diff --git a/docs/_posts/akrztrk/2024-12-19-medication_resolver_pipeline_en.md b/docs/_posts/akrztrk/2024-12-19-medication_resolver_pipeline_en.md
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+---
+layout: model
+title: Pipeline to Resolve Medication Codes
+author: John Snow Labs
+name: medication_resolver_pipeline
+date: 2024-12-19
+tags: [licensed, en, resolver, snomed, umls, rxnorm, ndc, ade, pipeline]
+task: [Pipeline Healthcare, Named Entity Recognition]
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+A pretrained resolver pipeline to extract medications and resolve their adverse reactions (ADE), RxNorm, UMLS, NDC, SNOMED CT codes, and action/treatments in clinical text.
+
+Action/treatments are available for branded medication, and SNOMED codes are available for non-branded medication.
+
+This pipeline can be used as Lightpipeline (with `annotate/fullAnnotate`). You can use `medication_resolver_transform_pipeline` for Spark transform.
+
+## Predicted Entities
+
+`DRUG`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/medication_resolver_pipeline_en_5.5.1_3.0_1734639447518.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/medication_resolver_pipeline_en_5.5.1_3.0_1734639447518.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+ner_pipeline = PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")
+
+result = ner_pipeline.annotate("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.
+""")
+
+```
+
+{:.jsl-block}
+```python
+
+ner_pipeline = nlp.PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")
+
+result = ner_pipeline.annotate("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.
+""")
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val ner_pipeline = PretrainedPipeline("medication_resolver_pipeline", "en", "clinical/models")
+
+val result = ner_pipeline.annotate("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.
+""")
+
+```
+</div>
+
+## Results
+
+```bash
+
+
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+|chunk                       |ner_label|ADE                        |RxNorm |Action                    |Treatment                                 |UMLS    |SNOMED_CT       |NDC_Product|NDC_Package  |
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+|Amlodopine Vallarta 10-320mg|DRUG     |Gynaecomastia              |722131 |NONE                      |NONE                                      |C1949334|1153435009      |00093-7693 |00093-7693-56|
+|Eviplera                    |DRUG     |Anxiety                    |217010 |Inhibitory Bone Resorption|Osteoporosis                              |C0720318|NONE            |NONE       |NONE         |
+|Lescol 40 MG                |DRUG     |NONE                       |103919 |Hypocholesterolemic       |Heterozygous Familial Hypercholesterolemia|C0353573|NONE            |00078-0234 |00078-0234-05|
+|Everolimus 1.5 mg tablet    |DRUG     |Acute myocardial infarction|2056895|NONE                      |NONE                                      |C4723581|1029521000202102|00054-0604 |00054-0604-21|
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|medication_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|3.3 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- TextMatcherInternalModel
+- ChunkMergeModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- Finisher
diff --git a/docs/_posts/akrztrk/2024-12-19-medication_resolver_transform_pipeline_en.md b/docs/_posts/akrztrk/2024-12-19-medication_resolver_transform_pipeline_en.md
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+---
+layout: model
+title: Pipeline to Resolve Medication Codes(Transform)
+author: John Snow Labs
+name: medication_resolver_transform_pipeline
+date: 2024-12-19
+tags: [licensed, en, resolver, snomed, umls, rxnorm, ndc, ade, pipeline]
+task: [Pipeline Healthcare, Named Entity Recognition]
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+A pretrained resolver pipeline to extract medications and resolve their adverse reactions (ADE), RxNorm, UMLS, NDC, SNOMED CT codes, and action/treatments in clinical text.
+
+Action/treatments are available for branded medication, and SNOMED codes are available for non-branded medication.
+
+This pipeline can be used with Spark transform. You can use `medication_resolver_pipeline` as Lightpipeline (with `annotate/fullAnnotate`).
+
+## Predicted Entities
+
+`DRUG`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/medication_resolver_transform_pipeline_en_5.5.1_3.0_1734635642246.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/medication_resolver_transform_pipeline_en_5.5.1_3.0_1734635642246.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+medication_resolver_pipeline = PretrainedPipeline("medication_resolver_transform_pipeline", "en", "clinical/models")
+
+text = """The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet."""
+
+data = spark.createDataFrame([[text]]).toDF("text")
+
+result = medication_resolver_pipeline.transform(data)
+
+```
+
+{:.jsl-block}
+```python
+
+medication_resolver_pipeline = nlp.PretrainedPipeline("medication_resolver_transform_pipeline", "en", "clinical/models")
+
+text = """The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet."""
+
+data = spark.createDataFrame([[text]]).toDF("text")
+
+result = medication_resolver_pipeline.transform(data)
+
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val medication_resolver_pipeline = new PretrainedPipeline("medication_resolver_transform_pipeline", "en", "clinical/models")
+
+val data = Seq("""The patient was prescribed Amlodopine Vallarta 10-320mg, Eviplera.
+The other patient is given Lescol 40 MG and Everolimus 1.5 mg tablet.""").toDS.toDF("text")
+
+val result = medication_resolver_pipeline.fit(data).transform(data)
+
+```
+</div>
+
+## Results
+
+```bash
+
+
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+|chunk                       |ner_label|ADE                        |RxNorm |Action                    |Treatment                                 |UMLS    |SNOMED_CT       |NDC_Product|NDC_Package  |
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+|Amlodopine Vallarta 10-320mg|DRUG     |Gynaecomastia              |722131 |NONE                      |NONE                                      |C1949334|1153435009      |00093-7693 |00093-7693-56|
+|Eviplera                    |DRUG     |Anxiety                    |217010 |Inhibitory Bone Resorption|Osteoporosis                              |C0720318|NONE            |NONE       |NONE         |
+|Lescol 40 MG                |DRUG     |NONE                       |103919 |Hypocholesterolemic       |Heterozygous Familial Hypercholesterolemia|C0353573|NONE            |00078-0234 |00078-0234-05|
+|Everolimus 1.5 mg tablet    |DRUG     |Acute myocardial infarction|2056895|NONE                      |NONE                                      |C4723581|1029521000202102|00054-0604 |00054-0604-21|
++----------------------------+---------+---------------------------+-------+--------------------------+------------------------------------------+--------+----------------+-----------+-------------+
+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|medication_resolver_transform_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|3.3 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetectorDLModel
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverterInternalModel
+- TextMatcherInternalModel
+- ChunkMergeModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger
+- Doc2Chunk
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperModel
+- Doc2Chunk
+- ChunkMapperModel
+- Finisher
diff --git a/docs/_posts/akrztrk/2024-12-23-snomed_multi_mapper_pipeline_en.md b/docs/_posts/akrztrk/2024-12-23-snomed_multi_mapper_pipeline_en.md
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+++ b/docs/_posts/akrztrk/2024-12-23-snomed_multi_mapper_pipeline_en.md
@@ -0,0 +1,100 @@
+---
+layout: model
+title: SNOMED Code Mapping Pipeline
+author: John Snow Labs
+name: snomed_multi_mapper_pipeline
+date: 2024-12-23
+tags: [licensed, en, snomed, pipeline]
+task: Pipeline Healthcare
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline maps SNOMED codes to their corresponding ICD-10, ICD-O, and UMLS codes.
+
+## Predicted Entities
+
+`snomed_code`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/snomed_multi_mapper_pipeline_en_5.5.1_3.0_1734953947952.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/snomed_multi_mapper_pipeline_en_5.5.1_3.0_1734953947952.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+mapper_pipeline = PretrainedPipeline("snomed_multi_mapper_pipeline", "en", "clinical/models")
+
+result = mapper_pipeline.fullAnnotate(["10000006", "128501000"])
+
+```
+
+{:.jsl-block}
+```python
+
+mapper_pipeline = nlp.PretrainedPipeline("snomed_multi_mapper_pipeline", "en", "clinical/models")
+
+result = mapper_pipeline.fullAnnotate(["10000006", "128501000"])
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val mapper_pipeline = PretrainedPipeline("snomed_multi_mapper_pipeline", "en", "clinical/models")
+
+val result = mapper_pipeline.fullAnnotate(["10000006", "128501000"])
+
+```
+</div>
+
+## Results
+
+```bash
+
++-----------+------------+---------+---------+
+|snomed_code|icd10cm_code|icdo_code|umls_code|
++-----------+------------+---------+---------+
+|   10000006|       R07.9|     NONE| C0232289|
+|  128501000|        NONE|    C49.5| C0448606|
++-----------+------------+---------+---------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|snomed_multi_mapper_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|9.4 MB|
+
+## Included Models
+
+- DocumentAssembler
+- DocMapperModel
+- DocMapperModel
+- DocMapperModel
diff --git a/docs/_posts/akrztrk/2024-12-23-umls_clinical_findings_resolver_pipeline_en.md b/docs/_posts/akrztrk/2024-12-23-umls_clinical_findings_resolver_pipeline_en.md
new file mode 100644
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+++ b/docs/_posts/akrztrk/2024-12-23-umls_clinical_findings_resolver_pipeline_en.md
@@ -0,0 +1,109 @@
+---
+layout: model
+title: Clinical Findings to UMLS Code Pipeline
+author: John Snow Labs
+name: umls_clinical_findings_resolver_pipeline
+date: 2024-12-23
+tags: [licensed, en, resolver, clinical, umls, pipeline]
+task: [Pipeline Healthcare, Chunk Mapping]
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline maps entities (Clinical Findings) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
+
+## Predicted Entities
+
+`PROBLEM`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/umls_clinical_findings_resolver_pipeline_en_5.5.1_3.0_1734985321381.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/umls_clinical_findings_resolver_pipeline_en_5.5.1_3.0_1734985321381.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+resolver_pipeline = PretrainedPipeline("umls_clinical_findings_resolver_pipeline", "en", "clinical/models")
+
+result = resolver_pipeline.annotate("""HTG-induced pancreatitis associated with an acute hepatitis, and obesity""")
+
+```
+
+{:.jsl-block}
+```python
+
+resolver_pipeline = nlp.PretrainedPipeline("umls_clinical_findings_resolver_pipeline", "en", "clinical/models")
+
+result = resolver_pipeline.annotate("""HTG-induced pancreatitis associated with an acute hepatitis, and obesity""")
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val resolver_pipeline = PretrainedPipeline("umls_clinical_findings_resolver_pipeline", "en", "clinical/models")
+
+val result = resolver_pipeline.annotate("""HTG-induced pancreatitis associated with an acute hepatitis, and obesity""")
+
+```
+</div>
+
+## Results
+
+```bash
+
++------------------------+---------+---------+
+|chunk                   |ner_label|umls_code|
++------------------------+---------+---------+
+|HTG-induced pancreatitis|PROBLEM  |C3808945 |
+|an acute hepatitis      |PROBLEM  |C4750596 |
+|obesity                 |PROBLEM  |C4759928 |
++------------------------+---------+---------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|umls_clinical_findings_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|4.4 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverter
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger
diff --git a/docs/_posts/akrztrk/2024-12-23-umls_drug_substance_resolver_pipeline_en.md b/docs/_posts/akrztrk/2024-12-23-umls_drug_substance_resolver_pipeline_en.md
new file mode 100644
index 0000000000..69a318ca8b
--- /dev/null
+++ b/docs/_posts/akrztrk/2024-12-23-umls_drug_substance_resolver_pipeline_en.md
@@ -0,0 +1,110 @@
+---
+layout: model
+title: Drug Substance to UMLS Code Pipeline
+author: John Snow Labs
+name: umls_drug_substance_resolver_pipeline
+date: 2024-12-23
+tags: [licensed, en, resolver, clinical, umls, drug, subtance, pipeline]
+task: [Pipeline Healthcare, Chunk Mapping]
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline maps entities (Drug Substances) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
+
+## Predicted Entities
+
+`DRUG`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/umls_drug_substance_resolver_pipeline_en_5.5.1_3.0_1734963236674.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/umls_drug_substance_resolver_pipeline_en_5.5.1_3.0_1734963236674.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+resolver_pipeline = PretrainedPipeline("umls_drug_substance_resolver_pipeline", "en", "clinical/models")
+
+result = resolver_pipeline.annotate("""The patient was given  metformin, lenvatinib and Magnesium hydroxide 100mg/1ml""")
+
+```
+
+{:.jsl-block}
+```python
+
+resolver_pipeline = nlp.PretrainedPipeline("umls_drug_substance_resolver_pipeline", "en", "clinical/models")
+
+result = resolver_pipeline.annotate("""The patient was given  metformin, lenvatinib and Magnesium hydroxide 100mg/1ml""")
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val resolver_pipeline = PretrainedPipeline("umls_drug_substance_resolver_pipeline", "en", "clinical/models")
+
+val result = resolver_pipeline.annotate("""The patient was given  metformin, lenvatinib and Magnesium hydroxide 100mg/1ml""")
+
+```
+</div>
+
+## Results
+
+```bash
+
++-----------------------------+---------+---------+
+|chunk                        |ner_label|umls_code|
++-----------------------------+---------+---------+
+|metformin                    |DRUG     |C0025598 |
+|lenvatinib                   |DRUG     |C2986924 |
+|Magnesium hydroxide 100mg/1ml|DRUG     |C1134402 |
++-----------------------------+---------+---------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|umls_drug_substance_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|5.1 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverter
+- ChunkMapperModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger
diff --git a/docs/_posts/akrztrk/2024-12-24-umls_disease_syndrome_resolver_pipeline_en.md b/docs/_posts/akrztrk/2024-12-24-umls_disease_syndrome_resolver_pipeline_en.md
new file mode 100644
index 0000000000..176bd758a5
--- /dev/null
+++ b/docs/_posts/akrztrk/2024-12-24-umls_disease_syndrome_resolver_pipeline_en.md
@@ -0,0 +1,113 @@
+---
+layout: model
+title: Diseases and Syndromes to UMLS Code Pipeline
+author: John Snow Labs
+name: umls_disease_syndrome_resolver_pipeline
+date: 2024-12-24
+tags: [licensed, en, resolver, clinical, umls, pipeline]
+task: [Pipeline Healthcare, Chunk Mapping]
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline maps entities (Diseases and Syndromes) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
+
+## Predicted Entities
+
+`PROBLEM`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/umls_disease_syndrome_resolver_pipeline_en_5.5.1_3.0_1735043968113.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/umls_disease_syndrome_resolver_pipeline_en_5.5.1_3.0_1735043968113.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+resolver_pipeline = PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
+
+result = resolver_pipeline.annotate("""A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria.""")
+
+```
+
+{:.jsl-block}
+```python
+
+resolver_pipeline = nlp.PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
+
+result = resolver_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 resolver_pipeline = PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
+
+val result = resolver_pipeline.annotate("""A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria.""")
+
+```
+</div>
+
+## Results
+
+```bash
+
++-----------------------------+---------+---------+
+|chunk                        |ner_label|umls_code|
++-----------------------------+---------+---------+
+|poor appetite                |PROBLEM  |C0003123 |
+|gestational diabetes mellitus|PROBLEM  |C0085207 |
+|acyclovir allergy            |PROBLEM  |C0571297 |
+|polyuria                     |PROBLEM  |C0011848 |
++-----------------------------+---------+---------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|umls_disease_syndrome_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|3.4 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverter
+- MedicalNerModel
+- NerConverter
+- ChunkMergeModel
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger
diff --git a/docs/_posts/akrztrk/2024-12-24-umls_drug_resolver_pipeline_en.md b/docs/_posts/akrztrk/2024-12-24-umls_drug_resolver_pipeline_en.md
new file mode 100644
index 0000000000..1f737a6b9a
--- /dev/null
+++ b/docs/_posts/akrztrk/2024-12-24-umls_drug_resolver_pipeline_en.md
@@ -0,0 +1,108 @@
+---
+layout: model
+title: Clinical Drugs to UMLS Code Mapping
+author: John Snow Labs
+name: umls_drug_resolver_pipeline
+date: 2024-12-24
+tags: [licensed, en, resolver, clinical, umls, pipeline]
+task: [Pipeline Healthcare, Chunk Mapping]
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline maps entities (Clinical Drugs) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
+
+## Predicted Entities
+
+`DRUG`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/umls_drug_resolver_pipeline_en_5.5.1_3.0_1735036946079.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/umls_drug_resolver_pipeline_en_5.5.1_3.0_1735036946079.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+resolver_pipeline = PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
+
+result = resolver_pipeline.annotate("""The patient was given Adapin 10 MG, coumadn 5 mg.""")
+
+```
+
+{:.jsl-block}
+```python
+
+resolver_pipeline = nlp.PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
+
+result = resolver_pipeline.annotate("""The patient was given Adapin 10 MG, coumadn 5 mg.""")
+
+```
+```scala
+
+import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
+
+val resolver_pipeline = PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
+
+val result = resolver_pipeline.annotate("""The patient was given Adapin 10 MG, coumadn 5 mg.""")
+
+```
+</div>
+
+## Results
+
+```bash
+
++------------+---------+---------+
+|chunk       |ner_label|umls_code|
++------------+---------+---------+
+|Adapin 10 MG|DRUG     |C1382178 |
+|coumadn 5 mg|DRUG     |C1368171 |
++------------+---------+---------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|umls_drug_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|4.0 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverter
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger
diff --git a/docs/_posts/akrztrk/2024-12-24-umls_major_concepts_resolver_pipeline_en.md b/docs/_posts/akrztrk/2024-12-24-umls_major_concepts_resolver_pipeline_en.md
new file mode 100644
index 0000000000..65e0e6f6b4
--- /dev/null
+++ b/docs/_posts/akrztrk/2024-12-24-umls_major_concepts_resolver_pipeline_en.md
@@ -0,0 +1,110 @@
+---
+layout: model
+title: Clinical Major Concepts to UMLS Code Pipeline
+author: John Snow Labs
+name: umls_major_concepts_resolver_pipeline
+date: 2024-12-24
+tags: [licensed, en, resolver, clinical, umls, pipeline]
+task: [Pipeline Healthcare, Chunk Mapping]
+language: en
+edition: Healthcare NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+This pretrained pipeline maps entities (Clinical Major Concepts) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
+
+## Predicted Entities
+
+`Qualitative_Concept`, `Mental_Process`, `Health_Care_Activity`, `Professional_or_Occupational_Group`, `Population_Group`, `Group`, `Pharmacologic_Substance`, `Research_Activity`, `Medical_Device`, `Diagnostic_Procedure`, `Molecular_Function`, `Spatial_Concept`, `Organic_Chemical`, `Amino_Acid`, `Peptide_or_Protein`, `Disease_or_Syndrome`, `Daily_or_Recreational_Activity`, `Quantitative_Concept`, `Biologic_Function`, `Organism_Attribute`, `Clinical_Attribute`, `Pathologic_Function`, `Eukaryote`, `Body_Part`, `Organ_or_Organ_Component`, `Anatomical_Structure`, `Cell_Component`, `Geographic_Area`, `Manufactured_Object`, `Tissue`, `Plant`, `Nucleic_Acid`, `Nucleoside_or_Nucleotide`, `Indicator`, `Reagent_or_Diagnostic_Aid`, `Prokaryote`, `Chemical`, `Therapeutic_or_Preventive_Procedure`, `Gene_or_Genome`, `Mammal`, `Laboratory_Procedure`, `Substance`, `Molecular_Biology_Research_Technique`, `Neoplastic_Process`, `Cell`, `Food`, `Genetic_Function`, `Mental_or_Behavioral_Dysfunction`, `Body_Substance`, `Sign_or_Symptom`, `Injury_or_Poisoning`, `Body_Location_or_Region`, `Organization`, `Body_System`, `Fungus`, `Virus`, `Nucleotide_Sequence`, `Biomedical_or_Dental_Material`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/clinical/models/umls_major_concepts_resolver_pipeline_en_5.5.1_3.0_1735048229500.zip){:.button.button-orange.button-orange-trans.arr.button-icon.hidden}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/clinical/models/umls_major_concepts_resolver_pipeline_en_5.5.1_3.0_1735048229500.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+  
+```python
+
+from sparknlp.pretrained import PretrainedPipeline
+
+resolver_pipeline = PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
+
+result = resolver_pipeline.annotate("""The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician""")
+
+```
+
+{:.jsl-block}
+```python
+
+resolver_pipeline = nlp.PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
+
+result = resolver_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 resolver_pipeline = PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
+
+val result = resolver_pipeline.annotate("""The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician""")
+
+```
+</div>
+
+## Results
+
+```bash
+
++----------------------+-----------------------------------+---------+
+|chunk                 |ner_label                          |umls_code|
++----------------------+-----------------------------------+---------+
+|pustules              |Sign_or_Symptom                    |C0241157 |
+|stairs                |Daily_or_Recreational_Activity     |C4300351 |
+|Arthroscopy           |Therapeutic_or_Preventive_Procedure|C0179144 |
+|primary care pyhsician|Health_Care_Activity               |C3266804 |
++----------------------+-----------------------------------+---------+
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|umls_major_concepts_resolver_pipeline|
+|Type:|pipeline|
+|Compatibility:|Healthcare NLP 5.5.1+|
+|License:|Licensed|
+|Edition:|Official|
+|Language:|en|
+|Size:|6.4 GB|
+
+## Included Models
+
+- DocumentAssembler
+- SentenceDetector
+- TokenizerModel
+- WordEmbeddingsModel
+- MedicalNerModel
+- NerConverter
+- ChunkMapperModel
+- ChunkMapperFilterer
+- Chunk2Doc
+- BertSentenceEmbeddings
+- SentenceEntityResolverModel
+- ResolverMerger