can progress to loss of skin color, a fast heart rate as it becomes more severe.  While it has been speculated that rabies virus, Lyssavirus and Ephemerovirus could be transmitted through aerosols, studies have concluded that this is only feasible in limited conditions.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label        |   confidence |
 |---:|:----------------|--------:|------:|:-----------------|-------------:|
 |  0 | Racecadotril    |       0 |    11 | Medicine         |     0.9468   |
@@ -107,9 +78,6 @@ Results
 |  6 | rabies virus    |     383 |   394 | Pathogen         |     0.95685  |
 |  7 | Lyssavirus      |     397 |   406 | Pathogen         |     0.9694   |
 |  8 | Ephemerovirus   |     412 |   424 | Pathogen         |     0.6919   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_pharmacology_pipeline_es.md b/docs/_posts/ahmedlone127/2023-06-16-ner_pharmacology_pipeline_es.md
index 631acc81fb..9392172b51 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_pharmacology_pipeline_es.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_pharmacology_pipeline_es.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_pharmacology](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-text = '''e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_pharmacology_pipeline", "es", "clinical/models")
-
-val text = "e realiza analítica destacando creatinkinasa 736 UI, LDH 545 UI, urea 63 mg/dl, CA 19.9 64,1 U/ml. Inmunofenotípicamente el tumor expresó vimentina, S-100, HMB-45 y actina. Se instauró el tratamiento con quimioterapia (Cisplatino, Interleukina II, Dacarbacina e Interferon alfa)."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,12 +69,6 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks      |   begin |   end | ner_label     |   confidence |
 |---:|:----------------|--------:|------:|:--------------|-------------:|
 |  0 | creatinkinasa   |      31 |    43 | PROTEINAS     |      0.9994  |
@@ -132,12 +83,6 @@ Results
 |  9 | Interleukina II |     231 |   245 | PROTEINAS     |      0.99955 |
 | 10 | Dacarbacina     |     248 |   258 | NORMALIZABLES |      0.9996  |
 | 11 | Interferon alfa |     262 |   276 | PROTEINAS     |      0.99935 |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_biobert_pipeline_en.md
index edf2465aca..b05e10b2bc 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_biobert](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.posology_biobert.pipeline").predict("""The patient was pres
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_biobert.pipeline").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | 1                   |      27 |    27 | DOSAGE      |     0.9993   |
@@ -115,9 +87,6 @@ Results
 | 14 | metformin           |     261 |   269 | DRUG        |     0.9999   |
 | 15 | 1000 mg             |     271 |   277 | STRENGTH    |     0.91255  |
 | 16 | two times a day     |     279 |   293 | FREQUENCY   |     0.9969   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_experimental_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_experimental_pipeline_en.md
index 045d0226c6..006032ee82 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_experimental_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_experimental_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_experimental](http
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -68,46 +69,11 @@ Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear th
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_experimental_pipeline", "en", "clinical/models")
-
-text = '''Y-90 Humanized Anti-Tac: 10 mCi (if a bone marrow transplant was part of the patient's previous therapy) or 15 mCi of yttrium labeled anti-TAC; followed by calcium trisodium Inj (Ca DTPA)..
-
-Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear the radioactive agent from the body.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_experimental_pipeline", "en", "clinical/models")
-
-val text = "Y-90 Humanized Anti-Tac: 10 mCi (if a bone marrow transplant was part of the patient's previous therapy) or 15 mCi of yttrium labeled anti-TAC; followed by calcium trisodium Inj (Ca DTPA)..
-
-Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear the radioactive agent from the body."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_experimental.pipeline").predict("""Y-90 Humanized Anti-Tac: 10 mCi (if a bone marrow transplant was part of the patient's previous therapy) or 15 mCi of yttrium labeled anti-TAC; followed by calcium trisodium Inj (Ca DTPA)..
-
-Calcium-DTPA: Ca-DTPA will be administered intravenously on Days 1-3 to clear the radioactive agent from the body.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                |   begin |   end | ner_label   |   confidence |
 |---:|:-------------------------|--------:|------:|:------------|-------------:|
 |  0 | Anti-Tac                 |      15 |    22 | Drug        |     0.8797   |
@@ -119,9 +85,6 @@ Results
 |  6 | Ca-DTPA                  |     205 |   211 | Drug        |     0.9544   |
 |  7 | intravenously            |     234 |   246 | Route       |     0.9518   |
 |  8 | Days 1-3                 |     251 |   258 | Cycleday    |     0.83325  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_greedy_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_greedy_pipeline_en.md
index 4f141334ff..ede7f21fbd 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_greedy_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_greedy_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_greedy](https://nl
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,9 @@ nlu.load("en.med_ner.posology_greedy.pipeline").predict("""The patient was presc
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_greedy_pipeline", "en", "clinical/models")
-
-text = '''The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_greedy_pipeline", "en", "clinical/models")
-
-val text = "The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_greedy.pipeline").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                   |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------------------------------|--------:|------:|:------------|-------------:|
 |  0 | 1 capsule of Advil 10 mg                    |      27 |    50 | DRUG        |     0.638183 |
@@ -107,9 +77,6 @@ Results
 |  6 | with meals                                  |     245 |   254 | FREQUENCY   |     0.79235  |
 |  7 | metformin 1000 mg                           |     261 |   277 | DRUG        |     0.707133 |
 |  8 | two times a day                             |     279 |   293 | FREQUENCY   |     0.700825 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_healthcare_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_healthcare_pipeline_en.md
index 7797bf0893..4aff4a8e2d 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_healthcare_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_healthcare_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_healthcare](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.posology.healthcare_pipeline").predict("""The patient is a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_healthcare_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_healthcare_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology.healthcare_pipeline").predict("""The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk     |   begin |   end | ner_label   |   confidence |
 |---:|:--------------|--------:|------:|:------------|-------------:|
 |  0 | Aspirin       |     267 |   273 | Drug        |      0.9983  |
@@ -110,9 +82,6 @@ Results
 |  9 | Nitroglycerin |     337 |   349 | Drug        |      0.9927  |
 | 10 | 1/150         |     351 |   355 | Strength    |      0.9565  |
 | 11 | sublingually. |     357 |   369 | Route       |      0.72065 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_large_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_large_biobert_pipeline_en.md
index faa7c3961d..00752f7b90 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_large_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_large_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_large_biobert](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.posology_biobert_large.pipeline").predict("""The patient wa
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_large_biobert_pipeline", "en", "clinical/models")
-
-text = '''The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_large_biobert_pipeline", "en", "clinical/models")
-
-val text = "The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_biobert_large.pipeline").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk           |   begin |   end | ner_label   |   confidence |
 |---:|:--------------------|--------:|------:|:------------|-------------:|
 |  0 | 1                   |      27 |    27 | DOSAGE      |     0.9998   |
@@ -116,9 +88,6 @@ Results
 | 15 | metformin           |     261 |   269 | DRUG        |     1        |
 | 16 | 1000 mg             |     271 |   277 | STRENGTH    |     0.69955  |
 | 17 | two times a day     |     279 |   293 | FREQUENCY   |     0.758125 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_large_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_large_pipeline_en.md
index 2f27aaf4db..b8943416f5 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_large_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_large_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_large](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.posoloy_large.pipeline").predict("""The patient is a 30-yea
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_large_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_large_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posoloy_large.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | insulin        |      59 |    65 | DRUG        |     0.9752   |
@@ -123,9 +95,6 @@ Results
 | 22 | 0.1 mg         |    1113 |  1118 | STRENGTH    |     0.9325   |
 | 23 | p.o.           |    1120 |  1123 | ROUTE       |     0.6783   |
 | 24 | daily          |    1125 |  1129 | FREQUENCY   |     0.9925   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_pipeline_en.md
index 9df49ead13..45a3a50490 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,10 @@ nlu.load("en.med_ner.posology_pipeline").predict("""The patient is a 30-year-old
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | insulin        |      59 |    65 | DRUG        |      0.9759  |
@@ -123,9 +94,6 @@ Results
 | 22 | 0.1 mg         |    1113 |  1118 | STRENGTH    |      0.7658  |
 | 23 | p.o            |    1120 |  1122 | ROUTE       |      0.9982  |
 | 24 | daily          |    1125 |  1129 | FREQUENCY   |      0.9983  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_small_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_small_pipeline_en.md
index 4cafd8782e..f3508ab7ed 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_posology_small_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_posology_small_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_posology_small](https://nlp
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.posology_small.pipeline").predict("""The patient is a 30-ye
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_posology_small_pipeline", "en", "clinical/models")
-
-text = '''The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_posology_small_pipeline", "en", "clinical/models")
-
-val text = "The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.posology_small.pipeline").predict("""The patient is a 30-year-old female with a long history of insulin dependent diabetes, type 2; coronary artery disease; chronic renal insufficiency; peripheral vascular disease, also secondary to diabetes; who was originally admitted to an outside hospital for what appeared to be acute paraplegia, lower extremities. She did receive a course of Bactrim for 14 days for UTI. Evidently, at some point in time, the patient was noted to develop a pressure-type wound on the sole of her left foot and left great toe. She was also noted to have a large sacral wound; this is in a similar location with her previous laminectomy, and this continues to receive daily care. The patient was transferred secondary to inability to participate in full physical and occupational therapy and continue medical management of her diabetes, the sacral decubitus, left foot pressure wound, and associated complications of diabetes. She is given Fragmin 5000 units subcutaneously daily, Xenaderm to wounds topically b.i.d., Lantus 40 units subcutaneously at bedtime, OxyContin 30 mg p.o. q.12 h., folic acid 1 mg daily, levothyroxine 0.1 mg p.o. daily, Prevacid 30 mg daily, Avandia 4 mg daily, Norvasc 10 mg daily, Lexapro 20 mg daily, aspirin 81 mg daily, Senna 2 tablets p.o. q.a.m., Neurontin 400 mg p.o. t.i.d., Percocet 5/325 mg 2 tablets q.4 h. p.r.n., magnesium citrate 1 bottle p.o. p.r.n., sliding scale coverage insulin, Wellbutrin 100 mg p.o. daily, and Bactrim DS b.i.d.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk      |   begin |   end | ner_label   |   confidence |
 |---:|:---------------|--------:|------:|:------------|-------------:|
 |  0 | insulin        |      59 |    65 | DRUG        |      0.9984  |
@@ -123,9 +95,6 @@ Results
 | 22 | 0.1 mg         |    1113 |  1118 | STRENGTH    |      0.99965 |
 | 23 | p.o            |    1120 |  1122 | ROUTE       |      0.999   |
 | 24 | daily          |    1125 |  1129 | FREQUENCY   |      0.9373  |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_profiling_biobert_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_profiling_biobert_en.md
index b008e9566a..309d662648 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_profiling_biobert_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_profiling_biobert_en.md
@@ -38,6 +38,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -67,66 +68,11 @@ nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline('ner_profiling_biobert', 'en', 'clinical/models')
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_biobert", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_biobert").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 ******************** ner_diseases_biobert Model Results ******************** 
 
 [('gestational diabetes mellitus', 'Disease'), ('type two diabetes mellitus', 'Disease'), ('T2DM', 'Disease'), ('HTG-induced pancreatitis', 'Disease'), ('hepatitis', 'Disease'), ('obesity', 'Disease'), ('polyuria', 'Disease'), ('polydipsia', 'Disease'), ('poor appetite', 'Disease'), ('vomiting', 'Disease')]
@@ -150,13 +96,6 @@ Results
 ******************** ner_risk_factors_biobert Model Results ******************** 
 
 [('diabetes mellitus', 'DIABETES'), ('subsequent type two diabetes mellitus', 'DIABETES'), ('obesity', 'OBESE')]
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_radiology_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_radiology_pipeline_en.md
index aca7f59235..5d2bbbb27f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_radiology_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_radiology_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_radiology](https://nlp.john
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.radiology.pipeline").predict("""Bilateral breast ultrasound
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_radiology_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_radiology_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.radiology.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                |   begin |   end | ner_label                 |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Bilateral breast                         |       0 |    15 | BodyPart                  |     0.945    |
@@ -110,9 +82,6 @@ Results
 |  9 | internal color flow                      |     294 |   312 | ImagingFindings           |     0.477233 |
 | 10 | benign fibrous tissue                    |     334 |   354 | ImagingFindings           |     0.524067 |
 | 11 | lipoma                                   |     361 |   366 | Disease_Syndrome_Disorder |     0.6081   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_radiology_wip_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_radiology_wip_clinical_pipeline_en.md
index 467e87fa0f..669e64f3e3 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_radiology_wip_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_radiology_wip_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_radiology_wip_clinical](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,40 +63,11 @@ nlu.load("en.med_ner.radiology.clinical_wip.pipeline").predict("""Bilateral brea
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_radiology_wip_clinical_pipeline", "en", "clinical/models")
-
-text = '''Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_radiology_wip_clinical_pipeline", "en", "clinical/models")
-
-val text = "Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.radiology.clinical_wip.pipeline").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks            |   begin |   end | ner_label                 |   confidence |
 |---:|:----------------------|--------:|------:|:--------------------------|-------------:|
 |  0 | Bilateral             |       0 |     8 | Direction                 |     0.9828   |
@@ -113,9 +85,6 @@ Results
 | 12 | internal color flow   |     294 |   312 | ImagingFindings           |     0.5153   |
 | 13 | benign fibrous tissue |     334 |   354 | ImagingFindings           |     0.394867 |
 | 14 | lipoma                |     361 |   366 | Disease_Syndrome_Disorder |     0.9142   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_risk_factors_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_risk_factors_biobert_pipeline_en.md
index 2944a40c0b..832185cbc1 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_risk_factors_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_risk_factors_biobert_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_risk_factors_biobert](https
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -86,64 +87,11 @@ FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_risk_factors_biobert_pipeline", "en", "clinical/models")
-
-text = '''ISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_risk_factors_biobert_pipeline", "en", "clinical/models")
-
-val text = "ISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother)."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.risk_factors_biobert.pipeline").predict("""ISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
 
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                                |   begin |   end | ner_label    |   confidence |
 |---:|:-----------------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | diabetic                                 |     135 |   142 | DIABETES     |     0.9689   |
@@ -153,9 +101,6 @@ Results
 |  4 | hypertension                             |    1341 |  1352 | HYPERTENSION |     0.956    |
 |  5 | coronary artery disease                  |    1355 |  1377 | CAD          |     0.7962   |
 |  6 | Smokes 2 packs of cigarettes per day     |    1480 |  1515 | SMOKER       |     0.461643 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_risk_factors_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_risk_factors_pipeline_en.md
index 026b13d582..18ac552a59 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_risk_factors_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_risk_factors_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_risk_factors](https://nlp.j
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -86,64 +87,11 @@ FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_risk_factors_pipeline", "en", "clinical/models")
-
-text = '''HISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_risk_factors_pipeline", "en", "clinical/models")
-
-val text = "HISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
-
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother)."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.med_ner.risk_factors.pipeline").predict("""HISTORY OF PRESENT ILLNESS: The patient is a 40-year-old white male who presents with a chief complaint of "chest pain". The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that his chest pain started yesterday evening and has been somewhat intermittent. The severity of the pain has progressively increased. He describes the pain as a sharp and heavy pain which radiates to his neck & left arm. He ranks the pain a 7 on a scale of 1-10. He admits some shortness of breath & diaphoresis. He states that he has had nausea & 3 episodes of vomiting tonight. He denies any fever or chills. He admits prior episodes of similar pain prior to his PTCA in 1995. He states the pain is somewhat worse with walking and seems to be relieved with rest. There is no change in pain with positioning. He states that he took 3 nitroglycerin tablets sublingually over the past 1 hour, which he states has partially relieved his pain. The patient ranks his present pain a 4 on a scale of 1-10. The most recent episode of pain has lasted one-hour. The patient denies any history of recent surgery, head trauma, recent stroke, abnormal bleeding such as blood in urine or stool or nosebleed.
-
-REVIEW OF SYSTEMS: All other systems reviewed & are negative.
-
-PAST MEDICAL HISTORY: Diabetes mellitus type II, hypertension, coronary artery disease, atrial fibrillation, status post PTCA in 1995 by Dr. ABC.
 
-SOCIAL HISTORY: Denies alcohol or drugs. Smokes 2 packs of cigarettes per day. Works as a banker.
-
-FAMILY HISTORY: Positive for coronary artery disease (father & brother).""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunk                            |   begin |   end | ner_label    |   confidence |
 |---:|:-------------------------------------|--------:|------:|:-------------|-------------:|
 |  0 | diabetic                             |     136 |   143 | DIABETES     |     0.9992   |
@@ -155,9 +103,6 @@ Results
 |  6 | ABC                                  |    1434 |  1436 | PHI          |     0.9999   |
 |  7 | Smokes 2 packs of cigarettes per day |    1481 |  1516 | SMOKER       |     0.634257 |
 |  8 | banker                               |    1530 |  1535 | PHI          |     0.9779   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-ner_supplement_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-ner_supplement_clinical_pipeline_en.md
index 22ffe25760..3913fc6498 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-ner_supplement_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-ner_supplement_clinical_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [ner_supplement_clinical](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-text = '''Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-val text = "Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-text = '''Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_supplement_clinical_pipeline", "en", "clinical/models")
-
-val text = "Excellent!. The state of health improves, nervousness disappears, and night sleep improves. It also promotes hair and nail growth. I recommend :"
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,24 +69,12 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | nervousness  |      42 |    52 | CONDITION   |      0.9999  |
 |  1 | night sleep  |      70 |    80 | BENEFIT     |      0.80775 |
 |  2 | hair         |     109 |   112 | BENEFIT     |      0.9997  |
 |  3 | nail growth  |     118 |   128 | BENEFIT     |      0.9997  |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-nerdl_tumour_demo_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-nerdl_tumour_demo_pipeline_en.md
index ed23a0d3b7..284caf94b8 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-nerdl_tumour_demo_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-nerdl_tumour_demo_pipeline_en.md
@@ -34,50 +34,7 @@ This pretrained pipeline is built on the top of [nerdl_tumour_demo](https://nlp.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
-
-val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -112,21 +69,9 @@ result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | ner_chunks       |   begin |   end | ner_label    | confidence   |
 |---:|:-----------------|--------:|------:|:-------------|:-------------|
 |  0 | breast carcinoma |      35 |    50 | Localization |              |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-oncology_biomarker_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-oncology_biomarker_pipeline_en.md
index e038c5651c..bf5e068a85 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-oncology_biomarker_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-oncology_biomarker_pipeline_en.md
@@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status and Relation E
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,72 +63,11 @@ nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was n
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-text = '''Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
 
-text = '''Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_biomarker_pipeline", "en", "clinical/models")
-
-val text = "Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_biomarker.pipeline").predict("""Immunohistochemistry was negative for thyroid transcription factor-1 and napsin A. The test was positive for ER and PR, and negative for HER2.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk                          | ner_label        |
@@ -253,13 +193,6 @@ Results
 | ER                   | Biomarker        | negative                       | Biomarker_Result | O             |
 | PR                   | Biomarker        | negative                       | Biomarker_Result | O             |
 | negative             | Biomarker_Result | HER2                           | Oncogene         | is_finding_of |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-oncology_therapy_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-oncology_therapy_pipeline_en.md
index 958202df80..4ee8144247 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-oncology_therapy_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-oncology_therapy_pipeline_en.md
@@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition and Assertion Status models to e
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -62,41 +63,11 @@ nlu.load("en.oncology_therpay.pipeline").predict("""The patient underwent a mast
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_therapy_pipeline", "en", "clinical/models")
-
-text = '''The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition.'''
 
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_therapy_pipeline", "en", "clinical/models")
-
-val text = "The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition."
-
-val result = pipeline.fullAnnotate(text)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_therpay.pipeline").predict("""The patient underwent a mastectomy two years ago. She is currently receiving her second cycle of adriamycin and cyclophosphamide, and is in good overall condition.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk            | ner_label      |
@@ -153,10 +124,6 @@ Results
 | mastectomy       | Cancer_Surgery | Present_Or_Past |
 | adriamycin       | Chemotherapy   | Present_Or_Past |
 | cyclophosphamide | Chemotherapy   | Present_Or_Past |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-re_bodypart_directions_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-re_bodypart_directions_pipeline_en.md
index 6fe9e0ed31..36be3a6c1b 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-re_bodypart_directions_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-re_bodypart_directions_pipeline_en.md
@@ -59,64 +59,11 @@ nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations | entity1                     | entity1_begin | entity1_end | chunk1     | entity2                     | entity2_end | entity2_end | chunk2        | confidence |
 |-------|-----------|-----------------------------|---------------|-------------|------------|-----------------------------|-------------|-------------|---------------|------------|
 | 0     | 1         | Direction                   | 35            | 39          | upper      | Internal_organ_or_component | 41          | 50          | brain stem    | 0.9999989  |
@@ -128,13 +75,6 @@ Results
 | 6     | 0         | Direction                   | 54            | 57          | left       | Internal_organ_or_component | 81          | 93          | basil ganglia | 0.97616416 |
 | 7     | 0         | Internal_organ_or_component | 59            | 68          | cerebellum | Direction                   | 75          | 79          | right         | 0.953046   |
 | 8     | 1         | Direction                   | 75            | 79          | right      | Internal_organ_or_component | 81          | 93          | basil ganglia | 1.0        |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-re_bodypart_proceduretest_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-re_bodypart_proceduretest_pipeline_en.md
index 951ff20334..f7c065358e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-re_bodypart_proceduretest_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-re_bodypart_proceduretest_pipeline_en.md
@@ -59,74 +59,14 @@ nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations | entity1                      | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2              | confidence |
 |-------|-----------|------------------------------|---------------|-------------|--------|---------|-------------|-------------|---------------------|------------|
 | 0     | 1         | External_body_part_or_region | 94            | 98          | chest  | Test    | 117         | 135         | portable ultrasound | 1.0        |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-re_human_phenotype_gene_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-re_human_phenotype_gene_clinical_pipeline_en.md
index 8a21b03a37..0a7f034e64 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-re_human_phenotype_gene_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-re_human_phenotype_gene_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_human_phenotype_gene_clinica
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
 
@@ -56,59 +57,11 @@ nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobo
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
 
 
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_human_phenotype_gene_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.human_gene_clinical.pipeline").predict("""Bilateral colobomatous microphthalmia and developmental delay in whom genetic studies identified a homozygous TENM3""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    |   relation | entity1   |   entity1_begin |   entity1_end | chunk1              | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+===========+=================+===============+=====================+===========+=================+===============+=====================+==============+
@@ -116,12 +69,6 @@ Results
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 |          1 | HP        |              23 |            36 | microphthalmia      | GENE      |             110 |           114 | TENM3               |     0.999999 |
 +----+------------+-----------+-----------------+---------------+---------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-re_temporal_events_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-re_temporal_events_clinical_pipeline_en.md
index a1f3650c81..b4eb9fd924 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-re_temporal_events_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-re_temporal_events_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_clinical](ht
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
 
@@ -56,59 +57,10 @@ nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temporal_event_clinical.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |    | relation   | entity1    |   entity1_begin |   entity1_end | chunk1                   | entity2   |   entity2_begin |   entity2_end | chunk2              |   confidence |
 +====+============+============+=================+===============+==========================+===========+=================+===============+=====================+==============+
@@ -116,12 +68,6 @@ Results
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
 |  1 | OVERLAP    | DATE       |             171 |           179 | that time                | PROBLEM   |             201 |           219 | any specific injury |     0.956654 |
 +----+------------+------------+-----------------+---------------+--------------------------+-----------+-----------------+---------------+---------------------+--------------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-re_temporal_events_enriched_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-re_temporal_events_enriched_clinical_pipeline_en.md
index fbd378e199..aff481dd33 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-re_temporal_events_enriched_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-re_temporal_events_enriched_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [re_temporal_events_enriched_cli
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
 
@@ -56,59 +57,11 @@ nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 5
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
 
 
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-```scala
-val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
-
-
-pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
-```
-</div>
-
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |    | relation   | entity1   |   entity1_begin |   entity1_end | chunk1                                        | entity2    |   entity2_begin |   entity2_end | chunk2                   |   confidence |
 +====+============+===========+=================+===============+===============================================+============+=================+===============+==========================+==============+
@@ -116,12 +69,6 @@ Results
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
 |  1 | AFTER      | DATE      |             171 |           179 | that time                                     | PROBLEM    |             201 |           219 | any specific injury      |     0.577288 |
 +----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-re_test_problem_finding_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-re_test_problem_finding_pipeline_en.md
index 8d78610b14..e5ca6e19ee 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-re_test_problem_finding_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-re_test_problem_finding_pipeline_en.md
@@ -59,74 +59,14 @@ nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | 1            | PROCEDURE    | biopsy              | SYMPTOM      |  lesion | 
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-re_test_result_date_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-re_test_result_date_pipeline_en.md
index 8f899d942e..90617ce513 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-re_test_result_date_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-re_test_result_date_pipeline_en.md
@@ -59,76 +59,15 @@ nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised ches
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
-
-pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 | index | relations    | entity1      | chunk1              | entity2      |  chunk2 |
 |-------|--------------|--------------|---------------------|--------------|---------|
 | 0     | O            | TEST         | chest X-ray         | MEASUREMENTS |  93%    | 
 | 1     | O            | TEST         | CT scan             | MEASUREMENTS |  93%    |
 | 2     | is_result_of | TEST         | SpO2                | MEASUREMENTS |  93%    |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-recognize_entities_posology_en.md b/docs/_posts/ahmedlone127/2023-06-16-recognize_entities_posology_en.md
index 444c9f9ff1..2ea8147da6 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-recognize_entities_posology_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-recognize_entities_posology_en.md
@@ -34,6 +34,7 @@ A pipeline with `ner_posology`. It will only extract medication entities.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
 
@@ -61,67 +62,11 @@ She was seen by the endocrinology service and discharged on 40 units of insulin
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
-
-res = pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-```scala
-val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "clinical/models")
-
-val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")(0)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-pipeline = PretrainedPipeline('recognize_entities_posology', 'en', 'clinical/models')
-
-res = pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-```scala
-val era_pipeline = new PretrainedPipeline("recognize_entities_posology", "en", "clinical/models")
 
-val result = era_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")(0)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.recognize_entities.posology").predict("""A 28-year-old female with a history of gestational diabetes mellitus, used to take metformin 1000 mg two times a day, presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting .
-She was seen by the endocrinology service and discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals.
-""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 |    | chunk            |   begin |   end | entity    |
 |---:|:-----------------|--------:|------:|:----------|
 |  0 | metformin        |      83 |    91 | DRUG      |
@@ -133,13 +78,6 @@ Results
 |  6 | 12 units         |     309 |   316 | DOSAGE    |
 |  7 | insulin lispro   |     321 |   334 | DRUG      |
 |  8 | with meals       |     336 |   345 | FREQUENCY |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-rxnorm_mesh_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-rxnorm_mesh_mapping_en.md
index 3e0c842b63..eee6125705 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-rxnorm_mesh_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-rxnorm_mesh_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps RxNorm codes to MeSH codes without using any text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline 
 pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
@@ -54,55 +55,11 @@ nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline 
-pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-pipeline.annotate("1191 6809 47613")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-val result = pipeline.annotate("1191 6809 47613")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline 
-pipeline = PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-pipeline.annotate("1191 6809 47613")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-val pipeline = new PretrainedPipeline("rxnorm_mesh_mapping","en","clinical/models")
-val result = pipeline.annotate("1191 6809 47613")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.rxnorm.mesh").predict("""1191 6809 47613""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
 {'rxnorm': ['1191', '6809', '47613'],
 'mesh': ['D001241', 'D008687', 'D019355']}
 
@@ -120,12 +77,6 @@ Note:
 | D001241    | Aspirin             |
 | D008687    | Metformin           |
 | D019355    | Calcium Citrate     |
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-rxnorm_ndc_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-rxnorm_ndc_mapping_en.md
index 987cc602a2..69dc5e7161 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-rxnorm_ndc_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-rxnorm_ndc_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps RXNORM codes to NDC codes without using any text d
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,75 +59,15 @@ nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1652674 259934)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1652674 259934)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1652674 259934)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_ndc_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1652674 259934)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.rxnorm_to_ndc.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 {'document': ['1652674 259934'],
 'package_ndc': ['62135-0625-60', '13349-0010-39'],
 'product_ndc': ['46708-0499', '13349-0010'],
 'rxnorm_code': ['1652674', '259934']}
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-rxnorm_umls_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-rxnorm_umls_mapping_en.md
index 942b4ae98e..c9b9d75468 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-rxnorm_umls_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-rxnorm_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `rxnorm_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1161611 315677)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1161611 315677)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(1161611 315677)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rxnorm_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(1161611 315677)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.rxnorm.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | rxnorm_code      | umls_code           |
 |---:|:-----------------|:--------------------|
 |  0 | 1161611 | 315677 | C3215948 | C0984912 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-snomed_icd10cm_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-snomed_icd10cm_mapping_en.md
index 209811a2c0..2f4e2a590e 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-snomed_icd10cm_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-snomed_icd10cm_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icd10cm_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here."
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(128041000119107 292278006 293072005)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | snomed_code                             | icd10cm_code               |
 |---:|:----------------------------------------|:---------------------------|
 |  0 | 128041000119107 | 292278006 | 293072005 | K22.70 | T43.595 | T37.1X5 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-snomed_icdo_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-snomed_icdo_mapping_en.md
index 45f7123bf2..7797c2f46f 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-snomed_icdo_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-snomed_icdo_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_icdo_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_icdo_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(10376009 2026006 26638004)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.snomed_to_icdo.pipe").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | snomed_code                   | icdo_code                |
 |---:|:------------------------------|:-------------------------|
 |  0 | 10376009 | 2026006 | 26638004 | 8050/2 | 9014/0 | 8322/0 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-snomed_umls_mapping_en.md b/docs/_posts/ahmedlone127/2023-06-16-snomed_umls_mapping_en.md
index c7c0174cd1..880882a8a4 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-snomed_umls_mapping_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-snomed_umls_mapping_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of `snomed_umls_mapper` model.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,74 +59,14 @@ nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("snomed_umls_mapping", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(733187009 449433008 51264003)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.snomed.umls.mapping").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-Results
-
-
-
 |    | snomed_code                      | umls_code                      |
 |---:|:---------------------------------|:-------------------------------|
 |  0 | 733187009 | 449433008 | 51264003 | C4546029 | C3164619 | C0271267 |
-
-
-
-{:.model-param}
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-16-spellcheck_clinical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-16-spellcheck_clinical_pipeline_en.md
index 6c129fce5c..ff313ace30 100644
--- a/docs/_posts/ahmedlone127/2023-06-16-spellcheck_clinical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-16-spellcheck_clinical_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained medical spellchecker pipeline is built on the top of `spellcheck
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -66,44 +67,10 @@ nlu.load("en.spell.clinical.pipeline").predict("""Witth the hell of phisical ter
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("spellcheck_clinical_pipeline", "en", "clinical/models")
-example = ["Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.",
-           "With paint wel controlled on orall pain medications, she was discharged too reihabilitation facilitay.",
-           "Abdomen is sort, nontender, and nonintended.",
-           "Patient not showing pain or any wealth problems.",
-           "No cute distress"]
-pipeline.fullAnnotate(example)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("spellcheck_clinical_pipeline", "en", "clinical/models")
-val example = Array("Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.",
-           "With paint wel controlled on orall pain medications, she was discharged too reihabilitation facilitay.",
-           "Abdomen is sort, nontender, and nonintended.",
-           "Patient not showing pain or any wealth problems.",
-           "No cute distress")
-pipeline.fullAnnotate(example)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.spell.clinical.pipeline").predict("""Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 [{'checked': ['With','the','cell','of','physical','therapy','the','patient','was','ambulated','and','on','postoperative',',','the','patient','tolerating','a','post','surgical','soft','diet','.'],
   'document': ['Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.'],
   'token': ['Witth','the','hell','of','phisical','terapy','the','patient','was','imbulated','and','on','postoperative',',','the','impatient','tolerating','a','post','curgical','soft','diet','.']},
@@ -123,9 +90,6 @@ Results
  {'checked': ['No', 'acute', 'distress'],
   'document': ['No cute distress'],
   'token': ['No', 'cute', 'distress']}]
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-19-ner_profiling_clinical_en.md b/docs/_posts/ahmedlone127/2023-06-19-ner_profiling_clinical_en.md
index 7db7d536e4..b226935104 100644
--- a/docs/_posts/ahmedlone127/2023-06-19-ner_profiling_clinical_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-19-ner_profiling_clinical_en.md
@@ -38,6 +38,7 @@ Here are the NER models that this pretrained pipeline includes:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 
 from sparknlp.pretrained import PretrainedPipeline
@@ -67,38 +68,11 @@ nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-ner_profiling_pipeline = PretrainedPipeline("ner_profiling_clinical", "en", "clinical/models")
-
-result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val ner_profiling_pipeline = PretrainedPipeline("ner_profiling_clinical", "en", "clinical/models")
-
-val result = ner_profiling_pipeline.annotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-
-{:.nlu-block}
-```python
-import nlu
-
-nlu.load("en.med_ner.profiling_clinical").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM ), one prior episode of HTG-induced pancreatitis three years prior to presentation , associated with an acute hepatitis , and obesity with a body mass index ( BMI ) of 33.5 kg/m2 , presented with a one-week history of polyuria , polydipsia , poor appetite , and vomiting.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 ******************** ner_jsl Model Results ******************** 
 
 [('28-year-old', 'Age'), ('female', 'Gender'), ('gestational diabetes mellitus', 'Diabetes'), ('eight years prior', 'RelativeDate'), ('subsequent', 'Modifier'), ('type two diabetes mellitus', 'Diabetes'), ('T2DM', 'Diabetes'), ('HTG-induced pancreatitis', 'Disease_Syndrome_Disorder'), ('three years prior', 'RelativeDate'), ('acute', 'Modifier'), ('hepatitis', 'Communicable_Disease'), ('obesity', 'Obesity'), ('body mass index', 'Symptom'), ('33.5 kg/m2', 'Weight'), ('one-week', 'Duration'), ('polyuria', 'Symptom'), ('polydipsia', 'Symptom'), ('poor appetite', 'Symptom'), ('vomiting', 'Symptom')]
@@ -124,10 +98,6 @@ Results
 [('female', 'Organism_Attribute'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('diabetes mellitus', 'Disease_or_Syndrome'), ('T2DM', 'Disease_or_Syndrome'), ('HTG-induced pancreatitis', 'Disease_or_Syndrome'), ('associated with', 'Qualitative_Concept'), ('acute hepatitis', 'Disease_or_Syndrome'), ('obesity', 'Disease_or_Syndrome'), ('body mass index', 'Clinical_Attribute'), ('BMI', 'Clinical_Attribute'), ('polyuria', 'Sign_or_Symptom'), ('polydipsia', 'Sign_or_Symptom'), ('poor appetite', 'Sign_or_Symptom'), ('vomiting', 'Sign_or_Symptom')]
 
 ...
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-21-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-21-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md
index ce805de5b7..b536061938 100644
--- a/docs/_posts/ahmedlone127/2023-06-21-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-21-bert_sequence_classifier_vop_hcp_consult_pipeline_en.md
@@ -34,24 +34,7 @@ This pretrained pipeline includes the Medical Bert for Sequence Classification m
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_sequence_classifier_vop_hcp_consult_pipeline", "en", "clinical/models")
-
-pipeline.annotate("My son has been to two doctors who gave him antibiotic drops but they also say the problem might related to allergies.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_sequence_classifier_vop_hcp_consult_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate(My son has been to two doctors who gave him antibiotic drops but they also say the problem might related to allergies.)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -71,16 +54,9 @@ val result = pipeline.annotate(My son has been to two doctors who gave him antib
 ## Results
 
 ```bash
-Results
-
-
 | text                                                                                                                   | prediction       |
 |:-----------------------------------------------------------------------------------------------------------------------|:-----------------|
 | My son has been to two doctors who gave him antibiotic drops but they also say the problem might related to allergies. | Consulted_By_HCP |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-bert_sequence_classifier_vop_sound_medical_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-bert_sequence_classifier_vop_sound_medical_pipeline_en.md
index 4dd637b7bd..4ca1519e97 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-bert_sequence_classifier_vop_sound_medical_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-bert_sequence_classifier_vop_sound_medical_pipeline_en.md
@@ -34,25 +34,7 @@ This pretrained pipeline includes the Medical Bert for Sequence Classification m
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("bert_sequence_classifier_vop_sound_medical_pipeline", "en", "clinical/models")
-
-pipeline.annotate("I had a lung surgery for emphyema and after surgery my xray showing some recovery.")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("bert_sequence_classifier_vop_sound_medical_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate(I had a lung surgery for emphyema and after surgery my xray showing some recovery.)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-  
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -69,6 +51,7 @@ val result = pipeline.annotate(I had a lung surgery for emphyema and after surge
 ```
 </div>
 
+
 ## Results
 
 ```bash
diff --git a/docs/_posts/ahmedlone127/2023-06-22-clinical_deidentification_ar.md b/docs/_posts/ahmedlone127/2023-06-22-clinical_deidentification_ar.md
index 10c5681af1..78f3501355 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-clinical_deidentification_ar.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-clinical_deidentification_ar.md
@@ -34,6 +34,7 @@ This pipeline can be used to deidentify Arabic PHI information from medical text
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -118,99 +119,11 @@ val result = deid_pipeline.annotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-deid_pipeline = PretrainedPipeline("clinical_deidentification", "ar", "clinical/models")
-
-text = '''
-
-ملاحظات سريرية - مريض الربو:
-
-التاريخ: 30 مايو 2023
-اسم المريضة: ليلى حسن
-
-تم تسجيل المريض في النظام باستخدام رقم الضمان الاجتماعي 123456789012.
-
-العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة
-الرمز البريدي: 54321
-البلد: المملكة العربية السعودية
-اسم المستشفى: مستشفى النور
-اسم الطبيب: د. أميرة أحمد
-
-تفاصيل الحالة:
-المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة.
-
-الخطة:
-
-تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج.
-يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة.
-يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة.
-يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو.
-تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح
 
-'''
-result = deid_pipeline.annotate(text)
-
-print("\nMasked with entity labels")
-print("-"*30)
-print("\n".join(result['masked_with_entity']))
-print("\nMasked with chars")
-print("-"*30)
-print("\n".join(result['masked_with_chars']))
-print("\nMasked with fixed length chars")
-print("-"*30)
-print("\n".join(result['masked_fixed_length_chars']))
-print("\nObfuscated")
-print("-"*30)
-print("\n".join(result['obfuscated']))
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val deid_pipeline = new PretrainedPipeline("clinical_deidentification","ar","clinical/models")
-
-val text = '''
-
-ملاحظات سريرية - مريض الربو:
-
-التاريخ: 30 مايو 2023
-اسم المريضة: ليلى حسن
-
-تم تسجيل المريض في النظام باستخدام رقم الضمان الاجتماعي 123456789012.
-
-العنوان: شارع المعرفة، مبنى رقم 789، حي الأمانة، جدة
-الرمز البريدي: 54321
-البلد: المملكة العربية السعودية
-اسم المستشفى: مستشفى النور
-اسم الطبيب: د. أميرة أحمد
-
-تفاصيل الحالة:
-المريضة ليلى حسن، البالغة من العمر 35 عامًا، تعاني من مرض الربو المزمن. تشكو من ضيق التنفس والسعال المتكرر والشهيق الشديد. تم تشخيصها بمرض الربو بناءً على تاريخها الطبي واختبارات وظائف الرئة.
-
-الخطة:
-
-تم وصف مضادات الالتهاب غير الستيرويدية والموسعات القصبية لتحسين التنفس وتقليل التهيج.
-يجب على المريضة حمل معها جهاز الاستنشاق في حالة حدوث نوبة ربو حادة.
-يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة.
-يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو.
-تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح
-
-'''
-
-val result = deid_pipeline.annotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 Masked with entity labels
 ------------------------------
 ملاحظات سريرية - مريض الربو:
@@ -306,9 +219,6 @@ Obfuscated
 يتعين على المريضة تجنب التحسس من العوامل المسببة للربو، مثل الدخان والغبار والحيوانات الأليفة.
 يجب مراقبة وظائف الرئة بانتظام ومتابعة التعليمات الطبية المتعلقة بمرض الربو.
 تعليم المريضة بشأن كيفية استخدام جهاز الاستنشاق بشكل صحيح وتقنيات التنفس الصحيح
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-cvx_resolver_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-cvx_resolver_pipeline_en.md
index 90d42b4417..0ba6853f95 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-cvx_resolver_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-cvx_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities with their corresponding CVX codes. You
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,48 +61,16 @@ nlu.load("en.resolve.cvx_pipeline").predict("""The patient has a history of infl
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-resolver_pipeline = PretrainedPipeline("cvx_resolver_pipeline", "en", "clinical/models")
-
-text= "The patient has a history of influenza vaccine, tetanus and DTaP"
-
-result = resolver_pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val resolver_pipeline = new PretrainedPipeline("cvx_resolver_pipeline", "en", "clinical/models")
-
-val result = resolver_pipeline.fullAnnotate("The patient has a history of influenza vaccine, tetanus and DTaP")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.cvx_pipeline").predict("""The patient has a history of influenza vaccine, tetanus and DTaP""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-+-----------------+---------+--------+
 |chunk            |ner_chunk|cvx_code|
 +-----------------+---------+--------+
 |influenza vaccine|Vaccine  |160     |
 |tetanus          |Vaccine  |35      |
 |DTaP             |Vaccine  |20      |
-+-----------------+---------+--------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-icd10cm_resolver_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-icd10cm_resolver_pipeline_en.md
index a9e015d12c..e12169e260 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-icd10cm_resolver_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-icd10cm_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities with their corresponding ICD-10-CM codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,48 +61,16 @@ nlu.load("en.icd10cm_resolver.pipeline").predict("""A 28-year-old female with a
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-resolver_pipeline = PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")
-
-text = """A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage"""
-
-result = resolver_pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val resolver_pipeline = new PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")
-
-val result = resolver_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.icd10cm_resolver.pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-+-----------------------------+---------+------------+
 |chunk                        |ner_chunk|icd10cm_code|
 +-----------------------------+---------+------------+
 |gestational diabetes mellitus|PROBLEM  |O24.919     |
 |anisakiasis                  |PROBLEM  |B81.0       |
 |fetal and neonatal hemorrhage|PROBLEM  |P545        |
-+-----------------------------+---------+------------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-icd9_resolver_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-icd9_resolver_pipeline_en.md
index 6854bbff48..3adc67a30e 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-icd9_resolver_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-icd9_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities with their corresponding ICD-9-CM codes.
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -60,50 +61,16 @@ nlu.load("en.resolve.icd9.pipeline").predict("""Put your text here.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("icd9_resolver_pipeline", "en", "clinical/models")
-
-text= A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("icd9_resolver_pipeline", "en", "clinical/models")
-
-val result = pipeline.fullAnnotate(A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.resolve.icd9.pipeline").predict("""Put your text here.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
-+-----------------------------+---------+---------+
 |chunk                        |ner_chunk|icd9_code|
 +-----------------------------+---------+---------+
 |gestational diabetes mellitus|PROBLEM  |V12.21   |
 |anisakiasis                  |PROBLEM  |127.1    |
 |fetal and neonatal hemorrhage|PROBLEM  |772      |
-+-----------------------------+---------+---------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md
index ad678d7d76..e037b712f5 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-rct_binary_classifier_biobert_pipeline_en.md
@@ -60,49 +60,16 @@ nlu.load("en.classify.rct_binary_biobert.pipeline").predict("""Abstract:Based on
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-
-pipeline = PretrainedPipeline("rct_binary_classifier_biobert_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rct_binary_classifier_biobert_pipeline", "en", "clinical/models")
-
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.rct_binary_biobert.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |true|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-rct_binary_classifier_use_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-rct_binary_classifier_use_pipeline_en.md
index 16d233b979..b4e6e85694 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-rct_binary_classifier_use_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-rct_binary_classifier_use_pipeline_en.md
@@ -59,48 +59,16 @@ nlu.load("en.classify.rct_binary_use.pipeline").predict("""Abstract:Based on the
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("rct_binary_classifier_use_pipeline", "en", "clinical/models")
-
-result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("rct_binary_classifier_use_pipeline", "en", "clinical/models")
 
-val result = pipeline.annotate("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.classify.rct_binary_use.pipeline").predict("""Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. """)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |rct |text                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 |true|Abstract:Based on the American Society of Anesthesiologists' Practice Guidelines for Sedation and Analgesia by Non-Anesthesiologists (ASA-SED), a sedation training course aimed at improving medical safety was developed by the Japanese Association for Medical Simulation in 2011. This study evaluated the effect of debriefing on participants' perceptions of the essential points of the ASA-SED. A total of 38 novice doctors participated in the sedation training course during the research period. Of these doctors, 18 participated in the debriefing group, and 20 participated in non-debriefing group. Scoring of participants' guideline perceptions was conducted using an evaluation sheet (nine items, 16 points) created based on the ASA-SED. The debriefing group showed a greater perception of the ASA-SED, as reflected in the significantly higher scores on the evaluation sheet (median, 16 points) than the control group (median, 13 points; p < 0.05). No significant differences were identified before or during sedation, but the difference after sedation was significant (p < 0.05). Debriefing after sedation training courses may contribute to better perception of the ASA-SED, and may lead to enhanced attitudes toward medical safety during sedation and analgesia. |
 +----+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md
index db8a624116..85f88abb5b 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-summarizer_biomedical_pubmed_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_biomedical_pubmed](h
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -54,40 +55,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_biomedical_pubmed_pipeline", "en", "clinical/models")
-
-text = """Residual disease after initial surgery for ovarian cancer is the strongest prognostic factor for survival. However, the extent of surgical resection required to achieve optimal cytoreduction is controversial. Our goal was to estimate the effect of aggressive surgical resection on ovarian cancer patient survival.\n A retrospective cohort study of consecutive patients with International Federation of Gynecology and Obstetrics stage IIIC ovarian cancer undergoing primary surgery was conducted between January 1, 1994, and December 31, 1998. The main outcome measures were residual disease after cytoreduction, frequency of radical surgical resection, and 5-year disease-specific survival.\n The study comprised 194 patients, including 144 with carcinomatosis. The mean patient age and follow-up time were 64.4 and 3.5 years, respectively. After surgery, 131 (67.5%) of the 194 patients had less than 1 cm of residual disease (definition of optimal cytoreduction). Considering all patients, residual disease was the only independent predictor of survival; the need to perform radical procedures to achieve optimal cytoreduction was not associated with a decrease in survival. For the subgroup of patients with carcinomatosis, residual disease and the performance of radical surgical procedures were the only independent predictors. Disease-specific survival was markedly improved for patients with carcinomatosis operated on by surgeons who most frequently used radical procedures compared with those least likely to use radical procedures (44% versus 17%, P < .001).\n Overall, residual disease was the only independent predictor of survival. Minimizing residual disease through aggressive surgical resection was beneficial, especially in patients with carcinomatosis."""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_biomedical_pubmed_pipeline", "en", "clinical/models")
-
-val text = """Residual disease after initial surgery for ovarian cancer is the strongest prognostic factor for survival. However, the extent of surgical resection required to achieve optimal cytoreduction is controversial. Our goal was to estimate the effect of aggressive surgical resection on ovarian cancer patient survival.\n A retrospective cohort study of consecutive patients with International Federation of Gynecology and Obstetrics stage IIIC ovarian cancer undergoing primary surgery was conducted between January 1, 1994, and December 31, 1998. The main outcome measures were residual disease after cytoreduction, frequency of radical surgical resection, and 5-year disease-specific survival.\n The study comprised 194 patients, including 144 with carcinomatosis. The mean patient age and follow-up time were 64.4 and 3.5 years, respectively. After surgery, 131 (67.5%) of the 194 patients had less than 1 cm of residual disease (definition of optimal cytoreduction). Considering all patients, residual disease was the only independent predictor of survival; the need to perform radical procedures to achieve optimal cytoreduction was not associated with a decrease in survival. For the subgroup of patients with carcinomatosis, residual disease and the performance of radical surgical procedures were the only independent predictors. Disease-specific survival was markedly improved for patients with carcinomatosis operated on by surgeons who most frequently used radical procedures compared with those least likely to use radical procedures (44% versus 17%, P < .001).\n Overall, residual disease was the only independent predictor of survival. Minimizing residual disease through aggressive surgical resection was beneficial, especially in patients with carcinomatosis."""
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 The results of this review suggest that aggressive ovarian cancer surgery is associated with a significant reduction in the risk of recurrence and a reduction in the number of radical versus conservative surgical resections. However, the results of this review are based on only one small trial. Further research is needed to determine the role of aggressive ovarian cancer surgery in women with stage IIIC ovarian cancer.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md
index 9cc9201368..18a70e05c1 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_guidelines_large_pipeline_en.md
@@ -34,84 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_guidelines_
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_guidelines_large_pipeline", "en", "clinical/models")
-
-text = """Clinical Guidelines for Breast Cancer:
-
-Breast cancer is the most common type of cancer among women. It occurs when the cells in the breast start growing abnormally, forming a lump or mass. This can result in the spread of cancerous cells to other parts of the body. Breast cancer may occur in both men and women but is more prevalent in women.
-
-The exact cause of breast cancer is unknown. However, several risk factors can increase your likelihood of developing breast cancer, such as:
-- A personal or family history of breast cancer
-- A genetic mutation, such as BRCA1 or BRCA2
-- Exposure to radiation
-- Age (most commonly occurring in women over 50)
-- Early onset of menstruation or late menopause
-- Obesity
-- Hormonal factors, such as taking hormone replacement therapy
-
-Breast cancer may not present symptoms during its early stages. Symptoms typically manifest as the disease progresses. Some notable symptoms include:
-- A lump or thickening in the breast or underarm area
-- Changes in the size or shape of the breast
-- Nipple discharge
-- Nipple changes in appearance, such as inversion or flattening
-- Redness or swelling in the breast
-
-Treatment for breast cancer depends on several factors, including the stage of the cancer, the location of the tumor, and the individual's overall health. Common treatment options include:
-- Surgery (such as lumpectomy or mastectomy)
-- Radiation therapy
-- Chemotherapy
-- Hormone therapy
-- Targeted therapy
-
-Early detection is crucial for the successful treatment of breast cancer. Women are advised to routinely perform self-examinations and undergo regular mammogram testing starting at age 40. If you notice any changes in your breast tissue, consult with your healthcare provider immediately.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_guidelines_large_pipeline", "en", "clinical/models")
-
-val text = """Clinical Guidelines for Breast Cancer:
 
-Breast cancer is the most common type of cancer among women. It occurs when the cells in the breast start growing abnormally, forming a lump or mass. This can result in the spread of cancerous cells to other parts of the body. Breast cancer may occur in both men and women but is more prevalent in women.
-
-The exact cause of breast cancer is unknown. However, several risk factors can increase your likelihood of developing breast cancer, such as:
-- A personal or family history of breast cancer
-- A genetic mutation, such as BRCA1 or BRCA2
-- Exposure to radiation
-- Age (most commonly occurring in women over 50)
-- Early onset of menstruation or late menopause
-- Obesity
-- Hormonal factors, such as taking hormone replacement therapy
-
-Breast cancer may not present symptoms during its early stages. Symptoms typically manifest as the disease progresses. Some notable symptoms include:
-- A lump or thickening in the breast or underarm area
-- Changes in the size or shape of the breast
-- Nipple discharge
-- Nipple changes in appearance, such as inversion or flattening
-- Redness or swelling in the breast
-
-Treatment for breast cancer depends on several factors, including the stage of the cancer, the location of the tumor, and the individual's overall health. Common treatment options include:
-- Surgery (such as lumpectomy or mastectomy)
-- Radiation therapy
-- Chemotherapy
-- Hormone therapy
-- Targeted therapy
-
-Early detection is crucial for the successful treatment of breast cancer. Women are advised to routinely perform self-examinations and undergo regular mammogram testing starting at age 40. If you notice any changes in your breast tissue, consult with your healthcare provider immediately.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -188,13 +111,10 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
-
 Overview of the disease: Breast cancer is the most common type of cancer among women, occurring when the cells in the breast start growing abnormally, forming a lump or mass. It can result in the spread of cancerous cells to other parts of the body. 
 
 Causes: The exact cause of breast cancer is unknown, but several risk factors can increase the likelihood of developing it, such as a personal or family history, a genetic mutation, exposure to radiation, age, early onset of menstruation or late menopause, obesity, and hormonal factors. 
@@ -202,10 +122,6 @@ Causes: The exact cause of breast cancer is unknown, but several risk factors ca
 Symptoms: Symptoms of breast cancer typically manifest as the disease progresses, including a lump or thickening in the breast or underarm area, changes in the size or shape of the breast, nipple discharge, nipple changes in appearance, and redness or swelling in the breast. 
 
 Treatment recommendations: Treatment for breast cancer depends on several factors, including the stage of the cancer, the location of the tumor, and the individual's overall health. Common treatment options include surgery, radiation therapy, chemotherapy, hormone therapy, and targeted therapy. Early detection is crucial for successful treatment of breast cancer. Women are advised to routinely perform self-examinations and undergo regular mammogram testing starting at age 40.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md
index 910fb47759..31fa692bc9 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_jsl_augmented_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_jsl_augment
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -70,56 +71,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_jsl_augmented_pipeline", "en", "clinical/models")
-
-text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_jsl_augmented_pipeline", "en", "clinical/models")
-
-val text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 A 78-year-old female with hypertension, syncope, and spinal stenosis returns for a recheck. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema. Her medications include Atenolol, Premarin, calcium with vitamin D, multivitamin, aspirin, and TriViFlor. She also has Elocon cream and Synalar cream for rash.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_jsl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_jsl_pipeline_en.md
index 151dbcd80d..e8ec38b93e 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_jsl_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_jsl](https:
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -70,56 +71,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_jsl_pipeline", "en", "clinical/models")
-
-text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_jsl_pipeline", "en", "clinical/models")
-
-val text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 A 78-year-old female with hypertension, syncope, and spinal stenosis returns for recheck. She denies chest pain, palpations, orthopnea, nocturnal dyspnea, or edema. She is on multiple medications and has Elocon cream and Synalar cream for rash.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_laymen_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_laymen_pipeline_en.md
index 122197944e..5e0a064285 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_laymen_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_laymen_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_laymen](htt
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -98,84 +99,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_clinical_laymen_pipeline", "en", "clinical/models")
-
-text = """
-Olivia Smith was seen in my office for evaluation for elective surgical weight loss on October 6, 2008. Olivia Smith is a 34-year-old female with a BMI of 43. She is 5'6" tall and weighs 267 pounds. She is motivated to attempt surgical weight loss because she has been overweight for over 20 years and wants to have more energy and improve her self-image. She is not only affected physically, but also socially by her weight. When she loses weight she always regains it and she always gains back more weight than she has lost. At one time, she lost 100 pounds and gained the weight back within a year. She has tried numerous commercial weight loss programs including Weight Watcher's for four months in 1992 with 15-pound weight loss, RS for two months in 1990 with six-pound weight loss, Slim Fast for six weeks in 2004 with eight-pound weight loss, an exercise program for two months in 2007 with a five-pound weight loss, Atkin's Diet for three months in 2008 with a ten-pound weight loss, and Dexatrim for one month in 2005 with a five-pound weight loss. She has also tried numerous fat reduction or fad diets. She was on Redux for nine months with a 100-pound weight loss.
-
-PAST MEDICAL HISTORY: She has a history of hypertension and shortness of breath.
-
-PAST SURGICAL HISTORY: Pertinent for cholecystectomy.
-
-PSYCHOLOGICAL HISTORY: Negative.
-
-SOCIAL HISTORY: She is single. She drinks alcohol once a week. She does not smoke.
-
-FAMILY HISTORY: Pertinent for obesity and hypertension.
-
-MEDICATIONS: Include Topamax 100 mg twice daily, Zoloft 100 mg twice daily, Abilify 5 mg daily, Motrin 800 mg daily, and a multivitamin.
-
-ALLERGIES: She has no known drug allergies.
-
-REVIEW OF SYSTEMS: Negative.
-
-PHYSICAL EXAM: This is a pleasant female in no acute distress. Alert and oriented x 3. HEENT: Normocephalic, atraumatic. Extraocular muscles intact, nonicteric sclerae. Chest is clear to auscultation bilaterally. Cardiovascular is normal sinus rhythm. Abdomen is obese, soft, nontender and nondistended. Extremities show no edema, clubbing or cyanosis.
-
-ASSESSMENT/PLAN: This is a 34-year-old female with a BMI of 43 who is interested in surgical weight via the gastric bypass as opposed to Lap-Band. Olivia Smith will be asking for a letter of medical necessity from Dr. Andrew Johnson. She will also see my nutritionist and social worker and have an upper endoscopy. Once this is completed, we will submit her to her insurance company for approval.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_laymen_pipeline", "en", "clinical/models")
-
-val text = """
-Olivia Smith was seen in my office for evaluation for elective surgical weight loss on October 6, 2008. Olivia Smith is a 34-year-old female with a BMI of 43. She is 5'6" tall and weighs 267 pounds. She is motivated to attempt surgical weight loss because she has been overweight for over 20 years and wants to have more energy and improve her self-image. She is not only affected physically, but also socially by her weight. When she loses weight she always regains it and she always gains back more weight than she has lost. At one time, she lost 100 pounds and gained the weight back within a year. She has tried numerous commercial weight loss programs including Weight Watcher's for four months in 1992 with 15-pound weight loss, RS for two months in 1990 with six-pound weight loss, Slim Fast for six weeks in 2004 with eight-pound weight loss, an exercise program for two months in 2007 with a five-pound weight loss, Atkin's Diet for three months in 2008 with a ten-pound weight loss, and Dexatrim for one month in 2005 with a five-pound weight loss. She has also tried numerous fat reduction or fad diets. She was on Redux for nine months with a 100-pound weight loss.
-
-PAST MEDICAL HISTORY: She has a history of hypertension and shortness of breath.
-
-PAST SURGICAL HISTORY: Pertinent for cholecystectomy.
-
-PSYCHOLOGICAL HISTORY: Negative.
-
-SOCIAL HISTORY: She is single. She drinks alcohol once a week. She does not smoke.
-
-FAMILY HISTORY: Pertinent for obesity and hypertension.
-
-MEDICATIONS: Include Topamax 100 mg twice daily, Zoloft 100 mg twice daily, Abilify 5 mg daily, Motrin 800 mg daily, and a multivitamin.
-
-ALLERGIES: She has no known drug allergies.
-
-REVIEW OF SYSTEMS: Negative.
-
-PHYSICAL EXAM: This is a pleasant female in no acute distress. Alert and oriented x 3. HEENT: Normocephalic, atraumatic. Extraocular muscles intact, nonicteric sclerae. Chest is clear to auscultation bilaterally. Cardiovascular is normal sinus rhythm. Abdomen is obese, soft, nontender and nondistended. Extremities show no edema, clubbing or cyanosis.
 
-ASSESSMENT/PLAN: This is a 34-year-old female with a BMI of 43 who is interested in surgical weight via the gastric bypass as opposed to Lap-Band. Olivia Smith will be asking for a letter of medical necessity from Dr. Andrew Johnson. She will also see my nutritionist and social worker and have an upper endoscopy. Once this is completed, we will submit her to her insurance company for approval.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 This is a clinical note about a 34-year-old woman who is interested in having weight loss surgery. She has been overweight for over 20 years and wants to have more energy and improve her self-image. She has tried many diets and weight loss programs, but has not been successful in keeping the weight off. She has a history of hypertension and shortness of breath, but is not allergic to any medications. She will have an upper endoscopy and will be contacted by a nutritionist and social worker. The plan is to have her weight loss surgery through the gastric bypass, rather than Lap-Band.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_questions_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_questions_pipeline_en.md
index dbb70046a7..4f23cf9155 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_questions_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-summarizer_clinical_questions_pipeline_en.md
@@ -34,32 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_clinical_questions](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
 
-pipeline = PretrainedPipeline("summarizer_clinical_questions_pipeline", "en", "clinical/models")
-
-text = """
-Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_clinical_questions_pipeline", "en", "clinical/models")
-
-val text = """
-Hello,I'm 20 year old girl. I'm diagnosed with hyperthyroid 1 month ago. I was feeling weak, light headed,poor digestion, panic attacks, depression, left chest pain, increased heart rate, rapidly weight loss, from 4 months. Because of this, I stayed in the hospital and just discharged from hospital. I had many other blood tests, brain mri, ultrasound scan, endoscopy because of some dumb doctors bcs they were not able to diagnose actual problem. Finally I got an appointment with a homeopathy doctor finally he find that i was suffering from hyperthyroid and my TSH was 0.15 T3 and T4 is normal . Also i have b12 deficiency and vitamin D deficiency so I'm taking weekly supplement of vitamin D and 1000 mcg b12 daily. I'm taking homeopathy medicine for 40 days and took 2nd test after 30 days. My TSH is 0.5 now. I feel a little bit relief from weakness and depression but I'm facing with 2 new problem from last week that is breathtaking problem and very rapid heartrate. I just want to know if i should start allopathy medicine or homeopathy is okay? Bcs i heard that thyroid take time to start recover. So please let me know if both of medicines take same time. Because some of my friends advising me to start allopathy and never take a chance as i can develop some serious problems.Sorry for my poor english😐Thank you.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -84,18 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
-
 What are the treatments for hyperthyroidism?
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-summarizer_generic_jsl_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-summarizer_generic_jsl_pipeline_en.md
index f82b7eeab9..d8db682285 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-summarizer_generic_jsl_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-summarizer_generic_jsl_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_generic_jsl](https:/
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -70,56 +71,12 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_generic_jsl_pipeline", "en", "clinical/models")
-
-text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_generic_jsl_pipeline", "en", "clinical/models")
-
-val text = """Patient with hypertension, syncope, and spinal stenosis - for recheck.
-(Medical Transcription Sample Report)
-SUBJECTIVE:
-The patient is a 78-year-old female who returns for recheck. She has hypertension. She denies difficulty with chest pain, palpations, orthopnea, nocturnal dyspnea, or edema.
-PAST MEDICAL HISTORY / SURGERY / HOSPITALIZATIONS:
-Reviewed and unchanged from the dictation on 12/03/2003.
-MEDICATIONS:
-Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin as needed, and TriViFlor 25 mg two pills daily. She also has Elocon cream 0.1% and Synalar cream 0.01% that she uses as needed for rash.
-"""
 
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-
 The patient is 78 years old and has hypertension. She has a history of chest pain, palpations, orthopedics, and spinal stenosis. She has a prescription of Atenolol 50 mg daily, Premarin 0.625 mg daily, calcium with vitamin D two to three pills daily, multivitamin daily, aspirin, and TriViFlor 25 mg two pills daily.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-22-summarizer_radiology_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-22-summarizer_radiology_pipeline_en.md
index 20287f824f..65b2a145d7 100644
--- a/docs/_posts/ahmedlone127/2023-06-22-summarizer_radiology_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-22-summarizer_radiology_pipeline_en.md
@@ -34,60 +34,7 @@ This pretrained pipeline is built on the top of [summarizer_radiology](https://n
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("summarizer_radiology_pipeline", "en", "clinical/models")
-
-text = """INDICATIONS: Peripheral vascular disease with claudication.
-
-RIGHT:
-1. Normal arterial imaging of right lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic.
-4. Ankle brachial index is 0.96.
-
-LEFT:
-1. Normal arterial imaging of left lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic throughout except in posterior tibial artery where it is biphasic.
-4. Ankle brachial index is 1.06.
-
-IMPRESSION: 
-Normal arterial imaging of both lower lobes.
-"""
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("summarizer_radiology_pipeline", "en", "clinical/models")
-
-val text = """INDICATIONS: Peripheral vascular disease with claudication.
-
-RIGHT:
-1. Normal arterial imaging of right lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic.
-4. Ankle brachial index is 0.96.
-
-LEFT:
-1. Normal arterial imaging of left lower extremity.
-2. Peak systolic velocity is normal.
-3. Arterial waveform is triphasic throughout except in posterior tibial artery where it is biphasic.
-4. Ankle brachial index is 1.06.
-
-IMPRESSION: 
-Normal arterial imaging of both lower lobes.
-"""
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -143,15 +90,7 @@ val result = pipeline.fullAnnotate(text)
 ## Results
 
 ```bash
-Results
-
-
-
 The patient has peripheral vascular disease with claudication. The right lower extremity shows normal arterial imaging, but the peak systolic velocity is normal. The arterial waveform is triphasic throughout, except for the posterior tibial artery, which is biphasic. The ankle brachial index is 0.96. The impression is normal arterial imaging of both lower lobes.
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md
index 0e11cc0ca0..a4d8dfc6fa 100644
--- a/docs/_posts/ahmedlone127/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-23-umls_disease_syndrome_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities (Diseases and Syndromes) with their corre
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,34 +59,11 @@ nlu.load("en.map_entity.umls_disease_syndrome_resolver").predict("""A 34-year-ol
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline= PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
-pipeline.annotate("A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline= PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
-val pipeline.annotate("A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria")
-```
-
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.umls_disease_syndrome_resolver").predict("""A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-----------------------------+---------+---------+
 |chunk                        |ner_label|umls_code|
 +-----------------------------+---------+---------+
@@ -94,10 +72,6 @@ Results
 |acyclovir allergy            |PROBLEM  |C0571297 |
 |polyuria                     |PROBLEM  |C0018965 |
 +-----------------------------+---------+---------+
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-23-umls_drug_resolver_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-23-umls_drug_resolver_pipeline_en.md
index 093f10894b..4911836c50 100644
--- a/docs/_posts/ahmedlone127/2023-06-23-umls_drug_resolver_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-23-umls_drug_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities (Clinical Drugs) with their corresponding
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -56,43 +57,17 @@ nlu.load("en.map_entity.umls_drug_resolver").predict("""The patient was given Ad
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
-pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
-val pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.umls_drug_resolver").predict("""The patient was given Adapin 10 MG, coumadn 5 mg""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +------------+---------+---------+
 |chunk       |ner_label|umls_code|
 +------------+---------+---------+
 |Adapin 10 MG|DRUG     |C2930083 |
 |coumadn 5 mg|DRUG     |C2723075 |
 +------------+---------+---------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-23-umls_major_concepts_resolver_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-23-umls_major_concepts_resolver_pipeline_en.md
index b5ef126ef3..8e31ce54ec 100644
--- a/docs/_posts/ahmedlone127/2023-06-23-umls_major_concepts_resolver_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-23-umls_major_concepts_resolver_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline maps entities (Clinical Major Concepts) with their corr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -56,34 +57,11 @@ nlu.load("en.map_entity.umls_major_concepts_resolver").predict("""The patient co
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline= PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
-pipeline.annotate("The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician")
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline= PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
-val pipeline.annotate("The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician")
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.map_entity.umls_major_concepts_resolver").predict("""The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 +-----------+-----------------------------------+---------+
 |chunk      |ner_label                          |umls_code|
 +-----------+-----------------------------------+---------+
@@ -91,9 +69,6 @@ Results
 |stairs     |Daily_or_Recreational_Activity     |C4300351 |
 |Arthroscopy|Therapeutic_or_Preventive_Procedure|C0179144 |
 +-----------+-----------------------------------+---------+
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md
index b7a600d10b..75da8ccbd7 100644
--- a/docs/_posts/ahmedlone127/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-26-ner_oncology_anatomy_granular_pipeline_en.md
@@ -34,28 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology_anatomy_granular](
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_anatomy_granular_pipeline", "en", "clinical/models")
-
-text = '''The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
 
-val pipeline = new PretrainedPipeline("ner_oncology_anatomy_granular_pipeline", "en", "clinical/models")
-
-val text = "The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
-
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -76,21 +55,16 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
+
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks   |   begin |   end | ner_label   |   confidence |
 |---:|:-------------|--------:|------:|:------------|-------------:|
 |  0 | left         |      36 |    39 | Direction   |       0.9981 |
 |  1 | breast       |      41 |    46 | Site_Breast |       0.9969 |
 |  2 | lungs        |      82 |    86 | Site_Lung   |       0.9978 |
 |  3 | liver        |      99 |   103 | Site_Liver  |       0.9999 |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-26-ner_oncology_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-26-ner_oncology_pipeline_en.md
index 67b3b2cd99..1626861f38 100644
--- a/docs/_posts/ahmedlone127/2023-06-26-ner_oncology_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-26-ner_oncology_pipeline_en.md
@@ -34,6 +34,7 @@ This pretrained pipeline is built on the top of [ner_oncology](https://nlp.johns
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -58,38 +59,11 @@ val result = pipeline.fullAnnotate(text)
 ```
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("ner_oncology_pipeline", "en", "clinical/models")
-
-text = '''The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("ner_oncology_pipeline", "en", "clinical/models")
 
-val text = "The had previously undergone a left mastectomy and an axillary lymph node dissection for a left breast cancer twenty years ago.
-The tumor was positive for ER and PR. Postoperatively, radiotherapy was administered to the residual breast.
-The cancer recurred as a right lung metastasis 13 years later. The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses, as first line therapy."
-
-val result = pipeline.fullAnnotate(text)
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
 |    | ner_chunks                     |   begin |   end | ner_label             |   confidence |
 |---:|:-------------------------------|--------:|------:|:----------------------|-------------:|
 |  0 | left                           |      31 |    34 | Direction             |     0.9913   |
@@ -116,9 +90,6 @@ Results
 | 21 | 600 mg/m2                      |     390 |   398 | Dosage                |     0.9647   |
 | 22 | six courses                    |     406 |   416 | Cycle_Count           |     0.6798   |
 | 23 | first line                     |     422 |   431 | Line_Of_Therapy       |     0.9792   |
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/ahmedlone127/2023-06-26-oncology_diagnosis_pipeline_en.md b/docs/_posts/ahmedlone127/2023-06-26-oncology_diagnosis_pipeline_en.md
index b2040bcdb1..004b83e45b 100644
--- a/docs/_posts/ahmedlone127/2023-06-26-oncology_diagnosis_pipeline_en.md
+++ b/docs/_posts/ahmedlone127/2023-06-26-oncology_diagnosis_pipeline_en.md
@@ -34,6 +34,7 @@ This pipeline includes Named-Entity Recognition, Assertion Status, Relation Extr
 
 <div class="tabs-box" markdown="1">
 {% include programmingLanguageSelectScalaPythonNLU.html %}
+
 ```python
 from sparknlp.pretrained import PretrainedPipeline
 
@@ -65,44 +66,11 @@ According to her last CT, she has no lung metastases.""")
 
 </div>
 
-<div class="tabs-box" markdown="1">
-{% include programmingLanguageSelectScalaPythonNLU.html %}
-```python
-from sparknlp.pretrained import PretrainedPipeline
-
-pipeline = PretrainedPipeline("oncology_diagnosis_pipeline", "en", "clinical/models")
-
-text = '''Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
-According to her last CT, she has no lung metastases.'''
-
-result = pipeline.fullAnnotate(text)
-```
-```scala
-import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
-
-val pipeline = new PretrainedPipeline("oncology_diagnosis_pipeline", "en", "clinical/models")
-
-val text = "Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
-According to her last CT, she has no lung metastases."
-
-val result = pipeline.fullAnnotate(text)
-```
 
-{:.nlu-block}
-```python
-import nlu
-nlu.load("en.oncology_diagnosis.pipeline").predict("""Two years ago, the patient presented with a 4-cm tumor in her left breast. She was diagnosed with ductal carcinoma.
-According to her last CT, she has no lung metastases.""")
-```
-</div>
 
 ## Results
 
 ```bash
-Results
-
-
-"
 ******************** ner_oncology_wip results ********************
 
 | chunk      | ner_label         |
@@ -199,10 +167,6 @@ Results
 | carcinoma  | Cancer_Dx         | 8010/3 | carcinoma         |
 | lung       | Site_Lung         | C34.9  | lung              |
 | metastases | Metastasis        | 8000/6 | tumor, metastatic |
-
-
-
-{:.model-param}
 ```
 
 {:.model-param}
diff --git a/docs/_posts/egenc/2022-01-04-bert_token_classifier_ner_ade_en.md b/docs/_posts/egenc/2022-01-04-bert_token_classifier_ner_ade_en.md
index 5ca86a2619..5cd7369ad7 100644
--- a/docs/_posts/egenc/2022-01-04-bert_token_classifier_ner_ade_en.md
+++ b/docs/_posts/egenc/2022-01-04-bert_token_classifier_ner_ade_en.md
@@ -68,7 +68,7 @@ ner_converter = NerConverter() \
 
 pipeline = Pipeline(stages=[documentAssembler, tokenizer, tokenClassifier, ner_converter])
 
-data = spark.createDataFrame([["""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay."""
+data = spark.createDataFrame([["""I have an allergic reaction to vancomycin so I have itchy skin, sore throat/burning/itching, numbness of tongue and gums. I would not recommend this drug to anyone, especially since I have never had such an adverse reaction to any other medication."""
 ]]).toDF("text")
 
 result = pipeline.fit(data).transform(data)
@@ -94,7 +94,7 @@ val ner_converter = new NerConverter()
 
 val pipeline =  new Pipeline().setStages(Array(document_assembler, tokenizer, tokenClassifier, ner_converter))
 
-val data = Seq("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.""").toDS.toDF("text")
+val data = Seq("""I have an allergic reaction to vancomycin so I have itchy skin, sore throat/burning/itching, numbness of tongue and gums. I would not recommend this drug to anyone, especially since I have never had such an adverse reaction to any other medication.""").toDS.toDF("text")
 
 val result = pipeline.fit(data).transform(data)
 ```
@@ -103,7 +103,7 @@ val result = pipeline.fit(data).transform(data)
 {:.nlu-block}
 ```python
 import nlu
-nlu.load("en.classify.token_bert.ner_ade").predict("""Both the erbA IRES and the erbA/myb virus constructs transformed erythroid cells after infection of bone marrow or blastoderm cultures. The erbA/myb IRES virus exhibited a 5-10-fold higher transformed colony forming efficiency than the erbA IRES virus in the blastoderm assay.""")
+nlu.load("en.classify.token_bert.ner_ade").predict("""I have an allergic reaction to vancomycin so I have itchy skin, sore throat/burning/itching, numbness of tongue and gums. I would not recommend this drug to anyone, especially since I have never had such an adverse reaction to any other medication.""")
 ```
 
 </div>
@@ -113,14 +113,15 @@ nlu.load("en.classify.token_bert.ner_ade").predict("""Both the erbA IRES and the
 
 
 ```bash
-+--------------+---------+
-|chunk         |ner_label|
-+--------------+---------+
-|Lipitor       |DRUG     |
-|severe fatigue|ADE      |
-|voltaren      |DRUG     |
-|cramps        |ADE      |
-+--------------+---------+
++-----------+---------------------------+-----+---+---------+
+|sentence_id|chunk                      |begin|end|ner_label|
++-----------+---------------------------+-----+---+---------+
+|0          |allergic reaction          |10   |26 |ADE      |
+|0          |vancomycin                 |31   |40 |DRUG     |
+|0          |itchy skin                 |52   |61 |ADE      |
+|0          |sore throat/burning/itching|64   |90 |ADE      |
+|0          |numbness of tongue and gums|93   |119|ADE      |
++-----------+---------------------------+-----+---+---------+
 ```
 
 
@@ -152,12 +153,12 @@ This model is trained on a custom dataset by John Snow Labs.
 
 
 ```bash
-label  precision    recall  f1-score   support
-B-ADE       0.93      0.79      0.85      2694
-B-DRUG       0.97      0.87      0.92      9539
-I-ADE       0.93      0.73      0.82      3236
-I-DRUG       0.95      0.82      0.88      6115
-accuracy        -         -        0.83     21584
-macro-avg       0.84      0.84      0.84     21584
-weighted-avg       0.95      0.83      0.89     21584
+label    precision    recall  f1-score   support
+B-ADE         0.93      0.79      0.85      2694
+B-DRUG        0.97      0.87      0.92      9539
+I-ADE         0.93      0.73      0.82      3236
+I-DRUG        0.95      0.82      0.88      6115
+accuracy       -         -        0.83     21584
+macro-avg     0.84      0.84      0.84     21584
+weighted-avg  0.95      0.83      0.89     21584
 ```