diff --git a/.github/workflows/nlu_test_flow.yaml b/.github/workflows/nlu_test_flow.yaml
index 0d9df573..d232f089 100644
--- a/.github/workflows/nlu_test_flow.yaml
+++ b/.github/workflows/nlu_test_flow.yaml
@@ -23,7 +23,7 @@ jobs:
run: |
python -m pip install --upgrade pip
pip install pypandoc sklearn
- pip install wheel dataclasses pandas numpy pytest modin[ray] pyspark==3.0.1 spark-nlp==3.0.1
+ pip install wheel dataclasses pandas numpy pytest modin[ray] pyspark==3.0.1 spark-nlp
java -version
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
# ! echo 2 | update-alternatives --config java
diff --git a/docs/assets/streamlit_docs_assets/gif/sentence_embedding_dimension_reduction/good_quality.mp4 b/docs/assets/streamlit_docs_assets/gif/sentence_embedding_dimension_reduction/good_quality.mp4
new file mode 100644
index 00000000..da93d8c7
Binary files /dev/null and b/docs/assets/streamlit_docs_assets/gif/sentence_embedding_dimension_reduction/good_quality.mp4 differ
diff --git a/docs/assets/streamlit_docs_assets/gif/sentence_embedding_dimension_reduction/sentence_manifold_low_qual.gif b/docs/assets/streamlit_docs_assets/gif/sentence_embedding_dimension_reduction/sentence_manifold_low_qual.gif
new file mode 100644
index 00000000..325b10b7
Binary files /dev/null and b/docs/assets/streamlit_docs_assets/gif/sentence_embedding_dimension_reduction/sentence_manifold_low_qual.gif differ
diff --git a/docs/en/examples.md b/docs/en/examples.md
index 92975bb9..f0350f4d 100644
--- a/docs/en/examples.md
+++ b/docs/en/examples.md
@@ -780,7 +780,7 @@ nlu.load('spell').predict('I liek pentut buttr ant jely')
```python
-nlu.load('dep.untyped').predict('Untyped Dependencies represent a grammatical tree structure')
+nlu.load('dep.untyped').predict('Untyped Dependencies represent a grammatical tree structure.md')
```
@@ -802,7 +802,7 @@ nlu.load('dep.untyped').predict('Untyped Dependencies represent a grammatical tr
[Typed Dependency Parsing example](https://colab.research.google.com/drive/1KXUqcF8e-LU9cXnHE8ni8z758LuFPvY7?usp=sharing)
```python
-nlu.load('dep').predict('Typed Dependencies represent a grammatical tree structure where every edge has a label')
+nlu.load('dep').predict('Typed Dependencies represent a grammatical tree structure.md where every edge has a label')
```
diff --git a/docs/en/examples_healthcare.md b/docs/en/examples_healthcare.md
index 8134e11d..298a81b6 100644
--- a/docs/en/examples_healthcare.md
+++ b/docs/en/examples_healthcare.md
@@ -193,6 +193,108 @@ df = nlu.load('de_identify').predict(data)
See the [Models Hub for all avaiable De-Identification Models](https://nlp.johnsnowlabs.com/models?task=De-identification)
+## Drug Normalizer
+[Drug Normalizer tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/healthcare/drug_normalization/drug_norm.ipynb)
+
+Normalize raw text from clinical documents, e.g. scraped web pages or xml document. Removes all dirty characters from text following one or more input regex patterns. Can apply non wanted character removal which a specific policy. Can apply lower case normalization.
+
+**Parameters are**
+- lowercase: whether to convert strings to lowercase. Default is False.
+- `policy`: rule to remove patterns from text. Valid policy values are: `all` `abbreviations`, `dosages`
+Defaults is `all`. `abbreviation` policy used to expend common drugs abbreviations, `dosages` policy used to convert drugs dosages and values to the standard form (see examples bellow).
+
+```python
+data = ["Agnogenic one half cup","adalimumab 54.5 + 43.2 gm","aspirin 10 meq/ 5 ml oral sol","interferon alfa-2b 10 million unit ( 1 ml ) injec","Sodium Chloride/Potassium Chloride 13bag"]
+nlu.load('norm_drugs').predict(data)
+```
+
+
+| drug_norm | text |
+|:-----------------------------------------------------|:--------------------------------------------------|
+| Agnogenic 0.5 oral solution | Agnogenic one half cup |
+| adalimumab 97700 mg | adalimumab 54.5 + 43.2 gm |
+| aspirin 2 meq/ml oral solution | aspirin 10 meq/ 5 ml oral sol |
+| interferon alfa - 2b 10000000 unt ( 1 ml ) injection | interferon alfa-2b 10 million unit ( 1 ml ) injec |
+| Sodium Chloride / Potassium Chloride 13 bag | Sodium Chloride/Potassium Chloride 13bag |
+
+
+## Rule based NER with Context Matcher
+[Rule based NER with context matching tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/rule_based_named_entity_recognition_and_resolution/rule_based_NER_and_resolution_with_context_matching.ipynb)
+Define a rule based NER algorithm by providing Regex Patterns and resolution mappings.
+The confidence value is computed using a heuristic approach based on how many matches it has.
+A dictionary can be provided with setDictionary to map extracted entities to a unified representation. The first column of the dictionary file should be the representation with following columns the possible matches.
+
+
+```python
+import nlu
+import json
+# Define helper functions to write NER rules to file
+"""Generate json with dict contexts at target path"""
+def dump_dict_to_json_file(dict, path):
+ with open(path, 'w') as f: json.dump(dict, f)
+
+"""Dump raw text file """
+def dump_file_to_csv(data,path):
+ with open(path, 'w') as f:f.write(data)
+sample_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 . 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 . Twenty days ago. Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . At birth the typical boy is growing slightly faster than the typical girl, but the velocities become equal at about seven months, and then the girl grows faster until four years. From then until adolescence no differences in velocity can be detected. 21-02-2020 21/04/2020 """
+
+# Define Gender NER matching rules
+gender_rules = {
+ "entity": "Gender",
+ "ruleScope": "sentence",
+ "completeMatchRegex": "true" }
+
+# Define dict data in csv format
+gender_data = '''male,man,male,boy,gentleman,he,him
+female,woman,female,girl,lady,old-lady,she,her
+neutral,neutral'''
+
+# Dump configs to file
+dump_dict_to_json_file(gender_data, 'gender.csv')
+dump_dict_to_json_file(gender_rules, 'gender.json')
+gender_NER_pipe = nlu.load('match.context')
+gender_NER_pipe.print_info()
+gender_NER_pipe['context_matcher'].setJsonPath('gender.json')
+gender_NER_pipe['context_matcher'].setDictionary('gender.csv', options={"delimiter":","})
+gender_NER_pipe.predict(sample_text)
+```
+
+| context_match | context_match_confidence |
+|:----------------|---------------------------:|
+| female | 0.13 |
+| she | 0.13 |
+| she | 0.13 |
+| she | 0.13 |
+| she | 0.13 |
+| boy | 0.13 |
+| girl | 0.13 |
+| girl | 0.13 |
+
+### Context Matcher Parameters
+You can define the following parameters in your rules.json file to define the entities to be matched
+
+| Parameter | Type | Description|
+|------------|-------|-----------|
+| entity | `str `| The name of this rule |
+| regex | `Optional[str] `| Regex Pattern to extract candidates |
+| contextLength | `Optional[int] `| defines the maximum distance a prefix and suffix words can be away from the word to match,whereas context are words that must be immediately after or before the word to match |
+| prefix | `Optional[List[str]] `| Words preceding the regex match, that are at most `contextLength` characters aways |
+| regexPrefix | `Optional[str] `| RegexPattern of words preceding the regex match, that are at most `contextLength` characters aways |
+| suffix | `Optional[List[str]] `| Words following the regex match, that are at most `contextLength` characters aways |
+| regexSuffix | `Optional[str] `| RegexPattern of words following the regex match, that are at most `contextLength` distance aways |
+| context | `Optional[List[str]] `| list of words that must be immediatly before/after a match |
+| contextException | `Optional[List[str]] `| ?? List of words that may not be immediatly before/after a match |
+| exceptionDistance | `Optional[int] `| Distance exceptions must be away from a match |
+| regexContextException | `Optional[str] `| Regex Pattern of exceptions that may not be within `exceptionDistance` range of the match |
+| matchScope | `Optional[str]`| Either `token` or `sub-token` to match on character basis |
+| completeMatchRegex | `Optional[str]`| Wether to use complete or partial matching, either `"true"` or `"false"` |
+| ruleScope | `str` | currently only `sentence` supported |
+
+
+
+
+
+
## Authorize access to licensed features and install healthcare dependencies
You need a set of **credentials** to access the licensed healthcare features.
[You can grab one here](https://www.johnsnowlabs.com/spark-nlp-try-free/)
diff --git a/docs/en/streamlit_viz_examples.md b/docs/en/streamlit_viz_examples.md
index 54911642..b4e2ad6c 100644
--- a/docs/en/streamlit_viz_examples.md
+++ b/docs/en/streamlit_viz_examples.md
@@ -348,7 +348,7 @@ Additionally, you can color the lower dimensional points with a label that has b
```python
-nlu.load('bert',verbose=True).viz_streamlit_word_embed_manifold(default_texts=THE_MATRIX_ARCHITECT_SCRIPT.split('\n'),default_algos_to_apply=['TSNE'],MAX_DISPLAY_NUM=5)
+nlu.load('bert',verbose=True).viz_streamlit_word_embed_manifold(default_texts=['I love NLU <3', 'I love streamlit <3'],default_algos_to_apply=['TSNE'],MAX_DISPLAY_NUM=5)
```
@@ -402,6 +402,7 @@ nlu.load('bert',verbose=True).viz_streamlit_word_embed_manifold(default_texts=TH
- [FactorAnalysis](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.FactorAnalysis.html#sklearn.decomposition.FactorAnalysis)
- [FastICA](https://scikit-learn.org/stable/modules/generated/fastica-function.html#sklearn.decomposition.fastica)
- [KernelPCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.KernelPCA.html#sklearn.decomposition.KernelPCA)
+- [Latent Dirichlet Allocation](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html)
diff --git a/docs/en/training.md b/docs/en/training.md
index 5df3d6c9..7836d791 100644
--- a/docs/en/training.md
+++ b/docs/en/training.md
@@ -121,6 +121,124 @@ fitted_pipe = nlu.load('bert train.ner').fit(dataset_path=train_path)
```
+# Chunk Entity Resolver Training
+[Chunk Entity Resolver Training Tutorial Notebook]()
+Named Entities are sub pieces in textual data which are labled with classes.
+These classes and strings are still ambious though and it is not possible to group semantically identically entities withouth any definition of `terminology`.
+With the `Chunk Resolver` you can train a state of the art deep learning architecture to map entities to their unique terminological representation.
+
+Train a chunk resolver on a dataset with columns named `y` , `_y` and `text`. `y` is a label, `_y` is an extra identifier label, `text` is the raw text
+
+```python
+import pandas as pd
+dataset = pd.DataFrame({
+ 'text': ['The Tesla company is good to invest is', 'TSLA is good to invest','TESLA INC. we should buy','PUT ALL MONEY IN TSLA inc!!'],
+ 'y': ['23','23','23','23']
+ '_y': ['TESLA','TESLA','TESLA','TESLA'],
+
+})
+
+
+trainable_pipe = nlu.load('train.resolve_chunks')
+fitted_pipe = trainable_pipe.fit(dataset)
+res = fitted_pipe.predict(dataset)
+fitted_pipe.predict(["Peter told me to buy Tesla ", 'I have money to loose, is TSLA a good option?'])
+```
+
+| entity_resolution_confidence | entity_resolution_code | entity_resolution | document |
+|:-------------------------------|:-------------------------|:--------------------|:----------------------------------------------|
+| '1.0000' | '23] | 'TESLA' | Peter told me to buy Tesla |
+| '1.0000' | '23] | 'TESLA' | I have money to loose, is TSLA a good option? |
+
+
+### Train with default glove embeddings
+```python
+untrained_chunk_resolver = nlu.load('train.resolve_chunks')
+trained_chunk_resolver = untrained_chunk_resolver.fit(df)
+trained_chunk_resolver.predict(df)
+```
+
+### Train with custom embeddings
+```python
+# Use BIo GLove
+untrained_chunk_resolver = nlu.load('en.embed.glove.biovec train.resolve_chunks')
+trained_chunk_resolver = untrained_chunk_resolver.fit(df)
+trained_chunk_resolver.predict(df)
+ ```
+
+
+
+# Rule based NER with Context Matcher
+[Rule based NER with context matching tutorial notebook](https://github.com/JohnSnowLabs/nlu/blob/master/examples/colab/Training/rule_based_named_entity_recognition_and_resolution/rule_based_NER_and_resolution_with_context_matching.ipynb)
+Define a rule based NER algorithm by providing Regex Patterns and resolution mappings.
+The confidence value is computed using a heuristic approach based on how many matches it has.
+A dictionary can be provided with setDictionary to map extracted entities to a unified representation. The first column of the dictionary file should be the representation with following columns the possible matches.
+
+
+```python
+import nlu
+import json
+# Define helper functions to write NER rules to file
+"""Generate json with dict contexts at target path"""
+def dump_dict_to_json_file(dict, path):
+ with open(path, 'w') as f: json.dump(dict, f)
+
+"""Dump raw text file """
+def dump_file_to_csv(data,path):
+ with open(path, 'w') as f:f.write(data)
+sample_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 . 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 . Twenty days ago. Her euDKA was thought to be precipitated by her respiratory tract infection in the setting of SGLT2 inhibitor use . At birth the typical boy is growing slightly faster than the typical girl, but the velocities become equal at about seven months, and then the girl grows faster until four years. From then until adolescence no differences in velocity can be detected. 21-02-2020 21/04/2020 """
+
+# Define Gender NER matching rules
+gender_rules = {
+ "entity": "Gender",
+ "ruleScope": "sentence",
+ "completeMatchRegex": "true" }
+
+# Define dict data in csv format
+gender_data = '''male,man,male,boy,gentleman,he,him
+female,woman,female,girl,lady,old-lady,she,her
+neutral,neutral'''
+
+# Dump configs to file
+dump_dict_to_json_file(gender_data, 'gender.csv')
+dump_dict_to_json_file(gender_rules, 'gender.json')
+gender_NER_pipe = nlu.load('match.context')
+gender_NER_pipe.print_info()
+gender_NER_pipe['context_matcher'].setJsonPath('gender.json')
+gender_NER_pipe['context_matcher'].setDictionary('gender.csv', options={"delimiter":","})
+gender_NER_pipe.predict(sample_text)
+```
+
+| context_match | context_match_confidence |
+| :------------ | -----------------------: |
+| female | 0.13 |
+| she | 0.13 |
+| she | 0.13 |
+| she | 0.13 |
+| she | 0.13 |
+| boy | 0.13 |
+| girl | 0.13 |
+| girl | 0.13 |
+
+### Context Matcher Parameters
+You can define the following parameters in your rules.json file to define the entities to be matched
+
+| Parameter | Type | Description |
+| --------------------- | ----------------------- | ------------------------------------------------------------ |
+| entity | `str ` | The name of this rule |
+| regex | `Optional[str] ` | Regex Pattern to extract candidates |
+| contextLength | `Optional[int] ` | defines the maximum distance a prefix and suffix words can be away from the word to match,whereas context are words that must be immediately after or before the word to match |
+| prefix | `Optional[List[str]] ` | Words preceding the regex match, that are at most `contextLength` characters aways |
+| regexPrefix | `Optional[str] ` | RegexPattern of words preceding the regex match, that are at most `contextLength` characters aways |
+| suffix | `Optional[List[str]] ` | Words following the regex match, that are at most `contextLength` characters aways |
+| regexSuffix | `Optional[str] ` | RegexPattern of words following the regex match, that are at most `contextLength` distance aways |
+| context | `Optional[List[str]] ` | list of words that must be immediatly before/after a match |
+| contextException | `Optional[List[str]] ` | ?? List of words that may not be immediatly before/after a match |
+| exceptionDistance | `Optional[int] ` | Distance exceptions must be away from a match |
+| regexContextException | `Optional[str] ` | Regex Pattern of exceptions that may not be within `exceptionDistance` range of the match |
+| matchScope | `Optional[str]` | Either `token` or `sub-token` to match on character basis |
+| completeMatchRegex | `Optional[str]` | Wether to use complete or partial matching, either `"true"` or `"false"` |
+| ruleScope | `str` | currently only `sentence` supported |
# Saving a NLU pipeline to disk
diff --git a/docs/index.md b/docs/index.md
index 3fa1a3ce..cef5195a 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -690,7 +690,7 @@ nlu.load('spell').predict('I liek pentut buttr ant jely')
```python
-nlu.load('dep.untyped').predict('Untyped Dependencies represent a grammatical tree structure')
+nlu.load('dep.untyped').predict('Untyped Dependencies represent a grammatical tree structure.md')
```
@@ -714,7 +714,7 @@ nlu.load('dep.untyped').predict('Untyped Dependencies represent a grammatical tr
[Typed Dependency Parsing example](https://colab.research.google.com/drive/1KXUqcF8e-LU9cXnHE8ni8z758LuFPvY7?usp=sharing)
```python
-nlu.load('dep').predict('Typed Dependencies represent a grammatical tree structure where every edge has a label')
+nlu.load('dep').predict('Typed Dependencies represent a grammatical tree structure.md where every edge has a label')
```
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_negation_classifier_demo_biological_texts.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_negation_classifier_demo_biological_texts.ipynb
deleted file mode 100644
index 74ae76b6..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_negation_classifier_demo_biological_texts.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_negation_classifier_demo_biological_texts.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_negation_classifier_demo_biological_texts.ipynb)\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 Class Biological Negation Classifer Training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data : \n","\n"," \n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data : \n","\n"," \n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620187471950,"user_tz":-120,"elapsed":121254,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"4abf85d5-354d-4b91-dac8-96cf4fabc546"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:02:31-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 04:02:31 (36.7 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 72kB/s \n","\u001b[K |████████████████████████████████| 153kB 51.4MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 49.6MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Negation Bilogical Texts dataset \n","https://www.kaggle.com/ma7555/bioscope-corpus-negation-annotated\n","#Context\n","The BioScope corpus consists of medical and biological texts annotated for negation and their linguistic scope. This was done to allow a comparison between the development of systems for negation/hedge detection and scope resolution.\n","The corpus is publicly available for research purposes.\n","\n","You can use this corpus to fine-tune a BERT-like model for negation detection.\n","\n","This dataset was created in this format during the COVID-19 crisis as a training set for detecting negations regarding treatment of specific drugs in the released research papers.\n","\n","Creators of the original dataset: MTA-SZTE Research Group on Artificial Intelligence - RGAI\n","https://rgai.inf.u-szeged.hu/node/105\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620187472933,"user_tz":-120,"elapsed":122227,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c4e16951-2733-4f4f-c59b-a6b517dc3774"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/bioscope_abstract.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:04:31-- http://ckl-it.de/wp-content/uploads/2021/02/bioscope_abstract.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 802898 (784K) [text/csv]\n","Saving to: ‘bioscope_abstract.csv’\n","\n","bioscope_abstract.c 100%[===================>] 784.08K 1.21MB/s in 0.6s \n","\n","2021-05-05 04:04:32 (1.21 MB/s) - ‘bioscope_abstract.csv’ saved [802898/802898]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620187474073,"user_tz":-120,"elapsed":123361,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"d0700a4a-7d4e-4fe9-8d96-84ecb1ef03ea"},"source":["import pandas as pd\n","train_path = '/content/bioscope_abstract.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","columns=['text','y']\n","train_df = train_df[columns]\n","train_df = train_df.dropna()\n","train_df = train_df.sample(frac=1).reset_index(drop=True)\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
85
\n","
The mAb failed to induce NF-kappa B/Rel nuclea...
\n","
positive
\n","
\n","
\n","
937
\n","
Because these induced gene products have NF-ka...
\n","
negative
\n","
\n","
\n","
1707
\n","
H2O2-induced NF-kappaB activation in Wurzburg ...
\n","
positive
\n","
\n","
\n","
1029
\n","
The carboxyl-terminal cytoplasmic domain of CD...
\n","
negative
\n","
\n","
\n","
258
\n","
Pretreatment with actinomycin D and cyclohexim...
\n","
negative
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
516
\n","
The finding that dexamethasone has no effect o...
\n","
positive
\n","
\n","
\n","
1870
\n","
IL-4 secreted by activated T cells is a pleiot...
\n","
negative
\n","
\n","
\n","
380
\n","
In contrast to wild-type B cells, neither of t...
\n","
positive
\n","
\n","
\n","
684
\n","
The IL-12 nonresponsiveness of the Th2 clones ...
\n","
positive
\n","
\n","
\n","
1830
\n","
Synergism between two distinct elements of the...
\n","
negative
\n","
\n"," \n","
\n","
1600 rows × 2 columns
\n","
"],"text/plain":[" text y\n","85 The mAb failed to induce NF-kappa B/Rel nuclea... positive\n","937 Because these induced gene products have NF-ka... negative\n","1707 H2O2-induced NF-kappaB activation in Wurzburg ... positive\n","1029 The carboxyl-terminal cytoplasmic domain of CD... negative\n","258 Pretreatment with actinomycin D and cyclohexim... negative\n","... ... ...\n","516 The finding that dexamethasone has no effect o... positive\n","1870 IL-4 secreted by activated T cells is a pleiot... negative\n","380 In contrast to wild-type B cells, neither of t... positive\n","684 The IL-12 nonresponsiveness of the Th2 clones ... positive\n","1830 Synergism between two distinct elements of the... negative\n","\n","[1600 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620188018837,"user_tz":-120,"elapsed":10641,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"1d2af806-3dc3-4a6e-839b-892fc20497a2"},"source":["import nlu \n","from sklearn.metrics import classification_report\n","\n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.00 0.00 0.00 24\n"," positive 0.52 1.00 0.68 26\n","\n"," accuracy 0.52 50\n"," macro avg 0.26 0.50 0.34 50\n","weighted avg 0.27 0.52 0.36 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
"],"text/plain":[" trained_sentiment ... trained_sentiment_confidence\n","0 positive ... 0.67287\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## 5. Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620188242045,"user_tz":-120,"elapsed":1004,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"9fc40e4e-8b18-45d7-d003-3be9a40c7481"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@1ff4ede2) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@1ff4ede2\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## 6. Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620188276333,"user_tz":-120,"elapsed":4993,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0ccd6fea-1606-44f8-8dd0-632fbb3bee1b"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.00 0.00 0.00 24\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.76 0.96 0.85 26\n","\n"," accuracy 0.50 50\n"," macro avg 0.25 0.32 0.28 50\n","weighted avg 0.39 0.50 0.44 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
trained_sentiment
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_use
\n","
trained_sentiment_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
positive
\n","
The mAb failed to induce NF-kappa B/Rel nuclea...
\n","
positive
\n","
[The mAb failed to induce NF-kappa B/Rel nucle...
\n","
85
\n","
The mAb failed to induce NF-kappa B/Rel nuclea...
\n","
[-0.021044651046395302, -0.012281016446650028,...
\n","
0.750314
\n","
\n","
\n","
1
\n","
negative
\n","
Because these induced gene products have NF-ka...
\n","
neutral
\n","
[Because these induced gene products have NF-k...
\n","
937
\n","
Because these induced gene products have NF-ka...
\n","
[-0.018397482112050056, -0.0002952178183477372...
\n","
0.524412
\n","
\n","
\n","
2
\n","
positive
\n","
H2O2-induced NF-kappaB activation in Wurzburg ...
\n","
positive
\n","
[H2O2-induced NF-kappaB activation in Wurzburg...
\n","
1707
\n","
H2O2-induced NF-kappaB activation in Wurzburg ...
\n","
[0.04317500814795494, 0.00023781821073498577, ...
\n","
0.771501
\n","
\n","
\n","
3
\n","
negative
\n","
The carboxyl-terminal cytoplasmic domain of CD...
\n","
positive
\n","
[The carboxyl-terminal cytoplasmic domain of C...
\n","
1029
\n","
The carboxyl-terminal cytoplasmic domain of CD...
\n","
[0.0399024672806263, 0.06178785115480423, -0.0...
\n","
0.703551
\n","
\n","
\n","
4
\n","
negative
\n","
Pretreatment with actinomycin D and cyclohexim...
\n","
positive
\n","
[Pretreatment with actinomycin D and cyclohexi...
\n","
258
\n","
Pretreatment with actinomycin D and cyclohexim...
\n","
[0.03217654675245285, -0.031980931758880615, 0...
\n","
0.695075
\n","
\n","
\n","
5
\n","
positive
\n","
On the other hand, phorbol ester-induced produ...
\n","
positive
\n","
[On the other hand, phorbol ester-induced prod...
\n","
1839
\n","
On the other hand, phorbol ester-induced produ...
\n","
[0.05933626368641853, 0.06317632645368576, 0.0...
\n","
0.776027
\n","
\n","
\n","
6
\n","
positive
\n","
Activated Rac-1 could mimic activated p21ras t...
\n","
positive
\n","
[Activated Rac-1 could mimic activated p21ras ...
\n","
1552
\n","
Activated Rac-1 could mimic activated p21ras t...
\n","
[0.012313981540501118, -0.05240459367632866, -...
\n","
0.816325
\n","
\n","
\n","
7
\n","
negative
\n","
After a 2-day incubation in LDL, the binding o...
\n","
positive
\n","
[After a 2-day incubation in LDL, the binding ...
\n","
942
\n","
After a 2-day incubation in LDL, the binding o...
\n","
[0.01922391913831234, -0.05796779692173004, -0...
\n","
0.645191
\n","
\n","
\n","
8
\n","
negative
\n","
As evidenced by electro mobility shift assay (...
\n","
positive
\n","
[As evidenced by electro mobility shift assay ...
\n","
383
\n","
As evidenced by electro mobility shift assay (...
\n","
[0.004417878575623035, -0.020263247191905975, ...
\n","
0.670496
\n","
\n","
\n","
9
\n","
negative
\n","
Mutations at the B2 site abolish this transcri...
\n","
neutral
\n","
[Mutations at the B2 site abolish this transcr...
\n","
1515
\n","
Mutations at the B2 site abolish this transcri...
\n","
[0.021449951454997063, 0.017173701897263527, -...
\n","
0.548251
\n","
\n","
\n","
10
\n","
positive
\n","
Electrophoretic mobility-shift assays of HUVEC...
\n","
positive
\n","
[Electrophoretic mobility-shift assays of HUVE...
\n","
1313
\n","
Electrophoretic mobility-shift assays of HUVEC...
\n","
[0.008786042220890522, 0.03473477438092232, -0...
\n","
0.838780
\n","
\n","
\n","
11
\n","
positive
\n","
In comparison to other activators of NF-kappa ...
\n","
positive
\n","
[In comparison to other activators of NF-kappa...
\n","
1972
\n","
In comparison to other activators of NF-kappa ...
\n","
[0.05494653061032295, 0.05886928364634514, 0.0...
\n","
0.709446
\n","
\n","
\n","
12
\n","
negative
\n","
Here we show that the I alpha1 promoter contai...
\n","
neutral
\n","
[Here we show that the I alpha1 promoter conta...
\n","
1279
\n","
Here we show that the I alpha1 promoter contai...
\n","
[0.024258537217974663, 0.003928740043193102, 0...
\n","
0.514196
\n","
\n","
\n","
13
\n","
positive
\n","
Thus these data suggest that the phorbol myris...
\n","
positive
\n","
[Thus these data suggest that the phorbol myri...
\n","
1094
\n","
Thus these data suggest that the phorbol myris...
\n","
[0.04496181383728981, 0.006568090058863163, -0...
\n","
0.803803
\n","
\n","
\n","
14
\n","
positive
\n","
A 40-fold stimulation of chloramphenicol acety...
\n","
positive
\n","
[A 40-fold stimulation of chloramphenicol acet...
\n","
1528
\n","
A 40-fold stimulation of chloramphenicol acety...
\n","
[0.02669776789844036, 0.0031935900915414095, 0...
\n","
0.719408
\n","
\n","
\n","
15
\n","
negative
\n","
Third, a coimmunoprecipitation assay showed th...
\n","
neutral
\n","
[Third, a coimmunoprecipitation assay showed t...
\n","
1195
\n","
Third, a coimmunoprecipitation assay showed th...
\n","
[0.03080531395971775, -0.01305992528796196, 0....
\n","
0.577207
\n","
\n","
\n","
16
\n","
negative
\n","
Phosphorylation of Jak2 in tax transformed cel...
\n","
neutral
\n","
[Phosphorylation of Jak2 in tax transformed ce...
\n","
1725
\n","
Phosphorylation of Jak2 in tax transformed cel...
\n","
[0.05668512359261513, -0.014115522615611553, 0...
\n","
0.535941
\n","
\n","
\n","
17
\n","
positive
\n","
This peptide potently inhibited NFAT activatio...
\n","
positive
\n","
[This peptide potently inhibited NFAT activati...
\n","
1924
\n","
This peptide potently inhibited NFAT activatio...
\n","
[0.051138270646333694, 0.019551211968064308, 0...
\n","
0.761979
\n","
\n","
\n","
18
\n","
positive
\n","
Overexpression of Bcl-2 in tumor cells blocks ...
\n","
positive
\n","
[Overexpression of Bcl-2 in tumor cells blocks...
\n","
527
\n","
Overexpression of Bcl-2 in tumor cells blocks ...
\n","
[0.029882941395044327, 0.031224790960550308, -...
\n","
0.715134
\n","
\n","
\n","
19
\n","
negative
\n","
Nuclear factor kappa B (NF-kappa B) is a pleio...
\n","
neutral
\n","
[Nuclear factor kappa B (NF-kappa B) is a plei...
\n","
284
\n","
Nuclear factor kappa B (NF-kappa B) is a pleio...
\n","
[0.04925459995865822, 0.030116116628050804, -0...
\n","
0.522261
\n","
\n","
\n","
20
\n","
positive
\n","
An NFAT oligonucleotide carrying mutations in ...
\n","
positive
\n","
[An NFAT oligonucleotide carrying mutations in...
\n","
524
\n","
An NFAT oligonucleotide carrying mutations in ...
\n","
[0.014317388646304607, 0.01018354669213295, 0....
\n","
0.605652
\n","
\n","
\n","
21
\n","
negative
\n","
In addition Spi-B as well as PU.1 were able to...
\n","
neutral
\n","
[In addition Spi-B as well as PU.1 were able t...
\n","
521
\n","
In addition Spi-B as well as PU.1 were able to...
\n","
[0.044076837599277496, -0.007634499575942755, ...
\n","
0.559043
\n","
\n","
\n","
22
\n","
positive
\n","
Nuclear run-on assays demonstrate that : ( 1 )...
\n","
positive
\n","
[Nuclear run-on assays demonstrate that : ( 1 ...
\n","
1064
\n","
Nuclear run-on assays demonstrate that : ( 1 )...
\n","
[0.03107193484902382, 0.020781097933650017, 0....
\n","
0.723425
\n","
\n","
\n","
23
\n","
positive
\n","
Finally, we conclude that this effect of CIITA...
\n","
neutral
\n","
[Finally, we conclude that this effect of CIIT...
\n","
1539
\n","
Finally, we conclude that this effect of CIITA...
\n","
[0.033310066908597946, -0.03267408534884453, 0...
\n","
0.566672
\n","
\n","
\n","
24
\n","
positive
\n","
We found that ALD induce a transient activatio...
\n","
positive
\n","
[We found that ALD induce a transient activati...
\n","
1994
\n","
We found that ALD induce a transient activatio...
\n","
[0.049336668103933334, -0.016328582540154457, ...
\n","
0.777087
\n","
\n","
\n","
25
\n","
positive
\n","
AFR behaves like ascorbate, while DHA and asco...
\n","
positive
\n","
[AFR behaves like ascorbate, while DHA and asc...
\n","
203
\n","
AFR behaves like ascorbate, while DHA and asco...
\n","
[0.06031205505132675, 0.00172607006970793, 0.0...
\n","
0.827045
\n","
\n","
\n","
26
\n","
positive
\n","
It has been shown recently that in wild-type C...
\n","
positive
\n","
[It has been shown recently that in wild-type ...
\n","
428
\n","
It has been shown recently that in wild-type C...
\n","
[0.06434260308742523, 0.035497453063726425, -0...
\n","
0.763702
\n","
\n","
\n","
27
\n","
positive
\n","
Oncogenic forms of NOTCH1 lacking either the p...
\n","
positive
\n","
[Oncogenic forms of NOTCH1 lacking either the ...
\n","
112
\n","
Oncogenic forms of NOTCH1 lacking either the p...
\n","
[0.036650002002716064, -0.005419247783720493, ...
\n","
0.761244
\n","
\n","
\n","
28
\n","
positive
\n","
Rhabdomyosarcomas do not contain mutations in ...
\n","
positive
\n","
[Rhabdomyosarcomas do not contain mutations in...
\n","
1258
\n","
Rhabdomyosarcomas do not contain mutations in ...
\n","
[0.04726873338222504, -0.012257322669029236, -...
\n","
0.607541
\n","
\n","
\n","
29
\n","
negative
\n","
Characterization of CD40 signaling determinant...
\n","
positive
\n","
[Characterization of CD40 signaling determinan...
\n","
151
\n","
Characterization of CD40 signaling determinant...
\n","
[0.005249931011348963, -0.01369913388043642, 0...
\n","
0.617174
\n","
\n","
\n","
30
\n","
negative
\n","
Thus, a component of LDL-enhanced endothelial ...
\n","
neutral
\n","
[Thus, a component of LDL-enhanced endothelial...
\n","
1669
\n","
Thus, a component of LDL-enhanced endothelial ...
\n","
[0.06682103872299194, -0.043387044221162796, -...
\n","
0.532349
\n","
\n","
\n","
31
\n","
positive
\n","
However, stimulation with MBP did not produce ...
\n","
positive
\n","
[However, stimulation with MBP did not produce...
\n","
1627
\n","
However, stimulation with MBP did not produce ...
\n","
[0.07646981626749039, 0.05336544290184975, -0....
\n","
0.803395
\n","
\n","
\n","
32
\n","
negative
\n","
We analyzed the activity of the enhancer, the ...
\n","
neutral
\n","
[We analyzed the activity of the enhancer, the...
\n","
1819
\n","
We analyzed the activity of the enhancer, the ...
\n","
[0.058565203100442886, 0.017934346571564674, 0...
\n","
0.525206
\n","
\n","
\n","
33
\n","
negative
\n","
Death-inducing ligands (DILs) such as tumor ne...
\n","
positive
\n","
[Death-inducing ligands (DILs) such as tumor n...
\n","
148
\n","
Death-inducing ligands (DILs) such as tumor ne...
\n","
[0.018583133816719055, 0.05835242569446564, 0....
\n","
0.721663
\n","
\n","
\n","
34
\n","
positive
\n","
Using electrophoretic mobility shift assays, w...
\n","
positive
\n","
[Using electrophoretic mobility shift assays, ...
\n","
857
\n","
Using electrophoretic mobility shift assays, w...
\n","
[0.01857064664363861, -1.7747517631505616e-05,...
\n","
0.762466
\n","
\n","
\n","
35
\n","
negative
\n","
cAMP signaling inhibited Stat1 at several diff...
\n","
neutral
\n","
[cAMP signaling inhibited Stat1 at several dif...
\n","
285
\n","
cAMP signaling inhibited Stat1 at several diff...
\n","
[0.0070323762483894825, -0.036651283502578735,...
\n","
0.517350
\n","
\n","
\n","
36
\n","
positive
\n","
In addition, JNK activation by PMA plus ionoph...
\n","
positive
\n","
[In addition, JNK activation by PMA plus ionop...
\n","
36
\n","
In addition, JNK activation by PMA plus ionoph...
\n","
[0.04152128845453262, 0.014748011715710163, 0....
\n","
0.800704
\n","
\n","
\n","
37
\n","
negative
\n","
The viral protein Tax induces the activation a...
\n","
neutral
\n","
[The viral protein Tax induces the activation ...
\n","
679
\n","
The viral protein Tax induces the activation a...
\n","
[0.06768079847097397, -0.028039786964654922, 0...
\n","
0.559131
\n","
\n","
\n","
38
\n","
negative
\n","
Furthermore, OFT-1 appeared to have approximat...
\n","
positive
\n","
[Furthermore, OFT-1 appeared to have approxima...
\n","
1527
\n","
Furthermore, OFT-1 appeared to have approximat...
\n","
[0.07009289413690567, 0.03128473833203316, -0....
\n","
0.662930
\n","
\n","
\n","
39
\n","
negative
\n","
A principal objective of the present study was...
\n","
neutral
\n","
[A principal objective of the present study wa...
\n","
479
\n","
A principal objective of the present study was...
\n","
[0.017783455550670624, -0.026092059910297394, ...
\n","
0.569664
\n","
\n","
\n","
40
\n","
positive
\n","
We conclude that downregulation of WT1 during ...
\n","
positive
\n","
[We conclude that downregulation of WT1 during...
\n","
1561
\n","
We conclude that downregulation of WT1 during ...
\n","
[0.00441081915050745, 0.021023083478212357, -0...
\n","
0.700463
\n","
\n","
\n","
41
\n","
negative
\n","
In these transfected CL-01 cells, CD40:CD40L e...
\n","
neutral
\n","
[In these transfected CL-01 cells, CD40:CD40L ...
\n","
1188
\n","
In these transfected CL-01 cells, CD40:CD40L e...
\n","
[0.010555184446275234, -0.005831445567309856, ...
\n","
0.548869
\n","
\n","
\n","
42
\n","
negative
\n","
Recent studies indicate that mutations in the ...
\n","
neutral
\n","
[Recent studies indicate that mutations in the...
\n","
667
\n","
Recent studies indicate that mutations in the ...
\n","
[0.05200810357928276, -0.0026498467195779085, ...
\n","
0.535857
\n","
\n","
\n","
43
\n","
negative
\n","
To confirm the importance of the three cis-act...
\n","
neutral
\n","
[To confirm the importance of the three cis-ac...
\n","
734
\n","
To confirm the importance of the three cis-act...
\n","
[0.05337677150964737, -0.028393635526299477, 0...
\n","
0.538683
\n","
\n","
\n","
44
\n","
positive
\n","
No significant difference was detected in the ...
\n","
positive
\n","
[No significant difference was detected in the...
\n","
1805
\n","
No significant difference was detected in the ...
\n","
[0.05019189417362213, 0.01725945621728897, 0.0...
\n","
0.838974
\n","
\n","
\n","
45
\n","
positive
\n","
In contrast, anti-CD3 and anti-CD28 stimulated...
\n","
positive
\n","
[In contrast, anti-CD3 and anti-CD28 stimulate...
\n","
1473
\n","
In contrast, anti-CD3 and anti-CD28 stimulated...
\n","
[0.053164657205343246, 0.015389195643365383, 0...
\n","
0.727595
\n","
\n","
\n","
46
\n","
negative
\n","
In this study we analyzed the effect of CD40 s...
\n","
neutral
\n","
[In this study we analyzed the effect of CD40 ...
\n","
418
\n","
In this study we analyzed the effect of CD40 s...
\n","
[0.016681713983416557, 0.06071694567799568, -0...
\n","
0.515776
\n","
\n","
\n","
47
\n","
positive
\n","
A dramatic increase in the intracellular level...
\n","
positive
\n","
[A dramatic increase in the intracellular leve...
\n","
689
\n","
A dramatic increase in the intracellular level...
\n","
[0.032944243401288986, -0.005736679304391146, ...
\n","
0.699241
\n","
\n","
\n","
48
\n","
positive
\n","
Qualitatively different effects were observed ...
\n","
positive
\n","
[Qualitatively different effects were observed...
\n","
1228
\n","
Qualitatively different effects were observed ...
\n","
[0.06803048402070999, 0.039302267134189606, 0....
\n","
0.795765
\n","
\n","
\n","
49
\n","
negative
\n","
A third tyrosine within the amino-terminal reg...
\n","
positive
\n","
[A third tyrosine within the amino-terminal re...
\n","
1899
\n","
A third tyrosine within the amino-terminal reg...
\n","
[0.08541002124547958, 0.0034846188500523567, 0...
\n","
0.600163
\n","
\n"," \n","
\n","
"],"text/plain":[" y ... trained_sentiment_confidence\n","0 positive ... 0.750314\n","1 negative ... 0.524412\n","2 positive ... 0.771501\n","3 negative ... 0.703551\n","4 negative ... 0.695075\n","5 positive ... 0.776027\n","6 positive ... 0.816325\n","7 negative ... 0.645191\n","8 negative ... 0.670496\n","9 negative ... 0.548251\n","10 positive ... 0.838780\n","11 positive ... 0.709446\n","12 negative ... 0.514196\n","13 positive ... 0.803803\n","14 positive ... 0.719408\n","15 negative ... 0.577207\n","16 negative ... 0.535941\n","17 positive ... 0.761979\n","18 positive ... 0.715134\n","19 negative ... 0.522261\n","20 positive ... 0.605652\n","21 negative ... 0.559043\n","22 positive ... 0.723425\n","23 positive ... 0.566672\n","24 positive ... 0.777087\n","25 positive ... 0.827045\n","26 positive ... 0.763702\n","27 positive ... 0.761244\n","28 positive ... 0.607541\n","29 negative ... 0.617174\n","30 negative ... 0.532349\n","31 positive ... 0.803395\n","32 negative ... 0.525206\n","33 negative ... 0.721663\n","34 positive ... 0.762466\n","35 negative ... 0.517350\n","36 positive ... 0.800704\n","37 negative ... 0.559131\n","38 negative ... 0.662930\n","39 negative ... 0.569664\n","40 positive ... 0.700463\n","41 negative ... 0.548869\n","42 negative ... 0.535857\n","43 negative ... 0.538683\n","44 positive ... 0.838974\n","45 positive ... 0.727595\n","46 negative ... 0.515776\n","47 positive ... 0.699241\n","48 positive ... 0.795765\n","49 negative ... 0.600163\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["# 7. Try training with different Embeddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxWFzQOhjWC8","executionInfo":{"status":"ok","timestamp":1620188276334,"user_tz":-120,"elapsed":4701,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b4d5e2b4-e0a5-4431-8ecd-6adf5f8a718c"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IKK_Ii_gjJfF","executionInfo":{"status":"ok","timestamp":1620189387010,"user_tz":-120,"elapsed":1115278,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"6b006f1b-dc1b-454d-f65a-95d87fd62f07"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(120) \n","trainable_pipe['sentiment_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.95 0.86 0.90 791\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.91 0.92 0.92 809\n","\n"," accuracy 0.89 1600\n"," macro avg 0.62 0.59 0.61 1600\n","weighted avg 0.93 0.89 0.91 1600\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 7.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fxx4yNkNVGFl","executionInfo":{"status":"ok","timestamp":1620189890877,"user_tz":-120,"elapsed":85461,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"ce8e9318-9a86-4c78-d5de-cd73ba3de282"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.92 0.81 0.86 209\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.85 0.89 0.87 191\n","\n"," accuracy 0.85 400\n"," macro avg 0.59 0.57 0.58 400\n","weighted avg 0.89 0.85 0.86 400\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 8. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620190077725,"user_tz":-120,"elapsed":272045,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"35f50714-cad7-4123-d117-f6aea7095f79"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 9. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SO4uz45MoRgp","executionInfo":{"status":"ok","timestamp":1620190092860,"user_tz":-120,"elapsed":286890,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c891d7d9-52c8-456c-a354-0bcc81af43eb"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('The virus had a direct impact on the nervous system')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentiment_confidence
\n","
sentence
\n","
text
\n","
sentence_embedding_from_disk
\n","
sentiment
\n","
document
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.9973864]
\n","
[The virus had a direct impact on the nervous ...
\n","
The virus had a direct impact on the nervous s...
\n","
[[0.19975340366363525, 0.40417489409446716, 0....
\n","
[negative]
\n","
The virus had a direct impact on the nervous s...
\n","
8589934592
\n","
\n"," \n","
\n","
"],"text/plain":[" sentiment_confidence ... origin_index\n","0 [0.9973864] ... 8589934592\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e0CVlkk9v6Qi","executionInfo":{"status":"ok","timestamp":1620190092863,"user_tz":-120,"elapsed":286783,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"9f4b2eb0-6144-4e6b-d6c6-c782ddd192eb"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3a4ced43) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3a4ced43\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentiment_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"CtOuwWgAvqXw"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_sarcasam_classifier_demo_news_headlines.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_sarcasam_classifier_demo_news_headlines.ipynb
deleted file mode 100644
index 75bce94f..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_sarcasam_classifier_demo_news_headlines.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_sarcasam_classifier_demo_news_headlines.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sarcasam_classifier_demo_news_headlines.ipynb)\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 Class News Headlines Sarcasam Training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n","\n"," \n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n","\n"," \n","\n","\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620187463238,"user_tz":-120,"elapsed":101354,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"1ab394d0-dce6-49cd-939b-c96e47fde49b"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:02:42-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 04:02:42 (33.7 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 74kB/s \n","\u001b[K |████████████████████████████████| 153kB 68.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 20.5MB/s \n","\u001b[K |████████████████████████████████| 204kB 79.6MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download News Headlines Sarcsam dataset \n","https://www.kaggle.com/rmisra/news-headlines-dataset-for-sarcasm-detection\n","#Context\n","Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets and detecting sarcasm in these requires the availability of contextual tweets.\n","\n","To overcome the limitations related to noise in Twitter datasets, this News Headlines dataset for Sarcasm Detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events and we collected all the headlines from News in Brief and News in Photos categories (which are sarcastic). We collect real (and non-sarcastic) news headlines from HuffPost.\n","\n","This new dataset has following advantages over the existing Twitter datasets:\n","\n","Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings.\n","\n","Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, we get high-quality labels with much less noise as compared to Twitter datasets.\n","\n","Unlike tweets which are replies to other tweets, the news headlines we obtained are self-contained. This would help us in teasing apart the real sarcastic elements.\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620187463651,"user_tz":-120,"elapsed":101758,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"4b81d821-074f-4409-ef50-7a401f6706be"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/Sarcasm_Headlines_Dataset_v2.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:04:22-- http://ckl-it.de/wp-content/uploads/2021/02/Sarcasm_Headlines_Dataset_v2.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 2381880 (2.3M) [text/csv]\n","Saving to: ‘Sarcasm_Headlines_Dataset_v2.csv’\n","\n","Sarcasm_Headlines_D 100%[===================>] 2.27M --.-KB/s in 0.1s \n","\n","2021-05-05 04:04:23 (15.7 MB/s) - ‘Sarcasm_Headlines_Dataset_v2.csv’ saved [2381880/2381880]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620187464360,"user_tz":-120,"elapsed":102461,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f3e8cdcc-271a-4fa6-e8c4-4aed44da42dc"},"source":["import pandas as pd\n","test_path = '/content/Sarcasm_Headlines_Dataset_v2.csv'\n","train_df = pd.read_csv(test_path,sep=\",\")\n","cols = [\"y\",\"text\"]\n","train_df = train_df[cols]\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df\n","\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
\n"," \n"," \n","
\n","
1463
\n","
positive
\n","
red cross installs blood drop-off bins for don...
\n","
\n","
\n","
9543
\n","
positive
\n","
president-elect edwards seen entering chinatow...
\n","
\n","
\n","
2930
\n","
positive
\n","
crowd at trump rally realizes they've been cha...
\n","
\n","
\n","
644
\n","
positive
\n","
kerry captures bin laden one week too late
\n","
\n","
\n","
2390
\n","
negative
\n","
'new hampshire' episode 4: not just for old, w...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
9034
\n","
negative
\n","
deputy interior secretary met with lobbyist fo...
\n","
\n","
\n","
6330
\n","
negative
\n","
parents of kidnapped girls make desperate plea
\n","
\n","
\n","
7127
\n","
negative
\n","
image vs. substance in your self-made journey
\n","
\n","
\n","
7289
\n","
positive
\n","
'secretary clinton is a different person than ...
\n","
\n","
\n","
9077
\n","
positive
\n","
cnn producer on hunt for saddest-looking fuck ...
\n","
\n"," \n","
\n","
8000 rows × 2 columns
\n","
"],"text/plain":[" y text\n","1463 positive red cross installs blood drop-off bins for don...\n","9543 positive president-elect edwards seen entering chinatow...\n","2930 positive crowd at trump rally realizes they've been cha...\n","644 positive kerry captures bin laden one week too late\n","2390 negative 'new hampshire' episode 4: not just for old, w...\n","... ... ...\n","9034 negative deputy interior secretary met with lobbyist fo...\n","6330 negative parents of kidnapped girls make desperate plea\n","7127 negative image vs. substance in your self-made journey\n","7289 positive 'secretary clinton is a different person than ...\n","9077 positive cnn producer on hunt for saddest-looking fuck ...\n","\n","[8000 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620188373251,"user_tz":-120,"elapsed":10920,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0f669bc1-ee54-423d-81ff-d52fd9b53808"},"source":["import nlu \n","from sklearn.metrics import classification_report\n","\n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.00 0.00 0.00 24\n"," neutral 0.00 0.00 0.00 0\n"," positive 1.00 0.23 0.38 26\n","\n"," accuracy 0.12 50\n"," macro avg 0.33 0.08 0.12 50\n","weighted avg 0.52 0.12 0.20 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
"],"text/plain":[" trained_sentiment document ... sentence origin_index\n","0 neutral Aliens are immortal! ... [Aliens are immortal!] 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## 5. Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620188373653,"user_tz":-120,"elapsed":10552,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"e43b4908-42f7-46fb-d110-210ae33b8ff9"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@9b0d688) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@9b0d688\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## 6. Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620188376889,"user_tz":-120,"elapsed":13708,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"86a3b364-8312-4dc0-beb0-37293e3ce167"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 1.00 0.88 0.93 24\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.96 0.92 0.94 26\n","\n"," accuracy 0.90 50\n"," macro avg 0.65 0.60 0.62 50\n","weighted avg 0.98 0.90 0.94 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment
\n","
document
\n","
sentence_embedding_use
\n","
text
\n","
trained_sentiment_confidence
\n","
sentence
\n","
y
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
positive
\n","
red cross installs blood drop-off bins for don...
\n","
[-0.07407097518444061, 0.020259270444512367, -...
\n","
red cross installs blood drop-off bins for don...
\n","
0.918347
\n","
[red cross installs blood drop-off bins for do...
\n","
positive
\n","
1463
\n","
\n","
\n","
1
\n","
neutral
\n","
president-elect edwards seen entering chinatow...
\n","
[0.07900547981262207, 0.06132232025265694, 0.0...
\n","
president-elect edwards seen entering chinatow...
\n","
0.591700
\n","
[president-elect edwards seen entering chinato...
\n","
positive
\n","
9543
\n","
\n","
\n","
2
\n","
positive
\n","
crowd at trump rally realizes they've been cha...
\n","
[0.00032250594813376665, -0.022321783006191254...
\n","
crowd at trump rally realizes they've been cha...
\n","
0.963672
\n","
[crowd at trump rally realizes they've been ch...
\n","
positive
\n","
2930
\n","
\n","
\n","
3
\n","
positive
\n","
kerry captures bin laden one week too late
\n","
[-0.004850792698562145, 0.017207739874720573, ...
\n","
kerry captures bin laden one week too late
\n","
0.846780
\n","
[kerry captures bin laden one week too late]
\n","
positive
\n","
644
\n","
\n","
\n","
4
\n","
negative
\n","
'new hampshire' episode 4: not just for old, w...
\n","
[-0.04294964671134949, -0.07175017148256302, -...
\n","
'new hampshire' episode 4: not just for old, w...
\n","
0.888615
\n","
['new hampshire' episode 4: not just for old, ...
\n","
negative
\n","
2390
\n","
\n","
\n","
5
\n","
negative
\n","
12 indie spots in hong kong
\n","
[-0.036156971007585526, -0.014244569465517998,...
\n","
12 indie spots in hong kong
\n","
0.889380
\n","
[12 indie spots in hong kong]
\n","
negative
\n","
5450
\n","
\n","
\n","
6
\n","
neutral
\n","
former refugee fights for her dream to abolish...
\n","
[-0.0035593388602137566, 0.008986725471913815,...
\n","
former refugee fights for her dream to abolish...
\n","
0.516011
\n","
[former refugee fights for her dream to abolis...
\n","
negative
\n","
7739
\n","
\n","
\n","
7
\n","
negative
\n","
watch out, sephora. h&m beauty is coming for you.
\n","
[-0.02806154452264309, -0.04734545946121216, 0...
\n","
watch out, sephora. h&m beauty is coming for you.
\n","
0.904426
\n","
[watch out, sephora., h&m beauty is coming for...
\n","
negative
\n","
4370
\n","
\n","
\n","
8
\n","
positive
\n","
computer scientists say ai's underdeveloped et...
\n","
[-0.007209080271422863, -0.01665995828807354, ...
\n","
computer scientists say ai's underdeveloped et...
\n","
0.881709
\n","
[computer scientists say ai's underdeveloped e...
\n","
positive
\n","
9418
\n","
\n","
\n","
9
\n","
negative
\n","
past armageddon and on to zippori, one of isra...
\n","
[-0.0013639864046126604, -0.05516374856233597,...
\n","
past armageddon and on to zippori, one of isra...
\n","
0.887740
\n","
[past armageddon and on to zippori, one of isr...
\n","
negative
\n","
4357
\n","
\n","
\n","
10
\n","
positive
\n","
obama caves to girl scout lobby, wears tiara i...
\n","
[-0.019499918445944786, -0.01798412762582302, ...
\n","
obama caves to girl scout lobby, wears tiara i...
\n","
0.654486
\n","
[obama caves to girl scout lobby, wears tiara ...
\n","
negative
\n","
2418
\n","
\n","
\n","
11
\n","
positive
\n","
israel passes law cementing itself as exclusiv...
\n","
[0.05780275911092758, -0.04909253492951393, -0...
\n","
israel passes law cementing itself as exclusiv...
\n","
0.803852
\n","
[israel passes law cementing itself as exclusi...
\n","
positive
\n","
7882
\n","
\n","
\n","
12
\n","
positive
\n","
local man's fear of snakes increases with each...
\n","
[0.056381646543741226, -0.057368289679288864, ...
\n","
local man's fear of snakes increases with each...
\n","
0.973072
\n","
[local man's fear of snakes increases with eac...
\n","
positive
\n","
6117
\n","
\n","
\n","
13
\n","
negative
\n","
media ethics: whose standards?
\n","
[0.0406915545463562, 0.03338606283068657, -0.0...
\n","
media ethics: whose standards?
\n","
0.884770
\n","
[media ethics: whose standards?]
\n","
negative
\n","
7992
\n","
\n","
\n","
14
\n","
positive
\n","
rare species of frog may hold cure to...ah, ne...
\n","
[0.079066202044487, 0.021108830347657204, -0.0...
\n","
rare species of frog may hold cure to...ah, ne...
\n","
0.942009
\n","
[rare species of frog may hold cure to., ..ah,...
\n","
positive
\n","
6445
\n","
\n","
\n","
15
\n","
positive
\n","
police officers waving everyone over to take a...
\n","
[-0.011674991808831692, -0.07644280791282654, ...
\n","
police officers waving everyone over to take a...
\n","
0.616271
\n","
[police officers waving everyone over to take ...
\n","
positive
\n","
8222
\n","
\n","
\n","
16
\n","
negative
\n","
nate berkus and jeremiah brent are married!
\n","
[0.03597276285290718, 0.05780172348022461, -0....
\n","
nate berkus and jeremiah brent are married!
\n","
0.904110
\n","
[nate berkus and jeremiah brent are married!]
\n","
negative
\n","
3847
\n","
\n","
\n","
17
\n","
neutral
\n","
summer camp hierarchy thrown into chaos after ...
\n","
[-0.07574271410703659, 0.037431202828884125, 0...
\n","
summer camp hierarchy thrown into chaos after ...
\n","
0.586359
\n","
[summer camp hierarchy thrown into chaos after...
\n","
positive
\n","
253
\n","
\n","
\n","
18
\n","
negative
\n","
iraqi officer under saddam masterminded the ri...
\n","
[0.02313143201172352, -0.05338258296251297, -0...
\n","
iraqi officer under saddam masterminded the ri...
\n","
0.712658
\n","
[iraqi officer under saddam masterminded the r...
\n","
negative
\n","
2504
\n","
\n","
\n","
19
\n","
negative
\n","
cop crashed cruiser into ditch after this owl ...
\n","
[-0.0011760082561522722, -0.07277680933475494,...
\n","
cop crashed cruiser into ditch after this owl ...
\n","
0.835989
\n","
[cop crashed cruiser into ditch after this owl...
\n","
negative
\n","
6992
\n","
\n","
\n","
20
\n","
positive
\n","
bush elected president of iraq
\n","
[-0.02786075882613659, 0.044202402234077454, -...
\n","
bush elected president of iraq
\n","
0.928596
\n","
[bush elected president of iraq]
\n","
positive
\n","
5882
\n","
\n","
\n","
21
\n","
negative
\n","
senate does equifax a favor as a former execut...
\n","
[0.048467256128787994, -0.020521214231848717, ...
\n","
senate does equifax a favor as a former execut...
\n","
0.908127
\n","
[senate does equifax a favor as a former execu...
\n","
negative
\n","
9721
\n","
\n","
\n","
22
\n","
positive
\n","
texan feels emotionally empty after chili cook...
\n","
[-0.03747135400772095, 0.021678565070033073, -...
\n","
texan feels emotionally empty after chili cook...
\n","
0.953778
\n","
[texan feels emotionally empty after chili coo...
\n","
positive
\n","
8362
\n","
\n","
\n","
23
\n","
neutral
\n","
lgbtq activists organizing massive dance prote...
\n","
[0.03160602226853371, -0.031953465193510056, -...
\n","
lgbtq activists organizing massive dance prote...
\n","
0.548180
\n","
[lgbtq activists organizing massive dance prot...
\n","
negative
\n","
1073
\n","
\n","
\n","
24
\n","
negative
\n","
samsung slashes profit forecast after pulling ...
\n","
[0.06239231675863266, -0.03570358455181122, -0...
\n","
samsung slashes profit forecast after pulling ...
\n","
0.912397
\n","
[samsung slashes profit forecast after pulling...
\n","
negative
\n","
7620
\n","
\n","
\n","
25
\n","
positive
\n","
unpublished twain autobiography rails against ...
\n","
[-0.030312832444906235, 0.06083317846059799, 0...
\n","
unpublished twain autobiography rails against ...
\n","
0.709892
\n","
[unpublished twain autobiography rails against...
\n","
positive
\n","
6476
\n","
\n","
\n","
26
\n","
positive
\n","
vegetarian begins sad, private routine of scan...
\n","
[-0.07400622963905334, -0.04156332463026047, -...
\n","
vegetarian begins sad, private routine of scan...
\n","
0.845192
\n","
[vegetarian begins sad, private routine of sca...
\n","
positive
\n","
6011
\n","
\n","
\n","
27
\n","
negative
\n","
down with cutesy cleaning supplies!
\n","
[-0.05738799646496773, 0.06619091331958771, -0...
\n","
down with cutesy cleaning supplies!
\n","
0.853556
\n","
[down with cutesy cleaning supplies!]
\n","
negative
\n","
7466
\n","
\n","
\n","
28
\n","
positive
\n","
hillary clinton sets personal single rep squat...
\n","
[0.05143097788095474, 0.03707878664135933, -0....
\n","
hillary clinton sets personal single rep squat...
\n","
0.908901
\n","
[hillary clinton sets personal single rep squa...
\n","
positive
\n","
2975
\n","
\n","
\n","
29
\n","
negative
\n","
traveling tips for families with special diet
\n","
[-0.05301835760474205, -0.04789021238684654, -...
\n","
traveling tips for families with special diet
\n","
0.788320
\n","
[traveling tips for families with special diet]
\n","
negative
\n","
9610
\n","
\n","
\n","
30
\n","
positive
\n","
tearful biden carefully takes down blacklight ...
\n","
[0.02181081660091877, -0.05245676264166832, 0....
\n","
tearful biden carefully takes down blacklight ...
\n","
0.836097
\n","
[tearful biden carefully takes down blacklight...
\n","
positive
\n","
9186
\n","
\n","
\n","
31
\n","
negative
\n","
8-year-old girl dies after drinking boiling wa...
\n","
[-0.07436364144086838, -0.042152464389801025, ...
\n","
8-year-old girl dies after drinking boiling wa...
\n","
0.888045
\n","
[8-year-old girl dies after drinking boiling w...
\n","
negative
\n","
5053
\n","
\n","
\n","
32
\n","
negative
\n","
woody harrelson applies to open a marijuana di...
\n","
[0.0774756669998169, -0.06551078706979752, -0....
\n","
woody harrelson applies to open a marijuana di...
\n","
0.916998
\n","
[woody harrelson applies to open a marijuana d...
\n","
negative
\n","
2457
\n","
\n","
\n","
33
\n","
negative
\n","
interview with elaine jung
\n","
[-0.0419117696583271, -0.02243213728070259, 0....
\n","
interview with elaine jung
\n","
0.909372
\n","
[interview with elaine jung]
\n","
negative
\n","
4830
\n","
\n","
\n","
34
\n","
negative
\n","
how your sleep changes with the moon
\n","
[0.005925372242927551, 0.0024853269569575787, ...
\n","
how your sleep changes with the moon
\n","
0.908831
\n","
[how your sleep changes with the moon]
\n","
negative
\n","
9055
\n","
\n","
\n","
35
\n","
positive
\n","
dr. scholl's introduces new cartilage inserts ...
\n","
[0.05181374028325081, 0.061040107160806656, -0...
\n","
dr. scholl's introduces new cartilage inserts ...
\n","
0.901216
\n","
[dr. scholl's introduces new cartilage inserts...
\n","
positive
\n","
1651
\n","
\n","
\n","
36
\n","
positive
\n","
nation's dogs dangerously underpetted, say dogs
\n","
[-0.01876039244234562, -0.03308898210525513, -...
\n","
nation's dogs dangerously underpetted, say dogs
\n","
0.959039
\n","
[nation's dogs dangerously underpetted, say dogs]
\n","
positive
\n","
5842
\n","
\n","
\n","
37
\n","
negative
\n","
sean bean's most memorable death wasn't 'lord ...
\n","
[-0.013742960058152676, 0.05260717123746872, -...
\n","
sean bean's most memorable death wasn't 'lord ...
\n","
0.888762
\n","
[sean bean's most memorable death wasn't 'lord...
\n","
negative
\n","
3328
\n","
\n","
\n","
38
\n","
positive
\n","
wife too busy videotaping elk attack to save h...
\n","
[-0.02963477373123169, -0.024700473994016647, ...
\n","
wife too busy videotaping elk attack to save h...
\n","
0.892977
\n","
[wife too busy videotaping elk attack to save ...
\n","
positive
\n","
3869
\n","
\n","
\n","
39
\n","
positive
\n","
obama, romney remain about equally powerful
\n","
[0.046225905418395996, 0.020606394857168198, -...
\n","
obama, romney remain about equally powerful
\n","
0.950219
\n","
[obama, romney remain about equally powerful]
\n","
positive
\n","
6087
\n","
\n","
\n","
40
\n","
positive
\n","
man arriving late forced to use excuse he was ...
\n","
[0.007267913315445185, 0.012945346534252167, -...
\n","
man arriving late forced to use excuse he was ...
\n","
0.854663
\n","
[man arriving late forced to use excuse he was...
\n","
positive
\n","
6329
\n","
\n","
\n","
41
\n","
negative
\n","
news roundup for june 19, 2017
\n","
[0.006106187589466572, -0.07620107382535934, -...
\n","
news roundup for june 19, 2017
\n","
0.926237
\n","
[news roundup for june 19, 2017]
\n","
negative
\n","
4470
\n","
\n","
\n","
42
\n","
positive
\n","
report: 1 in 5 air ducts contains person looki...
\n","
[-0.03163425996899605, 0.012088724412024021, -...
\n","
report: 1 in 5 air ducts contains person looki...
\n","
0.952198
\n","
[report: 1 in 5 air ducts contains person look...
\n","
positive
\n","
4423
\n","
\n","
\n","
43
\n","
negative
\n","
arrested but innocent? the internet still thin...
\n","
[0.0008291222620755434, -0.07605188339948654, ...
\n","
arrested but innocent? the internet still thin...
\n","
0.936171
\n","
[arrested but innocent?, the internet still t...
\n","
negative
\n","
7363
\n","
\n","
\n","
44
\n","
negative
\n","
this desperate dad is trying to ward off the t...
\n","
[-0.009455329738557339, -0.0632706880569458, -...
\n","
this desperate dad is trying to ward off the t...
\n","
0.873377
\n","
[this desperate dad is trying to ward off the ...
\n","
negative
\n","
6400
\n","
\n","
\n","
45
\n","
negative
\n","
in defense of the promposal
\n","
[0.04038773849606514, 0.06097205728292465, -0....
\n","
in defense of the promposal
\n","
0.866386
\n","
[in defense of the promposal]
\n","
negative
\n","
9190
\n","
\n","
\n","
46
\n","
positive
\n","
reason man turning to religion later in life m...
\n","
[0.024827271699905396, -0.00032991680200211704...
\n","
reason man turning to religion later in life m...
\n","
0.901040
\n","
[reason man turning to religion later in life ...
\n","
positive
\n","
234
\n","
\n","
\n","
47
\n","
positive
\n","
bunch of hick nobodies sue for toxic-waste exp...
\n","
[0.02218099869787693, 0.0022194429766386747, 0...
\n","
bunch of hick nobodies sue for toxic-waste exp...
\n","
0.874898
\n","
[bunch of hick nobodies sue for toxic-waste ex...
\n","
positive
\n","
5098
\n","
\n","
\n","
48
\n","
positive
\n","
no one in family sure who trip to arboretum is...
\n","
[-0.03585259988903999, 0.017737973481416702, -...
\n","
no one in family sure who trip to arboretum is...
\n","
0.859806
\n","
[no one in family sure who trip to arboretum i...
\n","
positive
\n","
2552
\n","
\n","
\n","
49
\n","
positive
\n","
voice coming from dnc sound system during sand...
\n","
[-0.009395286440849304, -0.0020206505432724953...
\n","
voice coming from dnc sound system during sand...
\n","
0.943251
\n","
[voice coming from dnc sound system during san...
\n","
positive
\n","
1449
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment ... origin_index\n","0 positive ... 1463\n","1 neutral ... 9543\n","2 positive ... 2930\n","3 positive ... 644\n","4 negative ... 2390\n","5 negative ... 5450\n","6 neutral ... 7739\n","7 negative ... 4370\n","8 positive ... 9418\n","9 negative ... 4357\n","10 positive ... 2418\n","11 positive ... 7882\n","12 positive ... 6117\n","13 negative ... 7992\n","14 positive ... 6445\n","15 positive ... 8222\n","16 negative ... 3847\n","17 neutral ... 253\n","18 negative ... 2504\n","19 negative ... 6992\n","20 positive ... 5882\n","21 negative ... 9721\n","22 positive ... 8362\n","23 neutral ... 1073\n","24 negative ... 7620\n","25 positive ... 6476\n","26 positive ... 6011\n","27 negative ... 7466\n","28 positive ... 2975\n","29 negative ... 9610\n","30 positive ... 9186\n","31 negative ... 5053\n","32 negative ... 2457\n","33 negative ... 4830\n","34 negative ... 9055\n","35 positive ... 1651\n","36 positive ... 5842\n","37 negative ... 3328\n","38 positive ... 3869\n","39 positive ... 6087\n","40 positive ... 6329\n","41 negative ... 4470\n","42 positive ... 4423\n","43 negative ... 7363\n","44 negative ... 6400\n","45 negative ... 9190\n","46 positive ... 234\n","47 positive ... 5098\n","48 positive ... 2552\n","49 positive ... 1449\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["# 7. Try training with different Embeddings"]},{"cell_type":"code","metadata":{"id":"nxWFzQOhjWC8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188377296,"user_tz":-120,"elapsed":14031,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"504a2809-2e9f-45b1-812d-4cd953323d9f"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IKK_Ii_gjJfF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620190483755,"user_tz":-120,"elapsed":2120443,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f6ff250f-0e4f-4425-a92e-18f08d6f1334"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(120) \n","trainable_pipe['sentiment_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.90 0.88 0.89 4023\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.90 0.88 0.89 3977\n","\n"," accuracy 0.88 8000\n"," macro avg 0.60 0.58 0.59 8000\n","weighted avg 0.90 0.88 0.89 8000\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 7.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"id":"Fxx4yNkNVGFl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620190639161,"user_tz":-120,"elapsed":2275776,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"d7f7c06c-aae2-43fe-f7c6-34a9fc87482b"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.84 0.82 0.83 977\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.86 0.81 0.83 1023\n","\n"," accuracy 0.82 2000\n"," macro avg 0.57 0.55 0.56 2000\n","weighted avg 0.85 0.82 0.83 2000\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 8. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620190792311,"user_tz":-120,"elapsed":2428848,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"5f8bc338-d089-4a82-e4b6-0eb286d14b0d"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 9. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620190810537,"user_tz":-120,"elapsed":2446986,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2ad4167d-9332-4880-d27b-6a1679256f41"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Aliens are immortal!')\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
text
\n","
sentiment
\n","
origin_index
\n","
sentence
\n","
sentiment_confidence
\n","
sentence_embedding_from_disk
\n","
\n"," \n"," \n","
\n","
0
\n","
Aliens are immortal!
\n","
Aliens are immortal!
\n","
[negative]
\n","
8589934592
\n","
[Aliens are immortal!]
\n","
[0.9999138]
\n","
[[0.30930545926094055, 0.12947328388690948, 0....
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence_embedding_from_disk\n","0 Aliens are immortal! ... [[0.30930545926094055, 0.12947328388690948, 0....\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620190810538,"user_tz":-120,"elapsed":2446902,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"4f7f8f8f-477e-430a-c17c-5b84c3ebe8fb"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@6cf015ef) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@6cf015ef\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentiment_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"aq3RCRU4wHsv"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo.ipynb
deleted file mode 100644
index 14749a8d..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_sentiment_classifier_demo.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo.ipynb)\n","\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl-multi-class-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620215193255,"user_tz":-120,"elapsed":126309,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"ede615d6-a5e6-41a3-aa1e-1f0c3c841234"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 11:44:27-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 11:44:27 (1.49 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 58kB/s \n","\u001b[K |████████████████████████████████| 153kB 46.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 18.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 52.9MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Stock Market Sentiment dataset \n","https://www.kaggle.com/yash612/stockmarket-sentiment-dataset"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620215194079,"user_tz":-120,"elapsed":127118,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f161f492-1d88-4652-aeb8-1a0f67e0272c"},"source":["! wget http://ckl-it.de/wp-content/uploads/2020/11/stock_data.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 11:46:32-- http://ckl-it.de/wp-content/uploads/2020/11/stock_data.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 479973 (469K) [text/csv]\n","Saving to: ‘stock_data.csv’\n","\n","stock_data.csv 100%[===================>] 468.72K 846KB/s in 0.6s \n","\n","2021-05-05 11:46:33 (846 KB/s) - ‘stock_data.csv’ saved [479973/479973]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uDGIOASY_fRj","executionInfo":{"status":"ok","timestamp":1620215245366,"user_tz":-120,"elapsed":178400,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"fcf975df-6fe9-4a6a-f7fd-10439b2b541c"},"source":["import nlu\n","sentiment = nlu.load('sentiment')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["analyze_sentiment download started this may take some time.\n","Approx size to download 4.9 MB\n","[OK!]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":76},"id":"U0ENiuMc_kyb","executionInfo":{"status":"ok","timestamp":1620215255157,"user_tz":-120,"elapsed":188186,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b77e7958-75b4-4bf7-aff8-e416450b168a"},"source":["sentiment.predict(\"I'm very very not at all happy\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
token
\n","
sentiment
\n","
sentence
\n","
origin_index
\n","
spell
\n","
document
\n","
sentiment_confidence
\n","
text
\n","
\n"," \n"," \n","
\n","
0
\n","
[I'm, very, very, not, at, all, happy]
\n","
[positive]
\n","
[I'm very very not at all happy]
\n","
8589934592
\n","
[I'm, very, very, not, at, all, happy]
\n","
I'm very very not at all happy
\n","
[0.3043]
\n","
I'm very very not at all happy
\n","
\n"," \n","
\n","
"],"text/plain":[" token ... text\n","0 [I'm, very, very, not, at, all, happy] ... I'm very very not at all happy\n","\n","[1 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620215255159,"user_tz":-120,"elapsed":188185,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"42f2b380-7742-49ee-f640-c62d4c969fce"},"source":["import pandas as pd\n","train_path = '/content/stock_data.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","# the label column must have name 'y' name be of type str\n","train_df.columns=['text','y']\n","train_df.y = train_df.y.astype(str)\n","train_df.y = train_df.y.str.replace('-1','negative')\n","train_df.y = train_df.y.str.replace('1','positive')\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
0
\n","
Kickers on my watchlist XIDE TIT SOQ PNK CPW B...
\n","
positive
\n","
\n","
\n","
1
\n","
user: AAP MOVIE. 55% return for the FEA/GEED i...
\n","
positive
\n","
\n","
\n","
2
\n","
user I'd be afraid to short AMZN - they are lo...
\n","
positive
\n","
\n","
\n","
3
\n","
MNTA Over 12.00
\n","
positive
\n","
\n","
\n","
4
\n","
OI Over 21.37
\n","
positive
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
5786
\n","
Industry body CII said #discoms are likely to ...
\n","
negative
\n","
\n","
\n","
5787
\n","
#Gold prices slip below Rs 46,000 as #investor...
\n","
negative
\n","
\n","
\n","
5788
\n","
Workers at Bajaj Auto have agreed to a 10% wag...
\n","
positive
\n","
\n","
\n","
5789
\n","
#Sharemarket LIVE: Sensex off day’s high, up 6...
\n","
positive
\n","
\n","
\n","
5790
\n","
#Sensex, #Nifty climb off day's highs, still u...
\n","
positive
\n","
\n"," \n","
\n","
5791 rows × 2 columns
\n","
"],"text/plain":[" text y\n","0 Kickers on my watchlist XIDE TIT SOQ PNK CPW B... positive\n","1 user: AAP MOVIE. 55% return for the FEA/GEED i... positive\n","2 user I'd be afraid to short AMZN - they are lo... positive\n","3 MNTA Over 12.00 positive\n","4 OI Over 21.37 positive\n","... ... ...\n","5786 Industry body CII said #discoms are likely to ... negative\n","5787 #Gold prices slip below Rs 46,000 as #investor... negative\n","5788 Workers at Bajaj Auto have agreed to a 10% wag... positive\n","5789 #Sharemarket LIVE: Sensex off day’s high, up 6... positive\n","5790 #Sensex, #Nifty climb off day's highs, still u... positive\n","\n","[5791 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":651},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620215420196,"user_tz":-120,"elapsed":353218,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0eea644b-5461-4347-b47e-c300e441be80"},"source":["from sklearn.metrics import classification_report\n","import nlu \n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df)\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.61 0.58 0.59 2106\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.79 0.71 0.75 3685\n","\n"," accuracy 0.66 5791\n"," macro avg 0.47 0.43 0.45 5791\n","weighted avg 0.73 0.66 0.69 5791\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
y
\n","
document
\n","
trained_sentiment_confidence
\n","
text
\n","
trained_sentiment
\n","
sentence_embedding_use
\n","
\n"," \n"," \n","
\n","
0
\n","
[Kickers on my watchlist XIDE TIT SOQ PNK CPW ...
\n","
0
\n","
positive
\n","
Kickers on my watchlist XIDE TIT SOQ PNK CPW B...
\n","
0.985571
\n","
Kickers on my watchlist XIDE TIT SOQ PNK CPW B...
\n","
positive
\n","
[0.006487144622951746, -0.042024899274110794, ...
\n","
\n","
\n","
1
\n","
[user: AAP MOVIE. 55% return for the FEA/GEED ...
\n","
1
\n","
positive
\n","
user: AAP MOVIE. 55% return for the FEA/GEED i...
\n","
0.720251
\n","
user: AAP MOVIE. 55% return for the FEA/GEED i...
\n","
positive
\n","
[0.04891366884112358, -0.07381151616573334, -0...
\n","
\n","
\n","
2
\n","
[user I'd be afraid to short AMZN - they are l...
\n","
2
\n","
positive
\n","
user I'd be afraid to short AMZN - they are lo...
\n","
0.580390
\n","
user I'd be afraid to short AMZN - they are lo...
\n","
neutral
\n","
[0.05556508153676987, -0.016491785645484924, 0...
\n","
\n","
\n","
3
\n","
[MNTA Over 12.00]
\n","
3
\n","
positive
\n","
MNTA Over 12.00
\n","
0.941032
\n","
MNTA Over 12.00
\n","
positive
\n","
[-0.010976563207805157, -0.029801178723573685,...
\n","
\n","
\n","
4
\n","
[OI Over 21.37]
\n","
4
\n","
positive
\n","
OI Over 21.37
\n","
0.535012
\n","
OI Over 21.37
\n","
neutral
\n","
[0.024849383160471916, 0.04679657891392708, -0...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
5786
\n","
[Industry body CII said #discoms are likely to...
\n","
5786
\n","
negative
\n","
Industry body CII said #discoms are likely to ...
\n","
0.939032
\n","
Industry body CII said #discoms are likely to ...
\n","
negative
\n","
[0.020985640585422516, -0.03145354241132736, -...
\n","
\n","
\n","
5787
\n","
[#Gold prices slip below Rs 46,000 as #investo...
\n","
5787
\n","
negative
\n","
#Gold prices slip below Rs 46,000 as #investor...
\n","
0.983991
\n","
#Gold prices slip below Rs 46,000 as #investor...
\n","
negative
\n","
[0.05627664923667908, 0.012842322699725628, -0...
\n","
\n","
\n","
5788
\n","
[Workers at Bajaj Auto have agreed to a 10% wa...
\n","
5788
\n","
positive
\n","
Workers at Bajaj Auto have agreed to a 10% wag...
\n","
0.918838
\n","
Workers at Bajaj Auto have agreed to a 10% wag...
\n","
negative
\n","
[0.019935883581638336, -0.031780488789081573, ...
\n","
\n","
\n","
5789
\n","
[#Sharemarket LIVE: Sensex off day’s high, up ...
\n","
5789
\n","
positive
\n","
#Sharemarket LIVE: Sensex off day’s high, up 6...
\n","
0.761864
\n","
#Sharemarket LIVE: Sensex off day’s high, up 6...
\n","
positive
\n","
[0.0031773506198078394, -0.04296385496854782, ...
\n","
\n","
\n","
5790
\n","
[#Sensex, #Nifty climb off day's highs, still ...
\n","
5790
\n","
positive
\n","
#Sensex, #Nifty climb off day's highs, still u...
\n","
0.904347
\n","
#Sensex, #Nifty climb off day's highs, still u...
\n","
positive
\n","
[0.04823731631040573, -0.012027987278997898, -...
\n","
\n"," \n","
\n","
5791 rows × 8 columns
\n","
"],"text/plain":[" sentence ... sentence_embedding_use\n","0 [Kickers on my watchlist XIDE TIT SOQ PNK CPW ... ... [0.006487144622951746, -0.042024899274110794, ...\n","1 [user: AAP MOVIE. 55% return for the FEA/GEED ... ... [0.04891366884112358, -0.07381151616573334, -0...\n","2 [user I'd be afraid to short AMZN - they are l... ... [0.05556508153676987, -0.016491785645484924, 0...\n","3 [MNTA Over 12.00] ... [-0.010976563207805157, -0.029801178723573685,...\n","4 [OI Over 21.37] ... [0.024849383160471916, 0.04679657891392708, -0...\n","... ... ... ...\n","5786 [Industry body CII said #discoms are likely to... ... [0.020985640585422516, -0.03145354241132736, -...\n","5787 [#Gold prices slip below Rs 46,000 as #investo... ... [0.05627664923667908, 0.012842322699725628, -0...\n","5788 [Workers at Bajaj Auto have agreed to a 10% wa... ... [0.019935883581638336, -0.031780488789081573, ...\n","5789 [#Sharemarket LIVE: Sensex off day’s high, up ... ... [0.0031773506198078394, -0.04296385496854782, ...\n","5790 [#Sensex, #Nifty climb off day's highs, still ... ... [0.04823731631040573, -0.012027987278997898, -...\n","\n","[5791 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"lVyOE2wV0fw_"},"source":["# Test the fitted pipe on new example"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":76},"id":"qdCUg2MR0PD2","executionInfo":{"status":"ok","timestamp":1620215420883,"user_tz":-120,"elapsed":353902,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"4ebad73c-0dc4-40cd-d173-a8134f269886"},"source":["fitted_pipe.predict(\"Bitcoin is going to the moon!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_use
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin is going to the moon!]
\n","
0
\n","
Bitcoin is going to the moon!
\n","
0.713436
\n","
positive
\n","
[0.06468033790588379, -0.040837567299604416, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... sentence_embedding_use\n","0 [Bitcoin is going to the moon!] ... [0.06468033790588379, -0.040837567299604416, -...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620215420884,"user_tz":-120,"elapsed":353900,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0a8cbe1d-a0be-47d9-e9d4-fac1f907b178"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@4ae24199) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@4ae24199\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":553},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620215480821,"user_tz":-120,"elapsed":413834,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"a07fb934-0fcb-4213-f693-118b873437c5"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.83 0.38 0.52 2106\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.75 0.94 0.83 3685\n","\n"," accuracy 0.74 5791\n"," macro avg 0.53 0.44 0.45 5791\n","weighted avg 0.78 0.74 0.72 5791\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
y
\n","
document
\n","
trained_sentiment_confidence
\n","
text
\n","
trained_sentiment
\n","
sentence_embedding_use
\n","
\n"," \n"," \n","
\n","
0
\n","
[Kickers on my watchlist XIDE TIT SOQ PNK CPW ...
\n","
0
\n","
positive
\n","
Kickers on my watchlist XIDE TIT SOQ PNK CPW B...
\n","
1.000000
\n","
Kickers on my watchlist XIDE TIT SOQ PNK CPW B...
\n","
positive
\n","
[0.006487144622951746, -0.042024899274110794, ...
\n","
\n","
\n","
1
\n","
[user: AAP MOVIE. 55% return for the FEA/GEED ...
\n","
1
\n","
positive
\n","
user: AAP MOVIE. 55% return for the FEA/GEED i...
\n","
0.934187
\n","
user: AAP MOVIE. 55% return for the FEA/GEED i...
\n","
positive
\n","
[0.04891366884112358, -0.07381151616573334, -0...
\n","
\n","
\n","
2
\n","
[user I'd be afraid to short AMZN - they are l...
\n","
2
\n","
positive
\n","
user I'd be afraid to short AMZN - they are lo...
\n","
0.625671
\n","
user I'd be afraid to short AMZN - they are lo...
\n","
negative
\n","
[0.05556508153676987, -0.016491785645484924, 0...
\n","
\n","
\n","
3
\n","
[MNTA Over 12.00]
\n","
3
\n","
positive
\n","
MNTA Over 12.00
\n","
0.999983
\n","
MNTA Over 12.00
\n","
positive
\n","
[-0.010976563207805157, -0.029801178723573685,...
\n","
\n","
\n","
4
\n","
[OI Over 21.37]
\n","
4
\n","
positive
\n","
OI Over 21.37
\n","
0.985523
\n","
OI Over 21.37
\n","
positive
\n","
[0.024849383160471916, 0.04679657891392708, -0...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
5786
\n","
[Industry body CII said #discoms are likely to...
\n","
5786
\n","
negative
\n","
Industry body CII said #discoms are likely to ...
\n","
0.733400
\n","
Industry body CII said #discoms are likely to ...
\n","
negative
\n","
[0.020985640585422516, -0.03145354241132736, -...
\n","
\n","
\n","
5787
\n","
[#Gold prices slip below Rs 46,000 as #investo...
\n","
5787
\n","
negative
\n","
#Gold prices slip below Rs 46,000 as #investor...
\n","
0.967702
\n","
#Gold prices slip below Rs 46,000 as #investor...
\n","
negative
\n","
[0.05627664923667908, 0.012842322699725628, -0...
\n","
\n","
\n","
5788
\n","
[Workers at Bajaj Auto have agreed to a 10% wa...
\n","
5788
\n","
positive
\n","
Workers at Bajaj Auto have agreed to a 10% wag...
\n","
0.778937
\n","
Workers at Bajaj Auto have agreed to a 10% wag...
\n","
negative
\n","
[0.019935883581638336, -0.031780488789081573, ...
\n","
\n","
\n","
5789
\n","
[#Sharemarket LIVE: Sensex off day’s high, up ...
\n","
5789
\n","
positive
\n","
#Sharemarket LIVE: Sensex off day’s high, up 6...
\n","
0.999009
\n","
#Sharemarket LIVE: Sensex off day’s high, up 6...
\n","
positive
\n","
[0.0031773506198078394, -0.04296385496854782, ...
\n","
\n","
\n","
5790
\n","
[#Sensex, #Nifty climb off day's highs, still ...
\n","
5790
\n","
positive
\n","
#Sensex, #Nifty climb off day's highs, still u...
\n","
0.999243
\n","
#Sensex, #Nifty climb off day's highs, still u...
\n","
positive
\n","
[0.04823731631040573, -0.012027987278997898, -...
\n","
\n"," \n","
\n","
5791 rows × 8 columns
\n","
"],"text/plain":[" sentence ... sentence_embedding_use\n","0 [Kickers on my watchlist XIDE TIT SOQ PNK CPW ... ... [0.006487144622951746, -0.042024899274110794, ...\n","1 [user: AAP MOVIE. 55% return for the FEA/GEED ... ... [0.04891366884112358, -0.07381151616573334, -0...\n","2 [user I'd be afraid to short AMZN - they are l... ... [0.05556508153676987, -0.016491785645484924, 0...\n","3 [MNTA Over 12.00] ... [-0.010976563207805157, -0.029801178723573685,...\n","4 [OI Over 21.37] ... [0.024849383160471916, 0.04679657891392708, -0...\n","... ... ... ...\n","5786 [Industry body CII said #discoms are likely to... ... [0.020985640585422516, -0.03145354241132736, -...\n","5787 [#Gold prices slip below Rs 46,000 as #investo... ... [0.05627664923667908, 0.012842322699725628, -0...\n","5788 [Workers at Bajaj Auto have agreed to a 10% wa... ... [0.019935883581638336, -0.031780488789081573, ...\n","5789 [#Sharemarket LIVE: Sensex off day’s high, up ... ... [0.0031773506198078394, -0.04296385496854782, ...\n","5790 [#Sensex, #Nifty climb off day's highs, still ... ... [0.04823731631040573, -0.012027987278997898, -...\n","\n","[5791 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["# Try training with different Embeddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxWFzQOhjWC8","executionInfo":{"status":"ok","timestamp":1620215481023,"user_tz":-120,"elapsed":414033,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"69e55a4f-67b2-48e2-a5f1-395f5486dc31"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":651},"id":"IKK_Ii_gjJfF","executionInfo":{"status":"ok","timestamp":1620215663304,"user_tz":-120,"elapsed":596311,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"776428c8-8135-4f41-edfd-358ee5572e2f"},"source":["trainable_pipe = nlu.load('embed_sentence.bert train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(40) \n","trainable_pipe['sentiment_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L2_128 download started this may take some time.\n","Approximate size to download 16.1 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.68 0.24 0.35 2106\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.72 0.84 0.77 3685\n","\n"," accuracy 0.62 5791\n"," macro avg 0.47 0.36 0.37 5791\n","weighted avg 0.71 0.62 0.62 5791\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
y
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
text
\n","
sentence_embedding_bert
\n","
\n"," \n"," \n","
\n","
0
\n","
[Kickers on my watchlist XIDE TIT SOQ PNK CPW ...
\n","
0
\n","
positive
\n","
Kickers on my watchlist XIDE TIT SOQ PNK CPW B...
\n","
0.864774
\n","
positive
\n","
Kickers on my watchlist XIDE TIT SOQ PNK CPW B...
\n","
[-0.9207566380500793, 0.21013399958610535, 0.1...
\n","
\n","
\n","
1
\n","
[user: AAP MOVIE. 55% return for the FEA/GEED ...
\n","
1
\n","
positive
\n","
user: AAP MOVIE. 55% return for the FEA/GEED i...
\n","
0.648291
\n","
positive
\n","
user: AAP MOVIE. 55% return for the FEA/GEED i...
\n","
[-0.43004706501960754, 0.5101228952407837, -0....
\n","
\n","
\n","
2
\n","
[user I'd be afraid to short AMZN - they are l...
\n","
2
\n","
positive
\n","
user I'd be afraid to short AMZN - they are lo...
\n","
0.793143
\n","
positive
\n","
user I'd be afraid to short AMZN - they are lo...
\n","
[0.3040030300617218, 0.22862930595874786, -0.5...
\n","
\n","
\n","
3
\n","
[MNTA Over 12.00]
\n","
3
\n","
positive
\n","
MNTA Over 12.00
\n","
0.964940
\n","
positive
\n","
MNTA Over 12.00
\n","
[-1.8103482723236084, -0.4799136519432068, -0....
\n","
\n","
\n","
4
\n","
[OI Over 21.37]
\n","
4
\n","
positive
\n","
OI Over 21.37
\n","
0.959243
\n","
positive
\n","
OI Over 21.37
\n","
[-2.4639298915863037, 0.3879586458206177, -0.6...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
5786
\n","
[Industry body CII said #discoms are likely to...
\n","
5786
\n","
negative
\n","
Industry body CII said #discoms are likely to ...
\n","
0.753365
\n","
negative
\n","
Industry body CII said #discoms are likely to ...
\n","
[-0.09503882378339767, 0.6293947696685791, 0.0...
\n","
\n","
\n","
5787
\n","
[#Gold prices slip below Rs 46,000 as #investo...
\n","
5787
\n","
negative
\n","
#Gold prices slip below Rs 46,000 as #investor...
\n","
0.724050
\n","
negative
\n","
#Gold prices slip below Rs 46,000 as #investor...
\n","
[-0.12879370152950287, 0.28170245885849, 0.028...
\n","
\n","
\n","
5788
\n","
[Workers at Bajaj Auto have agreed to a 10% wa...
\n","
5788
\n","
positive
\n","
Workers at Bajaj Auto have agreed to a 10% wag...
\n","
0.781417
\n","
negative
\n","
Workers at Bajaj Auto have agreed to a 10% wag...
\n","
[-0.3395586907863617, 0.9124063849449158, -0.3...
\n","
\n","
\n","
5789
\n","
[#Sharemarket LIVE: Sensex off day’s high, up ...
\n","
5789
\n","
positive
\n","
#Sharemarket LIVE: Sensex off day’s high, up 6...
\n","
0.520319
\n","
neutral
\n","
#Sharemarket LIVE: Sensex off day’s high, up 6...
\n","
[-0.6081282496452332, 0.2732301652431488, 0.25...
\n","
\n","
\n","
5790
\n","
[#Sensex, #Nifty climb off day's highs, still ...
\n","
5790
\n","
positive
\n","
#Sensex, #Nifty climb off day's highs, still u...
\n","
0.542672
\n","
neutral
\n","
#Sensex, #Nifty climb off day's highs, still u...
\n","
[-0.4486270248889923, 0.43264666199684143, 0.0...
\n","
\n"," \n","
\n","
5791 rows × 8 columns
\n","
"],"text/plain":[" sentence ... sentence_embedding_bert\n","0 [Kickers on my watchlist XIDE TIT SOQ PNK CPW ... ... [-0.9207566380500793, 0.21013399958610535, 0.1...\n","1 [user: AAP MOVIE. 55% return for the FEA/GEED ... ... [-0.43004706501960754, 0.5101228952407837, -0....\n","2 [user I'd be afraid to short AMZN - they are l... ... [0.3040030300617218, 0.22862930595874786, -0.5...\n","3 [MNTA Over 12.00] ... [-1.8103482723236084, -0.4799136519432068, -0....\n","4 [OI Over 21.37] ... [-2.4639298915863037, 0.3879586458206177, -0.6...\n","... ... ... ...\n","5786 [Industry body CII said #discoms are likely to... ... [-0.09503882378339767, 0.6293947696685791, 0.0...\n","5787 [#Gold prices slip below Rs 46,000 as #investo... ... [-0.12879370152950287, 0.28170245885849, 0.028...\n","5788 [Workers at Bajaj Auto have agreed to a 10% wa... ... [-0.3395586907863617, 0.9124063849449158, -0.3...\n","5789 [#Sharemarket LIVE: Sensex off day’s high, up ... ... [-0.6081282496452332, 0.2732301652431488, 0.25...\n","5790 [#Sensex, #Nifty climb off day's highs, still ... ... [-0.4486270248889923, 0.43264666199684143, 0.0...\n","\n","[5791 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620215683754,"user_tz":-120,"elapsed":616758,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"d32a77d0-d25b-414d-e849-d6fee69b169c"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":76},"executionInfo":{"status":"ok","timestamp":1620215688728,"user_tz":-120,"elapsed":621729,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"d8ab883e-ae6e-442f-e92c-c2ce7147e3ee"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Tesla plans to invest 10M into the ML sector')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentiment
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_from_disk
\n","
sentiment_confidence
\n","
text
\n","
\n"," \n"," \n","
\n","
0
\n","
[positive]
\n","
[Tesla plans to invest 10M into the ML sector]
\n","
8589934592
\n","
Tesla plans to invest 10M into the ML sector
\n","
[[-0.07111598551273346, 0.9532928466796875, -1...
\n","
[0.9114534]
\n","
Tesla plans to invest 10M into the ML sector
\n","
\n"," \n","
\n","
"],"text/plain":[" sentiment ... text\n","0 [positive] ... Tesla plans to invest 10M into the ML sector\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620215688730,"user_tz":-120,"elapsed":621726,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"e5f153e0-cb42-4779-dfa2-ccefb4f1fba4"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@63c37ce4) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@63c37ce4\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L2_128'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L2_128'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L2_128'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L2_128'].setDimension(128) | Info: Number of embedding dimensions | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L2_128'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L2_128'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L2_128'].setStorageRef('sent_small_bert_L2_128') | Info: unique reference name for identification | Currently set to : sent_small_bert_L2_128\n",">>> pipe['sentiment_dl@sent_small_bert_L2_128'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L2_128'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L2_128'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L2_128'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L2_128'].setStorageRef('sent_small_bert_L2_128') | Info: unique reference name for identification | Currently set to : sent_small_bert_L2_128\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"73rQbUy-KLpb"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb
deleted file mode 100644
index e74cce7e..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_sentiment_classifier_demo_IMDB.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"],"toc_visible":true},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_IMDB.ipynb)\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 class IMDB Movie sentiment classifier training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n","\n"," \n","\n","\n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n","\n"," \n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620193225748,"user_tz":-120,"elapsed":116140,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"9ac3d81e-7a53-4711-fa24-e7320fd99723"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:38:30-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \rInstalling NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 05:38:30 (63.2 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 67kB/s \n","\u001b[K |████████████████████████████████| 153kB 73.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 23.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 65.5MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download IMDB dataset\n","https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews\n","\n","IMDB dataset having 50K movie reviews for natural language processing or Text analytics.\n","This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training and 25,000 for testing. So, predict the number of positive and negative reviews using either classification or deep learning algorithms.\n","For more dataset information, please go through the following link,\n","http://ai.stanford.edu/~amaas/data/sentiment/"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620193226173,"user_tz":-120,"elapsed":116555,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"827e3c03-e794-491f-ef27-a5951ecfa716"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/01/IMDB-Dataset.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:40:25-- http://ckl-it.de/wp-content/uploads/2021/01/IMDB-Dataset.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 3288450 (3.1M) [text/csv]\n","Saving to: ‘IMDB-Dataset.csv’\n","\n","IMDB-Dataset.csv 100%[===================>] 3.14M 20.6MB/s in 0.2s \n","\n","2021-05-05 05:40:25 (20.6 MB/s) - ‘IMDB-Dataset.csv’ saved [3288450/3288450]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620193227021,"user_tz":-120,"elapsed":117397,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"91412e47-6ce0-4d25-b00d-e2238146895b"},"source":["import pandas as pd\n","train_path = '/content/IMDB-Dataset.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","# the label column must have name 'y' name be of type str\n","columns=['text','y']\n","train_df = train_df[columns]\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
1459
\n","
Absolutely wonderful drama and Ros is top notc...
\n","
positive
\n","
\n","
\n","
2428
\n","
I was totally surprised just how good this mov...
\n","
positive
\n","
\n","
\n","
2093
\n","
This is a fine musical with a timeless score b...
\n","
positive
\n","
\n","
\n","
2370
\n","
Saw it yesterday night at the Midnight Slam of...
\n","
negative
\n","
\n","
\n","
2064
\n","
This show was Fabulous. It was intricate and w...
\n","
positive
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
353
\n","
This film held my interest enough to watch it ...
\n","
positive
\n","
\n","
\n","
2322
\n","
After reading the other tepid reviews and comm...
\n","
positive
\n","
\n","
\n","
1997
\n","
This movie, despite its list of B, C, and D li...
\n","
negative
\n","
\n","
\n","
1585
\n","
One of the most magnificent movies ever made. ...
\n","
positive
\n","
\n","
\n","
237
\n","
This movie is not worth anything. I mean, if y...
\n","
negative
\n","
\n"," \n","
\n","
2000 rows × 2 columns
\n","
"],"text/plain":[" text y\n","1459 Absolutely wonderful drama and Ros is top notc... positive\n","2428 I was totally surprised just how good this mov... positive\n","2093 This is a fine musical with a timeless score b... positive\n","2370 Saw it yesterday night at the Midnight Slam of... negative\n","2064 This show was Fabulous. It was intricate and w... positive\n","... ... ...\n","353 This film held my interest enough to watch it ... positive\n","2322 After reading the other tepid reviews and comm... positive\n","1997 This movie, despite its list of B, C, and D li... negative\n","1585 One of the most magnificent movies ever made. ... positive\n","237 This movie is not worth anything. I mean, if y... negative\n","\n","[2000 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620195366745,"user_tz":-120,"elapsed":11481,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f826ae3a-a84b-4aab-f612-edb38026ac36"},"source":["import nlu \n","from sklearn.metrics import classification_report\n","\n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.82 0.88 0.85 26\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.85 0.71 0.77 24\n","\n"," accuracy 0.80 50\n"," macro avg 0.56 0.53 0.54 50\n","weighted avg 0.84 0.80 0.81 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_use
\n","
trained_sentiment_confidence
\n","
text
\n","
document
\n","
trained_sentiment
\n","
sentence
\n","
y
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.06351275742053986, 0.040804557502269745, -2...
\n","
0.971989
\n","
Absolutely wonderful drama and Ros is top notc...
\n","
Absolutely wonderful drama and Ros is top notc...
\n","
negative
\n","
[Absolutely wonderful drama and Ros is top not...
\n","
positive
\n","
1459
\n","
\n","
\n","
1
\n","
[0.06992700695991516, 0.0012252129381522536, -...
\n","
0.972120
\n","
I was totally surprised just how good this mov...
\n","
I was totally surprised just how good this mov...
\n","
positive
\n","
[I was totally surprised just how good this mo...
\n","
positive
\n","
2428
\n","
\n","
\n","
2
\n","
[0.052442848682403564, -0.017640504986047745, ...
\n","
0.989897
\n","
This is a fine musical with a timeless score b...
\n","
This is a fine musical with a timeless score b...
\n","
positive
\n","
[This is a fine musical with a timeless score ...
\n","
positive
\n","
2093
\n","
\n","
\n","
3
\n","
[0.04445473477244377, 0.018118631094694138, -0...
\n","
0.999111
\n","
Saw it yesterday night at the Midnight Slam of...
\n","
Saw it yesterday night at the Midnight Slam of...
\n","
negative
\n","
[Saw it yesterday night at the Midnight Slam o...
\n","
negative
\n","
2370
\n","
\n","
\n","
4
\n","
[0.01753336377441883, 0.05620148777961731, -0....
\n","
0.954813
\n","
This show was Fabulous. It was intricate and w...
\n","
This show was Fabulous. It was intricate and w...
\n","
positive
\n","
[This show was Fabulous., It was intricate and...
\n","
positive
\n","
2064
\n","
\n","
\n","
5
\n","
[0.02791544236242771, -0.028735769912600517, -...
\n","
0.506074
\n","
Jean Dujardin gets Connery's mannerisms down p...
\n","
Jean Dujardin gets Connery's mannerisms down p...
\n","
neutral
\n","
[Jean Dujardin gets Connery's mannerisms down ...
\n","
positive
\n","
1988
\n","
\n","
\n","
6
\n","
[0.06690913438796997, 0.03236781805753708, -0....
\n","
0.982164
\n","
This is a tongue in cheek movie from the very ...
\n","
This is a tongue in cheek movie from the very ...
\n","
positive
\n","
[This is a tongue in cheek movie from the very...
\n","
positive
\n","
1483
\n","
\n","
\n","
7
\n","
[0.05750656872987747, -0.059703629463911057, -...
\n","
0.989845
\n","
I have seen most, if not all of the Laurel & H...
\n","
I have seen most, if not all of the Laurel & H...
\n","
negative
\n","
[I have seen most, if not all of the Laurel & ...
\n","
negative
\n","
207
\n","
\n","
\n","
8
\n","
[-0.04340153560042381, -0.015529784373939037, ...
\n","
0.991958
\n","
Although there is melodrama at the center or r...
\n","
Although there is melodrama at the center or r...
\n","
positive
\n","
[Although there is melodrama at the center or ...
\n","
positive
\n","
2030
\n","
\n","
\n","
9
\n","
[-0.032444775104522705, -0.018744193017482758,...
\n","
0.986264
\n","
\"Hey Babu Riba\" is a film about a young woman,...
\n","
\"Hey Babu Riba\" is a film about a young woman,...
\n","
positive
\n","
[\"Hey Babu Riba\" is a film about a young woman...
\n","
positive
\n","
397
\n","
\n","
\n","
10
\n","
[0.04818158969283104, 0.006010068114846945, 0....
\n","
0.991307
\n","
I have to say the first I watched this film wa...
\n","
I have to say the first I watched this film wa...
\n","
negative
\n","
[I have to say the first I watched this film w...
\n","
negative
\n","
1079
\n","
\n","
\n","
11
\n","
[-0.011373912915587425, 0.033458177000284195, ...
\n","
0.997292
\n","
Well, there's no real plot to speak of, it's j...
\n","
Well, there's no real plot to speak of, it's j...
\n","
negative
\n","
[Well, there's no real plot to speak of, it's ...
\n","
negative
\n","
308
\n","
\n","
\n","
12
\n","
[0.059722382575273514, -0.04133075103163719, -...
\n","
0.999755
\n","
Why can't a movie be rated a zero? Or even a n...
\n","
Why can't a movie be rated a zero? Or even a n...
\n","
negative
\n","
[Why can't a movie be rated a zero?, Or even a...
\n","
negative
\n","
175
\n","
\n","
\n","
13
\n","
[-0.0024044793099164963, 0.01555465068668127, ...
\n","
0.915433
\n","
PREY <br /><br />Aspect ratio: 1.37:1<br /><br...
\n","
PREY <br /><br />Aspect ratio: 1.37:1<br /><br...
\n","
positive
\n","
[PREY <br /><br />Aspect ratio: 1.37:1<br /><b...
\n","
negative
\n","
610
\n","
\n","
\n","
14
\n","
[0.014560171402990818, 0.07181794941425323, 0....
\n","
0.954957
\n","
Don't bother trying to watch this terrible min...
\n","
Don't bother trying to watch this terrible min...
\n","
negative
\n","
[Don't bother trying to watch this terrible mi...
\n","
negative
\n","
1516
\n","
\n","
\n","
15
\n","
[0.047474540770053864, -0.01399887353181839, -...
\n","
0.992878
\n","
This is one of the most ridiculous westerns th...
\n","
This is one of the most ridiculous westerns th...
\n","
negative
\n","
[This is one of the most ridiculous westerns t...
\n","
negative
\n","
1558
\n","
\n","
\n","
16
\n","
[0.04194203019142151, 0.014816542156040668, -0...
\n","
0.994948
\n","
Please don't waste your time. This movie rehas...
\n","
Please don't waste your time. This movie rehas...
\n","
negative
\n","
[Please don't waste your time., This movie reh...
\n","
negative
\n","
1691
\n","
\n","
\n","
17
\n","
[-0.01830967515707016, 0.0020676907151937485, ...
\n","
0.835862
\n","
Nice character development in a pretty cool mi...
\n","
Nice character development in a pretty cool mi...
\n","
positive
\n","
[Nice character development in a pretty cool m...
\n","
positive
\n","
227
\n","
\n","
\n","
18
\n","
[-0.028767548501491547, -0.04782490059733391, ...
\n","
0.994877
\n","
The Elegant Documentary -<br /><br />Don't wat...
\n","
The Elegant Documentary -<br /><br />Don't wat...
\n","
positive
\n","
[The Elegant Documentary -<br /><br />Don't wa...
\n","
positive
\n","
172
\n","
\n","
\n","
19
\n","
[0.020619677379727364, -0.0368206612765789, -0...
\n","
0.993641
\n","
The script was VERY weak w/o enough character ...
\n","
The script was VERY weak w/o enough character ...
\n","
negative
\n","
[The script was VERY weak w/o enough character...
\n","
negative
\n","
1687
\n","
\n","
\n","
20
\n","
[0.025141961872577667, 0.03661772608757019, -0...
\n","
0.972840
\n","
Though structured totally different from the b...
\n","
Though structured totally different from the b...
\n","
positive
\n","
[Though structured totally different from the ...
\n","
positive
\n","
2496
\n","
\n","
\n","
21
\n","
[0.05255172401666641, 0.0014638856519013643, -...
\n","
0.971560
\n","
I am so excited that Greek is back! This seaso...
\n","
I am so excited that Greek is back! This seaso...
\n","
positive
\n","
[I am so excited that Greek is back!, This sea...
\n","
positive
\n","
2162
\n","
\n","
\n","
22
\n","
[0.05090215429663658, 0.04202255234122276, -0....
\n","
0.986716
\n","
Petter Mattei's \"Love in the Time of Money\" is...
\n","
Petter Mattei's \"Love in the Time of Money\" is...
\n","
positive
\n","
[Petter Mattei's \"Love in the Time of Money\" i...
\n","
positive
\n","
4
\n","
\n","
\n","
23
\n","
[0.028773412108421326, 0.025388378649950027, -...
\n","
0.998700
\n","
Most Lorne Michaels films seem to fail because...
\n","
Most Lorne Michaels films seem to fail because...
\n","
negative
\n","
[Most Lorne Michaels films seem to fail becaus...
\n","
negative
\n","
654
\n","
\n","
\n","
24
\n","
[0.0029449418652802706, -0.054734643548727036,...
\n","
0.999298
\n","
\"Revolt of the Zombies\" proves that having the...
\n","
\"Revolt of the Zombies\" proves that having the...
\n","
negative
\n","
[\"Revolt of the Zombies\" proves that having th...
\n","
negative
\n","
126
\n","
\n","
\n","
25
\n","
[0.045008499175310135, 0.06566429883241653, -0...
\n","
0.760545
\n","
Necessarily ridiculous film version the litera...
\n","
Necessarily ridiculous film version the litera...
\n","
positive
\n","
[Necessarily ridiculous film version the liter...
\n","
negative
\n","
2316
\n","
\n","
\n","
26
\n","
[0.03256276622414589, -0.041013315320014954, -...
\n","
0.964842
\n","
Supposedly a \"social commentary\" on racism and...
\n","
Supposedly a \"social commentary\" on racism and...
\n","
negative
\n","
[Supposedly a \"social commentary\" on racism an...
"],"text/plain":[" sentence_embedding_use ... origin_index\n","0 [-0.022810865193605423, 0.015739120543003082, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## 5. Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620195367250,"user_tz":-120,"elapsed":11235,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"40bfeffd-ac62-4ddb-9bfb-dbc13a32ab76"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@260a728d) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@260a728d\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## 6. Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620195371203,"user_tz":-120,"elapsed":15108,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c558685c-7b89-42f6-f5d2-75bda38aebef"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.92 0.92 0.92 26\n"," neutral 0.00 0.00 0.00 0\n"," positive 1.00 0.75 0.86 24\n","\n"," accuracy 0.84 50\n"," macro avg 0.64 0.56 0.59 50\n","weighted avg 0.96 0.84 0.89 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_use
\n","
trained_sentiment_confidence
\n","
text
\n","
document
\n","
trained_sentiment
\n","
sentence
\n","
y
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.06351275742053986, 0.040804557502269745, -2...
\n","
0.564160
\n","
Absolutely wonderful drama and Ros is top notc...
\n","
Absolutely wonderful drama and Ros is top notc...
\n","
neutral
\n","
[Absolutely wonderful drama and Ros is top not...
\n","
positive
\n","
1459
\n","
\n","
\n","
1
\n","
[0.06992700695991516, 0.0012252129381522536, -...
\n","
0.853955
\n","
I was totally surprised just how good this mov...
\n","
I was totally surprised just how good this mov...
\n","
positive
\n","
[I was totally surprised just how good this mo...
\n","
positive
\n","
2428
\n","
\n","
\n","
2
\n","
[0.052442848682403564, -0.017640504986047745, ...
\n","
0.866541
\n","
This is a fine musical with a timeless score b...
\n","
This is a fine musical with a timeless score b...
\n","
positive
\n","
[This is a fine musical with a timeless score ...
\n","
positive
\n","
2093
\n","
\n","
\n","
3
\n","
[0.04445473477244377, 0.018118631094694138, -0...
\n","
0.894689
\n","
Saw it yesterday night at the Midnight Slam of...
\n","
Saw it yesterday night at the Midnight Slam of...
\n","
negative
\n","
[Saw it yesterday night at the Midnight Slam o...
\n","
negative
\n","
2370
\n","
\n","
\n","
4
\n","
[0.01753336377441883, 0.05620148777961731, -0....
\n","
0.680551
\n","
This show was Fabulous. It was intricate and w...
\n","
This show was Fabulous. It was intricate and w...
\n","
positive
\n","
[This show was Fabulous., It was intricate and...
\n","
positive
\n","
2064
\n","
\n","
\n","
5
\n","
[0.02791544236242771, -0.028735769912600517, -...
\n","
0.566901
\n","
Jean Dujardin gets Connery's mannerisms down p...
\n","
Jean Dujardin gets Connery's mannerisms down p...
\n","
neutral
\n","
[Jean Dujardin gets Connery's mannerisms down ...
\n","
positive
\n","
1988
\n","
\n","
\n","
6
\n","
[0.06690913438796997, 0.03236781805753708, -0....
\n","
0.870670
\n","
This is a tongue in cheek movie from the very ...
\n","
This is a tongue in cheek movie from the very ...
\n","
positive
\n","
[This is a tongue in cheek movie from the very...
\n","
positive
\n","
1483
\n","
\n","
\n","
7
\n","
[0.05750656872987747, -0.059703629463911057, -...
\n","
0.811313
\n","
I have seen most, if not all of the Laurel & H...
\n","
I have seen most, if not all of the Laurel & H...
\n","
negative
\n","
[I have seen most, if not all of the Laurel & ...
\n","
negative
\n","
207
\n","
\n","
\n","
8
\n","
[-0.04340153560042381, -0.015529784373939037, ...
\n","
0.885475
\n","
Although there is melodrama at the center or r...
\n","
Although there is melodrama at the center or r...
\n","
positive
\n","
[Although there is melodrama at the center or ...
\n","
positive
\n","
2030
\n","
\n","
\n","
9
\n","
[-0.032444775104522705, -0.018744193017482758,...
\n","
0.880005
\n","
\"Hey Babu Riba\" is a film about a young woman,...
\n","
\"Hey Babu Riba\" is a film about a young woman,...
\n","
positive
\n","
[\"Hey Babu Riba\" is a film about a young woman...
\n","
positive
\n","
397
\n","
\n","
\n","
10
\n","
[0.04818158969283104, 0.006010068114846945, 0....
\n","
0.832985
\n","
I have to say the first I watched this film wa...
\n","
I have to say the first I watched this film wa...
\n","
negative
\n","
[I have to say the first I watched this film w...
\n","
negative
\n","
1079
\n","
\n","
\n","
11
\n","
[-0.011373912915587425, 0.033458177000284195, ...
\n","
0.881889
\n","
Well, there's no real plot to speak of, it's j...
\n","
Well, there's no real plot to speak of, it's j...
\n","
negative
\n","
[Well, there's no real plot to speak of, it's ...
\n","
negative
\n","
308
\n","
\n","
\n","
12
\n","
[0.059722382575273514, -0.04133075103163719, -...
\n","
0.939842
\n","
Why can't a movie be rated a zero? Or even a n...
\n","
Why can't a movie be rated a zero? Or even a n...
\n","
negative
\n","
[Why can't a movie be rated a zero?, Or even a...
\n","
negative
\n","
175
\n","
\n","
\n","
13
\n","
[-0.0024044793099164963, 0.01555465068668127, ...
\n","
0.566536
\n","
PREY <br /><br />Aspect ratio: 1.37:1<br /><br...
\n","
PREY <br /><br />Aspect ratio: 1.37:1<br /><br...
\n","
neutral
\n","
[PREY <br /><br />Aspect ratio: 1.37:1<br /><b...
\n","
negative
\n","
610
\n","
\n","
\n","
14
\n","
[0.014560171402990818, 0.07181794941425323, 0....
\n","
0.835540
\n","
Don't bother trying to watch this terrible min...
\n","
Don't bother trying to watch this terrible min...
\n","
negative
\n","
[Don't bother trying to watch this terrible mi...
\n","
negative
\n","
1516
\n","
\n","
\n","
15
\n","
[0.047474540770053864, -0.01399887353181839, -...
\n","
0.720724
\n","
This is one of the most ridiculous westerns th...
\n","
This is one of the most ridiculous westerns th...
\n","
negative
\n","
[This is one of the most ridiculous westerns t...
\n","
negative
\n","
1558
\n","
\n","
\n","
16
\n","
[0.04194203019142151, 0.014816542156040668, -0...
\n","
0.881261
\n","
Please don't waste your time. This movie rehas...
\n","
Please don't waste your time. This movie rehas...
\n","
negative
\n","
[Please don't waste your time., This movie reh...
\n","
negative
\n","
1691
\n","
\n","
\n","
17
\n","
[-0.01830967515707016, 0.0020676907151937485, ...
\n","
0.744092
\n","
Nice character development in a pretty cool mi...
\n","
Nice character development in a pretty cool mi...
\n","
positive
\n","
[Nice character development in a pretty cool m...
\n","
positive
\n","
227
\n","
\n","
\n","
18
\n","
[-0.028767548501491547, -0.04782490059733391, ...
\n","
0.875401
\n","
The Elegant Documentary -<br /><br />Don't wat...
\n","
The Elegant Documentary -<br /><br />Don't wat...
\n","
positive
\n","
[The Elegant Documentary -<br /><br />Don't wa...
\n","
positive
\n","
172
\n","
\n","
\n","
19
\n","
[0.020619677379727364, -0.0368206612765789, -0...
\n","
0.839296
\n","
The script was VERY weak w/o enough character ...
\n","
The script was VERY weak w/o enough character ...
\n","
negative
\n","
[The script was VERY weak w/o enough character...
\n","
negative
\n","
1687
\n","
\n","
\n","
20
\n","
[0.025141961872577667, 0.03661772608757019, -0...
\n","
0.849500
\n","
Though structured totally different from the b...
\n","
Though structured totally different from the b...
\n","
positive
\n","
[Though structured totally different from the ...
\n","
positive
\n","
2496
\n","
\n","
\n","
21
\n","
[0.05255172401666641, 0.0014638856519013643, -...
\n","
0.840122
\n","
I am so excited that Greek is back! This seaso...
\n","
I am so excited that Greek is back! This seaso...
\n","
positive
\n","
[I am so excited that Greek is back!, This sea...
\n","
positive
\n","
2162
\n","
\n","
\n","
22
\n","
[0.05090215429663658, 0.04202255234122276, -0....
\n","
0.874497
\n","
Petter Mattei's \"Love in the Time of Money\" is...
\n","
Petter Mattei's \"Love in the Time of Money\" is...
\n","
positive
\n","
[Petter Mattei's \"Love in the Time of Money\" i...
\n","
positive
\n","
4
\n","
\n","
\n","
23
\n","
[0.028773412108421326, 0.025388378649950027, -...
\n","
0.912337
\n","
Most Lorne Michaels films seem to fail because...
\n","
Most Lorne Michaels films seem to fail because...
\n","
negative
\n","
[Most Lorne Michaels films seem to fail becaus...
\n","
negative
\n","
654
\n","
\n","
\n","
24
\n","
[0.0029449418652802706, -0.054734643548727036,...
\n","
0.894369
\n","
\"Revolt of the Zombies\" proves that having the...
\n","
\"Revolt of the Zombies\" proves that having the...
\n","
negative
\n","
[\"Revolt of the Zombies\" proves that having th...
\n","
negative
\n","
126
\n","
\n","
\n","
25
\n","
[0.045008499175310135, 0.06566429883241653, -0...
\n","
0.562989
\n","
Necessarily ridiculous film version the litera...
\n","
Necessarily ridiculous film version the litera...
\n","
neutral
\n","
[Necessarily ridiculous film version the liter...
\n","
negative
\n","
2316
\n","
\n","
\n","
26
\n","
[0.03256276622414589, -0.041013315320014954, -...
\n","
0.753462
\n","
Supposedly a \"social commentary\" on racism and...
\n","
Supposedly a \"social commentary\" on racism and...
\n","
negative
\n","
[Supposedly a \"social commentary\" on racism an...
\n","
negative
\n","
250
\n","
\n","
\n","
27
\n","
[0.037413761019706726, -0.041003260761499405, ...
\n","
0.898136
\n","
I rented the DVD in a video store, as an alter...
\n","
I rented the DVD in a video store, as an alter...
\n","
negative
\n","
[I rented the DVD in a video store, as an alte...
\n","
negative
\n","
644
\n","
\n","
\n","
28
\n","
[-0.0654454156756401, 0.005620448384433985, -0...
\n","
0.765227
\n","
If you like original gut wrenching laughter yo...
\n","
If you like original gut wrenching laughter yo...
\n","
positive
\n","
[If you like original gut wrenching laughter y...
\n","
positive
\n","
9
\n","
\n","
\n","
29
\n","
[0.027594028040766716, -0.024396954104304314, ...
\n","
0.581867
\n","
Doctor Mordrid is one of those rare films that...
\n","
Doctor Mordrid is one of those rare films that...
\n","
neutral
\n","
[Doctor Mordrid is one of those rare films tha...
\n","
positive
\n","
376
\n","
\n","
\n","
30
\n","
[0.017458664253354073, 0.05214596167206764, -0...
\n","
0.814803
\n","
Back in 1982 a little film called MAKING LOVE ...
\n","
Back in 1982 a little film called MAKING LOVE ...
\n","
positive
\n","
[Back in 1982 a little film called MAKING LOVE...
\n","
positive
\n","
746
\n","
\n","
\n","
31
\n","
[0.04889684543013573, 0.029793666675686836, 0....
\n","
0.711966
\n","
1936 was the most prolific year for Astaire an...
\n","
1936 was the most prolific year for Astaire an...
\n","
positive
\n","
[1936 was the most prolific year for Astaire a...
\n","
positive
\n","
2293
\n","
\n","
\n","
32
\n","
[0.04028377681970596, 0.05781426653265953, -0....
\n","
0.823739
\n","
Tim Taylor is an abusive acholoic drug addict....
\n","
Tim Taylor is an abusive acholoic drug addict....
\n","
negative
\n","
[Tim Taylor is an abusive acholoic drug addict...
\n","
negative
\n","
2398
\n","
\n","
\n","
33
\n","
[0.033708442002534866, -0.06806037575006485, -...
\n","
0.880034
\n","
First of all yes I'm white, so I try to tread ...
\n","
First of all yes I'm white, so I try to tread ...
\n","
negative
\n","
[First of all yes I'm white, so I try to tread...
\n","
negative
\n","
784
\n","
\n","
\n","
34
\n","
[-0.05339590832591057, 0.03226976469159126, 0....
\n","
0.859471
\n","
I have to say that the events of 9/11 didn't h...
\n","
I have to say that the events of 9/11 didn't h...
\n","
positive
\n","
[I have to say that the events of 9/11 didn't ...
\n","
positive
\n","
713
\n","
\n","
\n","
35
\n","
[0.034553565084934235, -0.002318630227819085, ...
\n","
0.873029
\n","
This movie features Charlie Spradling dancing ...
\n","
This movie features Charlie Spradling dancing ...
\n","
negative
\n","
[This movie features Charlie Spradling dancing...
\n","
negative
\n","
358
\n","
\n","
\n","
36
\n","
[-0.03914014622569084, -0.04544996842741966, -...
\n","
0.919768
\n","
. . . and that is only if you like the sight o...
\n","
. . . and that is only if you like the sight o...
\n","
negative
\n","
[. . . and that is only if you like the sight ...
\n","
negative
\n","
1377
\n","
\n","
\n","
37
\n","
[-0.037124212831258774, 0.04466667398810387, -...
\n","
0.825637
\n","
An interesting movie with Jordana Brewster as ...
\n","
An interesting movie with Jordana Brewster as ...
\n","
positive
\n","
[An interesting movie with Jordana Brewster as...
\n","
positive
\n","
1902
\n","
\n","
\n","
38
\n","
[0.05217211693525314, -0.045366983860731125, -...
\n","
0.879427
\n","
Next to the slasher films of the 1970s and 80s...
\n","
Next to the slasher films of the 1970s and 80s...
\n","
negative
\n","
[Next to the slasher films of the 1970s and 80...
\n","
negative
\n","
2028
\n","
\n","
\n","
39
\n","
[0.054036945104599, -0.0035783019848167896, -0...
\n","
0.673891
\n","
I saw this movie in a theater while on vacatio...
\n","
I saw this movie in a theater while on vacatio...
\n","
negative
\n","
[I saw this movie in a theater while on vacati...
\n","
positive
\n","
929
\n","
\n","
\n","
40
\n","
[0.004495782777667046, 0.0002409428561804816, ...
\n","
0.870754
\n","
Did the first travesty actually make money? Th...
\n","
Did the first travesty actually make money? Th...
\n","
negative
\n","
[Did the first travesty actually make money?, ...
\n","
negative
\n","
659
\n","
\n","
\n","
41
\n","
[0.05336875841021538, 0.01685612089931965, -0....
\n","
0.839090
\n","
Jay Craven's criminally ignored film is a sobe...
\n","
Jay Craven's criminally ignored film is a sobe...
\n","
positive
\n","
[Jay Craven's criminally ignored film is a sob...
\n","
positive
\n","
2286
\n","
\n","
\n","
42
\n","
[0.06096196547150612, -0.018674220889806747, -...
\n","
0.880842
\n","
This is a great film with an amazing cast. Cri...
\n","
This is a great film with an amazing cast. Cri...
\n","
positive
\n","
[This is a great film with an amazing cast. Cr...
\n","
positive
\n","
1198
\n","
\n","
\n","
43
\n","
[-0.01874159276485443, -0.015269572846591473, ...
\n","
0.653215
\n","
I had a hard time staying awake for the two ho...
\n","
I had a hard time staying awake for the two ho...
\n","
negative
\n","
[I had a hard time staying awake for the two h...
\n","
negative
\n","
468
\n","
\n","
\n","
44
\n","
[0.0465228296816349, -0.03501952812075615, -0....
\n","
0.520477
\n","
Not the film to see if you want to be intellec...
\n","
Not the film to see if you want to be intellec...
\n","
neutral
\n","
[Not the film to see if you want to be intelle...
\n","
positive
\n","
1335
\n","
\n","
\n","
45
\n","
[-0.022962188348174095, -0.05519339069724083, ...
\n","
0.916282
\n","
I bought this (it was only $3, ok?) under the ...
\n","
I bought this (it was only $3, ok?) under the ...
\n","
negative
\n","
[I bought this (it was only $3, ok?) under the...
\n","
negative
\n","
1322
\n","
\n","
\n","
46
\n","
[0.05667755752801895, -0.004784214776009321, 0...
\n","
0.629980
\n","
I saw it tonight and fell asleep in the movie....
\n","
I saw it tonight and fell asleep in the movie....
\n","
negative
\n","
[I saw it tonight and fell asleep in the movie...
\n","
negative
\n","
2141
\n","
\n","
\n","
47
\n","
[-0.045412369072437286, -0.010548366233706474,...
\n","
0.944924
\n","
The opening scene keeps me from rating at abso...
\n","
The opening scene keeps me from rating at abso...
\n","
negative
\n","
[The opening scene keeps me from rating at abs...
\n","
negative
\n","
1065
\n","
\n","
\n","
48
\n","
[0.005430039018392563, -0.021274806931614876, ...
\n","
0.716253
\n","
If you are a fan of Zorro, Indiana Jones, or a...
\n","
If you are a fan of Zorro, Indiana Jones, or a...
\n","
negative
\n","
[If you are a fan of Zorro, Indiana Jones, or ...
\n","
positive
\n","
2103
\n","
\n","
\n","
49
\n","
[-0.012310817837715149, 0.036387305706739426, ...
\n","
0.831511
\n","
Stan Laurel and Oliver Hardy are the most famo...
\n","
Stan Laurel and Oliver Hardy are the most famo...
\n","
negative
\n","
[Stan Laurel and Oliver Hardy are the most fam...
\n","
negative
\n","
588
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_use ... origin_index\n","0 [0.06351275742053986, 0.040804557502269745, -2... ... 1459\n","1 [0.06992700695991516, 0.0012252129381522536, -... ... 2428\n","2 [0.052442848682403564, -0.017640504986047745, ... ... 2093\n","3 [0.04445473477244377, 0.018118631094694138, -0... ... 2370\n","4 [0.01753336377441883, 0.05620148777961731, -0.... ... 2064\n","5 [0.02791544236242771, -0.028735769912600517, -... ... 1988\n","6 [0.06690913438796997, 0.03236781805753708, -0.... ... 1483\n","7 [0.05750656872987747, -0.059703629463911057, -... ... 207\n","8 [-0.04340153560042381, -0.015529784373939037, ... ... 2030\n","9 [-0.032444775104522705, -0.018744193017482758,... ... 397\n","10 [0.04818158969283104, 0.006010068114846945, 0.... ... 1079\n","11 [-0.011373912915587425, 0.033458177000284195, ... ... 308\n","12 [0.059722382575273514, -0.04133075103163719, -... ... 175\n","13 [-0.0024044793099164963, 0.01555465068668127, ... ... 610\n","14 [0.014560171402990818, 0.07181794941425323, 0.... ... 1516\n","15 [0.047474540770053864, -0.01399887353181839, -... ... 1558\n","16 [0.04194203019142151, 0.014816542156040668, -0... ... 1691\n","17 [-0.01830967515707016, 0.0020676907151937485, ... ... 227\n","18 [-0.028767548501491547, -0.04782490059733391, ... ... 172\n","19 [0.020619677379727364, -0.0368206612765789, -0... ... 1687\n","20 [0.025141961872577667, 0.03661772608757019, -0... ... 2496\n","21 [0.05255172401666641, 0.0014638856519013643, -... ... 2162\n","22 [0.05090215429663658, 0.04202255234122276, -0.... ... 4\n","23 [0.028773412108421326, 0.025388378649950027, -... ... 654\n","24 [0.0029449418652802706, -0.054734643548727036,... ... 126\n","25 [0.045008499175310135, 0.06566429883241653, -0... ... 2316\n","26 [0.03256276622414589, -0.041013315320014954, -... ... 250\n","27 [0.037413761019706726, -0.041003260761499405, ... ... 644\n","28 [-0.0654454156756401, 0.005620448384433985, -0... ... 9\n","29 [0.027594028040766716, -0.024396954104304314, ... ... 376\n","30 [0.017458664253354073, 0.05214596167206764, -0... ... 746\n","31 [0.04889684543013573, 0.029793666675686836, 0.... ... 2293\n","32 [0.04028377681970596, 0.05781426653265953, -0.... ... 2398\n","33 [0.033708442002534866, -0.06806037575006485, -... ... 784\n","34 [-0.05339590832591057, 0.03226976469159126, 0.... ... 713\n","35 [0.034553565084934235, -0.002318630227819085, ... ... 358\n","36 [-0.03914014622569084, -0.04544996842741966, -... ... 1377\n","37 [-0.037124212831258774, 0.04466667398810387, -... ... 1902\n","38 [0.05217211693525314, -0.045366983860731125, -... ... 2028\n","39 [0.054036945104599, -0.0035783019848167896, -0... ... 929\n","40 [0.004495782777667046, 0.0002409428561804816, ... ... 659\n","41 [0.05336875841021538, 0.01685612089931965, -0.... ... 2286\n","42 [0.06096196547150612, -0.018674220889806747, -... ... 1198\n","43 [-0.01874159276485443, -0.015269572846591473, ... ... 468\n","44 [0.0465228296816349, -0.03501952812075615, -0.... ... 1335\n","45 [-0.022962188348174095, -0.05519339069724083, ... ... 1322\n","46 [0.05667755752801895, -0.004784214776009321, 0... ... 2141\n","47 [-0.045412369072437286, -0.010548366233706474,... ... 1065\n","48 [0.005430039018392563, -0.021274806931614876, ... ... 2103\n","49 [-0.012310817837715149, 0.036387305706739426, ... ... 588\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["# 7. Try training with different Embeddings"]},{"cell_type":"code","metadata":{"id":"nxWFzQOhjWC8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620195371204,"user_tz":-120,"elapsed":15026,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"cfc1e505-40b8-4021-e75d-228cae3cfe4c"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IKK_Ii_gjJfF","executionInfo":{"status":"ok","timestamp":1620201627380,"user_tz":-120,"elapsed":6271143,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"40fc246a-ca8b-4b81-f02d-30a189cd26f2"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(120) \n","trainable_pipe['sentiment_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.87 0.77 0.82 988\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.85 0.83 0.84 1012\n","\n"," accuracy 0.80 2000\n"," macro avg 0.57 0.53 0.55 2000\n","weighted avg 0.86 0.80 0.83 2000\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 7.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"id":"Fxx4yNkNVGFl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620201800537,"user_tz":-120,"elapsed":6444231,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"4cd26bf6-b452-4f97-d27e-125ba4adba24"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.85 0.75 0.80 246\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.84 0.81 0.83 254\n","\n"," accuracy 0.78 500\n"," macro avg 0.56 0.52 0.54 500\n","weighted avg 0.85 0.78 0.81 500\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 8. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620201947576,"user_tz":-120,"elapsed":6591195,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2722dd4c-b2c6-453c-9a76-b59de2034549"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 9. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":76},"id":"SO4uz45MoRgp","executionInfo":{"status":"ok","timestamp":1620201958142,"user_tz":-120,"elapsed":6601685,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"e7af7ac1-5347-4bf3-d23b-a1f65df85c97"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('It was one of the best films i have ever watched in my entire life !!')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentiment_confidence
\n","
sentence_embedding_from_disk
\n","
text
\n","
document
\n","
sentence
\n","
origin_index
\n","
sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.9986149, 0.9986149]
\n","
[[0.030373765155673027, 0.05577867478132248, 0...
\n","
It was one of the best films i have ever watch...
\n","
It was one of the best films i have ever watch...
\n","
[It was one of the best films i have ever watc...
\n","
8589934592
\n","
[positive, positive]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentiment_confidence ... sentiment\n","0 [0.9986149, 0.9986149] ... [positive, positive]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e0CVlkk9v6Qi","executionInfo":{"status":"ok","timestamp":1620201958143,"user_tz":-120,"elapsed":6601641,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"5dfa5379-198e-4796-c27b-18f7c92a6ffa"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@2350f35a) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@2350f35a\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentiment_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"mtDcALorKHIx"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_apple_twitter.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_apple_twitter.ipynb
deleted file mode 100644
index d8eca814..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_apple_twitter.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_sentiment_classifier_demo_apple_twitter.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"RIV-9vEqxTBB"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_apple_twitter.ipynb)\n","\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 class Apple Tweets sentiment classifier training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n"," \n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n","\n","\n"," \n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"05-mAOF6ol-0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620187468464,"user_tz":-120,"elapsed":103699,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c1058df3-e272-4d87-f61e-0589cfd22b84"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:02:45-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 04:02:45 (39.1 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 73kB/s \n","\u001b[K |████████████████████████████████| 153kB 46.7MB/s \n","\u001b[K |████████████████████████████████| 204kB 21.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 51.4MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download appple twitter Sentiment dataset \n","https://www.kaggle.com/seriousran/appletwittersentimenttexts\n","\n","this dataset contains tweets made towards apple and today we are going to train our model to predict whether the tweet contains sentiment!\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620187468785,"user_tz":-120,"elapsed":104015,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c6049d62-5149-4669-8f6e-8d4f0f0b7c63"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/01/apple-twitter-sentiment-texts.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:04:28-- http://ckl-it.de/wp-content/uploads/2021/01/apple-twitter-sentiment-texts.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 31678 (31K) [text/csv]\n","Saving to: ‘apple-twitter-sentiment-texts.csv’\n","\n","apple-twitter-senti 100%[===================>] 30.94K --.-KB/s in 0.1s \n","\n","2021-05-05 04:04:28 (250 KB/s) - ‘apple-twitter-sentiment-texts.csv’ saved [31678/31678]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620187470342,"user_tz":-120,"elapsed":105566,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"dcd0fde1-7df8-462f-bf4f-85174437b1a8"},"source":["import pandas as pd\n","train_path = '/content/apple-twitter-sentiment-texts.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","# the label column must have name 'y' name be of type str\n","columns=['text','y']\n","train_df = train_df[columns]\n","train_df = train_df[~train_df[\"y\"].isin([\"neuteral\"])]\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
36
\n","
Theyre not RT @Naivana_: You gotta be kidding ...
\n","
negative
\n","
\n","
\n","
7
\n","
Thank you @Apple for fixing the #Swift sourcek...
\n","
positive
\n","
\n","
\n","
272
\n","
Washed my earphones by accident, still works. ...
\n","
positive
\n","
\n","
\n","
146
\n","
@AppleOfficialll @apple I can't say enough abo...
\n","
positive
\n","
\n","
\n","
237
\n","
@OneRepublic @Apple So amazing
\n","
positive
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
247
\n","
Got to hear about the new patent by apple ... ...
\n","
positive
\n","
\n","
\n","
157
\n","
fuck you @apple
\n","
negative
\n","
\n","
\n","
93
\n","
#apple earns more #profit in on quarter than #...
\n","
positive
\n","
\n","
\n","
165
\n","
This is why I moved over to @Apple... http://t...
\n","
positive
\n","
\n","
\n","
52
\n","
I don't know what you're trying to do @apple b...
\n","
negative
\n","
\n"," \n","
\n","
228 rows × 2 columns
\n","
"],"text/plain":[" text y\n","36 Theyre not RT @Naivana_: You gotta be kidding ... negative\n","7 Thank you @Apple for fixing the #Swift sourcek... positive\n","272 Washed my earphones by accident, still works. ... positive\n","146 @AppleOfficialll @apple I can't say enough abo... positive\n","237 @OneRepublic @Apple So amazing positive\n",".. ... ...\n","247 Got to hear about the new patent by apple ... ... positive\n","157 fuck you @apple negative\n","93 #apple earns more #profit in on quarter than #... positive\n","165 This is why I moved over to @Apple... http://t... positive\n","52 I don't know what you're trying to do @apple b... negative\n","\n","[228 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620188334902,"user_tz":-120,"elapsed":10389,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"5fb3d813-68c4-4fc1-fd1a-d19b98dd828c"},"source":["from sklearn.metrics import classification_report\n","import nlu \n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.00 0.00 0.00 23.0\n"," neutral 0.00 0.00 0.00 0.0\n"," positive 0.00 0.00 0.00 27.0\n","\n"," accuracy 0.00 50.0\n"," macro avg 0.00 0.00 0.00 50.0\n","weighted avg 0.00 0.00 0.00 50.0\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
sentence_embedding_use
\n","
sentence
\n","
y
\n","
origin_index
\n","
document
\n","
trained_sentiment
\n","
trained_sentiment_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
Theyre not RT @Naivana_: You gotta be kidding ...
\n","
[0.053627125918865204, 0.03388773649930954, 0....
\n","
[Theyre not RT @Naivana_:, You gotta be kiddi...
\n","
negative
\n","
36
\n","
Theyre not RT @Naivana_: You gotta be kidding ...
\n","
neutral
\n","
0.519284
\n","
\n","
\n","
1
\n","
Thank you @Apple for fixing the #Swift sourcek...
\n","
[0.01400269940495491, 0.04662228375673294, 0.0...
\n","
[Thank you @Apple for fixing the #Swift source...
\n","
positive
\n","
7
\n","
Thank you @Apple for fixing the #Swift sourcek...
\n","
neutral
\n","
0.526175
\n","
\n","
\n","
2
\n","
Washed my earphones by accident, still works. ...
\n","
[0.028794147074222565, 0.060343481600284576, -...
\n","
[Washed my earphones by accident, still works....
\n","
positive
\n","
272
\n","
Washed my earphones by accident, still works. ...
\n","
neutral
\n","
0.546108
\n","
\n","
\n","
3
\n","
@AppleOfficialll @apple I can't say enough abo...
\n","
[0.04390808567404747, 0.0024722537491470575, -...
\n","
[@AppleOfficialll @apple I can't say enough ab...
\n","
positive
\n","
146
\n","
@AppleOfficialll @apple I can't say enough abo...
\n","
neutral
\n","
0.535401
\n","
\n","
\n","
4
\n","
@OneRepublic @Apple So amazing
\n","
[-0.0420607253909111, 0.05555792152881622, -0....
\n","
[@OneRepublic @Apple So amazing]
\n","
positive
\n","
237
\n","
@OneRepublic @Apple So amazing
\n","
neutral
\n","
0.562668
\n","
\n","
\n","
5
\n","
The 10 biggest differences between #Mac and #P...
\n","
[0.046186886727809906, 0.0493854284286499, 0.0...
\n","
[The 10 biggest differences between #Mac and #...
\n","
positive
\n","
213
\n","
The 10 biggest differences between #Mac and #P...
\n","
neutral
\n","
0.541398
\n","
\n","
\n","
6
\n","
@SamJam Agreed--have to give props to @Apple f...
\n","
[0.06067856401205063, -0.014465369284152985, -...
\n","
[@SamJam Agreed--have to give props to @Apple ...
\n","
positive
\n","
77
\n","
@SamJam Agreed--have to give props to @Apple f...
\n","
neutral
\n","
0.553645
\n","
\n","
\n","
7
\n","
@OneRepublic @Apple that looks amazing, don't ...
\n","
[0.016945000737905502, -0.019095521420240402, ...
\n","
[@OneRepublic @Apple that looks amazing, don't...
\n","
positive
\n","
56
\n","
@OneRepublic @Apple that looks amazing, don't ...
\n","
neutral
\n","
0.548541
\n","
\n","
\n","
8
\n","
Finally! Brooklyn Is Getting Its Own #Apple St...
\n","
[0.06617175042629242, -0.018538057804107666, -...
\n","
[Finally!, Brooklyn Is Getting Its Own #Apple...
\n","
positive
\n","
234
\n","
Finally! Brooklyn Is Getting Its Own #Apple St...
\n","
neutral
\n","
0.531661
\n","
\n","
\n","
9
\n","
RT @ehsonakbary: THE WORLD'S FIRST MURDER VIA ...
\n","
[0.07017797976732254, 0.03393903002142906, -0....
\n","
[RT @ehsonakbary: THE WORLD'S FIRST MURDER VIA...
\n","
negative
\n","
103
\n","
RT @ehsonakbary: THE WORLD'S FIRST MURDER VIA ...
\n","
neutral
\n","
0.541381
\n","
\n","
\n","
10
\n","
@SwiftKey @Apple Oh In wish you guys would por...
\n","
[0.04137858748435974, -0.03665991127490997, -0...
\n","
[@SwiftKey @Apple Oh In wish you guys would po...
\n","
negative
\n","
208
\n","
@SwiftKey @Apple Oh In wish you guys would por...
\n","
neutral
\n","
0.532717
\n","
\n","
\n","
11
\n","
The king of the phablets! Apple's iPhone 6 plu...
\n","
[0.07199912518262863, 0.0021514107938855886, -...
\n","
[The king of the phablets!, Apple's iPhone 6 ...
\n","
positive
\n","
250
\n","
The king of the phablets! Apple's iPhone 6 plu...
\n","
neutral
\n","
0.556155
\n","
\n","
\n","
12
\n","
iPhone6 fell 2 ft. Screen shattered like it wa...
\n","
[0.0498758926987648, 0.02919364534318447, -0.0...
\n","
[iPhone6 fell 2 ft., Screen shattered like it ...
\n","
negative
\n","
263
\n","
iPhone6 fell 2 ft. Screen shattered like it wa...
\n","
neutral
\n","
0.525769
\n","
\n","
\n","
13
\n","
Shockingly, iMessage on the desktop is fucked ...
\n","
[0.056961141526699066, -0.02297591045498848, -...
\n","
[Shockingly, iMessage on the desktop is fucked...
\n","
negative
\n","
107
\n","
Shockingly, iMessage on the desktop is fucked ...
\n","
neutral
\n","
0.515020
\n","
\n","
\n","
14
\n","
Yeeaaayyy....awesome OS X Yosemite 10.10.1 roc...
\n","
[0.03625739365816116, 0.0035043705720454454, -...
\n","
[Yeeaaayyy., ., ., .awesome OS X Yosemite 10.1...
\n","
positive
\n","
285
\n","
Yeeaaayyy....awesome OS X Yosemite 10.10.1 roc...
\n","
neutral
\n","
0.542655
\n","
\n","
\n","
15
\n","
@OneRepublic @Apple Thanks for sharing pics. A...
\n","
[0.020150689408183098, -0.03949593007564545, 0...
\n","
[@OneRepublic @Apple Thanks for sharing pics.,...
\n","
positive
\n","
138
\n","
@OneRepublic @Apple Thanks for sharing pics. A...
\n","
neutral
\n","
0.568121
\n","
\n","
\n","
16
\n","
@apple @iBooks is awesome. Thank You!
\n","
[0.0218979399651289, 0.005902431905269623, -0....
\n","
[@apple @iBooks is awesome., Thank You!]
\n","
positive
\n","
198
\n","
@apple @iBooks is awesome. Thank You!
\n","
neutral
\n","
0.559235
\n","
\n","
\n","
17
\n","
Why I Hate Apple - http://t.co/IbpyJXaOuW #AAP...
\n","
[0.055701617151498795, 0.04886789247393608, -0...
\n","
[Why I Hate Apple - http://t.co/IbpyJXaOuW #AA...
\n","
negative
\n","
109
\n","
Why I Hate Apple - http://t.co/IbpyJXaOuW #AAP...
\n","
neutral
\n","
0.525435
\n","
\n","
\n","
18
\n","
yo @Apple , why yall make it so hard to grip t...
\n","
[0.07655678689479828, 0.0038530321326106787, -...
\n","
[yo @Apple , why yall make it so hard to grip ...
\n","
negative
\n","
225
\n","
yo @Apple , why yall make it so hard to grip t...
\n","
neutral
\n","
0.526321
\n","
\n","
\n","
19
\n","
@apple Why is your NYC Grand Central store so ...
\n","
[0.06858145445585251, 0.052669648081064224, 0....
\n","
[@apple Why is your NYC Grand Central store so...
\n","
negative
\n","
131
\n","
@apple Why is your NYC Grand Central store so ...
\n","
neutral
\n","
0.518131
\n","
\n","
\n","
20
\n","
Love the @Apple is supporting #HourOfCode with...
\n","
[0.01483983639627695, -0.00843955110758543, -0...
\n","
[Love the @Apple is supporting #HourOfCode wit...
\n","
positive
\n","
219
\n","
Love the @Apple is supporting #HourOfCode with...
\n","
neutral
\n","
0.543223
\n","
\n","
\n","
21
\n","
Companies i admire : @3QDigital @vaynermedia @...
\n","
[0.015711167827248573, 0.064913809299469, 0.01...
\n","
[Companies i admire : @3QDigital @vaynermedia ...
\n","
positive
\n","
19
\n","
Companies i admire : @3QDigital @vaynermedia @...
\n","
neutral
\n","
0.539861
\n","
\n","
\n","
22
\n","
These Damn @Apple Commercials Are Getting Wors...
\n","
[0.00820611510425806, 0.004215271212160587, -0...
\n","
[These Damn @Apple Commercials Are Getting Wor...
\n","
negative
\n","
142
\n","
These Damn @Apple Commercials Are Getting Wors...
\n","
neutral
\n","
0.513193
\n","
\n","
\n","
23
\n","
Free s/o @apple for this nice iPad
\n","
[0.05512142553925514, 0.011973769403994083, -0...
\n","
[Free s/o @apple for this nice iPad]
\n","
positive
\n","
57
\n","
Free s/o @apple for this nice iPad
\n","
neutral
\n","
0.543708
\n","
\n","
\n","
24
\n","
if I tweet about NANDOS my phone puts it in ca...
\n","
[0.021839885041117668, 0.07290177792310715, -0...
\n","
[if I tweet about NANDOS my phone puts it in c...
\n","
positive
\n","
140
\n","
if I tweet about NANDOS my phone puts it in ca...
\n","
neutral
\n","
0.538120
\n","
\n","
\n","
25
\n","
RT @saigeist: the most offensive thing is the ...
\n","
[0.043079257011413574, -0.045617833733558655, ...
\n","
[RT @saigeist: the most offensive thing is the...
\n","
negative
\n","
178
\n","
RT @saigeist: the most offensive thing is the ...
\n","
neutral
\n","
0.512527
\n","
\n","
\n","
26
\n","
.@tim_cook That rage when dealing @Apple Geniu...
\n","
[0.05030369386076927, 0.023012423887848854, -0...
\n","
[., @tim_cook That rage when dealing @Apple Ge...
\n","
negative
\n","
96
\n","
.@tim_cook That rage when dealing @Apple Geniu...
\n","
neutral
\n","
0.517721
\n","
\n","
\n","
27
\n","
I hate my MacBook now. Fuck this update and fu...
\n","
[0.04070044681429863, 0.02998330444097519, 0.0...
\n","
[I hate my MacBook now., Fuck this update and...
\n","
negative
\n","
155
\n","
I hate my MacBook now. Fuck this update and fu...
\n","
neutral
\n","
0.510436
\n","
\n","
\n","
28
\n","
Kantar: iPhone 6 helps Apple gain share over A...
\n","
[0.06512415409088135, -0.009421378374099731, -...
\n","
[Kantar: iPhone 6 helps Apple gain share over ...
\n","
positive
\n","
41
\n","
Kantar: iPhone 6 helps Apple gain share over A...
\n","
neutral
\n","
0.550157
\n","
\n","
\n","
29
\n","
@tehhGOAT @Apple do you by accident have me bl...
\n","
[-0.019428884610533714, 0.055587127804756165, ...
\n","
[@tehhGOAT @Apple do you by accident have me b...
\n","
negative
\n","
17
\n","
@tehhGOAT @Apple do you by accident have me bl...
\n","
neutral
\n","
0.506774
\n","
\n","
\n","
30
\n","
@fullcircleone ThanX! Big @Apple ThanX goes 2 ...
\n","
[0.0020813194569200277, -0.025028454139828682,...
\n","
[@fullcircleone ThanX!, Big @Apple ThanX goes...
\n","
positive
\n","
194
\n","
@fullcircleone ThanX! Big @Apple ThanX goes 2 ...
\n","
neutral
\n","
0.553299
\n","
\n","
\n","
31
\n","
my phone keeps restarting on its own @apple do...
\n","
[-0.0074605816043913364, 0.026783859357237816,...
\n","
[my phone keeps restarting on its own @apple d...
\n","
negative
\n","
135
\n","
my phone keeps restarting on its own @apple do...
\n","
neutral
\n","
0.513831
\n","
\n","
\n","
32
\n","
my dad called now my musics arent playing jesu...
\n","
[0.008138233795762062, -0.026052597910165787, ...
\n","
[my dad called now my musics arent playing jes...
\n","
negative
\n","
187
\n","
my dad called now my musics arent playing jesu...
\n","
neutral
\n","
0.519269
\n","
\n","
\n","
33
\n","
@apple #greatservice thanks for helping me out...
\n","
[0.02643176168203354, 0.015369059517979622, -0...
\n","
[@apple #greatservice thanks for helping me ou...
\n","
positive
\n","
201
\n","
@apple #greatservice thanks for helping me out...
\n","
neutral
\n","
0.540207
\n","
\n","
\n","
34
\n","
@Apple fix addis' phone so we can text each ot...
\n","
[0.041290853172540665, 0.037915393710136414, -...
\n","
[@Apple fix addis' phone so we can text each o...
\n","
negative
\n","
261
\n","
@Apple fix addis' phone so we can text each ot...
\n","
neutral
\n","
0.521058
\n","
\n","
\n","
35
\n","
Sorry @samsung but I will be taking my smartph...
\n","
[0.07281249761581421, 0.0376746729016304, 0.01...
\n","
[Sorry @samsung but I will be taking my smartp...
\n","
positive
\n","
5
\n","
Sorry @samsung but I will be taking my smartph...
\n","
neutral
\n","
0.554886
\n","
\n","
\n","
36
\n","
@LittleWordBites @TravlandLeisure @Apple >...
\n","
[0.0581418015062809, -0.042348094284534454, -0...
\n","
[@LittleWordBites @TravlandLeisure @Apple >...
\n","
positive
\n","
195
\n","
@LittleWordBites @TravlandLeisure @Apple > ...
\n","
neutral
\n","
0.571530
\n","
\n","
\n","
37
\n","
RT @_emilymahon: @nosheenh2oo9 @Apple this is ...
\n","
[0.05870470777153969, 0.061276875436306, -0.02...
\n","
[RT @_emilymahon: @nosheenh2oo9 @Apple this is...
\n","
positive
\n","
8
\n","
RT @_emilymahon: @nosheenh2oo9 @Apple this is ...
\n","
neutral
\n","
0.546566
\n","
\n","
\n","
38
\n","
@FaZeNikan @Apple lol iPhone,weak. Get on that...
\n","
[0.07184943556785583, -0.025483926758170128, -...
\n","
[@FaZeNikan @Apple lol iPhone,weak., Get on t...
\n","
negative
\n","
191
\n","
@FaZeNikan @Apple lol iPhone,weak. Get on that...
\n","
neutral
\n","
0.526558
\n","
\n","
\n","
39
\n","
RT @hsmoghul: My @apple autocorrect changes Mu...
\n","
[0.031838081777095795, 0.009894482791423798, 0...
\n","
[RT @hsmoghul:, My @apple autocorrect changes...
\n","
positive
\n","
149
\n","
RT @hsmoghul: My @apple autocorrect changes Mu...
\n","
neutral
\n","
0.541348
\n","
\n","
\n","
40
\n","
RT @_iamGambino: Thank you @Apple
\n","
[0.018735762685537338, 0.07813401520252228, -0...
\n","
[RT @_iamGambino: Thank you @Apple]
\n","
positive
\n","
212
\n","
RT @_iamGambino: Thank you @Apple
\n","
neutral
\n","
0.538737
\n","
\n","
\n","
41
\n","
@lanadelreystan KILL YOURSELF @apple
\n","
[-0.018729694187641144, 0.05311402678489685, -...
\n","
[@lanadelreystan KILL YOURSELF @apple]
\n","
negative
\n","
245
\n","
@lanadelreystan KILL YOURSELF @apple
\n","
neutral
\n","
0.526904
\n","
\n","
\n","
42
\n","
Great time had @Apple store on Friday. @Russel...
\n","
[0.08689077943563461, 0.0028835677076131105, -...
\n","
[Great time had @Apple store on Friday., @Rus...
\n","
positive
\n","
72
\n","
Great time had @Apple store on Friday. @Russel...
\n","
neutral
\n","
0.557536
\n","
\n","
\n","
43
\n","
@CharlesJMeyer @Apple @Appy_Geek Hasn't Apple ...
\n","
[0.05127181485295296, 0.03388502821326256, -0....
\n","
[@CharlesJMeyer @Apple @Appy_Geek Hasn't Apple...
\n","
negative
\n","
181
\n","
@CharlesJMeyer @Apple @Appy_Geek Hasn't Apple ...
\n","
neutral
\n","
0.518989
\n","
\n","
\n","
44
\n","
Left the hoos we 100% now got 50 iPhones are d...
\n","
[0.009334878996014595, 0.06441717594861984, 0....
\n","
[Left the hoos we 100% now got 50 iPhones are ...
\n","
negative
\n","
86
\n","
Left the hoos we 100% now got 50 iPhones are d...
\n","
neutral
\n","
0.526055
\n","
\n","
\n","
45
\n","
@apple why is it that your iPhone's alarm fail...
\n","
[0.013754838146269321, 0.05925201624631882, -0...
\n","
[@apple why is it that your iPhone's alarm fai...
\n","
negative
\n","
69
\n","
@apple why is it that your iPhone's alarm fail...
\n","
neutral
\n","
0.515201
\n","
\n","
\n","
46
\n","
Changing words that aren't even misspelled lik...
\n","
[0.005594303365796804, 0.03357389196753502, -0...
\n","
[Changing words that aren't even misspelled li...
\n","
negative
\n","
50
\n","
Changing words that aren't even misspelled lik...
\n","
neutral
\n","
0.510925
\n","
\n","
\n","
47
\n","
that yosemite update is so annoying i regret i...
\n","
[0.011035434901714325, -0.046652231365442276, ...
\n","
[that yosemite update is so annoying i regret ...
\n","
negative
\n","
159
\n","
that yosemite update is so annoying i regret i...
\n","
neutral
\n","
0.512430
\n","
\n","
\n","
48
\n","
Photo: Amazing customer service today @Apple. ...
\n","
[0.0008898481610231102, 0.025655869394540787, ...
\n","
[Photo: Amazing customer service today @Apple....
\n","
positive
\n","
256
\n","
Photo: Amazing customer service today @Apple. ...
\n","
neutral
\n","
0.563457
\n","
\n","
\n","
49
\n","
Awesome! @Apple invented peanut-butter-sandwic...
\n","
[0.0439443401992321, 0.043788887560367584, -0....
\n","
[Awesome!, @Apple invented peanut-butter-sandw...
\n","
positive
\n","
20
\n","
Awesome! @Apple invented peanut-butter-sandwic...
\n","
neutral
\n","
0.547759
\n","
\n"," \n","
\n","
"],"text/plain":[" text ... trained_sentiment_confidence\n","0 Theyre not RT @Naivana_: You gotta be kidding ... ... 0.519284\n","1 Thank you @Apple for fixing the #Swift sourcek... ... 0.526175\n","2 Washed my earphones by accident, still works. ... ... 0.546108\n","3 @AppleOfficialll @apple I can't say enough abo... ... 0.535401\n","4 @OneRepublic @Apple So amazing ... 0.562668\n","5 The 10 biggest differences between #Mac and #P... ... 0.541398\n","6 @SamJam Agreed--have to give props to @Apple f... ... 0.553645\n","7 @OneRepublic @Apple that looks amazing, don't ... ... 0.548541\n","8 Finally! Brooklyn Is Getting Its Own #Apple St... ... 0.531661\n","9 RT @ehsonakbary: THE WORLD'S FIRST MURDER VIA ... ... 0.541381\n","10 @SwiftKey @Apple Oh In wish you guys would por... ... 0.532717\n","11 The king of the phablets! Apple's iPhone 6 plu... ... 0.556155\n","12 iPhone6 fell 2 ft. Screen shattered like it wa... ... 0.525769\n","13 Shockingly, iMessage on the desktop is fucked ... ... 0.515020\n","14 Yeeaaayyy....awesome OS X Yosemite 10.10.1 roc... ... 0.542655\n","15 @OneRepublic @Apple Thanks for sharing pics. A... ... 0.568121\n","16 @apple @iBooks is awesome. Thank You! ... 0.559235\n","17 Why I Hate Apple - http://t.co/IbpyJXaOuW #AAP... ... 0.525435\n","18 yo @Apple , why yall make it so hard to grip t... ... 0.526321\n","19 @apple Why is your NYC Grand Central store so ... ... 0.518131\n","20 Love the @Apple is supporting #HourOfCode with... ... 0.543223\n","21 Companies i admire : @3QDigital @vaynermedia @... ... 0.539861\n","22 These Damn @Apple Commercials Are Getting Wors... ... 0.513193\n","23 Free s/o @apple for this nice iPad ... 0.543708\n","24 if I tweet about NANDOS my phone puts it in ca... ... 0.538120\n","25 RT @saigeist: the most offensive thing is the ... ... 0.512527\n","26 .@tim_cook That rage when dealing @Apple Geniu... ... 0.517721\n","27 I hate my MacBook now. Fuck this update and fu... ... 0.510436\n","28 Kantar: iPhone 6 helps Apple gain share over A... ... 0.550157\n","29 @tehhGOAT @Apple do you by accident have me bl... ... 0.506774\n","30 @fullcircleone ThanX! Big @Apple ThanX goes 2 ... ... 0.553299\n","31 my phone keeps restarting on its own @apple do... ... 0.513831\n","32 my dad called now my musics arent playing jesu... ... 0.519269\n","33 @apple #greatservice thanks for helping me out... ... 0.540207\n","34 @Apple fix addis' phone so we can text each ot... ... 0.521058\n","35 Sorry @samsung but I will be taking my smartph... ... 0.554886\n","36 @LittleWordBites @TravlandLeisure @Apple >... ... 0.571530\n","37 RT @_emilymahon: @nosheenh2oo9 @Apple this is ... ... 0.546566\n","38 @FaZeNikan @Apple lol iPhone,weak. Get on that... ... 0.526558\n","39 RT @hsmoghul: My @apple autocorrect changes Mu... ... 0.541348\n","40 RT @_iamGambino: Thank you @Apple ... 0.538737\n","41 @lanadelreystan KILL YOURSELF @apple ... 0.526904\n","42 Great time had @Apple store on Friday. @Russel... ... 0.557536\n","43 @CharlesJMeyer @Apple @Appy_Geek Hasn't Apple ... ... 0.518989\n","44 Left the hoos we 100% now got 50 iPhones are d... ... 0.526055\n","45 @apple why is it that your iPhone's alarm fail... ... 0.515201\n","46 Changing words that aren't even misspelled lik... ... 0.510925\n","47 that yosemite update is so annoying i regret i... ... 0.512430\n","48 Photo: Amazing customer service today @Apple. ... ... 0.563457\n","49 Awesome! @Apple invented peanut-butter-sandwic... ... 0.547759\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"lVyOE2wV0fw_"},"source":["#4. Test the fitted pipe on new example"]},{"cell_type":"code","metadata":{"id":"qdCUg2MR0PD2","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620188335247,"user_tz":-120,"elapsed":10456,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"31d1c18f-b30b-4507-cd1c-4aa13c0bec6d"},"source":["fitted_pipe.predict('I hate the newest update')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_use
\n","
sentence
\n","
origin_index
\n","
document
\n","
trained_sentiment
\n","
trained_sentiment_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.023322951048612595, -0.04157407209277153, ...
\n","
[I hate the newest update]
\n","
0
\n","
I hate the newest update
\n","
neutral
\n","
0.509875
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_use ... trained_sentiment_confidence\n","0 [-0.023322951048612595, -0.04157407209277153, ... ... 0.509875\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["##5. Configure pipe training parameters"]},{"cell_type":"code","metadata":{"id":"UtsAUGTmOTms","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188335248,"user_tz":-120,"elapsed":10187,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f0702b13-6ac0-4a3d-87eb-57924a6760b1"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@41fb48fb) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@41fb48fb\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["##6. Retrain with new parameters"]},{"cell_type":"code","metadata":{"id":"mptfvHx-MMMX","colab":{"base_uri":"https://localhost:8080/","height":759},"executionInfo":{"status":"ok","timestamp":1620188339611,"user_tz":-120,"elapsed":14279,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c580024a-2436-4b70-b7bb-c121cd3d6e80"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:100])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.94 0.94 0.94 47\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.98 0.91 0.94 53\n","\n"," accuracy 0.92 100\n"," macro avg 0.64 0.61 0.63 100\n","weighted avg 0.96 0.92 0.94 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
sentence_embedding_use
\n","
sentence
\n","
y
\n","
origin_index
\n","
document
\n","
trained_sentiment
\n","
trained_sentiment_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
Theyre not RT @Naivana_: You gotta be kidding ...
\n","
[0.053627125918865204, 0.03388773649930954, 0....
\n","
[Theyre not RT @Naivana_:, You gotta be kiddi...
\n","
negative
\n","
36
\n","
Theyre not RT @Naivana_: You gotta be kidding ...
\n","
negative
\n","
0.947914
\n","
\n","
\n","
1
\n","
Thank you @Apple for fixing the #Swift sourcek...
\n","
[0.01400269940495491, 0.04662228375673294, 0.0...
\n","
[Thank you @Apple for fixing the #Swift source...
\n","
positive
\n","
7
\n","
Thank you @Apple for fixing the #Swift sourcek...
\n","
positive
\n","
0.926184
\n","
\n","
\n","
2
\n","
Washed my earphones by accident, still works. ...
\n","
[0.028794147074222565, 0.060343481600284576, -...
\n","
[Washed my earphones by accident, still works....
\n","
positive
\n","
272
\n","
Washed my earphones by accident, still works. ...
\n","
positive
\n","
0.867718
\n","
\n","
\n","
3
\n","
@AppleOfficialll @apple I can't say enough abo...
\n","
[0.04390808567404747, 0.0024722537491470575, -...
\n","
[@AppleOfficialll @apple I can't say enough ab...
\n","
positive
\n","
146
\n","
@AppleOfficialll @apple I can't say enough abo...
\n","
negative
\n","
0.707370
\n","
\n","
\n","
4
\n","
@OneRepublic @Apple So amazing
\n","
[-0.0420607253909111, 0.05555792152881622, -0....
\n","
[@OneRepublic @Apple So amazing]
\n","
positive
\n","
237
\n","
@OneRepublic @Apple So amazing
\n","
positive
\n","
0.989958
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
Apple Inc. CEO Donates $291K To Pennsylvania S...
\n","
[0.036090489476919174, 0.033749453723430634, -...
\n","
[Apple Inc. CEO Donates $291K To Pennsylvania ...
\n","
positive
\n","
4
\n","
Apple Inc. CEO Donates $291K To Pennsylvania S...
\n","
neutral
\n","
0.598268
\n","
\n","
\n","
96
\n","
Photo: Yaaass. Shoutout to @apple.holidays and...
\n","
[0.062088269740343094, -0.0338711179792881, -0...
\n","
[Photo:, Yaaass., Shoutout to @apple., holiday...
\n","
positive
\n","
275
\n","
Photo: Yaaass. Shoutout to @apple.holidays and...
\n","
positive
\n","
0.998650
\n","
\n","
\n","
97
\n","
RT @hypebot: Steve Job's Deposition in #iPod L...
\n","
[0.06204485893249512, 0.05829763785004616, -0....
\n","
[RT @hypebot: Steve Job's Deposition in #iPod ...
\n","
negative
\n","
141
\n","
RT @hypebot: Steve Job's Deposition in #iPod L...
\n","
neutral
\n","
0.581826
\n","
\n","
\n","
98
\n","
@apple I have been on hold for 30 minutes than...
\n","
[0.02501610852777958, 0.04794774204492569, -0....
\n","
[@apple I have been on hold for 30 minutes tha...
\n","
negative
\n","
128
\n","
@apple I have been on hold for 30 minutes than...
\n","
negative
\n","
0.933449
\n","
\n","
\n","
99
\n","
Wow. Yall needa step it up @Apple RT @heynyla:...
\n","
[0.027452174574136734, -0.004120523110032082, ...
\n","
[Wow. Yall needa step it up @Apple RT @heynyla...
\n","
negative
\n","
1
\n","
Wow. Yall needa step it up @Apple RT @heynyla:...
\n","
negative
\n","
0.729602
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" text ... trained_sentiment_confidence\n","0 Theyre not RT @Naivana_: You gotta be kidding ... ... 0.947914\n","1 Thank you @Apple for fixing the #Swift sourcek... ... 0.926184\n","2 Washed my earphones by accident, still works. ... ... 0.867718\n","3 @AppleOfficialll @apple I can't say enough abo... ... 0.707370\n","4 @OneRepublic @Apple So amazing ... 0.989958\n",".. ... ... ...\n","95 Apple Inc. CEO Donates $291K To Pennsylvania S... ... 0.598268\n","96 Photo: Yaaass. Shoutout to @apple.holidays and... ... 0.998650\n","97 RT @hypebot: Steve Job's Deposition in #iPod L... ... 0.581826\n","98 @apple I have been on hold for 30 minutes than... ... 0.933449\n","99 Wow. Yall needa step it up @Apple RT @heynyla:... ... 0.729602\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["#7. Try training with different Embeddings"]},{"cell_type":"code","metadata":{"id":"nxWFzQOhjWC8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188339931,"user_tz":-120,"elapsed":14332,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b6acf5d2-2f7e-4200-a6eb-24c9c4b05592"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188495786,"user_tz":-120,"elapsed":170049,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"5043ddfe-4d7a-472d-c7b9-8cf34cc1bb06"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(110) \n","trainable_pipe['sentiment_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.97 0.83 0.90 114\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.88 0.93 0.90 114\n","\n"," accuracy 0.88 228\n"," macro avg 0.62 0.59 0.60 228\n","weighted avg 0.92 0.88 0.90 228\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 7.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"id":"Fxx4yNkNVGFl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188505405,"user_tz":-120,"elapsed":179404,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"35e0f9c4-8486-4b24-b00e-387c5f7af5dd"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.94 0.59 0.72 29\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.79 0.93 0.86 29\n","\n"," accuracy 0.76 58\n"," macro avg 0.58 0.51 0.53 58\n","weighted avg 0.87 0.76 0.79 58\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 8. Lets save the model"]},{"cell_type":"code","metadata":{"id":"bZZpObLOtqo8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188675266,"user_tz":-120,"elapsed":348933,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"776368a9-02df-4524-e80d-b004a10c4306"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 9. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620188686839,"user_tz":-120,"elapsed":360106,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"32ac7000-0278-40e0-fcc2-5eef5cb56094"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('I hate the newest update')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
sentiment
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_from_disk
\n","
sentiment_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
I hate the newest update
\n","
[negative]
\n","
[I hate the newest update]
\n","
8589934592
\n","
I hate the newest update
\n","
[[-0.3084237277507782, -0.11030610650777817, 0...
\n","
[0.67131346]
\n","
\n"," \n","
\n","
"],"text/plain":[" text ... sentiment_confidence\n","0 I hate the newest update ... [0.67131346]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188686840,"user_tz":-120,"elapsed":359738,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b7915040-d049-4447-c1aa-21dcb0eb27db"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3102445a) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3102445a\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentiment_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"cbwS4bE0uT7d"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_covid_19.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_covid_19.ipynb
deleted file mode 100644
index 9b222591..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_covid_19.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_sentiment_classifier_demo_covid_19.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_covid_19.ipynb)\n","\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 Class COVID-19 Sentiment Classifer Training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n","\n"," \n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n","\n"," \n","\n",""]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hFGnBCHavltY","executionInfo":{"status":"ok","timestamp":1620188963860,"user_tz":-120,"elapsed":108208,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0258b3b7-e331-4f13-8667-00f107198737"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:27:36-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 04:27:36 (35.7 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 64kB/s \n","\u001b[K |████████████████████████████████| 153kB 46.6MB/s \n","\u001b[K |████████████████████████████████| 204kB 24.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 59.8MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Coivd19 NLP Text Sentiemnt Classifcation dataset \n","https://www.kaggle.com/datatattle/covid-19-nlp-text-classification\n","#Context\n","\n","This is a Dataset made of tweets about coivid 19 "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620188965300,"user_tz":-120,"elapsed":109641,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2e5ecb0f-1fac-4dce-9275-e7a34ec3ff99"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/Corona_NLP_train.csv"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:29:23-- http://ckl-it.de/wp-content/uploads/2021/02/Corona_NLP_train.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 5293639 (5.0M) [text/csv]\n","Saving to: ‘Corona_NLP_train.csv’\n","\n","Corona_NLP_train.cs 100%[===================>] 5.05M 4.58MB/s in 1.1s \n","\n","2021-05-05 04:29:25 (4.58 MB/s) - ‘Corona_NLP_train.csv’ saved [5293639/5293639]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620188966112,"user_tz":-120,"elapsed":110448,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0946f3a9-aa1e-490d-9763-c34787c6f6b9"},"source":["import pandas as pd\n","train_path = '/content/Corona_NLP_train.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","columns=['text','y']\n","train_df = train_df[columns]\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
"],"text/plain":[" text y\n","4204 When I was a kid I always wanted to go on the ... positive\n","5622 The selfish morons who panic bought toilet rol... negative\n","6545 Profitero studied keyword search patterns and ... positive\n","9049 From listed funds to builders to landlords, @k... negative\n","8568 Against the back drop of NHS workers, civil se... positive\n","... ... ...\n","9513 Crude prices could go negative while Alberta's... negative\n","5209 Thank you so much to all of the amazing Health... positive\n","8606 Crime in the COVID 19 era says a man s charged... negative\n","3066 I need a sugar daddy fast! Cause this covid-19... positive\n","5140 #auspol \\r\\r\\r\\n#StayAtHome\\r\\r\\r\\n#COVID?19\\r... negative\n","\n","[8000 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620189094731,"user_tz":-120,"elapsed":239061,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"58819459-f54c-4139-82fa-1de8e437d848"},"source":["import nlu \n","from sklearn.metrics import classification_report\n","\n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.00 0.00 0.00 19\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.65 1.00 0.78 31\n","\n"," accuracy 0.62 50\n"," macro avg 0.22 0.33 0.26 50\n","weighted avg 0.40 0.62 0.49 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_use
\n","
text
\n","
y
\n","
origin_index
\n","
document
\n","
trained_sentiment_confidence
\n","
sentence
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.07024706155061722, -0.047160204499959946, ...
\n","
When I was a kid I always wanted to go on the ...
\n","
positive
\n","
4204
\n","
When I was a kid I always wanted to go on the ...
\n","
0.782395
\n","
[When I was a kid I always wanted to go on the...
\n","
positive
\n","
\n","
\n","
1
\n","
[-0.012395520694553852, -0.020829271525144577,...
\n","
The selfish morons who panic bought toilet rol...
\n","
negative
\n","
5622
\n","
The selfish morons who panic bought toilet rol...
\n","
0.628907
\n","
[The selfish morons who panic bought toilet ro...
\n","
positive
\n","
\n","
\n","
2
\n","
[-0.02502020262181759, -0.08026497811079025, 0...
\n","
Profitero studied keyword search patterns and ...
\n","
positive
\n","
6545
\n","
Profitero studied keyword search patterns and ...
\n","
0.716436
\n","
[Profitero studied keyword search patterns and...
\n","
positive
\n","
\n","
\n","
3
\n","
[0.06621277332305908, 0.011142794042825699, -0...
\n","
From listed funds to builders to landlords, @k...
\n","
negative
\n","
9049
\n","
From listed funds to builders to landlords, @k...
\n","
0.613169
\n","
[From listed funds to builders to landlords, @...
\n","
positive
\n","
\n","
\n","
4
\n","
[-0.024583933874964714, 0.06747899204492569, 0...
\n","
Against the back drop of NHS workers, civil se...
\n","
positive
\n","
8568
\n","
Against the back drop of NHS workers, civil se...
\n","
0.753840
\n","
[Against the back drop of NHS workers, civil s...
\n","
positive
\n","
\n","
\n","
5
\n","
[-0.030639173462986946, 0.07562850415706635, -...
\n","
\"Your going to lose people to the flu but you'...
\n","
negative
\n","
1645
\n","
\"Your going to lose people to the flu but you'...
\n","
0.642136
\n","
[\"Your going to lose people to the flu but you...
\n","
positive
\n","
\n","
\n","
6
\n","
[0.020836224779486656, -0.06270623952150345, -...
\n","
As the COVID 19 outbreak continues to spread f...
\n","
negative
\n","
4190
\n","
As the COVID 19 outbreak continues to spread f...
\n","
0.636920
\n","
[As the COVID 19 outbreak continues to spread ...
\n","
positive
\n","
\n","
\n","
7
\n","
[0.04782669618725777, -0.0003077308356296271, ...
\n","
We re tracking daily changes in economic uncer...
\n","
negative
\n","
1930
\n","
We re tracking daily changes in economic uncer...
\n","
0.618729
\n","
[We re tracking daily changes in economic unce...
\n","
positive
\n","
\n","
\n","
8
\n","
[-0.04952622205018997, -0.024686245247721672, ...
\n","
I'm slightly confused! All for social distanc...
\n","
positive
\n","
4717
\n","
I'm slightly confused! All for social distanci...
\n","
0.748444
\n","
[I'm slightly confused!, All for social distan...
\n","
positive
\n","
\n","
\n","
9
\n","
[0.015434695407748222, -0.0009663645178079605,...
\n","
The knock-on effects of COVID-19 are having a...
\n","
positive
\n","
3860
\n","
The knock-on effects of COVID-19 are having a...
\n","
0.778461
\n","
[The knock-on effects of COVID-19 are having ...
\n","
positive
\n","
\n","
\n","
10
\n","
[0.01979624293744564, -0.006749877706170082, -...
\n","
Praise #coronavirus! God Bless the little bug...
\n","
positive
\n","
7223
\n","
Praise #coronavirus! God Bless the little bugs...
\n","
0.820170
\n","
[Praise #coronavirus!, God Bless the little bu...
\n","
positive
\n","
\n","
\n","
11
\n","
[0.06731504201889038, 0.06525817513465881, -0....
\n","
For those that think #COVIDÂ19 #Coronavirus ...
\n","
positive
\n","
1540
\n","
For those that think #COVIDÂ19 #Coronavirus i...
\n","
0.854121
\n","
[For those that think #COVIDÂ19 #Coronavirus ...
\n","
positive
\n","
\n","
\n","
12
\n","
[-0.03877340257167816, -0.04483730345964432, -...
\n","
Just managed to book a delivery with Sainsbury...
\n","
negative
\n","
1003
\n","
Just managed to book a delivery with Sainsbury...
\n","
0.658872
\n","
[Just managed to book a delivery with Sainsbur...
\n","
positive
\n","
\n","
\n","
13
\n","
[0.012202774174511433, 0.04586584493517876, -0...
\n","
CanÂt get any in the supermarket but can rece...
\n","
positive
\n","
9630
\n","
CanÂt get any in the supermarket but can rece...
\n","
0.777700
\n","
[CanÂt get any in the supermarket but can rec...
\n","
positive
\n","
\n","
\n","
14
\n","
[0.03157752379775047, -0.03581700101494789, -0...
\n","
Scary. Gun sales up in #USA. Due to food short...
\n","
negative
\n","
9278
\n","
Scary. Gun sales up in #USA. Due to food short...
\n","
0.597093
\n","
[Scary. Gun sales up in #USA., Due to food sho...
\n","
neutral
\n","
\n","
\n","
15
\n","
[0.07137540727853775, 0.004993322305381298, -0...
\n","
VR headset companies, now is the time to slash...
\n","
positive
\n","
6960
\n","
VR headset companies, now is the time to slash...
\n","
0.786807
\n","
[VR headset companies, now is the time to slas...
\n","
positive
\n","
\n","
\n","
16
\n","
[0.07225924730300903, 0.002497387118637562, 0....
\n","
31% CanÂt Pay the Rent: ÂItÂs Only Going to...
\n","
negative
\n","
3771
\n","
31% CanÂt Pay the Rent: ÂItÂs Only Going to...
\n","
0.643341
\n","
[31% CanÂt Pay the Rent: ÂItÂs Only Going t...
\n","
positive
\n","
\n","
\n","
17
\n","
[0.04617633670568466, 0.03097780980169773, -0....
\n","
COVID-19 is unprecedented. And to manage the c...
\n","
negative
\n","
2605
\n","
COVID-19 is unprecedented. And to manage the c...
\n","
0.652922
\n","
[COVID-19 is unprecedented., And to manage the...
\n","
positive
\n","
\n","
\n","
18
\n","
[0.02162887528538704, -0.07251725345849991, -0...
\n","
Even as government officials have appealed for...
\n","
negative
\n","
9976
\n","
Even as government officials have appealed for...
\n","
0.594582
\n","
[Even as government officials have appealed fo...
\n","
neutral
\n","
\n","
\n","
19
\n","
[-0.004043699707835913, -0.05827523022890091, ...
\n","
I'm just wondering where all these panic buyer...
\n","
negative
\n","
6191
\n","
I'm just wondering where all these panic buyer...
\n","
0.604813
\n","
[I'm just wondering where all these panic buye...
\n","
positive
\n","
\n","
\n","
20
\n","
[-0.006520306225866079, 0.04672585800290108, -...
\n","
To address the increased demand on our communi...
\n","
positive
\n","
4213
\n","
To address the increased demand on our communi...
\n","
0.754619
\n","
[To address the increased demand on our commun...
\n","
positive
\n","
\n","
\n","
21
\n","
[0.09055875241756439, -0.03801679238677025, -0...
\n","
Important phone conversation with @ReedHasting...
\n","
positive
\n","
4180
\n","
Important phone conversation with @ReedHasting...
\n","
0.824278
\n","
[Important phone conversation with @ReedHastin...
\n","
positive
\n","
\n","
\n","
22
\n","
[0.06983352452516556, 0.045526593923568726, 0....
\n","
@JeffreeStar hey,I hope you see this tweet,jus...
\n","
positive
\n","
4393
\n","
@JeffreeStar hey,I hope you see this tweet,jus...
\n","
0.712092
\n","
[@JeffreeStar hey,I hope you see this tweet,ju...
\n","
positive
\n","
\n","
\n","
23
\n","
[0.010531553998589516, -0.041734274476766586, ...
\n","
In addition to masks we re now also banning ha...
\n","
positive
\n","
7906
\n","
In addition to masks we re now also banning ha...
\n","
0.804328
\n","
[In addition to masks we re now also banning h...
\n","
positive
\n","
\n","
\n","
24
\n","
[-0.0046012867242097855, -0.00663403794169426,...
\n","
It'll make bump into April bumpier, drawing do...
\n","
positive
\n","
2178
\n","
It'll make bump into April bumpier, drawing do...
\n","
0.680699
\n","
[It'll make bump into April bumpier, drawing d...
\n","
positive
\n","
\n","
\n","
25
\n","
[0.02057724818587303, 0.02416279725730419, -0....
\n","
#ScamAlert! There are some awful folks out the...
\n","
negative
\n","
3000
\n","
#ScamAlert! There are some awful folks out the...
\n","
0.717378
\n","
[#ScamAlert!, There are some awful folks out ...
\n","
positive
\n","
\n","
\n","
26
\n","
[-0.0029535864014178514, 0.06930557638406754, ...
\n","
It's April and I'm on #ESA. So how has @GOVUK ...
\n","
positive
\n","
8967
\n","
It's April and I'm on #ESA. So how has @GOVUK ...
\n","
0.729225
\n","
[It's April and I'm on #ESA., So how has @GOVU...
\n","
positive
\n","
\n","
\n","
27
\n","
[-0.022838125005364418, 0.035331811755895615, ...
\n","
#THANKYOU times a million to all those on the ...
\n","
positive
\n","
8202
\n","
#THANKYOU times a million to all those on the ...
\n","
0.817317
\n","
[#THANKYOU times a million to all those on the...
\n","
positive
\n","
\n","
\n","
28
\n","
[0.0058709485456347466, 0.007139457855373621, ...
\n","
Online shopping doubles down during coronaviru...
\n","
negative
\n","
4932
\n","
Online shopping doubles down during coronaviru...
\n","
0.656595
\n","
[Online shopping doubles down during coronavir...
\n","
positive
\n","
\n","
\n","
29
\n","
[0.038493022322654724, -0.04178141430020332, 0...
\n","
Covid-19 is changing the world as we know it. ...
\n","
positive
\n","
525
\n","
Covid-19 is changing the world as we know it. ...
\n","
0.836061
\n","
[Covid-19 is changing the world as we know it....
\n","
positive
\n","
\n","
\n","
30
\n","
[0.001453789067454636, 0.005407411605119705, -...
\n","
The whole supermarket delivery model is broken...
\n","
negative
\n","
1389
\n","
The whole supermarket delivery model is broken...
\n","
0.656143
\n","
[The whole supermarket delivery model is broke...
\n","
positive
\n","
\n","
\n","
31
\n","
[0.07007955759763718, 0.02776280976831913, -0....
\n","
For brands interested in building long term eq...
\n","
positive
\n","
146
\n","
For brands interested in building long term eq...
\n","
0.717087
\n","
[For brands interested in building long term e...
\n","
positive
\n","
\n","
\n","
32
\n","
[0.014691045507788658, 0.052066899836063385, 0...
\n","
@masrour_barzani It is worth mentioning your e...
\n","
positive
\n","
2989
\n","
@masrour_barzani It is worth mentioning your e...
\n","
0.750604
\n","
[@masrour_barzani It is worth mentioning your ...
\n","
positive
\n","
\n","
\n","
33
\n","
[0.02123432233929634, 0.052282460033893585, -0...
\n","
When you realize health care workers grocery s...
\n","
positive
\n","
9206
\n","
When you realize health care workers grocery s...
\n","
0.808270
\n","
[When you realize health care workers grocery ...
\n","
positive
\n","
\n","
\n","
34
\n","
[-0.016208922490477562, 0.041428711265325546, ...
\n","
\"Sales of shelf-stable, fresh, and frozen seaf...
\n","
positive
\n","
5297
\n","
\"Sales of shelf-stable, fresh, and frozen seaf...
\n","
0.683432
\n","
[\"Sales of shelf-stable, fresh, and frozen sea...
\n","
positive
\n","
\n","
\n","
35
\n","
[0.0038951593451201916, 0.012588057667016983, ...
\n","
@HolidayInn @ChoiceHotels @OmniHotels all majo...
\n","
positive
\n","
7155
\n","
@HolidayInn @ChoiceHotels @OmniHotels all majo...
\n","
0.792782
\n","
[@HolidayInn @ChoiceHotels @OmniHotels all maj...
\n","
positive
\n","
\n","
\n","
36
\n","
[0.027244137600064278, 0.058916911482810974, -...
\n","
Some silver linings during this covid-19 crisi...
\n","
negative
\n","
3295
\n","
Some silver linings during this covid-19 crisi...
\n","
0.639766
\n","
[Some silver linings during this covid-19 cris...
\n","
positive
\n","
\n","
\n","
37
\n","
[0.013577781617641449, 0.004573680926114321, 0...
\n","
Ok people CAN WE STOP BUYING ALL THE HAND SANI...
\n","
positive
\n","
1259
\n","
Ok people CAN WE STOP BUYING ALL THE HAND SANI...
\n","
0.784130
\n","
[Ok people CAN WE STOP BUYING ALL THE HAND SAN...
\n","
positive
\n","
\n","
\n","
38
\n","
[0.011083973571658134, -0.004668612964451313, ...
\n","
Your well being is our top priority!\\r\\r\\r\\nWh...
\n","
positive
\n","
3510
\n","
Your well being is our top priority! While oth...
\n","
0.712732
\n","
[Your well being is our top priority!, While o...
\n","
positive
\n","
\n","
\n","
39
\n","
[0.010227790102362633, 0.03293986618518829, 0....
\n","
In #France, supermarket chains agree to a one-...
\n","
positive
\n","
9662
\n","
In #France, supermarket chains agree to a one-...
\n","
0.762242
\n","
[In #France, supermarket chains agree to a one...
\n","
positive
\n","
\n","
\n","
40
\n","
[-0.03131600469350815, 0.054168395698070526, 0...
\n","
In one week health care workers, truck drivers...
\n","
positive
\n","
8688
\n","
In one week health care workers, truck drivers...
\n","
0.787212
\n","
[In one week health care workers, truck driver...
\n","
positive
\n","
\n","
\n","
41
\n","
[-0.01420750841498375, 0.031081417575478554, 0...
\n","
Try your best to support local businesses duri...
\n","
positive
\n","
8111
\n","
Try your best to support local businesses duri...
\n","
0.765113
\n","
[Try your best to support local businesses dur...
\n","
positive
\n","
\n","
\n","
42
\n","
[0.023995142430067062, -0.014616046100854874, ...
\n","
Consumer Hero is wishing all businesses and co...
\n","
positive
\n","
6005
\n","
Consumer Hero is wishing all businesses and co...
\n","
0.732366
\n","
[Consumer Hero is wishing all businesses and c...
\n","
positive
\n","
\n","
\n","
43
\n","
[0.03285776078701019, 0.007155246566981077, -0...
\n","
The no longer hidden crisis in middle and poor...
\n","
negative
\n","
3965
\n","
The no longer hidden crisis in middle and poor...
\n","
0.626646
\n","
[The no longer hidden crisis in middle and poo...
\n","
positive
\n","
\n","
\n","
44
\n","
[0.061222951859235764, 0.04071924835443497, -0...
\n","
@osaeB @markets Two economic positives from CO...
\n","
positive
\n","
561
\n","
@osaeB @markets Two economic positives from CO...
\n","
0.702125
\n","
[@osaeB @markets Two economic positives from C...
\n","
positive
\n","
\n","
\n","
45
\n","
[0.027163468301296234, -0.029017480090260506, ...
\n","
Well the #Coronavirus food panic buying isnÂt...
\n","
negative
\n","
8352
\n","
Well the #Coronavirus food panic buying isnÂt...
\n","
0.610110
\n","
[Well the #Coronavirus food panic buying isnÂ...
\n","
positive
\n","
\n","
\n","
46
\n","
[0.04085322096943855, 0.04066244512796402, -0....
\n","
@Complex This is unacceptable! WhereÂs Peta? ...
\n","
positive
\n","
4370
\n","
@Complex This is unacceptable! WhereÂs Peta? ...
\n","
0.818300
\n","
[@Complex This is unacceptable!, WhereÂs Peta...
\n","
positive
\n","
\n","
\n","
47
\n","
[0.06088094785809517, 0.02243555523455143, -0....
\n","
STOP THE PANIC BUYING \\r\\r\\r\\nThere is no shor...
"],"text/plain":[" sentence_embedding_use ... trained_sentiment\n","0 [0.027917474508285522, -0.06684374064207077, -... ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## 5. Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620189095591,"user_tz":-120,"elapsed":239911,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"7d9e3f28-7bdc-4a4f-e1cb-2329d5b26dd1"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@d4b5f4f) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@d4b5f4f\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["##6. Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620189100229,"user_tz":-120,"elapsed":244544,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b7a16d53-fde9-4d97-bb0e-ac3be96d722d"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.00 0.00 0.00 19\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.97 1.00 0.98 31\n","\n"," accuracy 0.62 50\n"," macro avg 0.32 0.33 0.33 50\n","weighted avg 0.60 0.62 0.61 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_use
\n","
text
\n","
y
\n","
origin_index
\n","
document
\n","
trained_sentiment_confidence
\n","
sentence
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.07024706155061722, -0.047160204499959946, ...
\n","
When I was a kid I always wanted to go on the ...
\n","
positive
\n","
4204
\n","
When I was a kid I always wanted to go on the ...
\n","
0.978457
\n","
[When I was a kid I always wanted to go on the...
\n","
positive
\n","
\n","
\n","
1
\n","
[-0.012395520694553852, -0.020829271525144577,...
\n","
The selfish morons who panic bought toilet rol...
\n","
negative
\n","
5622
\n","
The selfish morons who panic bought toilet rol...
\n","
0.530837
\n","
[The selfish morons who panic bought toilet ro...
\n","
neutral
\n","
\n","
\n","
2
\n","
[-0.02502020262181759, -0.08026497811079025, 0...
\n","
Profitero studied keyword search patterns and ...
\n","
positive
\n","
6545
\n","
Profitero studied keyword search patterns and ...
\n","
0.845908
\n","
[Profitero studied keyword search patterns and...
\n","
positive
\n","
\n","
\n","
3
\n","
[0.06621277332305908, 0.011142794042825699, -0...
\n","
From listed funds to builders to landlords, @k...
\n","
negative
\n","
9049
\n","
From listed funds to builders to landlords, @k...
\n","
0.541567
\n","
[From listed funds to builders to landlords, @...
\n","
neutral
\n","
\n","
\n","
4
\n","
[-0.024583933874964714, 0.06747899204492569, 0...
\n","
Against the back drop of NHS workers, civil se...
\n","
positive
\n","
8568
\n","
Against the back drop of NHS workers, civil se...
\n","
0.961466
\n","
[Against the back drop of NHS workers, civil s...
\n","
positive
\n","
\n","
\n","
5
\n","
[-0.030639173462986946, 0.07562850415706635, -...
\n","
\"Your going to lose people to the flu but you'...
\n","
negative
\n","
1645
\n","
\"Your going to lose people to the flu but you'...
\n","
0.508357
\n","
[\"Your going to lose people to the flu but you...
\n","
neutral
\n","
\n","
\n","
6
\n","
[0.020836224779486656, -0.06270623952150345, -...
\n","
As the COVID 19 outbreak continues to spread f...
\n","
negative
\n","
4190
\n","
As the COVID 19 outbreak continues to spread f...
\n","
0.526309
\n","
[As the COVID 19 outbreak continues to spread ...
\n","
neutral
\n","
\n","
\n","
7
\n","
[0.04782669618725777, -0.0003077308356296271, ...
\n","
We re tracking daily changes in economic uncer...
\n","
negative
\n","
1930
\n","
We re tracking daily changes in economic uncer...
\n","
0.542942
\n","
[We re tracking daily changes in economic unce...
\n","
neutral
\n","
\n","
\n","
8
\n","
[-0.04952622205018997, -0.024686245247721672, ...
\n","
I'm slightly confused! All for social distanc...
\n","
positive
\n","
4717
\n","
I'm slightly confused! All for social distanci...
\n","
0.953167
\n","
[I'm slightly confused!, All for social distan...
\n","
positive
\n","
\n","
\n","
9
\n","
[0.015434695407748222, -0.0009663645178079605,...
\n","
The knock-on effects of COVID-19 are having a...
\n","
positive
\n","
3860
\n","
The knock-on effects of COVID-19 are having a...
\n","
0.974030
\n","
[The knock-on effects of COVID-19 are having ...
\n","
positive
\n","
\n","
\n","
10
\n","
[0.01979624293744564, -0.006749877706170082, -...
\n","
Praise #coronavirus! God Bless the little bug...
\n","
positive
\n","
7223
\n","
Praise #coronavirus! God Bless the little bugs...
\n","
0.983924
\n","
[Praise #coronavirus!, God Bless the little bu...
\n","
positive
\n","
\n","
\n","
11
\n","
[0.06731504201889038, 0.06525817513465881, -0....
\n","
For those that think #COVIDÂ19 #Coronavirus ...
\n","
positive
\n","
1540
\n","
For those that think #COVIDÂ19 #Coronavirus i...
\n","
0.994959
\n","
[For those that think #COVIDÂ19 #Coronavirus ...
\n","
positive
\n","
\n","
\n","
12
\n","
[-0.03877340257167816, -0.04483730345964432, -...
\n","
Just managed to book a delivery with Sainsbury...
\n","
negative
\n","
1003
\n","
Just managed to book a delivery with Sainsbury...
\n","
0.519039
\n","
[Just managed to book a delivery with Sainsbur...
\n","
neutral
\n","
\n","
\n","
13
\n","
[0.012202774174511433, 0.04586584493517876, -0...
\n","
CanÂt get any in the supermarket but can rece...
\n","
positive
\n","
9630
\n","
CanÂt get any in the supermarket but can rece...
\n","
0.976869
\n","
[CanÂt get any in the supermarket but can rec...
\n","
positive
\n","
\n","
\n","
14
\n","
[0.03157752379775047, -0.03581700101494789, -0...
\n","
Scary. Gun sales up in #USA. Due to food short...
\n","
negative
\n","
9278
\n","
Scary. Gun sales up in #USA. Due to food short...
\n","
0.534066
\n","
[Scary. Gun sales up in #USA., Due to food sho...
\n","
neutral
\n","
\n","
\n","
15
\n","
[0.07137540727853775, 0.004993322305381298, -0...
\n","
VR headset companies, now is the time to slash...
\n","
positive
\n","
6960
\n","
VR headset companies, now is the time to slash...
\n","
0.949657
\n","
[VR headset companies, now is the time to slas...
\n","
positive
\n","
\n","
\n","
16
\n","
[0.07225924730300903, 0.002497387118637562, 0....
\n","
31% CanÂt Pay the Rent: ÂItÂs Only Going to...
\n","
negative
\n","
3771
\n","
31% CanÂt Pay the Rent: ÂItÂs Only Going to...
\n","
0.517149
\n","
[31% CanÂt Pay the Rent: ÂItÂs Only Going t...
\n","
neutral
\n","
\n","
\n","
17
\n","
[0.04617633670568466, 0.03097780980169773, -0....
\n","
COVID-19 is unprecedented. And to manage the c...
\n","
negative
\n","
2605
\n","
COVID-19 is unprecedented. And to manage the c...
\n","
0.502484
\n","
[COVID-19 is unprecedented., And to manage the...
\n","
neutral
\n","
\n","
\n","
18
\n","
[0.02162887528538704, -0.07251725345849991, -0...
\n","
Even as government officials have appealed for...
\n","
negative
\n","
9976
\n","
Even as government officials have appealed for...
\n","
0.543885
\n","
[Even as government officials have appealed fo...
\n","
neutral
\n","
\n","
\n","
19
\n","
[-0.004043699707835913, -0.05827523022890091, ...
\n","
I'm just wondering where all these panic buyer...
\n","
negative
\n","
6191
\n","
I'm just wondering where all these panic buyer...
\n","
0.528756
\n","
[I'm just wondering where all these panic buye...
\n","
neutral
\n","
\n","
\n","
20
\n","
[-0.006520306225866079, 0.04672585800290108, -...
\n","
To address the increased demand on our communi...
\n","
positive
\n","
4213
\n","
To address the increased demand on our communi...
\n","
0.955490
\n","
[To address the increased demand on our commun...
\n","
positive
\n","
\n","
\n","
21
\n","
[0.09055875241756439, -0.03801679238677025, -0...
\n","
Important phone conversation with @ReedHasting...
\n","
positive
\n","
4180
\n","
Important phone conversation with @ReedHasting...
\n","
0.983021
\n","
[Important phone conversation with @ReedHastin...
\n","
positive
\n","
\n","
\n","
22
\n","
[0.06983352452516556, 0.045526593923568726, 0....
\n","
@JeffreeStar hey,I hope you see this tweet,jus...
\n","
positive
\n","
4393
\n","
@JeffreeStar hey,I hope you see this tweet,jus...
\n","
0.886496
\n","
[@JeffreeStar hey,I hope you see this tweet,ju...
\n","
positive
\n","
\n","
\n","
23
\n","
[0.010531553998589516, -0.041734274476766586, ...
\n","
In addition to masks we re now also banning ha...
\n","
positive
\n","
7906
\n","
In addition to masks we re now also banning ha...
\n","
0.980952
\n","
[In addition to masks we re now also banning h...
\n","
positive
\n","
\n","
\n","
24
\n","
[-0.0046012867242097855, -0.00663403794169426,...
\n","
It'll make bump into April bumpier, drawing do...
\n","
positive
\n","
2178
\n","
It'll make bump into April bumpier, drawing do...
\n","
0.780364
\n","
[It'll make bump into April bumpier, drawing d...
\n","
positive
\n","
\n","
\n","
25
\n","
[0.02057724818587303, 0.02416279725730419, -0....
\n","
#ScamAlert! There are some awful folks out the...
\n","
negative
\n","
3000
\n","
#ScamAlert! There are some awful folks out the...
\n","
0.547257
\n","
[#ScamAlert!, There are some awful folks out ...
\n","
neutral
\n","
\n","
\n","
26
\n","
[-0.0029535864014178514, 0.06930557638406754, ...
\n","
It's April and I'm on #ESA. So how has @GOVUK ...
\n","
positive
\n","
8967
\n","
It's April and I'm on #ESA. So how has @GOVUK ...
\n","
0.844140
\n","
[It's April and I'm on #ESA., So how has @GOVU...
\n","
positive
\n","
\n","
\n","
27
\n","
[-0.022838125005364418, 0.035331811755895615, ...
\n","
#THANKYOU times a million to all those on the ...
\n","
positive
\n","
8202
\n","
#THANKYOU times a million to all those on the ...
\n","
0.984350
\n","
[#THANKYOU times a million to all those on the...
\n","
positive
\n","
\n","
\n","
28
\n","
[0.0058709485456347466, 0.007139457855373621, ...
\n","
Online shopping doubles down during coronaviru...
\n","
negative
\n","
4932
\n","
Online shopping doubles down during coronaviru...
\n","
0.517497
\n","
[Online shopping doubles down during coronavir...
\n","
neutral
\n","
\n","
\n","
29
\n","
[0.038493022322654724, -0.04178141430020332, 0...
\n","
Covid-19 is changing the world as we know it. ...
\n","
positive
\n","
525
\n","
Covid-19 is changing the world as we know it. ...
\n","
0.994486
\n","
[Covid-19 is changing the world as we know it....
\n","
positive
\n","
\n","
\n","
30
\n","
[0.001453789067454636, 0.005407411605119705, -...
\n","
The whole supermarket delivery model is broken...
\n","
negative
\n","
1389
\n","
The whole supermarket delivery model is broken...
\n","
0.505307
\n","
[The whole supermarket delivery model is broke...
\n","
neutral
\n","
\n","
\n","
31
\n","
[0.07007955759763718, 0.02776280976831913, -0....
\n","
For brands interested in building long term eq...
\n","
positive
\n","
146
\n","
For brands interested in building long term eq...
\n","
0.887487
\n","
[For brands interested in building long term e...
\n","
positive
\n","
\n","
\n","
32
\n","
[0.014691045507788658, 0.052066899836063385, 0...
\n","
@masrour_barzani It is worth mentioning your e...
\n","
positive
\n","
2989
\n","
@masrour_barzani It is worth mentioning your e...
\n","
0.946808
\n","
[@masrour_barzani It is worth mentioning your ...
\n","
positive
\n","
\n","
\n","
33
\n","
[0.02123432233929634, 0.052282460033893585, -0...
\n","
When you realize health care workers grocery s...
\n","
positive
\n","
9206
\n","
When you realize health care workers grocery s...
\n","
0.988752
\n","
[When you realize health care workers grocery ...
\n","
positive
\n","
\n","
\n","
34
\n","
[-0.016208922490477562, 0.041428711265325546, ...
\n","
\"Sales of shelf-stable, fresh, and frozen seaf...
\n","
positive
\n","
5297
\n","
\"Sales of shelf-stable, fresh, and frozen seaf...
\n","
0.841267
\n","
[\"Sales of shelf-stable, fresh, and frozen sea...
\n","
positive
\n","
\n","
\n","
35
\n","
[0.0038951593451201916, 0.012588057667016983, ...
\n","
@HolidayInn @ChoiceHotels @OmniHotels all majo...
\n","
positive
\n","
7155
\n","
@HolidayInn @ChoiceHotels @OmniHotels all majo...
\n","
0.972513
\n","
[@HolidayInn @ChoiceHotels @OmniHotels all maj...
\n","
positive
\n","
\n","
\n","
36
\n","
[0.027244137600064278, 0.058916911482810974, -...
\n","
Some silver linings during this covid-19 crisi...
\n","
negative
\n","
3295
\n","
Some silver linings during this covid-19 crisi...
\n","
0.500515
\n","
[Some silver linings during this covid-19 cris...
\n","
neutral
\n","
\n","
\n","
37
\n","
[0.013577781617641449, 0.004573680926114321, 0...
\n","
Ok people CAN WE STOP BUYING ALL THE HAND SANI...
\n","
positive
\n","
1259
\n","
Ok people CAN WE STOP BUYING ALL THE HAND SANI...
\n","
0.953883
\n","
[Ok people CAN WE STOP BUYING ALL THE HAND SAN...
\n","
positive
\n","
\n","
\n","
38
\n","
[0.011083973571658134, -0.004668612964451313, ...
\n","
Your well being is our top priority!\\r\\r\\r\\nWh...
\n","
positive
\n","
3510
\n","
Your well being is our top priority! While oth...
\n","
0.848192
\n","
[Your well being is our top priority!, While o...
\n","
positive
\n","
\n","
\n","
39
\n","
[0.010227790102362633, 0.03293986618518829, 0....
\n","
In #France, supermarket chains agree to a one-...
\n","
positive
\n","
9662
\n","
In #France, supermarket chains agree to a one-...
\n","
0.961013
\n","
[In #France, supermarket chains agree to a one...
\n","
positive
\n","
\n","
\n","
40
\n","
[-0.03131600469350815, 0.054168395698070526, 0...
\n","
In one week health care workers, truck drivers...
\n","
positive
\n","
8688
\n","
In one week health care workers, truck drivers...
\n","
0.983875
\n","
[In one week health care workers, truck driver...
\n","
positive
\n","
\n","
\n","
41
\n","
[-0.01420750841498375, 0.031081417575478554, 0...
\n","
Try your best to support local businesses duri...
\n","
positive
\n","
8111
\n","
Try your best to support local businesses duri...
\n","
0.963043
\n","
[Try your best to support local businesses dur...
\n","
positive
\n","
\n","
\n","
42
\n","
[0.023995142430067062, -0.014616046100854874, ...
\n","
Consumer Hero is wishing all businesses and co...
\n","
positive
\n","
6005
\n","
Consumer Hero is wishing all businesses and co...
\n","
0.903110
\n","
[Consumer Hero is wishing all businesses and c...
\n","
positive
\n","
\n","
\n","
43
\n","
[0.03285776078701019, 0.007155246566981077, -0...
\n","
The no longer hidden crisis in middle and poor...
\n","
negative
\n","
3965
\n","
The no longer hidden crisis in middle and poor...
\n","
0.530567
\n","
[The no longer hidden crisis in middle and poo...
\n","
neutral
\n","
\n","
\n","
44
\n","
[0.061222951859235764, 0.04071924835443497, -0...
\n","
@osaeB @markets Two economic positives from CO...
\n","
positive
\n","
561
\n","
@osaeB @markets Two economic positives from CO...
\n","
0.764335
\n","
[@osaeB @markets Two economic positives from C...
\n","
positive
\n","
\n","
\n","
45
\n","
[0.027163468301296234, -0.029017480090260506, ...
\n","
Well the #Coronavirus food panic buying isnÂt...
\n","
negative
\n","
8352
\n","
Well the #Coronavirus food panic buying isnÂt...
\n","
0.536124
\n","
[Well the #Coronavirus food panic buying isnÂ...
\n","
neutral
\n","
\n","
\n","
46
\n","
[0.04085322096943855, 0.04066244512796402, -0....
\n","
@Complex This is unacceptable! WhereÂs Peta? ...
\n","
positive
\n","
4370
\n","
@Complex This is unacceptable! WhereÂs Peta? ...
\n","
0.983527
\n","
[@Complex This is unacceptable!, WhereÂs Peta...
\n","
positive
\n","
\n","
\n","
47
\n","
[0.06088094785809517, 0.02243555523455143, -0....
\n","
STOP THE PANIC BUYING \\r\\r\\r\\nThere is no shor...
\n","
negative
\n","
8241
\n","
STOP THE PANIC BUYING There is no shortage of ...
\n","
0.538391
\n","
[STOP THE PANIC BUYING There is no shortage of...
\n","
neutral
\n","
\n","
\n","
48
\n","
[-0.05397384986281395, 0.04183822497725487, -0...
\n","
We often rightly hear about the great work tha...
\n","
positive
\n","
5160
\n","
We often rightly hear about the great work tha...
\n","
0.989714
\n","
[We often rightly hear about the great work th...
\n","
positive
\n","
\n","
\n","
49
\n","
[-0.009696043096482754, 0.09022427350282669, 0...
\n","
In these tough and stressful times with 100 s ...
\n","
negative
\n","
5159
\n","
In these tough and stressful times with 100 s ...
\n","
0.822190
\n","
[In these tough and stressful times with 100 s...
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_use ... trained_sentiment\n","0 [-0.07024706155061722, -0.047160204499959946, ... ... positive\n","1 [-0.012395520694553852, -0.020829271525144577,... ... neutral\n","2 [-0.02502020262181759, -0.08026497811079025, 0... ... positive\n","3 [0.06621277332305908, 0.011142794042825699, -0... ... neutral\n","4 [-0.024583933874964714, 0.06747899204492569, 0... ... positive\n","5 [-0.030639173462986946, 0.07562850415706635, -... ... neutral\n","6 [0.020836224779486656, -0.06270623952150345, -... ... neutral\n","7 [0.04782669618725777, -0.0003077308356296271, ... ... neutral\n","8 [-0.04952622205018997, -0.024686245247721672, ... ... positive\n","9 [0.015434695407748222, -0.0009663645178079605,... ... positive\n","10 [0.01979624293744564, -0.006749877706170082, -... ... positive\n","11 [0.06731504201889038, 0.06525817513465881, -0.... ... positive\n","12 [-0.03877340257167816, -0.04483730345964432, -... ... neutral\n","13 [0.012202774174511433, 0.04586584493517876, -0... ... positive\n","14 [0.03157752379775047, -0.03581700101494789, -0... ... neutral\n","15 [0.07137540727853775, 0.004993322305381298, -0... ... positive\n","16 [0.07225924730300903, 0.002497387118637562, 0.... ... neutral\n","17 [0.04617633670568466, 0.03097780980169773, -0.... ... neutral\n","18 [0.02162887528538704, -0.07251725345849991, -0... ... neutral\n","19 [-0.004043699707835913, -0.05827523022890091, ... ... neutral\n","20 [-0.006520306225866079, 0.04672585800290108, -... ... positive\n","21 [0.09055875241756439, -0.03801679238677025, -0... ... positive\n","22 [0.06983352452516556, 0.045526593923568726, 0.... ... positive\n","23 [0.010531553998589516, -0.041734274476766586, ... ... positive\n","24 [-0.0046012867242097855, -0.00663403794169426,... ... positive\n","25 [0.02057724818587303, 0.02416279725730419, -0.... ... neutral\n","26 [-0.0029535864014178514, 0.06930557638406754, ... ... positive\n","27 [-0.022838125005364418, 0.035331811755895615, ... ... positive\n","28 [0.0058709485456347466, 0.007139457855373621, ... ... neutral\n","29 [0.038493022322654724, -0.04178141430020332, 0... ... positive\n","30 [0.001453789067454636, 0.005407411605119705, -... ... neutral\n","31 [0.07007955759763718, 0.02776280976831913, -0.... ... positive\n","32 [0.014691045507788658, 0.052066899836063385, 0... ... positive\n","33 [0.02123432233929634, 0.052282460033893585, -0... ... positive\n","34 [-0.016208922490477562, 0.041428711265325546, ... ... positive\n","35 [0.0038951593451201916, 0.012588057667016983, ... ... positive\n","36 [0.027244137600064278, 0.058916911482810974, -... ... neutral\n","37 [0.013577781617641449, 0.004573680926114321, 0... ... positive\n","38 [0.011083973571658134, -0.004668612964451313, ... ... positive\n","39 [0.010227790102362633, 0.03293986618518829, 0.... ... positive\n","40 [-0.03131600469350815, 0.054168395698070526, 0... ... positive\n","41 [-0.01420750841498375, 0.031081417575478554, 0... ... positive\n","42 [0.023995142430067062, -0.014616046100854874, ... ... positive\n","43 [0.03285776078701019, 0.007155246566981077, -0... ... neutral\n","44 [0.061222951859235764, 0.04071924835443497, -0... ... positive\n","45 [0.027163468301296234, -0.029017480090260506, ... ... neutral\n","46 [0.04085322096943855, 0.04066244512796402, -0.... ... positive\n","47 [0.06088094785809517, 0.02243555523455143, -0.... ... neutral\n","48 [-0.05397384986281395, 0.04183822497725487, -0... ... positive\n","49 [-0.009696043096482754, 0.09022427350282669, 0... ... positive\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["# 7. Try training with different Embeddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxWFzQOhjWC8","executionInfo":{"status":"ok","timestamp":1620189100230,"user_tz":-120,"elapsed":244540,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"04526125-5b61-4628-e7a4-9778daa6da8f"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IKK_Ii_gjJfF","executionInfo":{"status":"ok","timestamp":1620199760733,"user_tz":-120,"elapsed":6530,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"bb105ac7-fef2-4e67-d5ae-c48d6dd7107c"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(120) \n","trainable_pipe['sentiment_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.89 0.86 0.87 3982\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.90 0.85 0.87 4018\n","\n"," accuracy 0.85 8000\n"," macro avg 0.60 0.57 0.58 8000\n","weighted avg 0.90 0.85 0.87 8000\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 7.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fxx4yNkNVGFl","executionInfo":{"status":"ok","timestamp":1620200260263,"user_tz":-120,"elapsed":491614,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"eff0c644-2484-4180-8c2d-0295eca4d1c5"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.88 0.81 0.85 1018\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.86 0.85 0.85 982\n","\n"," accuracy 0.83 2000\n"," macro avg 0.58 0.55 0.57 2000\n","weighted avg 0.87 0.83 0.85 2000\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 8. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620200475138,"user_tz":-120,"elapsed":705445,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0223e739-965d-4882-cf48-ebc6d1fd45d4"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 9. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":76},"id":"SO4uz45MoRgp","executionInfo":{"status":"ok","timestamp":1620200488257,"user_tz":-120,"elapsed":716709,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"4347e200-966d-415b-d719-090e2e2efcd7"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Everything is under control !')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
sentiment
\n","
origin_index
\n","
document
\n","
sentiment_confidence
\n","
sentence_embedding_from_disk
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Everything is under control !
\n","
[negative]
\n","
8589934592
\n","
Everything is under control !
\n","
[0.7562715]
\n","
[[0.3778035342693329, 0.29955393075942993, 0.1...
\n","
[Everything is under control !]
\n","
\n"," \n","
\n","
"],"text/plain":[" text ... sentence\n","0 Everything is under control ! ... [Everything is under control !]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e0CVlkk9v6Qi","executionInfo":{"status":"ok","timestamp":1620200488259,"user_tz":-120,"elapsed":715702,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"a1f2e70d-ac08-428c-9e05-0c83c78f2db6"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@551e1ab0) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@551e1ab0\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentiment_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-S888rwObpEs"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_finanical_news.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_finanical_news.ipynb
deleted file mode 100644
index 7385af06..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_finanical_news.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_sentiment_classifier_demo_finanical_news.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_finanical_news.ipynb)\n","\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 class Finance News sentiment classifier training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n"," \n","\n","\n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n","\n"," \n","\n","\n",""]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191453470,"user_tz":-120,"elapsed":108117,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"128e625c-21cf-4c76-969d-d58ba57f0b9f"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:09:06-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 05:09:06 (1.82 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 64kB/s \n","\u001b[K |████████████████████████████████| 153kB 46.5MB/s \n","\u001b[K |████████████████████████████████| 204kB 21.5MB/s \n","\u001b[K |████████████████████████████████| 204kB 50.1MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Finanical News Sentiment dataset \n","https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news\n","\n","This dataset contains the sentiments for financial news headlines from the perspective of a retail investor. Further details about the dataset can be found in: Malo, P., Sinha, A., Takala, P., Korhonen, P. and Wallenius, J. (2014): “Good debt or bad debt: Detecting semantic orientations in economic texts.” Journal of the American Society for Information Science and Technology."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620191454511,"user_tz":-120,"elapsed":109150,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"6b206aaa-ecb1-43a7-8d4a-1eae242989d4"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/01/all-data.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:10:53-- http://ckl-it.de/wp-content/uploads/2021/01/all-data.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 704799 (688K) [text/csv]\n","Saving to: ‘all-data.csv’\n","\n","all-data.csv 100%[===================>] 688.28K 1.22MB/s in 0.5s \n","\n","2021-05-05 05:10:54 (1.22 MB/s) - ‘all-data.csv’ saved [704799/704799]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620191455016,"user_tz":-120,"elapsed":109649,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"a2eb3af1-eb9a-413f-ffb8-eedd69c82cd6"},"source":["import pandas as pd\n","train_path = '/content/all-data.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","# the label column must have name 'y' name be of type str\n","columns=['text','y']\n","train_df = train_df[columns]\n","train_df = train_df[~train_df[\"y\"].isin([\"neutral\"])]\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
951
\n","
Finnish software developer Basware Oyj said on...
\n","
positive
\n","
\n","
\n","
829
\n","
Comptel , a vendor of dynamic Operations Suppo...
\n","
positive
\n","
\n","
\n","
760
\n","
Espoon kaupunki awarded contracts for personal...
\n","
positive
\n","
\n","
\n","
770
\n","
Net cash flow from operations is expected to r...
\n","
positive
\n","
\n","
\n","
811
\n","
The shopping center to be opened in St. Peters...
\n","
positive
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
12
\n","
Finnish Talentum reports its operating profit ...
\n","
positive
\n","
\n","
\n","
981
\n","
The financial impact is estimated to be an ann...
\n","
positive
\n","
\n","
\n","
4773
\n","
`` I am extremely delighted with this project ...
\n","
positive
\n","
\n","
\n","
4080
\n","
Cost cutting measures , which have produced ar...
\n","
positive
\n","
\n","
\n","
1944
\n","
Paper maker Stora Enso Oyj said Friday it has ...
\n","
positive
\n","
\n"," \n","
\n","
1573 rows × 2 columns
\n","
"],"text/plain":[" text y\n","951 Finnish software developer Basware Oyj said on... positive\n","829 Comptel , a vendor of dynamic Operations Suppo... positive\n","760 Espoon kaupunki awarded contracts for personal... positive\n","770 Net cash flow from operations is expected to r... positive\n","811 The shopping center to be opened in St. Peters... positive\n","... ... ...\n","12 Finnish Talentum reports its operating profit ... positive\n","981 The financial impact is estimated to be an ann... positive\n","4773 `` I am extremely delighted with this project ... positive\n","4080 Cost cutting measures , which have produced ar... positive\n","1944 Paper maker Stora Enso Oyj said Friday it has ... positive\n","\n","[1573 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620191586887,"user_tz":-120,"elapsed":241515,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"da31ed6e-519a-420d-9bdd-3dd95b8d0c78"},"source":["import nlu \n","from sklearn.metrics import classification_report\n","\n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.00 0.00 0.00 14\n"," positive 0.72 1.00 0.84 36\n","\n"," accuracy 0.72 50\n"," macro avg 0.36 0.50 0.42 50\n","weighted avg 0.52 0.72 0.60 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
"],"text/plain":[" origin_index ... sentence_embedding_use\n","0 0 ... [0.009911456145346165, 0.04162858799099922, -0...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## 5. Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620191587670,"user_tz":-120,"elapsed":242288,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"75f6cff2-4c64-4eb3-a5a3-cf4d142940c8"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3933547a) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3933547a\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## 6. Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":759},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620191593284,"user_tz":-120,"elapsed":247897,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"eea9eda8-11d0-4568-c277-46105770062f"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:100])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.00 0.00 0.00 31\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.86 0.99 0.92 69\n","\n"," accuracy 0.68 100\n"," macro avg 0.29 0.33 0.31 100\n","weighted avg 0.59 0.68 0.63 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
y
\n","
text
\n","
document
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_use
\n","
\n"," \n"," \n","
\n","
0
\n","
951
\n","
0.999660
\n","
positive
\n","
Finnish software developer Basware Oyj said on...
\n","
Finnish software developer Basware Oyj said on...
\n","
positive
\n","
[Finnish software developer Basware Oyj said o...
\n","
[0.015306072309613228, -0.002737046219408512, ...
\n","
\n","
\n","
1
\n","
829
\n","
0.999917
\n","
positive
\n","
Comptel , a vendor of dynamic Operations Suppo...
\n","
Comptel , a vendor of dynamic Operations Suppo...
\n","
positive
\n","
[Comptel , a vendor of dynamic Operations Supp...
\n","
[0.04115989804267883, -0.013678845949470997, -...
\n","
\n","
\n","
2
\n","
760
\n","
0.997053
\n","
positive
\n","
Espoon kaupunki awarded contracts for personal...
\n","
Espoon kaupunki awarded contracts for personal...
\n","
positive
\n","
[Espoon kaupunki awarded contracts for persona...
\n","
[0.050158627331256866, 0.011978656984865665, 0...
\n","
\n","
\n","
3
\n","
770
\n","
0.958511
\n","
positive
\n","
Net cash flow from operations is expected to r...
\n","
Net cash flow from operations is expected to r...
\n","
positive
\n","
[Net cash flow from operations is expected to ...
\n","
[0.06134718284010887, 0.020957626402378082, -0...
\n","
\n","
\n","
4
\n","
811
\n","
0.978840
\n","
positive
\n","
The shopping center to be opened in St. Peters...
\n","
The shopping center to be opened in St. Peters...
\n","
positive
\n","
[The shopping center to be opened in St. Peter...
\n","
[0.030753908678889275, -0.009971680119633675, ...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
689
\n","
0.897502
\n","
positive
\n","
Finnish Sampo Bank , of Danish Danske Bank gro...
\n","
Finnish Sampo Bank , of Danish Danske Bank gro...
\n","
positive
\n","
[Finnish Sampo Bank , of Danish Danske Bank gr...
\n","
[0.06391362845897675, 0.028690673410892487, -0...
\n","
\n","
\n","
96
\n","
1998
\n","
0.979267
\n","
positive
\n","
Talentum expects that the net sales of its cor...
\n","
Talentum expects that the net sales of its cor...
\n","
positive
\n","
[Talentum expects that the net sales of its co...
\n","
[0.07739534974098206, 0.037561606615781784, -0...
\n","
\n","
\n","
97
\n","
4458
\n","
0.997499
\n","
positive
\n","
efficiency improvement measures 20 January 201...
\n","
efficiency improvement measures 20 January 201...
\n","
positive
\n","
[efficiency improvement measures 20 January 20...
\n","
[0.056025125086307526, 0.054798685014247894, -...
\n","
\n","
\n","
98
\n","
4415
\n","
0.518980
\n","
negative
\n","
Operating profit , excluding non-recurring ite...
\n","
Operating profit , excluding non-recurring ite...
\n","
neutral
\n","
[Operating profit , excluding non-recurring it...
\n","
[0.031145866960287094, 0.051556773483753204, -...
\n","
\n","
\n","
99
\n","
4752
\n","
0.516453
\n","
negative
\n","
Operating profit totaled EUR 9.4 mn , down fro...
\n","
Operating profit totaled EUR 9.4 mn , down fro...
\n","
neutral
\n","
[Operating profit totaled EUR 9.4 mn , down fr...
\n","
[0.04533316195011139, 0.06103566288948059, -0....
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" origin_index ... sentence_embedding_use\n","0 951 ... [0.015306072309613228, -0.002737046219408512, ...\n","1 829 ... [0.04115989804267883, -0.013678845949470997, -...\n","2 760 ... [0.050158627331256866, 0.011978656984865665, 0...\n","3 770 ... [0.06134718284010887, 0.020957626402378082, -0...\n","4 811 ... [0.030753908678889275, -0.009971680119633675, ...\n",".. ... ... ...\n","95 689 ... [0.06391362845897675, 0.028690673410892487, -0...\n","96 1998 ... [0.07739534974098206, 0.037561606615781784, -0...\n","97 4458 ... [0.056025125086307526, 0.054798685014247894, -...\n","98 4415 ... [0.031145866960287094, 0.051556773483753204, -...\n","99 4752 ... [0.04533316195011139, 0.06103566288948059, -0....\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["#7. Try training with different Embeddings"]},{"cell_type":"code","metadata":{"id":"nxWFzQOhjWC8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191593285,"user_tz":-120,"elapsed":247893,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c9c63c03-471b-4c63-bf5f-4bf1107c521b"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IKK_Ii_gjJfF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620192376887,"user_tz":-120,"elapsed":1031491,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"3beccb4a-b2c7-437a-8725-753d99011c4e"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(70) \n","trainable_pipe['sentiment_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.87 0.84 0.86 488\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.96 0.92 0.94 1085\n","\n"," accuracy 0.90 1573\n"," macro avg 0.61 0.59 0.60 1573\n","weighted avg 0.94 0.90 0.92 1573\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 7.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"id":"Fxx4yNkNVGFl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620192438491,"user_tz":-120,"elapsed":1093091,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2ca09f36-0316-41af-9eba-61d6aeaf1f54"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.84 0.76 0.80 116\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.95 0.90 0.93 278\n","\n"," accuracy 0.86 394\n"," macro avg 0.60 0.55 0.57 394\n","weighted avg 0.92 0.86 0.89 394\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 8. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620192662123,"user_tz":-120,"elapsed":1316719,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"a91b2529-2d79-400d-ce51-107c2afa2195"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 9. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":94},"executionInfo":{"status":"ok","timestamp":1620192674556,"user_tz":-120,"elapsed":1329148,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"864c1c25-cebd-4f7b-c5cd-06ff11db537b"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('According to the most recent update there has been a major decrese in the rate of oil')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentiment
\n","
sentence_embedding_from_disk
\n","
text
\n","
sentiment_confidence
\n","
document
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
8589934592
\n","
[negative]
\n","
[[-0.021685948595404625, 0.13073018193244934, ...
\n","
According to the most recent update there has ...
\n","
[0.86657214]
\n","
According to the most recent update there has ...
\n","
[According to the most recent update there has...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 8589934592 ... [According to the most recent update there has...\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620192674557,"user_tz":-120,"elapsed":1329144,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"53e0a6b8-7742-4428-d203-cfa5c1c499b0"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@dcd6682) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@dcd6682\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentiment_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_natural_disasters.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_natural_disasters.ipynb
deleted file mode 100644
index 3eca1abb..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_natural_disasters.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_sentiment_classifier_demo_natural_disasters.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_natural_disasters.ipynb)\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 Class Natural Disasters Sentiment Classifer Training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n","\n"," \n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n","\n"," \n","\n","\n",""]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620193265134,"user_tz":-120,"elapsed":142818,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"52bc1bd1-c902-4d24-b470-579f3f4ab6d2"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:38:43-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 05:38:43 (30.6 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 73kB/s \n","\u001b[K |████████████████████████████████| 153kB 45.7MB/s \n","\u001b[K |████████████████████████████████| 204kB 19.8MB/s \n","\u001b[K |████████████████████████████████| 204kB 41.4MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Disaster Sentiment dataset \n","https://www.kaggle.com/vstepanenko/disaster-tweets\n","#Context\n","\n","The file contains over 11,000 tweets associated with disaster keywords like “crash”, “quarantine”, and “bush fires” as well as the location and keyword itself. The data structure was inherited from Disasters on social media\n","\n","The tweets were collected on Jan 14th, 2020.\n","\n","Some of the topics people were tweeting:\n","\n","The eruption of Taal Volcano in Batangas, Philippines\n","Coronavirus\n","Bushfires in Australia\n","Iran downing of the airplane flight PS752\n","Disclaimer: The dataset contains text that may be considered profane, vulgar, or offensive."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620193268178,"user_tz":-120,"elapsed":145852,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"d989f5b9-0f5c-4afc-94d4-04eb3f7613a4"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/tweets.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:41:04-- http://ckl-it.de/wp-content/uploads/2021/02/tweets.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1207952 (1.2M) [text/csv]\n","Saving to: ‘tweets.csv’\n","\n","tweets.csv 100%[===================>] 1.15M 654KB/s in 1.8s \n","\n","2021-05-05 05:41:07 (654 KB/s) - ‘tweets.csv’ saved [1207952/1207952]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620193269115,"user_tz":-120,"elapsed":146783,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"5f4be6e9-1900-4776-ee93-9ad537c2daf0"},"source":["import pandas as pd\n","train_path = '/content/tweets.csv'\n","\n","train_df = pd.read_csv(train_path,sep=\",\", encoding='latin-1')\n","# the text data to use for classification should be in a column named 'text'\n","columns=['text','y']\n","train_df = train_df.dropna()\n","positive = train_df[train_df['y']==(\"positive\")].iloc[:1500]\n","negative = train_df[train_df['y']==(\"negative\")].iloc[:1500]\n","positive = positive.append(negative, ignore_index = True)\n","positive = positive.sample(frac=1).reset_index(drop=True)\n","train_df = positive\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
Unnamed: 0
\n","
id
\n","
keyword
\n","
location
\n","
text
\n","
target
\n","
y
\n","
\n"," \n"," \n","
\n","
2310
\n","
2375.0
\n","
6350.0
\n","
heat%20wave
\n","
Chandigarh, India
\n","
Bihar District Magistrate Issues Order to Clos...
\n","
1.0
\n","
positive
\n","
\n","
\n","
2044
\n","
1607.0
\n","
3937.0
\n","
destroyed
\n","
Scotland, but like at the top
\n","
YOU THERE, PACHIRISU PUNK, PREPARE TO BE DESTR...
\n","
0.0
\n","
negative
\n","
\n","
\n","
2473
\n","
809.0
\n","
272.0
\n","
annihilated
\n","
USA
\n","
A devastating and thorough refutation of and N...
\n","
0.0
\n","
negative
\n","
\n","
\n","
1369
\n","
152.0
\n","
3817.0
\n","
desolate
\n","
United States Air Force
\n","
bubby finally gonna help with desolate while i...
\n","
0.0
\n","
negative
\n","
\n","
\n","
1880
\n","
305.0
\n","
4609.0
\n","
earthquake
\n","
Chile
\n","
Temblor: mb 4.5 SOUTHEAST OF EASTER ISLAND: Ma...
\n","
1.0
\n","
positive
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1531
\n","
2759.0
\n","
110.0
\n","
aftershock
\n","
NYC
\n","
The magnitude 5.9 quake in #PuertoRico this mo...
\n","
1.0
\n","
positive
\n","
\n","
\n","
903
\n","
182.0
\n","
3256.0
\n","
debris
\n","
Republic of the Philippines
\n","
Hello everyone. Tito Sotto is wrong. Cloud see...
\n","
0.0
\n","
negative
\n","
\n","
\n","
2621
\n","
287.0
\n","
4008.0
\n","
destruction
\n","
Recife, Brasil
\n","
Bear witness to his destruction. West Coast, #...
\n","
0.0
\n","
negative
\n","
\n","
\n","
1235
\n","
5131.0
\n","
2863.0
\n","
curfew
\n","
Vehari
\n","
KASHMIR STILL UNDER CURFEW ðÂÂÂðÂÂÂðÂ...
\n","
1.0
\n","
positive
\n","
\n","
\n","
2264
\n","
1300.0
\n","
8294.0
\n","
quarantined
\n","
ÃÂT: 45.246915,-76.163963
\n","
Chinese woman with mystery virus quarantined i...
\n","
1.0
\n","
positive
\n","
\n"," \n","
\n","
2400 rows × 7 columns
\n","
"],"text/plain":[" Unnamed: 0 id ... target y\n","2310 2375.0 6350.0 ... 1.0 positive\n","2044 1607.0 3937.0 ... 0.0 negative\n","2473 809.0 272.0 ... 0.0 negative\n","1369 152.0 3817.0 ... 0.0 negative\n","1880 305.0 4609.0 ... 1.0 positive\n","... ... ... ... ... ...\n","1531 2759.0 110.0 ... 1.0 positive\n","903 182.0 3256.0 ... 0.0 negative\n","2621 287.0 4008.0 ... 0.0 negative\n","1235 5131.0 2863.0 ... 1.0 positive\n","2264 1300.0 8294.0 ... 1.0 positive\n","\n","[2400 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620195355299,"user_tz":-120,"elapsed":13046,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"72a7378b-68c0-42ae-f41c-44d717e82f46"},"source":["import nlu \n","from sklearn.metrics import classification_report\n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 1.00 0.20 0.33 25\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.86 0.96 0.91 25\n","\n"," accuracy 0.58 50\n"," macro avg 0.62 0.39 0.41 50\n","weighted avg 0.93 0.58 0.62 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
trained_sentiment
\n","
text
\n","
sentence
\n","
id
\n","
target
\n","
document
\n","
trained_sentiment_confidence
\n","
keyword
\n","
origin_index
\n","
sentence_embedding_use
\n","
Unnamed: 0
\n","
location
\n","
\n"," \n"," \n","
\n","
0
\n","
positive
\n","
positive
\n","
Bihar District Magistrate Issues Order to Clos...
\n","
[Bihar District Magistrate Issues Order to Clo...
\n","
6350.0
\n","
1.0
\n","
Bihar District Magistrate Issues Order to Clos...
\n","
0.714817
\n","
heat%20wave
\n","
2310
\n","
[0.01817215047776699, -0.042255766689777374, -...
\n","
2375.0
\n","
Chandigarh, India
\n","
\n","
\n","
1
\n","
negative
\n","
negative
\n","
YOU THERE, PACHIRISU PUNK, PREPARE TO BE DESTR...
\n","
[YOU THERE, PACHIRISU PUNK, PREPARE TO BE DEST...
\n","
3937.0
\n","
0.0
\n","
YOU THERE, PACHIRISU PUNK, PREPARE TO BE DESTR...
\n","
0.624722
\n","
destroyed
\n","
2044
\n","
[0.01567729189991951, 0.00028937371098436415, ...
\n","
1607.0
\n","
Scotland, but like at the top
\n","
\n","
\n","
2
\n","
negative
\n","
neutral
\n","
A devastating and thorough refutation of and N...
\n","
[A devastating and thorough refutation of and ...
\n","
272.0
\n","
0.0
\n","
A devastating and thorough refutation of and N...
\n","
0.516126
\n","
annihilated
\n","
2473
\n","
[0.030531354248523712, 0.018328234553337097, -...
\n","
809.0
\n","
USA
\n","
\n","
\n","
3
\n","
negative
\n","
negative
\n","
bubby finally gonna help with desolate while i...
\n","
[bubby finally gonna help with desolate while ...
\n","
3817.0
\n","
0.0
\n","
bubby finally gonna help with desolate while i...
\n","
0.635754
\n","
desolate
\n","
1369
\n","
[-0.010298581793904305, -0.023482605814933777,...
\n","
152.0
\n","
United States Air Force
\n","
\n","
\n","
4
\n","
positive
\n","
positive
\n","
Temblor: mb 4.5 SOUTHEAST OF EASTER ISLAND: Ma...
\n","
[Temblor: mb 4.5 SOUTHEAST OF EASTER ISLAND:, ...
\n","
4609.0
\n","
1.0
\n","
Temblor: mb 4.5 SOUTHEAST OF EASTER ISLAND: Ma...
\n","
0.688311
\n","
earthquake
\n","
1880
\n","
[-0.07005153596401215, -0.042087431997060776, ...
\n","
305.0
\n","
Chile
\n","
\n","
\n","
5
\n","
positive
\n","
positive
\n","
Human body parts have been found in a bag on a...
\n","
[Human body parts have been found in a bag on ...
\n","
1333.0
\n","
1.0
\n","
Human body parts have been found in a bag on a...
\n","
0.672013
\n","
body%20bag
\n","
936
\n","
[-0.01760876551270485, -0.07565091550350189, -...
\n","
1765.0
\n","
Dublin, Ireland
\n","
\n","
\n","
6
\n","
negative
\n","
neutral
\n","
JFC!!! Racist comment ! I guess all \"lazy\" may...
\n","
[JFC!!!, Racist comment !, I guess all \"lazy\" ...
\n","
1709.0
\n","
0.0
\n","
JFC!!! Racist comment ! I guess all \"lazy\" may...
\n","
0.577475
\n","
buildings%20burning
\n","
2032
\n","
[0.023510590195655823, 0.002154689282178879, -...
\n","
2141.0
\n","
Southern OBX
\n","
\n","
\n","
7
\n","
positive
\n","
positive
\n","
14/17:27 EST Severe Thunderstorm Warning for p...
\n","
[14/17:27 EST Severe Thunderstorm Warning for ...
\n","
10191.0
\n","
1.0
\n","
14/17:27 EST Severe Thunderstorm Warning for p...
\n","
0.727776
\n","
thunderstorm
\n","
647
\n","
[-0.0552973710000515, 0.024097146466374397, -0...
\n","
5108.0
\n","
Queensland, Australia
\n","
\n","
\n","
8
\n","
negative
\n","
positive
\n","
Roadworks are co-ordinated and approved by a g...
\n","
[Roadworks are co-ordinated and approved by a ...
\n","
4845.0
\n","
0.0
\n","
Roadworks are co-ordinated and approved by a g...
\n","
0.669093
\n","
emergency%20services
\n","
1081
\n","
[-0.05318903550505638, 0.018101226538419724, -...
\n","
2228.0
\n","
Edinburgh
\n","
\n","
\n","
9
\n","
positive
\n","
positive
\n","
Iran Protests: 1,500 killed 5,000 Injured 7,00...
\n","
[Iran Protests: 1,500 killed 5,000 Injured 7,0...
\n","
6776.0
\n","
1.0
\n","
Iran Protests: 1,500 killed 5,000 Injured 7,00...
\n","
0.727720
\n","
injured
\n","
1759
\n","
[0.03859921544790268, -0.010697060264647007, -...
\n","
4651.0
\n","
Ohio River Valley
\n","
\n","
\n","
10
\n","
negative
\n","
positive
\n","
Kwara State Governor, Mallam purchased Truck f...
\n","
[Kwara State Governor, Mallam purchased Truck ...
\n","
5632.0
\n","
0.0
\n","
Kwara State Governor, Mallam purchased Truck f...
\n","
0.653245
\n","
fire%20truck
\n","
1551
\n","
[-0.013514711521565914, -0.004037567414343357,...
\n","
2225.0
\n","
Ilorin, Nigeria
\n","
\n","
\n","
11
\n","
negative
\n","
neutral
\n","
IâÂÂm wondering if framing these pictures t...
\n","
[IâÂÂm wondering if framing these pictures ...
\n","
5718.0
\n","
0.0
\n","
IâÂÂm wondering if framing these pictures t...
\n","
0.545631
\n","
flattened
\n","
237
\n","
[0.03829433023929596, -0.08540338277816772, -0...
\n","
2171.0
\n","
ZellRepublikðÂÂÂ
\n","
\n","
\n","
12
\n","
negative
\n","
neutral
\n","
Transparency makes everything better: individu...
\n","
[Transparency makes everything better: individ...
\n","
5503.0
\n","
0.0
\n","
Transparency makes everything better: individu...
\n","
0.597684
\n","
fear
\n","
593
\n","
[0.0025042055640369654, 0.027974789962172508, ...
\n","
1037.0
\n","
North East England
\n","
\n","
\n","
13
\n","
positive
\n","
neutral
\n","
If I didn't need my Crutch I would seriously w...
\n","
[If I didn't need my Crutch I would seriously ...
\n","
1280.0
\n","
1.0
\n","
If I didn't need my Crutch I would seriously w...
\n","
0.583970
\n","
bloody
\n","
914
\n","
[0.043823741376399994, 0.057542573660612106, -...
\n","
819.0
\n","
Guildford
\n","
\n","
\n","
14
\n","
negative
\n","
positive
\n","
Imagine you get a phone call from the Israeli ...
\n","
[Imagine you get a phone call from the Israeli...
\n","
1552.0
\n","
0.0
\n","
Imagine you get a phone call from the Israeli ...
\n","
0.644661
\n","
bombed
\n","
596
\n","
[-0.02263369970023632, -0.04711006581783295, -...
\n","
2227.0
\n","
Uganda, Nigeria
\n","
\n","
\n","
15
\n","
negative
\n","
positive
\n","
[Breaking] Nirbhaya Rape: Supreme Court dismis...
\n","
[[Breaking] Nirbhaya Rape: Supreme Court dismi...
\n","
3127.0
\n","
0.0
\n","
[Breaking] Nirbhaya Rape: Supreme Court dismis...
\n","
0.660057
\n","
death
\n","
2715
\n","
[-0.014349669218063354, -0.07226337492465973, ...
\n","
812.0
\n","
India
\n","
\n","
\n","
16
\n","
positive
\n","
positive
\n","
this storm is quite violent oh lord imagine ir...
\n","
[this storm is quite violent oh lord imagine i...
\n","
10739.0
\n","
1.0
\n","
this storm is quite violent oh lord imagine ir...
\n","
0.662823
\n","
violent%20storm
\n","
1010
\n","
[-0.02750890888273716, 0.021164197474718094, -...
\n","
5069.0
\n","
ireland , 20
\n","
\n","
\n","
17
\n","
negative
\n","
neutral
\n","
Yeah France is doing fantastic with all the mi...
\n","
[Yeah France is doing fantastic with all the m...
\n","
2347.0
\n","
0.0
\n","
Yeah France is doing fantastic with all the mi...
\n","
0.536393
\n","
collapse
\n","
2853
\n","
[0.027280263602733612, 0.015566764399409294, -...
\n","
399.0
\n","
South Africa
\n","
\n","
\n","
18
\n","
negative
\n","
neutral
\n","
He seems to be crumbling more rapidly now. The...
\n","
[He seems to be crumbling more rapidly now., ...
\n","
3350.0
\n","
0.0
\n","
He seems to be crumbling more rapidly now. The...
\n","
0.568037
\n","
deluge
\n","
1481
\n","
[0.11189540475606918, -0.009536723606288433, 0...
\n","
375.0
\n","
Midwest Red State
\n","
\n","
\n","
19
\n","
negative
\n","
neutral
\n","
#coup 9/11 vs Cal Fires So is this an attack o...
\n","
[#coup 9/11 vs Cal Fires So is this an attack ...
\n","
1779.0
\n","
0.0
\n","
#coup 9/11 vs Cal Fires So is this an attack o...
\n","
0.583260
\n","
buildings%20on%20fire
\n","
429
\n","
[-0.015312806703150272, 0.030779404565691948, ...
\n","
2388.0
\n","
OnFreedomRoad.com
\n","
\n","
\n","
20
\n","
positive
\n","
positive
\n","
6 months of videos of protesters smashing, bur...
\n","
[6 months of videos of protesters smashing, bu...
\n","
3599.0
\n","
1.0
\n","
6 months of videos of protesters smashing, bur...
\n","
0.709840
\n","
derail
\n","
427
\n","
[-0.02566157653927803, 0.009822700172662735, -...
\n","
2900.0
\n","
hk island, govt held, not K&NT
\n","
\n","
\n","
21
\n","
positive
\n","
positive
\n","
Mexican Border City on High Alert Over âÂÂS...
\n","
[Mexican Border City on High Alert Over âÂÂ...
\n","
9696.0
\n","
1.0
\n","
Mexican Border City on High Alert Over âÂÂS...
\n","
0.687776
\n","
suicide%20bomber
\n","
2907
\n","
[0.015122084878385067, -0.0711512491106987, -0...
\n","
5733.0
\n","
Colorado, USA
\n","
\n","
\n","
22
\n","
negative
\n","
neutral
\n","
Amazing light show or a battle for your health...
\n","
[Amazing light show or a battle for your healt...
\n","
3909.0
\n","
0.0
\n","
Amazing light show or a battle for your health...
\n","
0.530186
\n","
destroy
\n","
107
\n","
[0.001234019990079105, -0.06394369900226593, 0...
\n","
1805.0
\n","
Brisbane, Queensland
\n","
\n","
\n","
23
\n","
positive
\n","
positive
\n","
Emergency services called to a collision on th...
\n","
[Emergency services called to a collision on t...
\n","
4877.0
\n","
1.0
\n","
Emergency services called to a collision on th...
\n","
0.689470
\n","
emergency%20services
\n","
2844
\n","
[-0.05413218215107918, -0.0021766768768429756,...
\n","
2548.0
\n","
United Kingdom
\n","
\n","
\n","
24
\n","
positive
\n","
positive
\n","
HEADLINE: \"6.4 earthquake strikes Puerto Rico,...
\n","
[HEADLINE: \"6.4 earthquake strikes Puerto Rico...
\n","
9125.0
\n","
1.0
\n","
HEADLINE: \"6.4 earthquake strikes Puerto Rico,...
"],"text/plain":[" document ... sentence\n","0 All the buildings in the capital were destroyed ... [All the buildings in the capital were destroyed]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## 5. Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620195355927,"user_tz":-120,"elapsed":12923,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"3245d07d-0864-4e67-8b47-44378f110787"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@29bdbc21) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@29bdbc21\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## 6. Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620195360521,"user_tz":-120,"elapsed":17440,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"012313a9-d3a0-4c42-9b27-656e59001a1e"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.96 0.96 0.96 25\n"," neutral 0.00 0.00 0.00 0\n"," positive 1.00 0.96 0.98 25\n","\n"," accuracy 0.96 50\n"," macro avg 0.65 0.64 0.65 50\n","weighted avg 0.98 0.96 0.97 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
trained_sentiment
\n","
text
\n","
sentence
\n","
id
\n","
target
\n","
document
\n","
trained_sentiment_confidence
\n","
keyword
\n","
origin_index
\n","
sentence_embedding_use
\n","
Unnamed: 0
\n","
location
\n","
\n"," \n"," \n","
\n","
0
\n","
positive
\n","
positive
\n","
Bihar District Magistrate Issues Order to Clos...
\n","
[Bihar District Magistrate Issues Order to Clo...
\n","
6350.0
\n","
1.0
\n","
Bihar District Magistrate Issues Order to Clos...
\n","
0.970833
\n","
heat%20wave
\n","
2310
\n","
[0.01817215047776699, -0.042255766689777374, -...
\n","
2375.0
\n","
Chandigarh, India
\n","
\n","
\n","
1
\n","
negative
\n","
negative
\n","
YOU THERE, PACHIRISU PUNK, PREPARE TO BE DESTR...
\n","
[YOU THERE, PACHIRISU PUNK, PREPARE TO BE DEST...
\n","
3937.0
\n","
0.0
\n","
YOU THERE, PACHIRISU PUNK, PREPARE TO BE DESTR...
\n","
0.982484
\n","
destroyed
\n","
2044
\n","
[0.01567729189991951, 0.00028937371098436415, ...
\n","
1607.0
\n","
Scotland, but like at the top
\n","
\n","
\n","
2
\n","
negative
\n","
negative
\n","
A devastating and thorough refutation of and N...
\n","
[A devastating and thorough refutation of and ...
\n","
272.0
\n","
0.0
\n","
A devastating and thorough refutation of and N...
\n","
0.969792
\n","
annihilated
\n","
2473
\n","
[0.030531354248523712, 0.018328234553337097, -...
\n","
809.0
\n","
USA
\n","
\n","
\n","
3
\n","
negative
\n","
negative
\n","
bubby finally gonna help with desolate while i...
\n","
[bubby finally gonna help with desolate while ...
\n","
3817.0
\n","
0.0
\n","
bubby finally gonna help with desolate while i...
\n","
0.985924
\n","
desolate
\n","
1369
\n","
[-0.010298581793904305, -0.023482605814933777,...
\n","
152.0
\n","
United States Air Force
\n","
\n","
\n","
4
\n","
positive
\n","
positive
\n","
Temblor: mb 4.5 SOUTHEAST OF EASTER ISLAND: Ma...
\n","
[Temblor: mb 4.5 SOUTHEAST OF EASTER ISLAND:, ...
\n","
4609.0
\n","
1.0
\n","
Temblor: mb 4.5 SOUTHEAST OF EASTER ISLAND: Ma...
\n","
0.973546
\n","
earthquake
\n","
1880
\n","
[-0.07005153596401215, -0.042087431997060776, ...
\n","
305.0
\n","
Chile
\n","
\n","
\n","
5
\n","
positive
\n","
positive
\n","
Human body parts have been found in a bag on a...
\n","
[Human body parts have been found in a bag on ...
\n","
1333.0
\n","
1.0
\n","
Human body parts have been found in a bag on a...
\n","
0.974148
\n","
body%20bag
\n","
936
\n","
[-0.01760876551270485, -0.07565091550350189, -...
\n","
1765.0
\n","
Dublin, Ireland
\n","
\n","
\n","
6
\n","
negative
\n","
negative
\n","
JFC!!! Racist comment ! I guess all \"lazy\" may...
\n","
[JFC!!!, Racist comment !, I guess all \"lazy\" ...
\n","
1709.0
\n","
0.0
\n","
JFC!!! Racist comment ! I guess all \"lazy\" may...
\n","
0.960352
\n","
buildings%20burning
\n","
2032
\n","
[0.023510590195655823, 0.002154689282178879, -...
\n","
2141.0
\n","
Southern OBX
\n","
\n","
\n","
7
\n","
positive
\n","
positive
\n","
14/17:27 EST Severe Thunderstorm Warning for p...
\n","
[14/17:27 EST Severe Thunderstorm Warning for ...
\n","
10191.0
\n","
1.0
\n","
14/17:27 EST Severe Thunderstorm Warning for p...
\n","
0.981230
\n","
thunderstorm
\n","
647
\n","
[-0.0552973710000515, 0.024097146466374397, -0...
\n","
5108.0
\n","
Queensland, Australia
\n","
\n","
\n","
8
\n","
negative
\n","
negative
\n","
Roadworks are co-ordinated and approved by a g...
\n","
[Roadworks are co-ordinated and approved by a ...
\n","
4845.0
\n","
0.0
\n","
Roadworks are co-ordinated and approved by a g...
\n","
0.755662
\n","
emergency%20services
\n","
1081
\n","
[-0.05318903550505638, 0.018101226538419724, -...
\n","
2228.0
\n","
Edinburgh
\n","
\n","
\n","
9
\n","
positive
\n","
positive
\n","
Iran Protests: 1,500 killed 5,000 Injured 7,00...
\n","
[Iran Protests: 1,500 killed 5,000 Injured 7,0...
\n","
6776.0
\n","
1.0
\n","
Iran Protests: 1,500 killed 5,000 Injured 7,00...
\n","
0.982878
\n","
injured
\n","
1759
\n","
[0.03859921544790268, -0.010697060264647007, -...
\n","
4651.0
\n","
Ohio River Valley
\n","
\n","
\n","
10
\n","
negative
\n","
negative
\n","
Kwara State Governor, Mallam purchased Truck f...
\n","
[Kwara State Governor, Mallam purchased Truck ...
\n","
5632.0
\n","
0.0
\n","
Kwara State Governor, Mallam purchased Truck f...
\n","
0.745413
\n","
fire%20truck
\n","
1551
\n","
[-0.013514711521565914, -0.004037567414343357,...
\n","
2225.0
\n","
Ilorin, Nigeria
\n","
\n","
\n","
11
\n","
negative
\n","
negative
\n","
IâÂÂm wondering if framing these pictures t...
\n","
[IâÂÂm wondering if framing these pictures ...
\n","
5718.0
\n","
0.0
\n","
IâÂÂm wondering if framing these pictures t...
\n","
0.951121
\n","
flattened
\n","
237
\n","
[0.03829433023929596, -0.08540338277816772, -0...
\n","
2171.0
\n","
ZellRepublikðÂÂÂ
\n","
\n","
\n","
12
\n","
negative
\n","
negative
\n","
Transparency makes everything better: individu...
\n","
[Transparency makes everything better: individ...
\n","
5503.0
\n","
0.0
\n","
Transparency makes everything better: individu...
\n","
0.979479
\n","
fear
\n","
593
\n","
[0.0025042055640369654, 0.027974789962172508, ...
\n","
1037.0
\n","
North East England
\n","
\n","
\n","
13
\n","
positive
\n","
negative
\n","
If I didn't need my Crutch I would seriously w...
\n","
[If I didn't need my Crutch I would seriously ...
\n","
1280.0
\n","
1.0
\n","
If I didn't need my Crutch I would seriously w...
\n","
0.620800
\n","
bloody
\n","
914
\n","
[0.043823741376399994, 0.057542573660612106, -...
\n","
819.0
\n","
Guildford
\n","
\n","
\n","
14
\n","
negative
\n","
negative
\n","
Imagine you get a phone call from the Israeli ...
\n","
[Imagine you get a phone call from the Israeli...
\n","
1552.0
\n","
0.0
\n","
Imagine you get a phone call from the Israeli ...
\n","
0.894990
\n","
bombed
\n","
596
\n","
[-0.02263369970023632, -0.04711006581783295, -...
\n","
2227.0
\n","
Uganda, Nigeria
\n","
\n","
\n","
15
\n","
negative
\n","
neutral
\n","
[Breaking] Nirbhaya Rape: Supreme Court dismis...
\n","
[[Breaking] Nirbhaya Rape: Supreme Court dismi...
\n","
3127.0
\n","
0.0
\n","
[Breaking] Nirbhaya Rape: Supreme Court dismis...
\n","
0.502236
\n","
death
\n","
2715
\n","
[-0.014349669218063354, -0.07226337492465973, ...
\n","
812.0
\n","
India
\n","
\n","
\n","
16
\n","
positive
\n","
positive
\n","
this storm is quite violent oh lord imagine ir...
\n","
[this storm is quite violent oh lord imagine i...
\n","
10739.0
\n","
1.0
\n","
this storm is quite violent oh lord imagine ir...
\n","
0.917719
\n","
violent%20storm
\n","
1010
\n","
[-0.02750890888273716, 0.021164197474718094, -...
\n","
5069.0
\n","
ireland , 20
\n","
\n","
\n","
17
\n","
negative
\n","
negative
\n","
Yeah France is doing fantastic with all the mi...
\n","
[Yeah France is doing fantastic with all the m...
\n","
2347.0
\n","
0.0
\n","
Yeah France is doing fantastic with all the mi...
\n","
0.935926
\n","
collapse
\n","
2853
\n","
[0.027280263602733612, 0.015566764399409294, -...
\n","
399.0
\n","
South Africa
\n","
\n","
\n","
18
\n","
negative
\n","
negative
\n","
He seems to be crumbling more rapidly now. The...
\n","
[He seems to be crumbling more rapidly now., ...
\n","
3350.0
\n","
0.0
\n","
He seems to be crumbling more rapidly now. The...
\n","
0.980057
\n","
deluge
\n","
1481
\n","
[0.11189540475606918, -0.009536723606288433, 0...
\n","
375.0
\n","
Midwest Red State
\n","
\n","
\n","
19
\n","
negative
\n","
negative
\n","
#coup 9/11 vs Cal Fires So is this an attack o...
\n","
[#coup 9/11 vs Cal Fires So is this an attack ...
\n","
1779.0
\n","
0.0
\n","
#coup 9/11 vs Cal Fires So is this an attack o...
\n","
0.926649
\n","
buildings%20on%20fire
\n","
429
\n","
[-0.015312806703150272, 0.030779404565691948, ...
\n","
2388.0
\n","
OnFreedomRoad.com
\n","
\n","
\n","
20
\n","
positive
\n","
positive
\n","
6 months of videos of protesters smashing, bur...
\n","
[6 months of videos of protesters smashing, bu...
\n","
3599.0
\n","
1.0
\n","
6 months of videos of protesters smashing, bur...
\n","
0.905132
\n","
derail
\n","
427
\n","
[-0.02566157653927803, 0.009822700172662735, -...
\n","
2900.0
\n","
hk island, govt held, not K&NT
\n","
\n","
\n","
21
\n","
positive
\n","
positive
\n","
Mexican Border City on High Alert Over âÂÂS...
\n","
[Mexican Border City on High Alert Over âÂÂ...
\n","
9696.0
\n","
1.0
\n","
Mexican Border City on High Alert Over âÂÂS...
\n","
0.714104
\n","
suicide%20bomber
\n","
2907
\n","
[0.015122084878385067, -0.0711512491106987, -0...
\n","
5733.0
\n","
Colorado, USA
\n","
\n","
\n","
22
\n","
negative
\n","
negative
\n","
Amazing light show or a battle for your health...
\n","
[Amazing light show or a battle for your healt...
\n","
3909.0
\n","
0.0
\n","
Amazing light show or a battle for your health...
\n","
0.970722
\n","
destroy
\n","
107
\n","
[0.001234019990079105, -0.06394369900226593, 0...
\n","
1805.0
\n","
Brisbane, Queensland
\n","
\n","
\n","
23
\n","
positive
\n","
positive
\n","
Emergency services called to a collision on th...
\n","
[Emergency services called to a collision on t...
\n","
4877.0
\n","
1.0
\n","
Emergency services called to a collision on th...
\n","
0.953050
\n","
emergency%20services
\n","
2844
\n","
[-0.05413218215107918, -0.0021766768768429756,...
\n","
2548.0
\n","
United Kingdom
\n","
\n","
\n","
24
\n","
positive
\n","
positive
\n","
HEADLINE: \"6.4 earthquake strikes Puerto Rico,...
\n","
[HEADLINE: \"6.4 earthquake strikes Puerto Rico...
\n","
9125.0
\n","
1.0
\n","
HEADLINE: \"6.4 earthquake strikes Puerto Rico,...
"],"text/plain":[" text ... sentence_embedding_from_disk\n","0 All the buildings in the capital were destroyed ... [[-0.393466055393219, 0.33815130591392517, -0....\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e0CVlkk9v6Qi","executionInfo":{"status":"ok","timestamp":1620197397239,"user_tz":-120,"elapsed":2051781,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"5c3d3b1b-32f4-48f1-c69f-2b7344657eba"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@103a8839) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@103a8839\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentiment_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"F_mfqyyyKGkV"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_reddit.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_reddit.ipynb
deleted file mode 100644
index 0a451b56..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_reddit.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_sentiment_classifier_demo_reddit.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_reddit.ipynb)\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 class Reddit comment sentiment classifier training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620193274509,"user_tz":-120,"elapsed":122976,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f1d2ff21-6e64-4c99-fcae-ae5d060d0530"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:39:12-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \rInstalling NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 05:39:12 (39.8 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 64kB/s \n","\u001b[K |████████████████████████████████| 153kB 47.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 17.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 49.5MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Reddit Sentiment dataset \n","https://www.kaggle.com/cosmos98/twitter-and-reddit-sentimental-analysis-dataset\n","#Context\n","\n","This is was a Dataset Created as a part of the university Project On Sentimental Analysis On Multi-Source Social Media Platforms using PySpark."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620193275331,"user_tz":-120,"elapsed":123790,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b398d517-d3af-4205-83ac-7994eb80fffc"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/01/Reddit_Data.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:41:14-- http://ckl-it.de/wp-content/uploads/2021/01/Reddit_Data.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 153265 (150K) [text/csv]\n","Saving to: ‘Reddit_Data.csv’\n","\n","Reddit_Data.csv 100%[===================>] 149.67K 402KB/s in 0.4s \n","\n","2021-05-05 05:41:14 (402 KB/s) - ‘Reddit_Data.csv’ saved [153265/153265]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620193275333,"user_tz":-120,"elapsed":123787,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"34fa4591-c90f-4147-f536-038a9f383019"},"source":["import pandas as pd\n","train_path = '/content/Reddit_Data.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","columns=['text','y']\n","train_df = train_df[columns]\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
0
\n","
its true they had cut the power what douchebag...
\n","
positive
\n","
\n","
\n","
1
\n","
fuck giroud better finishing like this month
\n","
positive
\n","
\n","
\n","
2
\n","
looks shit now but still proud made
\n","
positive
\n","
\n","
\n","
3
\n","
pelor the burning hate the best evil god
\n","
negative
\n","
\n","
\n","
4
\n","
can ask what you with something this powerful
\n","
positive
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
595
\n","
bangali desh bechne main sabse aage
\n","
positive
\n","
\n","
\n","
596
\n","
national media channels were gaged not cover t...
\n","
positive
\n","
\n","
\n","
597
\n","
been following these threads from the beginni...
\n","
negative
\n","
\n","
\n","
598
\n","
pretty sure this sarcasm satire the news 1500...
\n","
positive
\n","
\n","
\n","
599
\n","
much would love for namo our next hard imagin...
\n","
positive
\n","
\n"," \n","
\n","
600 rows × 2 columns
\n","
"],"text/plain":[" text y\n","0 its true they had cut the power what douchebag... positive\n","1 fuck giroud better finishing like this month positive\n","2 looks shit now but still proud made positive\n","3 pelor the burning hate the best evil god negative\n","4 can ask what you with something this powerful positive\n",".. ... ...\n","595 bangali desh bechne main sabse aage positive\n","596 national media channels were gaged not cover t... positive\n","597 been following these threads from the beginni... negative\n","598 pretty sure this sarcasm satire the news 1500... positive\n","599 much would love for namo our next hard imagin... positive\n","\n","[600 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620195831144,"user_tz":-120,"elapsed":11077,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"40e942ab-8923-4f23-acc6-e600cf4e7e16"},"source":["from sklearn.metrics import classification_report\n","import nlu \n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.00 0.00 0.00 24\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.72 1.00 0.84 26\n","\n"," accuracy 0.52 50\n"," macro avg 0.24 0.33 0.28 50\n","weighted avg 0.38 0.52 0.44 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
text
\n","
y
\n","
sentence
\n","
sentence_embedding_use
\n","
trained_sentiment
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
its true they had cut the power what douchebag...
\n","
positive
\n","
[its true they had cut the power what doucheba...
\n","
[0.033111296594142914, 0.053994592279195786, -...
\n","
positive
\n","
0.626655
\n","
its true they had cut the power what douchebag...
\n","
\n","
\n","
1
\n","
1
\n","
fuck giroud better finishing like this month
\n","
positive
\n","
[fuck giroud better finishing like this month]
\n","
[0.0678204670548439, 0.01411951333284378, -0.0...
\n","
positive
\n","
0.653644
\n","
fuck giroud better finishing like this month
\n","
\n","
\n","
2
\n","
2
\n","
looks shit now but still proud made
\n","
positive
\n","
[looks shit now but still proud made]
\n","
[0.03247417137026787, -0.09844466298818588, -0...
\n","
positive
\n","
0.660186
\n","
looks shit now but still proud made
\n","
\n","
\n","
3
\n","
3
\n","
pelor the burning hate the best evil god
\n","
negative
\n","
[pelor the burning hate the best evil god]
\n","
[0.04032062366604805, 0.07666622847318649, -0....
\n","
neutral
\n","
0.578461
\n","
pelor the burning hate the best evil god
\n","
\n","
\n","
4
\n","
4
\n","
can ask what you with something this powerful
\n","
positive
\n","
[can ask what you with something this powerful]
\n","
[0.015518003143370152, -0.05116305500268936, -...
\n","
positive
\n","
0.691478
\n","
can ask what you with something this powerful
\n","
\n","
\n","
5
\n","
5
\n","
aapâ shazia ilmi from puram constituency lag...
\n","
negative
\n","
[aapâ shazia ilmi from puram constituency la...
\n","
[0.02478150464594364, -0.06508146971464157, -0...
\n","
positive
\n","
0.612378
\n","
aapâ shazia ilmi from puram constituency lag...
\n","
\n","
\n","
6
\n","
6
\n","
fuck yeah
\n","
negative
\n","
[fuck yeah]
\n","
[0.046024102717638016, -0.02504798397421837, -...
\n","
neutral
\n","
0.586349
\n","
fuck yeah
\n","
\n","
\n","
7
\n","
7
\n","
honestly really surprised alice ranked that lo...
\n","
positive
\n","
[honestly really surprised alice ranked that l...
\n","
[-0.035716041922569275, -0.04127982258796692, ...
\n","
positive
\n","
0.654837
\n","
honestly really surprised alice ranked that lo...
\n","
\n","
\n","
8
\n","
8
\n","
didn care about politics before now hate
\n","
negative
\n","
[didn care about politics before now hate]
\n","
[-0.006816443987190723, 0.06221264228224754, -...
\n","
neutral
\n","
0.581450
\n","
didn care about politics before now hate
\n","
\n","
\n","
9
\n","
9
\n","
hard nips and goosebumps
\n","
negative
\n","
[hard nips and goosebumps]
\n","
[-0.02919699251651764, -0.030449824407696724, ...
\n","
neutral
\n","
0.580563
\n","
hard nips and goosebumps
\n","
\n","
\n","
10
\n","
10
\n","
varadabhai ndtv trying too well dilute bjp tre...
\n","
negative
\n","
[varadabhai ndtv trying too well dilute bjp tr...
\n","
[0.04727796092629433, -0.06792476028203964, -0...
\n","
positive
\n","
0.621601
\n","
varadabhai ndtv trying too well dilute bjp tre...
\n","
\n","
\n","
11
\n","
11
\n","
old man has lost his mind
\n","
positive
\n","
[old man has lost his mind]
\n","
[0.039657335728406906, -0.04277808964252472, -...
\n","
positive
\n","
0.639790
\n","
old man has lost his mind
\n","
\n","
\n","
12
\n","
12
\n","
why this being downvoted you might ask both mo...
\n","
negative
\n","
[why this being downvoted you might ask both m...
\n","
[0.06581216305494308, -0.06079106032848358, -0...
\n","
positive
\n","
0.629655
\n","
why this being downvoted you might ask both mo...
\n","
\n","
\n","
13
\n","
13
\n","
hasnt changed all apolitical before simply do...
\n","
positive
\n","
[hasnt changed all apolitical before simply do...
\n","
[0.03509754315018654, -0.004639611579477787, -...
\n","
positive
\n","
0.624468
\n","
hasnt changed all apolitical before simply don...
\n","
\n","
\n","
14
\n","
14
\n","
for one campaign pretty much just snatched the...
\n","
negative
\n","
[for one campaign pretty much just snatched th...
\n","
[0.017386479303240776, 0.0443551279604435, -0....
\n","
positive
\n","
0.618989
\n","
for one campaign pretty much just snatched the...
\n","
\n","
\n","
15
\n","
15
\n","
vajpayee managed forge much broader coalition ...
\n","
positive
\n","
[vajpayee managed forge much broader coalition...
\n","
[0.0372871570289135, -0.051079731434583664, -0...
\n","
positive
\n","
0.661443
\n","
vajpayee managed forge much broader coalition ...
\n","
\n","
\n","
16
\n","
16
\n","
lol this only proves how desperate they are ge...
\n","
positive
\n","
[lol this only proves how desperate they are g...
\n","
[0.05233633145689964, -0.03147873282432556, 0....
\n","
positive
\n","
0.615814
\n","
lol this only proves how desperate they are ge...
\n","
\n","
\n","
17
\n","
17
\n","
dont hate aap but your questions are example ...
\n","
negative
\n","
[dont hate aap but your questions are example ...
\n","
[0.026356501504778862, -0.04044199362397194, -...
\n","
positive
\n","
0.614039
\n","
dont hate aap but your questions are example w...
\n","
\n","
\n","
18
\n","
18
\n","
what were the other policies you discussed not...
\n","
negative
\n","
[what were the other policies you discussed no...
\n","
[-0.07521010935306549, 0.008543566800653934, 0...
\n","
neutral
\n","
0.581745
\n","
what were the other policies you discussed not...
\n","
\n","
\n","
19
\n","
19
\n","
wow lots favorites this bracket haqua tsukushi...
\n","
positive
\n","
[wow lots favorites this bracket haqua tsukush...
\n","
[-0.069316066801548, -0.015458517707884312, -0...
\n","
positive
\n","
0.663116
\n","
wow lots favorites this bracket haqua tsukushi...
\n","
\n","
\n","
20
\n","
20
\n","
sorry know this isn what you asked just ventin...
\n","
negative
\n","
[sorry know this isn what you asked just venti...
\n","
[0.016777774319052696, -0.05478339642286301, -...
\n","
positive
\n","
0.618847
\n","
sorry know this isn what you asked just ventin...
\n","
\n","
\n","
21
\n","
21
\n","
coming out strongly against gujarat chief min...
\n","
positive
\n","
[coming out strongly against gujarat chief min...
\n","
[0.06856724619865417, -0.019821861758828163, -...
\n","
positive
\n","
0.640057
\n","
coming out strongly against gujarat chief mini...
\n","
\n","
\n","
22
\n","
22
\n","
there one tool bjp can use their manifesto whi...
\n","
positive
\n","
[there one tool bjp can use their manifesto wh...
\n","
[0.057847339659929276, -0.05365725979208946, -...
\n","
positive
\n","
0.648467
\n","
there one tool bjp can use their manifesto whi...
\n","
\n","
\n","
23
\n","
23
\n","
jakiro spotted the middle top maybe
\n","
positive
\n","
[jakiro spotted the middle top maybe]
\n","
[-0.011690961197018623, -0.024473996832966805,...
\n","
positive
\n","
0.678376
\n","
jakiro spotted the middle top maybe
\n","
\n","
\n","
24
\n","
24
\n","
family mormon have never tried explain them t...
\n","
positive
\n","
[family mormon have never tried explain them t...
\n","
[0.03987010195851326, -0.0009543427731841803, ...
\n","
positive
\n","
0.678150
\n","
family mormon have never tried explain them th...
\n","
\n","
\n","
25
\n","
25
\n","
with these results would have grudgingly accep...
\n","
negative
\n","
[with these results would have grudgingly acce...
\n","
[0.034668292850255966, -0.05392604321241379, -...
\n","
positive
\n","
0.632179
\n","
with these results would have grudgingly accep...
\n","
\n","
\n","
26
\n","
26
\n","
tea partier expresses support for namo after ...
\n","
negative
\n","
[tea partier expresses support for namo after ...
\n","
[0.032365716993808746, -0.056087080389261246, ...
\n","
neutral
\n","
0.587468
\n","
tea partier expresses support for namo after e...
\n","
\n","
\n","
27
\n","
27
\n","
politically would stupid move take stand right...
\n","
negative
\n","
[politically would stupid move take stand righ...
\n","
[-0.00040777752292342484, -0.01262842211872339...
\n","
neutral
\n","
0.593538
\n","
politically would stupid move take stand right...
\n","
\n","
\n","
28
\n","
28
\n","
wtf why
\n","
negative
\n","
[wtf why]
\n","
[0.025807170197367668, -0.07080958038568497, -...
\n","
neutral
\n","
0.584124
\n","
wtf why
\n","
\n","
\n","
29
\n","
29
\n","
have actually seen lot users views change dur...
\n","
positive
\n","
[have actually seen lot users views change dur...
\n","
[-0.009333955124020576, 0.01388698909431696, -...
\n","
positive
\n","
0.649225
\n","
have actually seen lot users views change duri...
\n","
\n","
\n","
30
\n","
30
\n","
truth told there not insignificant percentage ...
\n","
positive
\n","
[truth told there not insignificant percentage...
\n","
[0.03927519917488098, -0.05597652122378349, -0...
\n","
positive
\n","
0.649703
\n","
truth told there not insignificant percentage ...
\n","
\n","
\n","
31
\n","
31
\n","
was anti bjp and neutral cong became anti bjp ...
\n","
positive
\n","
[was anti bjp and neutral cong became anti bjp...
\n","
[0.03805134445428848, -0.030298737809062004, -...
\n","
positive
\n","
0.639704
\n","
was anti bjp and neutral cong became anti bjp ...
\n","
\n","
\n","
32
\n","
32
\n","
most religions have dogmatic orthodox well eso...
\n","
positive
\n","
[most religions have dogmatic orthodox well es...
\n","
[0.03939439728856087, -0.02040349319577217, -0...
\n","
positive
\n","
0.684981
\n","
most religions have dogmatic orthodox well eso...
\n","
\n","
\n","
33
\n","
33
\n","
laureatte sen said christian schools are perfe...
\n","
positive
\n","
[laureatte sen said christian schools are perf...
\n","
[0.05267934128642082, 0.05836360529065132, 0.0...
\n","
positive
\n","
0.647901
\n","
laureatte sen said christian schools are perfe...
\n","
\n","
\n","
34
\n","
34
\n","
need stop watching the garbage that you watch...
\n","
positive
\n","
[need stop watching the garbage that you watch...
\n","
[-0.012382612563669682, 0.01988200470805168, 0...
\n","
positive
\n","
0.653579
\n","
need stop watching the garbage that you watch ...
\n","
\n","
\n","
35
\n","
35
\n","
gandhi mandela hitler mao plato chandragupt ma...
\n","
negative
\n","
[gandhi mandela hitler mao plato chandragupt m...
\n","
[0.027552243322134018, 0.013075066730380058, 0...
\n","
neutral
\n","
0.593626
\n","
gandhi mandela hitler mao plato chandragupt ma...
\n","
\n","
\n","
36
\n","
36
\n","
hate aap for the other thread points such the...
\n","
negative
\n","
[hate aap for the other thread points such the...
\n","
[0.014617362059652805, -0.038017578423023224, ...
\n","
positive
\n","
0.618540
\n","
hate aap for the other thread points such the ...
\n","
\n","
\n","
37
\n","
37
\n","
absolutely agree with you subsidies the worst ...
\n","
negative
\n","
[absolutely agree with you subsidies the worst...
\n","
[0.0109744006767869, 0.0033110964577645063, -0...
\n","
positive
\n","
0.623482
\n","
absolutely agree with you subsidies the worst ...
\n","
\n","
\n","
38
\n","
38
\n","
are you corrupt mind have you benefited throu...
\n","
negative
\n","
[are you corrupt mind have you benefited throu...
\n","
[0.03834373503923416, -0.06521473079919815, -0...
\n","
neutral
\n","
0.599771
\n","
are you corrupt mind have you benefited throug...
\n","
\n","
\n","
39
\n","
39
\n","
congress needs bogeyman modi without the bad g...
\n","
positive
\n","
[congress needs bogeyman modi without the bad ...
\n","
[0.03138439729809761, -0.06221967190504074, -0...
\n","
positive
\n","
0.649323
\n","
congress needs bogeyman modi without the bad g...
\n","
\n","
\n","
40
\n","
40
\n","
protip don type uppercase text all caps harder...
\n","
negative
\n","
[protip don type uppercase text all caps harde...
\n","
[0.044019922614097595, 0.025341013446450233, 0...
\n","
neutral
\n","
0.567544
\n","
protip don type uppercase text all caps harder...
\n","
\n","
\n","
41
\n","
41
\n","
brother trog very wrathful indeed but his wil...
\n","
positive
\n","
[brother trog very wrathful indeed but his wil...
\n","
[-0.024625714868307114, 0.06193268671631813, 0...
\n","
positive
\n","
0.654243
\n","
brother trog very wrathful indeed but his will...
\n","
\n","
\n","
42
\n","
42
\n","
start off saying that the craftsmanship this ...
\n","
positive
\n","
[start off saying that the craftsmanship this ...
\n","
[0.05780624598264694, -0.06291750818490982, -0...
\n","
positive
\n","
0.707086
\n","
start off saying that the craftsmanship this p...
\n","
\n","
\n","
43
\n","
43
\n","
have made request unban namoarmy hell moron h...
\n","
negative
\n","
[have made request unban namoarmy hell moron h...
\n","
[0.015555822290480137, -0.012748800218105316, ...
\n","
neutral
\n","
0.578515
\n","
have made request unban namoarmy hell moron ho...
\n","
\n","
\n","
44
\n","
44
\n","
child modi worked his fatherâ tea shop and ...
\n","
negative
\n","
[child modi worked his fatherâ tea shop and ...
\n","
[0.05774841457605362, -0.05956699699163437, -0...
\n","
positive
\n","
0.608462
\n","
child modi worked his fatherâ tea shop and y...
\n","
\n","
\n","
45
\n","
45
\n","
namo tea yuupea horrible rhyme know
\n","
negative
\n","
[namo tea yuupea horrible rhyme know]
\n","
[0.025534288957715034, 0.004176765214651823, -...
\n","
neutral
\n","
0.576838
\n","
namo tea yuupea horrible rhyme know
\n","
\n","
\n","
46
\n","
46
\n","
great agility from akpom cut back and bend
\n","
positive
\n","
[great agility from akpom cut back and bend]
\n","
[0.06865684688091278, -0.02164856530725956, -0...
\n","
positive
\n","
0.676614
\n","
great agility from akpom cut back and bend
\n","
\n","
\n","
47
\n","
47
\n","
from undecided pro aap they are not perfect bu...
\n","
positive
\n","
[from undecided pro aap they are not perfect b...
\n","
[0.01590304635465145, -0.0683458000421524, -0....
\n","
positive
\n","
0.651092
\n","
from undecided pro aap they are not perfect bu...
\n","
\n","
\n","
48
\n","
48
\n","
woah there don insane with pray mean you don w...
\n","
negative
\n","
[woah there don insane with pray mean you don ...
\n","
[0.050547026097774506, -0.01725909113883972, 0...
\n","
neutral
\n","
0.573394
\n","
woah there don insane with pray mean you don w...
\n","
\n","
\n","
49
\n","
49
\n","
porngress wont announce their candidate cuz th...
\n","
positive
\n","
[porngress wont announce their candidate cuz t...
\n","
[0.05935536324977875, -0.051609162241220474, -...
\n","
positive
\n","
0.662533
\n","
porngress wont announce their candidate cuz th...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... document\n","0 0 ... its true they had cut the power what douchebag...\n","1 1 ... fuck giroud better finishing like this month\n","2 2 ... looks shit now but still proud made\n","3 3 ... pelor the burning hate the best evil god\n","4 4 ... can ask what you with something this powerful\n","5 5 ... aapâ shazia ilmi from puram constituency lag...\n","6 6 ... fuck yeah\n","7 7 ... honestly really surprised alice ranked that lo...\n","8 8 ... didn care about politics before now hate\n","9 9 ... hard nips and goosebumps\n","10 10 ... varadabhai ndtv trying too well dilute bjp tre...\n","11 11 ... old man has lost his mind\n","12 12 ... why this being downvoted you might ask both mo...\n","13 13 ... hasnt changed all apolitical before simply don...\n","14 14 ... for one campaign pretty much just snatched the...\n","15 15 ... vajpayee managed forge much broader coalition ...\n","16 16 ... lol this only proves how desperate they are ge...\n","17 17 ... dont hate aap but your questions are example w...\n","18 18 ... what were the other policies you discussed not...\n","19 19 ... wow lots favorites this bracket haqua tsukushi...\n","20 20 ... sorry know this isn what you asked just ventin...\n","21 21 ... coming out strongly against gujarat chief mini...\n","22 22 ... there one tool bjp can use their manifesto whi...\n","23 23 ... jakiro spotted the middle top maybe\n","24 24 ... family mormon have never tried explain them th...\n","25 25 ... with these results would have grudgingly accep...\n","26 26 ... tea partier expresses support for namo after e...\n","27 27 ... politically would stupid move take stand right...\n","28 28 ... wtf why\n","29 29 ... have actually seen lot users views change duri...\n","30 30 ... truth told there not insignificant percentage ...\n","31 31 ... was anti bjp and neutral cong became anti bjp ...\n","32 32 ... most religions have dogmatic orthodox well eso...\n","33 33 ... laureatte sen said christian schools are perfe...\n","34 34 ... need stop watching the garbage that you watch ...\n","35 35 ... gandhi mandela hitler mao plato chandragupt ma...\n","36 36 ... hate aap for the other thread points such the ...\n","37 37 ... absolutely agree with you subsidies the worst ...\n","38 38 ... are you corrupt mind have you benefited throug...\n","39 39 ... congress needs bogeyman modi without the bad g...\n","40 40 ... protip don type uppercase text all caps harder...\n","41 41 ... brother trog very wrathful indeed but his will...\n","42 42 ... start off saying that the craftsmanship this p...\n","43 43 ... have made request unban namoarmy hell moron ho...\n","44 44 ... child modi worked his fatherâ tea shop and y...\n","45 45 ... namo tea yuupea horrible rhyme know\n","46 46 ... great agility from akpom cut back and bend\n","47 47 ... from undecided pro aap they are not perfect bu...\n","48 48 ... woah there don insane with pray mean you don w...\n","49 49 ... porngress wont announce their candidate cuz th...\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"lVyOE2wV0fw_"},"source":["# Test the fitted pipe on new example"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":77},"id":"qdCUg2MR0PD2","executionInfo":{"status":"ok","timestamp":1620195831996,"user_tz":-120,"elapsed":11264,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"68ed8568-654b-41bc-fa22-0ea363e1fb2d"},"source":["fitted_pipe.predict(\"Indian prime minister was assinated!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence
\n","
sentence_embedding_use
\n","
trained_sentiment
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[Indian prime minister was assinated!]
\n","
[0.012644989416003227, -0.04661174491047859, -...
\n","
positive
\n","
0.6117
\n","
Indian prime minister was assinated!
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... document\n","0 0 ... Indian prime minister was assinated!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620195831997,"user_tz":-120,"elapsed":11180,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"69dd4215-3f29-4231-9213-0fe956bc520f"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3350ae7a) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3350ae7a\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620195837227,"user_tz":-120,"elapsed":16321,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"a46b8835-74db-4f5e-dd91-87d9277cb5b3"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 1.00 0.83 0.91 24\n"," neutral 0.00 0.00 0.00 0\n"," positive 1.00 1.00 1.00 26\n","\n"," accuracy 0.92 50\n"," macro avg 0.67 0.61 0.64 50\n","weighted avg 1.00 0.92 0.96 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
text
\n","
y
\n","
sentence
\n","
sentence_embedding_use
\n","
trained_sentiment
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
its true they had cut the power what douchebag...
\n","
positive
\n","
[its true they had cut the power what doucheba...
\n","
[0.033111296594142914, 0.053994592279195786, -...
\n","
positive
\n","
0.761194
\n","
its true they had cut the power what douchebag...
\n","
\n","
\n","
1
\n","
1
\n","
fuck giroud better finishing like this month
\n","
positive
\n","
[fuck giroud better finishing like this month]
\n","
[0.0678204670548439, 0.01411951333284378, -0.0...
\n","
positive
\n","
0.938677
\n","
fuck giroud better finishing like this month
\n","
\n","
\n","
2
\n","
2
\n","
looks shit now but still proud made
\n","
positive
\n","
[looks shit now but still proud made]
\n","
[0.03247417137026787, -0.09844466298818588, -0...
\n","
positive
\n","
0.954937
\n","
looks shit now but still proud made
\n","
\n","
\n","
3
\n","
3
\n","
pelor the burning hate the best evil god
\n","
negative
\n","
[pelor the burning hate the best evil god]
\n","
[0.04032062366604805, 0.07666622847318649, -0....
\n","
negative
\n","
0.810980
\n","
pelor the burning hate the best evil god
\n","
\n","
\n","
4
\n","
4
\n","
can ask what you with something this powerful
\n","
positive
\n","
[can ask what you with something this powerful]
\n","
[0.015518003143370152, -0.05116305500268936, -...
\n","
positive
\n","
0.956043
\n","
can ask what you with something this powerful
\n","
\n","
\n","
5
\n","
5
\n","
aapâ shazia ilmi from puram constituency lag...
\n","
negative
\n","
[aapâ shazia ilmi from puram constituency la...
\n","
[0.02478150464594364, -0.06508146971464157, -0...
\n","
negative
\n","
0.708917
\n","
aapâ shazia ilmi from puram constituency lag...
\n","
\n","
\n","
6
\n","
6
\n","
fuck yeah
\n","
negative
\n","
[fuck yeah]
\n","
[0.046024102717638016, -0.02504798397421837, -...
\n","
negative
\n","
0.731940
\n","
fuck yeah
\n","
\n","
\n","
7
\n","
7
\n","
honestly really surprised alice ranked that lo...
\n","
positive
\n","
[honestly really surprised alice ranked that l...
\n","
[-0.035716041922569275, -0.04127982258796692, ...
\n","
positive
\n","
0.966494
\n","
honestly really surprised alice ranked that lo...
\n","
\n","
\n","
8
\n","
8
\n","
didn care about politics before now hate
\n","
negative
\n","
[didn care about politics before now hate]
\n","
[-0.006816443987190723, 0.06221264228224754, -...
\n","
negative
\n","
0.672320
\n","
didn care about politics before now hate
\n","
\n","
\n","
9
\n","
9
\n","
hard nips and goosebumps
\n","
negative
\n","
[hard nips and goosebumps]
\n","
[-0.02919699251651764, -0.030449824407696724, ...
\n","
negative
\n","
0.604969
\n","
hard nips and goosebumps
\n","
\n","
\n","
10
\n","
10
\n","
varadabhai ndtv trying too well dilute bjp tre...
\n","
negative
\n","
[varadabhai ndtv trying too well dilute bjp tr...
\n","
[0.04727796092629433, -0.06792476028203964, -0...
\n","
negative
\n","
0.639880
\n","
varadabhai ndtv trying too well dilute bjp tre...
\n","
\n","
\n","
11
\n","
11
\n","
old man has lost his mind
\n","
positive
\n","
[old man has lost his mind]
\n","
[0.039657335728406906, -0.04277808964252472, -...
\n","
positive
\n","
0.929136
\n","
old man has lost his mind
\n","
\n","
\n","
12
\n","
12
\n","
why this being downvoted you might ask both mo...
\n","
negative
\n","
[why this being downvoted you might ask both m...
\n","
[0.06581216305494308, -0.06079106032848358, -0...
\n","
neutral
\n","
0.546161
\n","
why this being downvoted you might ask both mo...
\n","
\n","
\n","
13
\n","
13
\n","
hasnt changed all apolitical before simply do...
\n","
positive
\n","
[hasnt changed all apolitical before simply do...
\n","
[0.03509754315018654, -0.004639611579477787, -...
\n","
positive
\n","
0.883017
\n","
hasnt changed all apolitical before simply don...
\n","
\n","
\n","
14
\n","
14
\n","
for one campaign pretty much just snatched the...
\n","
negative
\n","
[for one campaign pretty much just snatched th...
\n","
[0.017386479303240776, 0.0443551279604435, -0....
\n","
negative
\n","
0.636396
\n","
for one campaign pretty much just snatched the...
\n","
\n","
\n","
15
\n","
15
\n","
vajpayee managed forge much broader coalition ...
\n","
positive
\n","
[vajpayee managed forge much broader coalition...
\n","
[0.0372871570289135, -0.051079731434583664, -0...
\n","
positive
\n","
0.848566
\n","
vajpayee managed forge much broader coalition ...
\n","
\n","
\n","
16
\n","
16
\n","
lol this only proves how desperate they are ge...
\n","
positive
\n","
[lol this only proves how desperate they are g...
\n","
[0.05233633145689964, -0.03147873282432556, 0....
\n","
positive
\n","
0.819890
\n","
lol this only proves how desperate they are ge...
\n","
\n","
\n","
17
\n","
17
\n","
dont hate aap but your questions are example ...
\n","
negative
\n","
[dont hate aap but your questions are example ...
\n","
[0.026356501504778862, -0.04044199362397194, -...
\n","
negative
\n","
0.724538
\n","
dont hate aap but your questions are example w...
\n","
\n","
\n","
18
\n","
18
\n","
what were the other policies you discussed not...
\n","
negative
\n","
[what were the other policies you discussed no...
\n","
[-0.07521010935306549, 0.008543566800653934, 0...
\n","
negative
\n","
0.732422
\n","
what were the other policies you discussed not...
\n","
\n","
\n","
19
\n","
19
\n","
wow lots favorites this bracket haqua tsukushi...
\n","
positive
\n","
[wow lots favorites this bracket haqua tsukush...
\n","
[-0.069316066801548, -0.015458517707884312, -0...
\n","
positive
\n","
0.971349
\n","
wow lots favorites this bracket haqua tsukushi...
\n","
\n","
\n","
20
\n","
20
\n","
sorry know this isn what you asked just ventin...
\n","
negative
\n","
[sorry know this isn what you asked just venti...
\n","
[0.016777774319052696, -0.05478339642286301, -...
\n","
negative
\n","
0.623325
\n","
sorry know this isn what you asked just ventin...
\n","
\n","
\n","
21
\n","
21
\n","
coming out strongly against gujarat chief min...
\n","
positive
\n","
[coming out strongly against gujarat chief min...
\n","
[0.06856724619865417, -0.019821861758828163, -...
\n","
positive
\n","
0.736283
\n","
coming out strongly against gujarat chief mini...
\n","
\n","
\n","
22
\n","
22
\n","
there one tool bjp can use their manifesto whi...
\n","
positive
\n","
[there one tool bjp can use their manifesto wh...
\n","
[0.057847339659929276, -0.05365725979208946, -...
\n","
positive
\n","
0.870023
\n","
there one tool bjp can use their manifesto whi...
\n","
\n","
\n","
23
\n","
23
\n","
jakiro spotted the middle top maybe
\n","
positive
\n","
[jakiro spotted the middle top maybe]
\n","
[-0.011690961197018623, -0.024473996832966805,...
\n","
positive
\n","
0.965604
\n","
jakiro spotted the middle top maybe
\n","
\n","
\n","
24
\n","
24
\n","
family mormon have never tried explain them t...
\n","
positive
\n","
[family mormon have never tried explain them t...
\n","
[0.03987010195851326, -0.0009543427731841803, ...
\n","
positive
\n","
0.964053
\n","
family mormon have never tried explain them th...
\n","
\n","
\n","
25
\n","
25
\n","
with these results would have grudgingly accep...
\n","
negative
\n","
[with these results would have grudgingly acce...
\n","
[0.034668292850255966, -0.05392604321241379, -...
\n","
neutral
\n","
0.521401
\n","
with these results would have grudgingly accep...
\n","
\n","
\n","
26
\n","
26
\n","
tea partier expresses support for namo after ...
\n","
negative
\n","
[tea partier expresses support for namo after ...
\n","
[0.032365716993808746, -0.056087080389261246, ...
\n","
negative
\n","
0.837552
\n","
tea partier expresses support for namo after e...
\n","
\n","
\n","
27
\n","
27
\n","
politically would stupid move take stand right...
\n","
negative
\n","
[politically would stupid move take stand righ...
\n","
[-0.00040777752292342484, -0.01262842211872339...
\n","
neutral
\n","
0.541656
\n","
politically would stupid move take stand right...
\n","
\n","
\n","
28
\n","
28
\n","
wtf why
\n","
negative
\n","
[wtf why]
\n","
[0.025807170197367668, -0.07080958038568497, -...
\n","
negative
\n","
0.747054
\n","
wtf why
\n","
\n","
\n","
29
\n","
29
\n","
have actually seen lot users views change dur...
\n","
positive
\n","
[have actually seen lot users views change dur...
\n","
[-0.009333955124020576, 0.01388698909431696, -...
\n","
positive
\n","
0.818759
\n","
have actually seen lot users views change duri...
\n","
\n","
\n","
30
\n","
30
\n","
truth told there not insignificant percentage ...
\n","
positive
\n","
[truth told there not insignificant percentage...
\n","
[0.03927519917488098, -0.05597652122378349, -0...
\n","
positive
\n","
0.776765
\n","
truth told there not insignificant percentage ...
\n","
\n","
\n","
31
\n","
31
\n","
was anti bjp and neutral cong became anti bjp ...
\n","
positive
\n","
[was anti bjp and neutral cong became anti bjp...
\n","
[0.03805134445428848, -0.030298737809062004, -...
\n","
positive
\n","
0.630857
\n","
was anti bjp and neutral cong became anti bjp ...
\n","
\n","
\n","
32
\n","
32
\n","
most religions have dogmatic orthodox well eso...
\n","
positive
\n","
[most religions have dogmatic orthodox well es...
\n","
[0.03939439728856087, -0.02040349319577217, -0...
\n","
positive
\n","
0.972607
\n","
most religions have dogmatic orthodox well eso...
\n","
\n","
\n","
33
\n","
33
\n","
laureatte sen said christian schools are perfe...
\n","
positive
\n","
[laureatte sen said christian schools are perf...
\n","
[0.05267934128642082, 0.05836360529065132, 0.0...
\n","
positive
\n","
0.911020
\n","
laureatte sen said christian schools are perfe...
\n","
\n","
\n","
34
\n","
34
\n","
need stop watching the garbage that you watch...
\n","
positive
\n","
[need stop watching the garbage that you watch...
\n","
[-0.012382612563669682, 0.01988200470805168, 0...
\n","
positive
\n","
0.954440
\n","
need stop watching the garbage that you watch ...
\n","
\n","
\n","
35
\n","
35
\n","
gandhi mandela hitler mao plato chandragupt ma...
\n","
negative
\n","
[gandhi mandela hitler mao plato chandragupt m...
\n","
[0.027552243322134018, 0.013075066730380058, 0...
\n","
negative
\n","
0.767667
\n","
gandhi mandela hitler mao plato chandragupt ma...
\n","
\n","
\n","
36
\n","
36
\n","
hate aap for the other thread points such the...
\n","
negative
\n","
[hate aap for the other thread points such the...
\n","
[0.014617362059652805, -0.038017578423023224, ...
\n","
negative
\n","
0.690414
\n","
hate aap for the other thread points such the ...
\n","
\n","
\n","
37
\n","
37
\n","
absolutely agree with you subsidies the worst ...
\n","
negative
\n","
[absolutely agree with you subsidies the worst...
\n","
[0.0109744006767869, 0.0033110964577645063, -0...
\n","
neutral
\n","
0.581476
\n","
absolutely agree with you subsidies the worst ...
\n","
\n","
\n","
38
\n","
38
\n","
are you corrupt mind have you benefited throu...
\n","
negative
\n","
[are you corrupt mind have you benefited throu...
\n","
[0.03834373503923416, -0.06521473079919815, -0...
\n","
negative
\n","
0.783217
\n","
are you corrupt mind have you benefited throug...
\n","
\n","
\n","
39
\n","
39
\n","
congress needs bogeyman modi without the bad g...
\n","
positive
\n","
[congress needs bogeyman modi without the bad ...
\n","
[0.03138439729809761, -0.06221967190504074, -0...
\n","
positive
\n","
0.764358
\n","
congress needs bogeyman modi without the bad g...
\n","
\n","
\n","
40
\n","
40
\n","
protip don type uppercase text all caps harder...
\n","
negative
\n","
[protip don type uppercase text all caps harde...
\n","
[0.044019922614097595, 0.025341013446450233, 0...
\n","
negative
\n","
0.738550
\n","
protip don type uppercase text all caps harder...
\n","
\n","
\n","
41
\n","
41
\n","
brother trog very wrathful indeed but his wil...
\n","
positive
\n","
[brother trog very wrathful indeed but his wil...
\n","
[-0.024625714868307114, 0.06193268671631813, 0...
\n","
positive
\n","
0.923871
\n","
brother trog very wrathful indeed but his will...
\n","
\n","
\n","
42
\n","
42
\n","
start off saying that the craftsmanship this ...
\n","
positive
\n","
[start off saying that the craftsmanship this ...
\n","
[0.05780624598264694, -0.06291750818490982, -0...
\n","
positive
\n","
0.985073
\n","
start off saying that the craftsmanship this p...
\n","
\n","
\n","
43
\n","
43
\n","
have made request unban namoarmy hell moron h...
\n","
negative
\n","
[have made request unban namoarmy hell moron h...
\n","
[0.015555822290480137, -0.012748800218105316, ...
\n","
negative
\n","
0.796430
\n","
have made request unban namoarmy hell moron ho...
\n","
\n","
\n","
44
\n","
44
\n","
child modi worked his fatherâ tea shop and ...
\n","
negative
\n","
[child modi worked his fatherâ tea shop and ...
\n","
[0.05774841457605362, -0.05956699699163437, -0...
\n","
negative
\n","
0.709697
\n","
child modi worked his fatherâ tea shop and y...
\n","
\n","
\n","
45
\n","
45
\n","
namo tea yuupea horrible rhyme know
\n","
negative
\n","
[namo tea yuupea horrible rhyme know]
\n","
[0.025534288957715034, 0.004176765214651823, -...
\n","
negative
\n","
0.851523
\n","
namo tea yuupea horrible rhyme know
\n","
\n","
\n","
46
\n","
46
\n","
great agility from akpom cut back and bend
\n","
positive
\n","
[great agility from akpom cut back and bend]
\n","
[0.06865684688091278, -0.02164856530725956, -0...
\n","
positive
\n","
0.966416
\n","
great agility from akpom cut back and bend
\n","
\n","
\n","
47
\n","
47
\n","
from undecided pro aap they are not perfect bu...
\n","
positive
\n","
[from undecided pro aap they are not perfect b...
\n","
[0.01590304635465145, -0.0683458000421524, -0....
\n","
positive
\n","
0.891286
\n","
from undecided pro aap they are not perfect bu...
\n","
\n","
\n","
48
\n","
48
\n","
woah there don insane with pray mean you don w...
\n","
negative
\n","
[woah there don insane with pray mean you don ...
\n","
[0.050547026097774506, -0.01725909113883972, 0...
\n","
negative
\n","
0.798072
\n","
woah there don insane with pray mean you don w...
\n","
\n","
\n","
49
\n","
49
\n","
porngress wont announce their candidate cuz th...
\n","
positive
\n","
[porngress wont announce their candidate cuz t...
\n","
[0.05935536324977875, -0.051609162241220474, -...
\n","
positive
\n","
0.858500
\n","
porngress wont announce their candidate cuz th...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... document\n","0 0 ... its true they had cut the power what douchebag...\n","1 1 ... fuck giroud better finishing like this month\n","2 2 ... looks shit now but still proud made\n","3 3 ... pelor the burning hate the best evil god\n","4 4 ... can ask what you with something this powerful\n","5 5 ... aapâ shazia ilmi from puram constituency lag...\n","6 6 ... fuck yeah\n","7 7 ... honestly really surprised alice ranked that lo...\n","8 8 ... didn care about politics before now hate\n","9 9 ... hard nips and goosebumps\n","10 10 ... varadabhai ndtv trying too well dilute bjp tre...\n","11 11 ... old man has lost his mind\n","12 12 ... why this being downvoted you might ask both mo...\n","13 13 ... hasnt changed all apolitical before simply don...\n","14 14 ... for one campaign pretty much just snatched the...\n","15 15 ... vajpayee managed forge much broader coalition ...\n","16 16 ... lol this only proves how desperate they are ge...\n","17 17 ... dont hate aap but your questions are example w...\n","18 18 ... what were the other policies you discussed not...\n","19 19 ... wow lots favorites this bracket haqua tsukushi...\n","20 20 ... sorry know this isn what you asked just ventin...\n","21 21 ... coming out strongly against gujarat chief mini...\n","22 22 ... there one tool bjp can use their manifesto whi...\n","23 23 ... jakiro spotted the middle top maybe\n","24 24 ... family mormon have never tried explain them th...\n","25 25 ... with these results would have grudgingly accep...\n","26 26 ... tea partier expresses support for namo after e...\n","27 27 ... politically would stupid move take stand right...\n","28 28 ... wtf why\n","29 29 ... have actually seen lot users views change duri...\n","30 30 ... truth told there not insignificant percentage ...\n","31 31 ... was anti bjp and neutral cong became anti bjp ...\n","32 32 ... most religions have dogmatic orthodox well eso...\n","33 33 ... laureatte sen said christian schools are perfe...\n","34 34 ... need stop watching the garbage that you watch ...\n","35 35 ... gandhi mandela hitler mao plato chandragupt ma...\n","36 36 ... hate aap for the other thread points such the ...\n","37 37 ... absolutely agree with you subsidies the worst ...\n","38 38 ... are you corrupt mind have you benefited throug...\n","39 39 ... congress needs bogeyman modi without the bad g...\n","40 40 ... protip don type uppercase text all caps harder...\n","41 41 ... brother trog very wrathful indeed but his will...\n","42 42 ... start off saying that the craftsmanship this p...\n","43 43 ... have made request unban namoarmy hell moron ho...\n","44 44 ... child modi worked his fatherâ tea shop and y...\n","45 45 ... namo tea yuupea horrible rhyme know\n","46 46 ... great agility from akpom cut back and bend\n","47 47 ... from undecided pro aap they are not perfect bu...\n","48 48 ... woah there don insane with pray mean you don w...\n","49 49 ... porngress wont announce their candidate cuz th...\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["# Try training with different Embeddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxWFzQOhjWC8","executionInfo":{"status":"ok","timestamp":1620195837230,"user_tz":-120,"elapsed":16237,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2e5d8344-8b95-40fc-b43e-04c0b4aaaa45"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IKK_Ii_gjJfF","executionInfo":{"status":"ok","timestamp":1620196320027,"user_tz":-120,"elapsed":499014,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"4237cd32-fd39-444f-f6a9-9a6de947a62f"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(70) \n","trainable_pipe['sentiment_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.86 0.80 0.83 300\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.90 0.70 0.79 300\n","\n"," accuracy 0.75 600\n"," macro avg 0.59 0.50 0.54 600\n","weighted avg 0.88 0.75 0.81 600\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620196504424,"user_tz":-120,"elapsed":683325,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"798b31d7-cec2-48c8-ae29-7d4577ed9097"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":77},"id":"SO4uz45MoRgp","executionInfo":{"status":"ok","timestamp":1620196519636,"user_tz":-120,"elapsed":698457,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"662503b6-694f-4990-e121-80f6a9f2dcae"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Indian prime minister was assinated')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
text
\n","
sentence_embedding_from_disk
\n","
sentence
\n","
sentiment_confidence
\n","
sentiment
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
8589934592
\n","
Indian prime minister was assinated
\n","
[[-0.09739551693201065, 0.23939256370067596, 0...
\n","
[Indian prime minister was assinated]
\n","
[0.81195]
\n","
[negative]
\n","
Indian prime minister was assinated
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... document\n","0 8589934592 ... Indian prime minister was assinated\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e0CVlkk9v6Qi","executionInfo":{"status":"ok","timestamp":1620196519638,"user_tz":-120,"elapsed":698413,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"10c659fe-32a8-479b-e994-6827d8240a64"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@2d9c5454) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@2d9c5454\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentiment_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"2LJTK79JKF9-"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_stock_market.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_stock_market.ipynb
deleted file mode 100644
index ca953b92..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_stock_market.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_sentiment_classifier_demo_stock_market.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_stock_market.ipynb)\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 Class Demo Stock Market Sentiment Training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n","\n"," \n","\n","\n","\n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n","\n"," \n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620214514012,"user_tz":-120,"elapsed":124849,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"ef6ce0ba-22d9-46dc-f409-ed1c02167849"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 11:33:09-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 11:33:09 (53.5 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 70kB/s \n","\u001b[K |████████████████████████████████| 153kB 48.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.4MB/s \n","\u001b[K |████████████████████████████████| 204kB 49.2MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Stock Market Sentiment dataset \n","https://www.kaggle.com/yash612/stockmarket-sentiment-dataset\n","#Context\n","\n","Gathered Stock news from Multiple twitter Handles regarding Economic news dividing into two parts : Negative and positive."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620214515441,"user_tz":-120,"elapsed":126265,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2bfba990-0ff7-4660-8ba5-8821eb540304"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/stock_data.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 11:35:13-- http://ckl-it.de/wp-content/uploads/2021/02/stock_data.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 758217 (740K) [text/csv]\n","Saving to: ‘stock_data.csv’\n","\n","stock_data.csv 100%[===================>] 740.45K 819KB/s in 0.9s \n","\n","2021-05-05 11:35:15 (819 KB/s) - ‘stock_data.csv’ saved [758217/758217]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620214516379,"user_tz":-120,"elapsed":127196,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"9495fbc4-9641-44c5-bfbc-85b564fb7b12"},"source":["import pandas as pd\n","train_path = '/content/stock_data.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","columns=['text','y']\n","train_df = train_df[columns]\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
3761
\n","
P see what happened from October to Dec that s...
\n","
negative
\n","
\n","
\n","
1428
\n","
AAP Closed my short for positiveK Will short a...
\n","
negative
\n","
\n","
\n","
3091
\n","
AAP It may be wise to hold off on buying #AAP ...
\n","
negative
\n","
\n","
\n","
620
\n","
RT @DaveCBenoit: The banking system is not bui...
\n","
negative
\n","
\n","
\n","
1611
\n","
Sensex Slumps Over positive,000 Points From Da...
\n","
negative
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1281
\n","
The rodeo clown sent BK screaming into the S...
\n","
negative
\n","
\n","
\n","
1895
\n","
WMT breaking out of channel + All MAs are lini...
\n","
positive
\n","
\n","
\n","
3458
\n","
keep an eye on IDA presentation bout postive d...
\n","
positive
\n","
\n","
\n","
3399
\n","
DNDN Good place to lock in (some) profit at 7...
\n","
positive
\n","
\n","
\n","
2303
\n","
VVS breaking out of a flag setup. MACD cross-u...
\n","
positive
\n","
\n"," \n","
\n","
3200 rows × 2 columns
\n","
"],"text/plain":[" text y\n","3761 P see what happened from October to Dec that s... negative\n","1428 AAP Closed my short for positiveK Will short a... negative\n","3091 AAP It may be wise to hold off on buying #AAP ... negative\n","620 RT @DaveCBenoit: The banking system is not bui... negative\n","1611 Sensex Slumps Over positive,000 Points From Da... negative\n","... ... ...\n","1281 The rodeo clown sent BK screaming into the S... negative\n","1895 WMT breaking out of channel + All MAs are lini... positive\n","3458 keep an eye on IDA presentation bout postive d... positive\n","3399 DNDN Good place to lock in (some) profit at 7... positive\n","2303 VVS breaking out of a flag setup. MACD cross-u... positive\n","\n","[3200 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620215080750,"user_tz":-120,"elapsed":569,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2c6c1232-26f2-4727-e184-f343eb0d699f"},"source":["import nlu \n","from sklearn.metrics import classification_report\n","\n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.00 0.00 0.00 22\n"," neutral 0.00 0.00 0.00 0\n"," positive 1.00 0.11 0.19 28\n","\n"," accuracy 0.06 50\n"," macro avg 0.33 0.04 0.06 50\n","weighted avg 0.56 0.06 0.11 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
sentence_embedding_use
\n","
trained_sentiment_confidence
\n","
origin_index
\n","
trained_sentiment
\n","
document
\n","
text
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
negative
\n","
[-0.03631044551730156, -0.02809535153210163, -...
\n","
0.532420
\n","
3761
\n","
neutral
\n","
P see what happened from October to Dec that s...
\n","
P see what happened from October to Dec that s...
\n","
[P see what happened from October to Dec that ...
\n","
\n","
\n","
1
\n","
negative
\n","
[-0.019356619566679, 0.02282714657485485, 0.00...
\n","
0.528691
\n","
1428
\n","
neutral
\n","
AAP Closed my short for positiveK Will short a...
\n","
AAP Closed my short for positiveK Will short a...
\n","
[AAP Closed my short for positiveK Will short ...
\n","
\n","
\n","
2
\n","
negative
\n","
[0.03622237220406532, -0.06491724401712418, 0....
\n","
0.518308
\n","
3091
\n","
neutral
\n","
AAP It may be wise to hold off on buying #AAP ...
\n","
AAP It may be wise to hold off on buying #AAP ...
\n","
[AAP It may be wise to hold off on buying #AAP...
\n","
\n","
\n","
3
\n","
negative
\n","
[0.038874898105859756, 0.025729672983288765, -...
\n","
0.538483
\n","
620
\n","
neutral
\n","
RT @DaveCBenoit: The banking system is not bui...
\n","
RT @DaveCBenoit: The banking system is not bui...
\n","
[RT @DaveCBenoit:, The banking system is not ...
\n","
\n","
\n","
4
\n","
negative
\n","
[0.04230193793773651, -0.02588844671845436, -0...
\n","
0.526358
\n","
1611
\n","
neutral
\n","
Sensex Slumps Over positive,000 Points From Da...
\n","
Sensex Slumps Over positive,000 Points From Da...
\n","
[Sensex Slumps Over positive,000 Points From D...
\n","
\n","
\n","
5
\n","
negative
\n","
[-0.000432533270213753, -0.009179827757179737,...
\n","
0.530686
\n","
876
\n","
neutral
\n","
Selling your home to a computer was supposed t...
\n","
Selling your home to a computer was supposed t...
\n","
[Selling your home to a computer was supposed ...
\n","
\n","
\n","
6
\n","
positive
\n","
[0.06600425392389297, -0.022004421800374985, -...
\n","
0.581016
\n","
1678
\n","
neutral
\n","
ed Daily Triangle on HEO,..... pdating ong and...
\n","
ed Daily Triangle on HEO,..... pdating ong an...
\n","
[ed Daily Triangle on HEO,., .... pdating ong ...
\n","
\n","
\n","
7
\n","
positive
\n","
[-0.017160149291157722, -0.03617899864912033, ...
\n","
0.549800
\n","
482
\n","
neutral
\n","
P breaking out after expanding from Shark Patt...
\n","
P breaking out after expanding from Shark Patt...
\n","
[P breaking out after expanding from Shark Pat...
\n","
\n","
\n","
8
\n","
positive
\n","
[0.026538891717791557, -0.06604061275720596, -...
\n","
0.568579
\n","
2540
\n","
neutral
\n","
Acting well above 26.positive9 flat base trigg...
\n","
Acting well above 26.positive9 flat base trigg...
\n","
[Acting well above 26.positive9 flat base trig...
\n","
\n","
\n","
9
\n","
positive
\n","
[0.08514805138111115, -0.024943925440311432, -...
\n","
0.524013
\n","
2183
\n","
neutral
\n","
CYBX broke out to all time highs accompained b...
\n","
CYBX broke out to all time highs accompained b...
\n","
[CYBX broke out to all time highs accompained ...
\n","
\n","
\n","
10
\n","
positive
\n","
[0.05149979144334793, -0.061731286346912384, -...
\n","
0.518314
\n","
3998
\n","
neutral
\n","
solid day with MGM AAP SBX leading the way for...
\n","
solid day with MGM AAP SBX leading the way for...
\n","
[solid day with MGM AAP SBX leading the way fo...
\n","
\n","
\n","
11
\n","
negative
\n","
[0.03843201324343681, -0.0733688548207283, -0....
\n","
0.516091
\n","
19
\n","
neutral
\n","
AAP 465 is resistance and heading to 435 in th...
\n","
AAP 465 is resistance and heading to 435 in th...
\n","
[AAP 465 is resistance and heading to 435 in t...
\n","
\n","
\n","
12
\n","
negative
\n","
[0.02629232406616211, -0.014554189518094063, -...
\n","
0.522850
\n","
219
\n","
neutral
\n","
CO pointed out this wknd - huge Outside Day - ...
\n","
CO pointed out this wknd - huge Outside Day - ...
\n","
[CO pointed out this wknd - huge Outside Day -...
\n","
\n","
\n","
13
\n","
positive
\n","
[0.05966312810778618, -0.08997906744480133, -0...
\n","
0.611891
\n","
1041
\n","
positive
\n","
VVS ong set up:
\n","
VVS ong set up:
\n","
[VVS ong set up:]
\n","
\n","
\n","
14
\n","
positive
\n","
[-0.003521521808579564, -0.011022194288671017,...
\n","
0.545092
\n","
2763
\n","
neutral
\n","
DDD come on 40! I got some calls at 0.positive...
\n","
DDD come on 40! I got some calls at 0.positive...
\n","
[DDD come on 40!, I got some calls at 0.posit...
\n","
\n","
\n","
15
\n","
positive
\n","
[0.07560215145349503, -0.009755599312484264, -...
\n","
0.536057
\n","
1683
\n","
neutral
\n","
WMB might fill this gap and reverse.
\n","
WMB might fill this gap and reverse.
\n","
[WMB might fill this gap and reverse.]
\n","
\n","
\n","
16
\n","
negative
\n","
[-0.0117484824731946, -0.007429660763591528, -...
\n","
0.536093
\n","
2565
\n","
neutral
\n","
Heard on the Street: Funeral providers would s...
\n","
Heard on the Street: Funeral providers would s...
\n","
[Heard on the Street: Funeral providers would ...
\n","
\n","
\n","
17
\n","
positive
\n","
[-0.03314466401934624, -0.04437091201543808, -...
\n","
0.620777
\n","
2065
\n","
positive
\n","
user welcome to the TK club
\n","
user welcome to the TK club
\n","
[user welcome to the TK club]
\n","
\n","
\n","
18
\n","
positive
\n","
[-0.018701869994401932, 0.03903575986623764, -...
\n","
0.589417
\n","
2308
\n","
neutral
\n","
ed Weekly Triangle on DVAX,....Scaling P
\n","
ed Weekly Triangle on DVAX,....Scaling P
\n","
[ed Weekly Triangle on DVAX,., ., .., Scaling P]
\n","
\n","
\n","
19
\n","
positive
\n","
[-0.011695394292473793, 0.05609796568751335, -...
\n","
0.515030
\n","
3355
\n","
neutral
\n","
AAP What does Al Gore know that we don't?
\n","
AAP What does Al Gore know that we don't?
\n","
[AAP What does Al Gore know that we don't?]
\n","
\n","
\n","
20
\n","
positive
\n","
[0.04774581268429756, -0.03589113429188728, -0...
\n","
0.581980
\n","
2594
\n","
neutral
\n","
user CSN, yesterday I bough at 25Kpositive.237...
\n","
user CSN, yesterday I bough at 25Kpositive.237...
\n","
[user CSN, yesterday I bough at 25Kpositive., ...
\n","
\n","
\n","
21
\n","
positive
\n","
[0.04126836359500885, -0.0008074558572843671, ...
\n","
0.554633
\n","
3099
\n","
neutral
\n","
FAO is deciding its direction #waitforit
\n","
FAO is deciding its direction #waitforit
\n","
[FAO is deciding its direction #waitforit]
\n","
\n","
\n","
22
\n","
negative
\n","
[0.017080938443541527, 0.021576160565018654, 0...
\n","
0.519300
\n","
2091
\n","
neutral
\n","
EN Take your profits and run. Nice run up,but ...
\n","
EN Take your profits and run. Nice run up,but ...
\n","
[EN Take your profits and run., Nice run up,bu...
\n","
\n","
\n","
23
\n","
negative
\n","
[0.04716206341981888, -0.021414492279291153, -...
\n","
0.534085
\n","
1819
\n","
neutral
\n","
GOOG reminds me so much of AAP in Sept bouncin...
\n","
GOOG reminds me so much of AAP in Sept bounci...
\n","
[GOOG reminds me so much of AAP in Sept bounci...
\n","
\n","
\n","
24
\n","
positive
\n","
[0.023834621533751488, -0.07077112793922424, -...
\n","
0.500451
\n","
3913
\n","
neutral
\n","
AAP doji being put in on 60 min after 7 down c...
\n","
AAP doji being put in on 60 min after 7 down c...
\n","
[AAP doji being put in on 60 min after 7 down ...
\n","
\n","
\n","
25
\n","
negative
\n","
[0.1026904433965683, 0.013861684128642082, -0....
\n","
0.510388
\n","
3117
\n","
neutral
\n","
James Dinsmore attributes Bancroft Fundââ¬â...
\n","
James Dinsmore attributes Bancroft Fundââ¬â...
\n","
[James Dinsmore attributes Bancroft Fundââ¬â...
\n","
\n","
\n","
26
\n","
positive
\n","
[0.006304154172539711, -0.0644269809126854, -0...
\n","
0.518708
\n","
695
\n","
neutral
\n","
SEV nicely green on red market
\n","
SEV nicely green on red market
\n","
[SEV nicely green on red market]
\n","
\n","
\n","
27
\n","
negative
\n","
[0.06555721163749695, 0.023601243272423744, -0...
\n","
0.515442
\n","
2916
\n","
neutral
\n","
VS option trader closes out Feb 50C selling po...
\n","
VS option trader closes out Feb 50C selling po...
\n","
[VS option trader closes out Feb 50C selling p...
\n","
\n","
\n","
28
\n","
positive
\n","
[-0.0012494433904066682, -0.017743036150932312...
\n","
0.596320
\n","
692
\n","
neutral
\n","
AON Continuation on good volume
\n","
AON Continuation on good volume
\n","
[AON Continuation on good volume]
\n","
\n","
\n","
29
\n","
positive
\n","
[0.04852033779025078, 0.00623974809423089, -0....
\n","
0.513316
\n","
475
\n","
neutral
\n","
Watch for SGY to break its downward trend line...
\n","
Watch for SGY to break its downward trend line...
\n","
[Watch for SGY to break its downward trend lin...
\n","
\n","
\n","
30
\n","
positive
\n","
[0.05204714462161064, 0.03000013716518879, -0....
\n","
0.556872
\n","
3159
\n","
neutral
\n","
SXC - breaking above key downward channel leve...
\n","
SXC - breaking above key downward channel leve...
\n","
[SXC - breaking above key downward channel lev...
\n","
\n","
\n","
31
\n","
negative
\n","
[0.053190235048532486, -0.023420613259077072, ...
\n","
0.524574
\n","
2102
\n","
neutral
\n","
Government May Slash Borrowing From Market In ...
\n","
Government May Slash Borrowing From Market In ...
\n","
[Government May Slash Borrowing From Market In...
\n","
\n","
\n","
32
\n","
negative
\n","
[0.013695711269974709, 0.031249094754457474, -...
\n","
0.501941
\n","
1689
\n","
neutral
\n","
CS - To those that doubted me; today is you're...
\n","
CS - To those that doubted me; today is you're...
\n","
[CS - To those that doubted me; today is you'r...
\n","
\n","
\n","
33
\n","
positive
\n","
[0.03522776812314987, -0.00036802110844291747,...
\n","
0.507491
\n","
2455
\n","
neutral
\n","
PAB has resistance from June to Sept but Over ...
\n","
PAB has resistance from June to Sept but Over ...
\n","
[PAB has resistance from June to Sept but Over...
\n","
\n","
\n","
34
\n","
negative
\n","
[-0.05846807360649109, -0.027525130659341812, ...
\n","
0.523526
\n","
2337
\n","
neutral
\n","
BAC anyone think this might slush and fall bel...
\n","
BAC anyone think this might slush and fall bel...
\n","
[BAC anyone think this might slush and fall be...
\n","
\n","
\n","
35
\n","
positive
\n","
[0.05771354213356972, -0.03869543969631195, -0...
\n","
0.503713
\n","
3529
\n","
neutral
\n","
with little whips on pops and drops don't go a...
\n","
with little whips on pops and drops don't go a...
\n","
[with little whips on pops and drops don't go ...
\n","
\n","
\n","
36
\n","
negative
\n","
[-0.049992404878139496, -0.017947403714060783,...
\n","
0.536274
\n","
2076
\n","
neutral
\n","
Green Weekly Triangle on CYTX,....pdating
\n","
Green Weekly Triangle on CYTX,....pdating
\n","
[Green Weekly Triangle on CYTX,., ...pdating]
\n","
\n","
\n","
37
\n","
positive
\n","
[0.006133062299340963, -0.05417517572641373, -...
\n","
0.546319
\n","
3012
\n","
neutral
\n","
reports next Wed. user: ATW: Barclays starts a...
\n","
reports next Wed. user: ATW: Barclays starts ...
\n","
[reports next Wed. user: ATW:, Barclays start...
\n","
\n","
\n","
38
\n","
negative
\n","
[0.041309256106615067, 0.02620919793844223, -0...
\n","
0.518853
\n","
3805
\n","
neutral
\n","
NKD target positive50 - positive2 points to be...
\n","
NKD target positive50 - positive2 points to be...
\n","
[NKD target positive50 - positive2 points to b...
\n","
\n","
\n","
39
\n","
positive
\n","
[0.0385039821267128, -0.040522705763578415, -0...
\n","
0.522853
\n","
3789
\n","
neutral
\n","
Positive GOOG earnings pushed NQ_F higher and ...
\n","
Positive GOOG earnings pushed NQ_F higher and ...
\n","
[Positive GOOG earnings pushed NQ_F higher and...
\n","
\n","
\n","
40
\n","
positive
\n","
[0.05128449201583862, -0.04454624652862549, -0...
\n","
0.552043
\n","
3625
\n","
neutral
\n","
SSQ pulling back as suspected. uckily sold nea...
\n","
SSQ pulling back as suspected. uckily sold nea...
\n","
[SSQ pulling back as suspected., uckily sold n...
\n","
\n","
\n","
41
\n","
negative
\n","
[0.018300753086805344, -0.04134758561849594, -...
\n","
0.519100
\n","
851
\n","
neutral
\n","
NKE potential short for many reasons. Check F ...
\n","
NKE potential short for many reasons. Check F ...
\n","
[NKE potential short for many reasons., Check ...
\n","
\n","
\n","
42
\n","
negative
\n","
[0.09244795143604279, 0.015826726332306862, -0...
\n","
0.534414
\n","
608
\n","
neutral
\n","
Supply-chain finance has become popular in rec...
\n","
Supply-chain finance has become popular in rec...
\n","
[Supply-chain finance has become popular in re...
\n","
\n","
\n","
43
\n","
positive
\n","
[0.042959753423929214, 0.004588234703987837, -...
\n","
0.536310
\n","
546
\n","
neutral
\n","
AEG Over 28.23
\n","
AEG Over 28.23
\n","
[AEG Over 28.23]
\n","
\n","
\n","
44
\n","
negative
\n","
[0.004285166040062904, -0.07630395889282227, -...
\n","
0.506102
\n","
1225
\n","
neutral
\n","
AAP Free-falling Now to 457
\n","
AAP Free-falling Now to 457
\n","
[AAP Free-falling Now to 457]
\n","
\n","
\n","
45
\n","
positive
\n","
[0.017030321061611176, -0.022644072771072388, ...
\n","
0.500568
\n","
1227
\n","
neutral
\n","
BAC positivepositive.75 to positivepositive.80...
\n","
BAC positivepositive.75 to positivepositive.80...
\n","
[BAC positivepositive., 75 to positivepositive...
\n","
\n","
\n","
46
\n","
positive
\n","
[0.030308598652482033, 0.02380680851638317, -0...
\n","
0.513642
\n","
3991
\n","
neutral
\n","
NVDA not working so far still holding though.
\n","
NVDA not working so far still holding though.
\n","
[NVDA not working so far still holding though.]
\n","
\n","
\n","
47
\n","
negative
\n","
[0.05175251513719559, 0.05424535647034645, -0....
\n","
0.512999
\n","
1467
\n","
neutral
\n","
CAT Once she loses 86.50 ookout Below!!! Strai...
\n","
CAT Once she loses 86.50 ookout Below!!! Strai...
\n","
[CAT Once she loses 86.50 ookout Below!, !!, S...
\n","
\n","
\n","
48
\n","
positive
\n","
[-0.022420072928071022, -0.06559137254953384, ...
\n","
0.600178
\n","
1000
\n","
positive
\n","
My setup alerts went bonkers today..One of man...
\n","
My setup alerts went bonkers today..One of man...
\n","
[My setup alerts went bonkers today., ., One o...
\n","
\n","
\n","
49
\n","
positive
\n","
[0.03310457989573479, -0.038539715111255646, -...
\n","
0.554509
\n","
282
\n","
neutral
\n","
SWHC Obama/Biden to speak tomorrow on gun cont...
\n","
SWHC Obama/Biden to speak tomorrow on gun cont...
\n","
[SWHC Obama/Biden to speak tomorrow on gun con...
\n","
\n"," \n","
\n","
"],"text/plain":[" y ... sentence\n","0 negative ... [P see what happened from October to Dec that ...\n","1 negative ... [AAP Closed my short for positiveK Will short ...\n","2 negative ... [AAP It may be wise to hold off on buying #AAP...\n","3 negative ... [RT @DaveCBenoit:, The banking system is not ...\n","4 negative ... [Sensex Slumps Over positive,000 Points From D...\n","5 negative ... [Selling your home to a computer was supposed ...\n","6 positive ... [ed Daily Triangle on HEO,., .... pdating ong ...\n","7 positive ... [P breaking out after expanding from Shark Pat...\n","8 positive ... [Acting well above 26.positive9 flat base trig...\n","9 positive ... [CYBX broke out to all time highs accompained ...\n","10 positive ... [solid day with MGM AAP SBX leading the way fo...\n","11 negative ... [AAP 465 is resistance and heading to 435 in t...\n","12 negative ... [CO pointed out this wknd - huge Outside Day -...\n","13 positive ... [VVS ong set up:]\n","14 positive ... [DDD come on 40!, I got some calls at 0.posit...\n","15 positive ... [WMB might fill this gap and reverse.]\n","16 negative ... [Heard on the Street: Funeral providers would ...\n","17 positive ... [user welcome to the TK club]\n","18 positive ... [ed Weekly Triangle on DVAX,., ., .., Scaling P]\n","19 positive ... [AAP What does Al Gore know that we don't?]\n","20 positive ... [user CSN, yesterday I bough at 25Kpositive., ...\n","21 positive ... [FAO is deciding its direction #waitforit]\n","22 negative ... [EN Take your profits and run., Nice run up,bu...\n","23 negative ... [GOOG reminds me so much of AAP in Sept bounci...\n","24 positive ... [AAP doji being put in on 60 min after 7 down ...\n","25 negative ... [James Dinsmore attributes Bancroft Fundââ¬â...\n","26 positive ... [SEV nicely green on red market]\n","27 negative ... [VS option trader closes out Feb 50C selling p...\n","28 positive ... [AON Continuation on good volume]\n","29 positive ... [Watch for SGY to break its downward trend lin...\n","30 positive ... [SXC - breaking above key downward channel lev...\n","31 negative ... [Government May Slash Borrowing From Market In...\n","32 negative ... [CS - To those that doubted me; today is you'r...\n","33 positive ... [PAB has resistance from June to Sept but Over...\n","34 negative ... [BAC anyone think this might slush and fall be...\n","35 positive ... [with little whips on pops and drops don't go ...\n","36 negative ... [Green Weekly Triangle on CYTX,., ...pdating]\n","37 positive ... [reports next Wed. user: ATW:, Barclays start...\n","38 negative ... [NKD target positive50 - positive2 points to b...\n","39 positive ... [Positive GOOG earnings pushed NQ_F higher and...\n","40 positive ... [SSQ pulling back as suspected., uckily sold n...\n","41 negative ... [NKE potential short for many reasons., Check ...\n","42 negative ... [Supply-chain finance has become popular in re...\n","43 positive ... [AEG Over 28.23]\n","44 negative ... [AAP Free-falling Now to 457]\n","45 positive ... [BAC positivepositive., 75 to positivepositive...\n","46 positive ... [NVDA not working so far still holding though.]\n","47 negative ... [CAT Once she loses 86.50 ookout Below!, !!, S...\n","48 positive ... [My setup alerts went bonkers today., ., One o...\n","49 positive ... [SWHC Obama/Biden to speak tomorrow on gun con...\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"lVyOE2wV0fw_"},"source":["# 4. Test the fitted pipe on new example"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":76},"id":"qdCUg2MR0PD2","executionInfo":{"status":"ok","timestamp":1620215088558,"user_tz":-120,"elapsed":1570,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"54f6d037-8cbc-4273-8c9c-04e5599e76cf"},"source":["fitted_pipe.predict(\"Bitcoin dropped by 50 percent!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_use
\n","
trained_sentiment_confidence
\n","
origin_index
\n","
trained_sentiment
\n","
document
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.06509937345981598, -0.05708129703998566, -0...
\n","
0.502021
\n","
0
\n","
neutral
\n","
Bitcoin dropped by 50 percent!
\n","
[Bitcoin dropped by 50 percent!]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_use ... sentence\n","0 [0.06509937345981598, -0.05708129703998566, -0... ... [Bitcoin dropped by 50 percent!]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## 5. Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620215088559,"user_tz":-120,"elapsed":1284,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"e6190008-9310-45be-f3ac-e47fc541a70f"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@45181116) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@45181116\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## 6. Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620215094052,"user_tz":-120,"elapsed":6547,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b0386b8b-b130-4d65-d972-1ecb8cefbe17"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 1.00 0.73 0.84 22\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.90 1.00 0.95 28\n","\n"," accuracy 0.88 50\n"," macro avg 0.63 0.58 0.60 50\n","weighted avg 0.95 0.88 0.90 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
sentence_embedding_use
\n","
trained_sentiment_confidence
\n","
origin_index
\n","
trained_sentiment
\n","
document
\n","
text
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
negative
\n","
[-0.03631044551730156, -0.02809535153210163, -...
\n","
0.673180
\n","
3761
\n","
negative
\n","
P see what happened from October to Dec that s...
\n","
P see what happened from October to Dec that s...
\n","
[P see what happened from October to Dec that ...
\n","
\n","
\n","
1
\n","
negative
\n","
[-0.019356619566679, 0.02282714657485485, 0.00...
\n","
0.692276
\n","
1428
\n","
negative
\n","
AAP Closed my short for positiveK Will short a...
\n","
AAP Closed my short for positiveK Will short a...
\n","
[AAP Closed my short for positiveK Will short ...
\n","
\n","
\n","
2
\n","
negative
\n","
[0.03622237220406532, -0.06491724401712418, 0....
\n","
0.704943
\n","
3091
\n","
negative
\n","
AAP It may be wise to hold off on buying #AAP ...
\n","
AAP It may be wise to hold off on buying #AAP ...
\n","
[AAP It may be wise to hold off on buying #AAP...
\n","
\n","
\n","
3
\n","
negative
\n","
[0.038874898105859756, 0.025729672983288765, -...
\n","
0.737828
\n","
620
\n","
negative
\n","
RT @DaveCBenoit: The banking system is not bui...
\n","
RT @DaveCBenoit: The banking system is not bui...
\n","
[RT @DaveCBenoit:, The banking system is not ...
\n","
\n","
\n","
4
\n","
negative
\n","
[0.04230193793773651, -0.02588844671845436, -0...
\n","
0.690660
\n","
1611
\n","
negative
\n","
Sensex Slumps Over positive,000 Points From Da...
\n","
Sensex Slumps Over positive,000 Points From Da...
\n","
[Sensex Slumps Over positive,000 Points From D...
\n","
\n","
\n","
5
\n","
negative
\n","
[-0.000432533270213753, -0.009179827757179737,...
\n","
0.674662
\n","
876
\n","
negative
\n","
Selling your home to a computer was supposed t...
\n","
Selling your home to a computer was supposed t...
\n","
[Selling your home to a computer was supposed ...
\n","
\n","
\n","
6
\n","
positive
\n","
[0.06600425392389297, -0.022004421800374985, -...
\n","
0.983413
\n","
1678
\n","
positive
\n","
ed Daily Triangle on HEO,..... pdating ong and...
\n","
ed Daily Triangle on HEO,..... pdating ong an...
\n","
[ed Daily Triangle on HEO,., .... pdating ong ...
\n","
\n","
\n","
7
\n","
positive
\n","
[-0.017160149291157722, -0.03617899864912033, ...
\n","
0.958774
\n","
482
\n","
positive
\n","
P breaking out after expanding from Shark Patt...
\n","
P breaking out after expanding from Shark Patt...
\n","
[P breaking out after expanding from Shark Pat...
\n","
\n","
\n","
8
\n","
positive
\n","
[0.026538891717791557, -0.06604061275720596, -...
\n","
0.945701
\n","
2540
\n","
positive
\n","
Acting well above 26.positive9 flat base trigg...
\n","
Acting well above 26.positive9 flat base trigg...
\n","
[Acting well above 26.positive9 flat base trig...
\n","
\n","
\n","
9
\n","
positive
\n","
[0.08514805138111115, -0.024943925440311432, -...
\n","
0.897408
\n","
2183
\n","
positive
\n","
CYBX broke out to all time highs accompained b...
\n","
CYBX broke out to all time highs accompained b...
\n","
[CYBX broke out to all time highs accompained ...
\n","
\n","
\n","
10
\n","
positive
\n","
[0.05149979144334793, -0.061731286346912384, -...
\n","
0.839700
\n","
3998
\n","
positive
\n","
solid day with MGM AAP SBX leading the way for...
\n","
solid day with MGM AAP SBX leading the way for...
\n","
[solid day with MGM AAP SBX leading the way fo...
\n","
\n","
\n","
11
\n","
negative
\n","
[0.03843201324343681, -0.0733688548207283, -0....
\n","
0.649359
\n","
19
\n","
negative
\n","
AAP 465 is resistance and heading to 435 in th...
\n","
AAP 465 is resistance and heading to 435 in th...
\n","
[AAP 465 is resistance and heading to 435 in t...
\n","
\n","
\n","
12
\n","
negative
\n","
[0.02629232406616211, -0.014554189518094063, -...
\n","
0.696317
\n","
219
\n","
negative
\n","
CO pointed out this wknd - huge Outside Day - ...
\n","
CO pointed out this wknd - huge Outside Day - ...
\n","
[CO pointed out this wknd - huge Outside Day -...
\n","
\n","
\n","
13
\n","
positive
\n","
[0.05966312810778618, -0.08997906744480133, -0...
\n","
0.994020
\n","
1041
\n","
positive
\n","
VVS ong set up:
\n","
VVS ong set up:
\n","
[VVS ong set up:]
\n","
\n","
\n","
14
\n","
positive
\n","
[-0.003521521808579564, -0.011022194288671017,...
\n","
0.860149
\n","
2763
\n","
positive
\n","
DDD come on 40! I got some calls at 0.positive...
\n","
DDD come on 40! I got some calls at 0.positive...
\n","
[DDD come on 40!, I got some calls at 0.posit...
\n","
\n","
\n","
15
\n","
positive
\n","
[0.07560215145349503, -0.009755599312484264, -...
\n","
0.961940
\n","
1683
\n","
positive
\n","
WMB might fill this gap and reverse.
\n","
WMB might fill this gap and reverse.
\n","
[WMB might fill this gap and reverse.]
\n","
\n","
\n","
16
\n","
negative
\n","
[-0.0117484824731946, -0.007429660763591528, -...
\n","
0.703831
\n","
2565
\n","
negative
\n","
Heard on the Street: Funeral providers would s...
\n","
Heard on the Street: Funeral providers would s...
\n","
[Heard on the Street: Funeral providers would ...
\n","
\n","
\n","
17
\n","
positive
\n","
[-0.03314466401934624, -0.04437091201543808, -...
\n","
0.994179
\n","
2065
\n","
positive
\n","
user welcome to the TK club
\n","
user welcome to the TK club
\n","
[user welcome to the TK club]
\n","
\n","
\n","
18
\n","
positive
\n","
[-0.018701869994401932, 0.03903575986623764, -...
\n","
0.991520
\n","
2308
\n","
positive
\n","
ed Weekly Triangle on DVAX,....Scaling P
\n","
ed Weekly Triangle on DVAX,....Scaling P
\n","
[ed Weekly Triangle on DVAX,., ., .., Scaling P]
\n","
\n","
\n","
19
\n","
positive
\n","
[-0.011695394292473793, 0.05609796568751335, -...
\n","
0.725599
\n","
3355
\n","
positive
\n","
AAP What does Al Gore know that we don't?
\n","
AAP What does Al Gore know that we don't?
\n","
[AAP What does Al Gore know that we don't?]
\n","
\n","
\n","
20
\n","
positive
\n","
[0.04774581268429756, -0.03589113429188728, -0...
\n","
0.973669
\n","
2594
\n","
positive
\n","
user CSN, yesterday I bough at 25Kpositive.237...
\n","
user CSN, yesterday I bough at 25Kpositive.237...
\n","
[user CSN, yesterday I bough at 25Kpositive., ...
\n","
\n","
\n","
21
\n","
positive
\n","
[0.04126836359500885, -0.0008074558572843671, ...
\n","
0.986087
\n","
3099
\n","
positive
\n","
FAO is deciding its direction #waitforit
\n","
FAO is deciding its direction #waitforit
\n","
[FAO is deciding its direction #waitforit]
\n","
\n","
\n","
22
\n","
negative
\n","
[0.017080938443541527, 0.021576160565018654, 0...
\n","
0.675147
\n","
2091
\n","
negative
\n","
EN Take your profits and run. Nice run up,but ...
\n","
EN Take your profits and run. Nice run up,but ...
\n","
[EN Take your profits and run., Nice run up,bu...
\n","
\n","
\n","
23
\n","
negative
\n","
[0.04716206341981888, -0.021414492279291153, -...
\n","
0.759626
\n","
1819
\n","
negative
\n","
GOOG reminds me so much of AAP in Sept bouncin...
\n","
GOOG reminds me so much of AAP in Sept bounci...
\n","
[GOOG reminds me so much of AAP in Sept bounci...
\n","
\n","
\n","
24
\n","
positive
\n","
[0.023834621533751488, -0.07077112793922424, -...
\n","
0.693293
\n","
3913
\n","
positive
\n","
AAP doji being put in on 60 min after 7 down c...
\n","
AAP doji being put in on 60 min after 7 down c...
\n","
[AAP doji being put in on 60 min after 7 down ...
\n","
\n","
\n","
25
\n","
negative
\n","
[0.1026904433965683, 0.013861684128642082, -0....
\n","
0.587268
\n","
3117
\n","
neutral
\n","
James Dinsmore attributes Bancroft Fundââ¬â...
\n","
James Dinsmore attributes Bancroft Fundââ¬â...
\n","
[James Dinsmore attributes Bancroft Fundââ¬â...
\n","
\n","
\n","
26
\n","
positive
\n","
[0.006304154172539711, -0.0644269809126854, -0...
\n","
0.875854
\n","
695
\n","
positive
\n","
SEV nicely green on red market
\n","
SEV nicely green on red market
\n","
[SEV nicely green on red market]
\n","
\n","
\n","
27
\n","
negative
\n","
[0.06555721163749695, 0.023601243272423744, -0...
\n","
0.674148
\n","
2916
\n","
negative
\n","
VS option trader closes out Feb 50C selling po...
\n","
VS option trader closes out Feb 50C selling po...
\n","
[VS option trader closes out Feb 50C selling p...
\n","
\n","
\n","
28
\n","
positive
\n","
[-0.0012494433904066682, -0.017743036150932312...
\n","
0.984582
\n","
692
\n","
positive
\n","
AON Continuation on good volume
\n","
AON Continuation on good volume
\n","
[AON Continuation on good volume]
\n","
\n","
\n","
29
\n","
positive
\n","
[0.04852033779025078, 0.00623974809423089, -0....
\n","
0.778999
\n","
475
\n","
positive
\n","
Watch for SGY to break its downward trend line...
\n","
Watch for SGY to break its downward trend line...
\n","
[Watch for SGY to break its downward trend lin...
\n","
\n","
\n","
30
\n","
positive
\n","
[0.05204714462161064, 0.03000013716518879, -0....
\n","
0.983867
\n","
3159
\n","
positive
\n","
SXC - breaking above key downward channel leve...
\n","
SXC - breaking above key downward channel leve...
\n","
[SXC - breaking above key downward channel lev...
\n","
\n","
\n","
31
\n","
negative
\n","
[0.053190235048532486, -0.023420613259077072, ...
\n","
0.725456
\n","
2102
\n","
negative
\n","
Government May Slash Borrowing From Market In ...
\n","
Government May Slash Borrowing From Market In ...
\n","
[Government May Slash Borrowing From Market In...
\n","
\n","
\n","
32
\n","
negative
\n","
[0.013695711269974709, 0.031249094754457474, -...
\n","
0.577159
\n","
1689
\n","
neutral
\n","
CS - To those that doubted me; today is you're...
\n","
CS - To those that doubted me; today is you're...
\n","
[CS - To those that doubted me; today is you'r...
\n","
\n","
\n","
33
\n","
positive
\n","
[0.03522776812314987, -0.00036802110844291747,...
\n","
0.749848
\n","
2455
\n","
positive
\n","
PAB has resistance from June to Sept but Over ...
\n","
PAB has resistance from June to Sept but Over ...
\n","
[PAB has resistance from June to Sept but Over...
\n","
\n","
\n","
34
\n","
negative
\n","
[-0.05846807360649109, -0.027525130659341812, ...
\n","
0.683594
\n","
2337
\n","
negative
\n","
BAC anyone think this might slush and fall bel...
\n","
BAC anyone think this might slush and fall bel...
\n","
[BAC anyone think this might slush and fall be...
\n","
\n","
\n","
35
\n","
positive
\n","
[0.05771354213356972, -0.03869543969631195, -0...
\n","
0.776892
\n","
3529
\n","
positive
\n","
with little whips on pops and drops don't go a...
\n","
with little whips on pops and drops don't go a...
\n","
[with little whips on pops and drops don't go ...
\n","
\n","
\n","
36
\n","
negative
\n","
[-0.049992404878139496, -0.017947403714060783,...
\n","
0.915735
\n","
2076
\n","
positive
\n","
Green Weekly Triangle on CYTX,....pdating
\n","
Green Weekly Triangle on CYTX,....pdating
\n","
[Green Weekly Triangle on CYTX,., ...pdating]
\n","
\n","
\n","
37
\n","
positive
\n","
[0.006133062299340963, -0.05417517572641373, -...
\n","
0.925494
\n","
3012
\n","
positive
\n","
reports next Wed. user: ATW: Barclays starts a...
\n","
reports next Wed. user: ATW: Barclays starts ...
\n","
[reports next Wed. user: ATW:, Barclays start...
\n","
\n","
\n","
38
\n","
negative
\n","
[0.041309256106615067, 0.02620919793844223, -0...
\n","
0.725568
\n","
3805
\n","
positive
\n","
NKD target positive50 - positive2 points to be...
\n","
NKD target positive50 - positive2 points to be...
\n","
[NKD target positive50 - positive2 points to b...
\n","
\n","
\n","
39
\n","
positive
\n","
[0.0385039821267128, -0.040522705763578415, -0...
\n","
0.879851
\n","
3789
\n","
positive
\n","
Positive GOOG earnings pushed NQ_F higher and ...
\n","
Positive GOOG earnings pushed NQ_F higher and ...
\n","
[Positive GOOG earnings pushed NQ_F higher and...
\n","
\n","
\n","
40
\n","
positive
\n","
[0.05128449201583862, -0.04454624652862549, -0...
\n","
0.964138
\n","
3625
\n","
positive
\n","
SSQ pulling back as suspected. uckily sold nea...
\n","
SSQ pulling back as suspected. uckily sold nea...
\n","
[SSQ pulling back as suspected., uckily sold n...
\n","
\n","
\n","
41
\n","
negative
\n","
[0.018300753086805344, -0.04134758561849594, -...
\n","
0.644012
\n","
851
\n","
positive
\n","
NKE potential short for many reasons. Check F ...
\n","
NKE potential short for many reasons. Check F ...
\n","
[NKE potential short for many reasons., Check ...
\n","
\n","
\n","
42
\n","
negative
\n","
[0.09244795143604279, 0.015826726332306862, -0...
\n","
0.729759
\n","
608
\n","
negative
\n","
Supply-chain finance has become popular in rec...
\n","
Supply-chain finance has become popular in rec...
\n","
[Supply-chain finance has become popular in re...
\n","
\n","
\n","
43
\n","
positive
\n","
[0.042959753423929214, 0.004588234703987837, -...
\n","
0.985379
\n","
546
\n","
positive
\n","
AEG Over 28.23
\n","
AEG Over 28.23
\n","
[AEG Over 28.23]
\n","
\n","
\n","
44
\n","
negative
\n","
[0.004285166040062904, -0.07630395889282227, -...
\n","
0.604251
\n","
1225
\n","
negative
\n","
AAP Free-falling Now to 457
\n","
AAP Free-falling Now to 457
\n","
[AAP Free-falling Now to 457]
\n","
\n","
\n","
45
\n","
positive
\n","
[0.017030321061611176, -0.022644072771072388, ...
\n","
0.839384
\n","
1227
\n","
positive
\n","
BAC positivepositive.75 to positivepositive.80...
\n","
BAC positivepositive.75 to positivepositive.80...
\n","
[BAC positivepositive., 75 to positivepositive...
\n","
\n","
\n","
46
\n","
positive
\n","
[0.030308598652482033, 0.02380680851638317, -0...
\n","
0.888166
\n","
3991
\n","
positive
\n","
NVDA not working so far still holding though.
\n","
NVDA not working so far still holding though.
\n","
[NVDA not working so far still holding though.]
\n","
\n","
\n","
47
\n","
negative
\n","
[0.05175251513719559, 0.05424535647034645, -0....
\n","
0.569252
\n","
1467
\n","
neutral
\n","
CAT Once she loses 86.50 ookout Below!!! Strai...
\n","
CAT Once she loses 86.50 ookout Below!!! Strai...
\n","
[CAT Once she loses 86.50 ookout Below!, !!, S...
\n","
\n","
\n","
48
\n","
positive
\n","
[-0.022420072928071022, -0.06559137254953384, ...
\n","
0.986396
\n","
1000
\n","
positive
\n","
My setup alerts went bonkers today..One of man...
\n","
My setup alerts went bonkers today..One of man...
\n","
[My setup alerts went bonkers today., ., One o...
\n","
\n","
\n","
49
\n","
positive
\n","
[0.03310457989573479, -0.038539715111255646, -...
\n","
0.925892
\n","
282
\n","
positive
\n","
SWHC Obama/Biden to speak tomorrow on gun cont...
\n","
SWHC Obama/Biden to speak tomorrow on gun cont...
\n","
[SWHC Obama/Biden to speak tomorrow on gun con...
\n","
\n"," \n","
\n","
"],"text/plain":[" y ... sentence\n","0 negative ... [P see what happened from October to Dec that ...\n","1 negative ... [AAP Closed my short for positiveK Will short ...\n","2 negative ... [AAP It may be wise to hold off on buying #AAP...\n","3 negative ... [RT @DaveCBenoit:, The banking system is not ...\n","4 negative ... [Sensex Slumps Over positive,000 Points From D...\n","5 negative ... [Selling your home to a computer was supposed ...\n","6 positive ... [ed Daily Triangle on HEO,., .... pdating ong ...\n","7 positive ... [P breaking out after expanding from Shark Pat...\n","8 positive ... [Acting well above 26.positive9 flat base trig...\n","9 positive ... [CYBX broke out to all time highs accompained ...\n","10 positive ... [solid day with MGM AAP SBX leading the way fo...\n","11 negative ... [AAP 465 is resistance and heading to 435 in t...\n","12 negative ... [CO pointed out this wknd - huge Outside Day -...\n","13 positive ... [VVS ong set up:]\n","14 positive ... [DDD come on 40!, I got some calls at 0.posit...\n","15 positive ... [WMB might fill this gap and reverse.]\n","16 negative ... [Heard on the Street: Funeral providers would ...\n","17 positive ... [user welcome to the TK club]\n","18 positive ... [ed Weekly Triangle on DVAX,., ., .., Scaling P]\n","19 positive ... [AAP What does Al Gore know that we don't?]\n","20 positive ... [user CSN, yesterday I bough at 25Kpositive., ...\n","21 positive ... [FAO is deciding its direction #waitforit]\n","22 negative ... [EN Take your profits and run., Nice run up,bu...\n","23 negative ... [GOOG reminds me so much of AAP in Sept bounci...\n","24 positive ... [AAP doji being put in on 60 min after 7 down ...\n","25 negative ... [James Dinsmore attributes Bancroft Fundââ¬â...\n","26 positive ... [SEV nicely green on red market]\n","27 negative ... [VS option trader closes out Feb 50C selling p...\n","28 positive ... [AON Continuation on good volume]\n","29 positive ... [Watch for SGY to break its downward trend lin...\n","30 positive ... [SXC - breaking above key downward channel lev...\n","31 negative ... [Government May Slash Borrowing From Market In...\n","32 negative ... [CS - To those that doubted me; today is you'r...\n","33 positive ... [PAB has resistance from June to Sept but Over...\n","34 negative ... [BAC anyone think this might slush and fall be...\n","35 positive ... [with little whips on pops and drops don't go ...\n","36 negative ... [Green Weekly Triangle on CYTX,., ...pdating]\n","37 positive ... [reports next Wed. user: ATW:, Barclays start...\n","38 negative ... [NKD target positive50 - positive2 points to b...\n","39 positive ... [Positive GOOG earnings pushed NQ_F higher and...\n","40 positive ... [SSQ pulling back as suspected., uckily sold n...\n","41 negative ... [NKE potential short for many reasons., Check ...\n","42 negative ... [Supply-chain finance has become popular in re...\n","43 positive ... [AEG Over 28.23]\n","44 negative ... [AAP Free-falling Now to 457]\n","45 positive ... [BAC positivepositive., 75 to positivepositive...\n","46 positive ... [NVDA not working so far still holding though.]\n","47 negative ... [CAT Once she loses 86.50 ookout Below!, !!, S...\n","48 positive ... [My setup alerts went bonkers today., ., One o...\n","49 positive ... [SWHC Obama/Biden to speak tomorrow on gun con...\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["# 7. Try training with different Embeddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxWFzQOhjWC8","executionInfo":{"status":"ok","timestamp":1620215094053,"user_tz":-120,"elapsed":6283,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f410569a-7055-427d-c7dd-1388947b8f37"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IKK_Ii_gjJfF","executionInfo":{"status":"ok","timestamp":1620216889890,"user_tz":-120,"elapsed":1801997,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"3ef316f8-15cb-47fa-e781-d26594a43df4"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(120) \n","trainable_pipe['sentiment_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.81 0.68 0.74 1586\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.80 0.73 0.76 1614\n","\n"," accuracy 0.71 3200\n"," macro avg 0.54 0.47 0.50 3200\n","weighted avg 0.80 0.71 0.75 3200\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 7.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"id":"Fxx4yNkNVGFl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620217006865,"user_tz":-120,"elapsed":1918670,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"41c4545d-ae7f-47e8-8d6a-19d1a555a10c"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.76 0.62 0.68 414\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.72 0.67 0.69 386\n","\n"," accuracy 0.64 800\n"," macro avg 0.49 0.43 0.46 800\n","weighted avg 0.74 0.64 0.69 800\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 8. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620217179981,"user_tz":-120,"elapsed":2091513,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0f025bcd-eab2-47ee-8440-0425138fea27"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 9. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":76},"id":"SO4uz45MoRgp","executionInfo":{"status":"ok","timestamp":1620217194005,"user_tz":-120,"elapsed":2104313,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"004beb68-0b86-41b8-efb7-0c6382311f17"},"source":["hdd_pipe = nlu.load(path=\"./models/classifier_dl_trained\")\n","\n","preds = hdd_pipe.predict('Bitcoin dropped by 50 percent!!')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentiment
\n","
sentence_embedding_from_disk
\n","
sentiment_confidence
\n","
document
\n","
text
\n","
origin_index
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[negative, negative]
\n","
[[0.17410096526145935, 0.14491602778434753, 0....
\n","
[0.7221757, 0.7221757]
\n","
Bitcoin dropped by 50 percent!!
\n","
Bitcoin dropped by 50 percent!!
\n","
8589934592
\n","
[Bitcoin dropped by 50 percent!, !]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentiment ... sentence\n","0 [negative, negative] ... [Bitcoin dropped by 50 percent!, !]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e0CVlkk9v6Qi","executionInfo":{"status":"ok","timestamp":1620217194006,"user_tz":-120,"elapsed":2104171,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"206b4d33-57de-4b00-a731-8182c4359e00"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@160b9ba) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@160b9ba\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentiment_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"-CdcbSd7WEpm"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_twitter.ipynb b/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_twitter.ipynb
deleted file mode 100644
index f8dea6c0..00000000
--- a/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_twitter.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_sentiment_classifier_demo_twitter.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"],"history_visible":true},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/binary_text_classification/NLU_training_sentiment_classifier_demo_twitter.ipynb)\n","\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 class twitter classifier training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620214509349,"user_tz":-120,"elapsed":123909,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"faee9bf7-4d8f-4a48-be35-6966ac6ebd02"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 11:33:05-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 11:33:06 (47.6 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 67kB/s \n","\u001b[K |████████████████████████████████| 153kB 44.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 54.6MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download twitter Sentiment dataset \n","https://www.kaggle.com/cosmos98/twitter-and-reddit-sentimental-analysis-dataset\n","#Context\n","\n","This is was a Dataset Created as a part of the university Project On Sentimental Analysis On Multi-Source Social Media Platforms using PySpark."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620214509351,"user_tz":-120,"elapsed":123894,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"7beb4eab-22ec-4f40-d7bf-6460b1fe261d"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/01/Twitter_Data.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 11:35:08-- http://ckl-it.de/wp-content/uploads/2021/01/Twitter_Data.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 99657 (97K) [text/csv]\n","Saving to: ‘Twitter_Data.csv’\n","\n","Twitter_Data.csv 100%[===================>] 97.32K 310KB/s in 0.3s \n","\n","2021-05-05 11:35:08 (310 KB/s) - ‘Twitter_Data.csv’ saved [99657/99657]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620214509356,"user_tz":-120,"elapsed":123892,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"5e36e540-7943-4bd4-85e2-956979503db7"},"source":["import pandas as pd\n","train_path = '/content/Twitter_Data.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","# the label column must have name 'y' name be of type str\n","columns=['text','y']\n","train_df = train_df[columns]\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
0
\n","
how narendra modi has almost killed the indian...
\n","
negative
\n","
\n","
\n","
1
\n","
you think was modi behind that accident
\n","
negative
\n","
\n","
\n","
2
\n","
kamal haasan takes chowkidar modi kamal haasan...
\n","
negative
\n","
\n","
\n","
3
\n","
connected name with surname not bcz religion c...
\n","
negative
\n","
\n","
\n","
4
\n","
anyone better than modi when nehruji expired s...
\n","
positive
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
595
\n","
perception makes fool some call âforeign inv...
\n","
negative
\n","
\n","
\n","
596
\n","
when will see your tweet for justice for you a...
\n","
negative
\n","
\n","
\n","
597
\n","
haha congress going gaga over this after looti...
\n","
positive
\n","
\n","
\n","
598
\n","
this movie shows the life histiry narendra mod...
\n","
negative
\n","
\n","
\n","
599
\n","
modi left his year old wife and returned her r...
\n","
positive
\n","
\n"," \n","
\n","
600 rows × 2 columns
\n","
"],"text/plain":[" text y\n","0 how narendra modi has almost killed the indian... negative\n","1 you think was modi behind that accident negative\n","2 kamal haasan takes chowkidar modi kamal haasan... negative\n","3 connected name with surname not bcz religion c... negative\n","4 anyone better than modi when nehruji expired s... positive\n",".. ... ...\n","595 perception makes fool some call âforeign inv... negative\n","596 when will see your tweet for justice for you a... negative\n","597 haha congress going gaga over this after looti... positive\n","598 this movie shows the life histiry narendra mod... negative\n","599 modi left his year old wife and returned her r... positive\n","\n","[600 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620214645691,"user_tz":-120,"elapsed":260221,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c3d16617-f157-4732-8139-86dc380efcca"},"source":["from sklearn.metrics import classification_report\n","import nlu \n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# by default the Universal Sentence Encoder (USE) Sentence embeddings are used for generation\n","trainable_pipe = nlu.load('train.sentiment')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.54 1.00 0.70 27\n"," positive 0.00 0.00 0.00 23\n","\n"," accuracy 0.54 50\n"," macro avg 0.27 0.50 0.35 50\n","weighted avg 0.29 0.54 0.38 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\\r\\nthe foundation for new india 2022 has alre...
\n","
0.755853
\n","
the foundation for new india 2022 has already ...
\n","
[the foundation for new india 2022 has already...
\n","
\n","
\n","
19
\n","
[0.05615750327706337, -0.002462629694491625, -...
\n","
negative
\n","
negative
\n","
19
\n","
only rahul gandhis politics love can defeat th...
\n","
0.770993
\n","
only rahul gandhis politics love can defeat th...
\n","
[only rahul gandhis politics love can defeat t...
\n","
\n","
\n","
20
\n","
[0.030352214351296425, -0.06195472553372383, 0...
\n","
negative
\n","
negative
\n","
20
\n","
one step time navigating thru looteyns when ev...
\n","
0.758632
\n","
one step time navigating thru looteyns when ev...
\n","
[one step time navigating thru looteyns when e...
\n","
\n","
\n","
21
\n","
[0.07535804808139801, -0.05643236264586449, -0...
\n","
negative
\n","
negative
\n","
21
\n","
why sir mam shabana azami hate much that have ...
\n","
0.778967
\n","
why sir mam shabana azami hate much that have ...
\n","
[why sir mam shabana azami hate much that have...
\n","
\n","
\n","
22
\n","
[0.05986170098185539, -0.0674145296216011, -0....
\n","
negative
\n","
negative
\n","
22
\n","
modi will remain for next 510 years and till t...
\n","
0.782218
\n","
modi will remain for next 510 years and till t...
\n","
[modi will remain for next 510 years and till ...
\n","
\n","
\n","
23
\n","
[0.023959942162036896, -0.01397246215492487, -...
\n","
negative
\n","
positive
\n","
23
\n","
pledge your first vote for modi
\n","
0.753846
\n","
pledge your first vote for modi
\n","
[pledge your first vote for modi]
\n","
\n","
\n","
24
\n","
[0.04451165348291397, -0.06473662704229355, -0...
\n","
negative
\n","
positive
\n","
24
\n","
why need modi lead bjp government again 2019 j...
\n","
0.775316
\n","
why need modi lead bjp government again 2019 j...
\n","
[why need modi lead bjp government again 2019 ...
\n","
\n","
\n","
25
\n","
[0.06561190634965897, -0.0614917054772377, -0....
\n","
negative
\n","
negative
\n","
25
\n","
raghuram rajan sent list high profile bank fra...
\n","
0.791679
\n","
raghuram rajan sent list high profile bank fra...
\n","
[raghuram rajan sent list high profile bank fr...
\n","
\n","
\n","
26
\n","
[0.05217093229293823, -0.05785880237817764, -0...
\n","
negative
\n","
negative
\n","
26
\n","
modi govts slashing indias education budget cl...
\n","
0.764641
\n","
modi govts slashing indias education budget cl...
\n","
[modi govts slashing indias education budget c...
\n","
\n","
\n","
27
\n","
[0.04579754173755646, -0.051767487078905106, -...
\n","
negative
\n","
positive
\n","
27
\n","
why are you hell bent manoj tiwari just her ph...
\n","
0.748229
\n","
why are you hell bent manoj tiwari just her ph...
\n","
[why are you hell bent manoj tiwari just her p...
\n","
\n","
\n","
28
\n","
[0.047987841069698334, -0.050984784960746765, ...
\n","
negative
\n","
negative
\n","
28
\n","
know going into dirty details nehru family its...
\n","
0.768564
\n","
know going into dirty details nehru family its...
\n","
[know going into dirty details nehru family it...
\n","
\n","
\n","
29
\n","
[0.04509664326906204, -0.05019481107592583, -0...
\n","
negative
\n","
negative
\n","
29
\n","
momota begum will let her state become total s...
\n","
0.745878
\n","
momota begum will let her state become total s...
\n","
[momota begum will let her state become total ...
\n","
\n","
\n","
30
\n","
[0.04315190762281418, -0.04578147828578949, -0...
\n","
negative
\n","
positive
\n","
30
\n","
thanks anu sharma will vote and make sure peop...
\n","
0.768158
\n","
thanks anu sharma will vote and make sure peop...
\n","
[thanks anu sharma will vote and make sure peo...
\n","
\n","
\n","
31
\n","
[0.0144237345084548, -0.052222371101379395, -0...
\n","
negative
\n","
positive
\n","
31
\n","
those who themselves dont know how many father...
\n","
0.752480
\n","
those who themselves dont know how many father...
\n","
[those who themselves dont know how many fathe...
\n","
\n","
\n","
32
\n","
[0.02492097206413746, -0.0531931146979332, -0....
\n","
negative
\n","
positive
\n","
32
\n","
the star campaigner myth bjp lost more than as...
\n","
0.785754
\n","
the star campaigner myth bjp lost more than as...
\n","
[the star campaigner myth bjp lost more than a...
\n","
\n","
\n","
33
\n","
[0.040389616042375565, -0.06375984847545624, -...
\n","
negative
\n","
negative
\n","
33
\n","
modi also live for few years only like you not...
\n","
0.763549
\n","
modi also live for few years only like you not...
\n","
[modi also live for few years only like you no...
\n","
\n","
\n","
34
\n","
[0.06742898374795914, -0.060488566756248474, -...
\n","
negative
\n","
positive
\n","
34
\n","
narendra modi more brainy than all the drswamy...
\n","
0.741321
\n","
narendra modi more brainy than all the drswamy...
\n","
[narendra modi more brainy than all the drswam...
\n","
\n","
\n","
35
\n","
[0.06360629200935364, -0.06786973774433136, -0...
\n","
negative
\n","
negative
\n","
35
\n","
have started calling chowkidaar narendra modi ...
\n","
0.771774
\n","
have started calling chowkidaar narendra modi ...
\n","
[have started calling chowkidaar narendra modi...
\n","
\n","
\n","
36
\n","
[0.024233123287558556, -0.05243394151329994, -...
\n","
negative
\n","
positive
\n","
36
\n","
this the difference confident leaders call upo...
\n","
0.758803
\n","
this the difference confident leaders call upo...
\n","
[this the difference confident leaders call up...
\n","
\n","
\n","
37
\n","
[0.039280060678720474, -0.05146652087569237, -...
\n","
negative
\n","
negative
\n","
37
\n","
jawans killed the border\\r\\ncrimes against wom...
\n","
0.774826
\n","
jawans killed the border crimes against women ...
\n","
[jawans killed the border crimes against women...
\n","
\n","
\n","
38
\n","
[0.05051109194755554, -0.0660049319267273, 0.0...
\n","
negative
\n","
negative
\n","
38
\n","
tag this fast growing youtuber cared abt this ...
\n","
0.789172
\n","
tag this fast growing youtuber cared abt this ...
\n","
[tag this fast growing youtuber cared abt this...
\n","
\n","
\n","
39
\n","
[-0.010975896380841732, -0.059168506413698196,...
\n","
negative
\n","
positive
\n","
39
\n","
think hindus should back off and let them suff...
\n","
0.702905
\n","
think hindus should back off and let them suff...
\n","
[think hindus should back off and let them suf...
\n","
\n","
\n","
40
\n","
[0.02310813218355179, -0.027600247412919998, -...
\n","
negative
\n","
positive
\n","
40
\n","
yes cannot make any knee jerk moves drastic ac...
\n","
0.664961
\n","
yes cannot make any knee jerk moves drastic ac...
\n","
[yes cannot make any knee jerk moves drastic a...
\n","
\n","
\n","
41
\n","
[0.043231260031461716, -0.07101075351238251, -...
\n","
negative
\n","
negative
\n","
41
\n","
why picked chairman the devious aadhaar isnt h...
\n","
0.783011
\n","
why picked chairman the devious aadhaar isnt h...
\n","
[why picked chairman the devious aadhaar isnt ...
\n","
\n","
\n","
42
\n","
[0.04160398617386818, -0.06572042405605316, -0...
\n","
negative
\n","
positive
\n","
42
\n","
due automation and artificial intelligence fur...
\n","
0.748431
\n","
due automation and artificial intelligence fur...
\n","
[due automation and artificial intelligence fu...
\n","
\n","
\n","
43
\n","
[-0.00038854932063259184, -0.04599419981241226...
\n","
negative
\n","
positive
\n","
43
\n","
weak state capacity exacerbated excessive acco...
\n","
0.776327
\n","
weak state capacity exacerbated excessive acco...
\n","
[weak state capacity exacerbated excessive acc...
\n","
\n","
\n","
44
\n","
[-0.02063656784594059, -0.07548005133867264, -...
\n","
negative
\n","
positive
\n","
44
\n","
our narendra modi ordered indian air force tak...
\n","
0.734817
\n","
our narendra modi ordered indian air force tak...
\n","
[our narendra modi ordered indian air force ta...
\n","
\n","
\n","
45
\n","
[0.01779576577246189, -0.06789527088403702, -0...
\n","
negative
\n","
negative
\n","
45
\n","
why vote modi dynasty visionary 3no high level...
\n","
0.758230
\n","
why vote modi dynasty visionary 3no high level...
\n","
[why vote modi dynasty visionary 3no high leve...
\n","
\n","
\n","
46
\n","
[0.065566785633564, -0.04119298234581947, -0.0...
\n","
negative
\n","
negative
\n","
46
\n","
its modi chor corrupt maha thugbandhan janta w...
\n","
0.783157
\n","
its modi chor corrupt maha thugbandhan janta w...
\n","
[its modi chor corrupt maha thugbandhan janta ...
\n","
\n","
\n","
47
\n","
[0.03988223522901535, -0.04965453967452049, -0...
\n","
negative
\n","
positive
\n","
47
\n","
before modis arrival 2014 all supported him fo...
\n","
0.767342
\n","
before modis arrival 2014 all supported him fo...
\n","
[before modis arrival 2014 all supported him f...
\n","
\n","
\n","
48
\n","
[0.010842484422028065, 0.01363383699208498, -0...
\n","
negative
\n","
positive
\n","
48
\n","
think you forgot dollar india handled exceptio...
\n","
0.767096
\n","
think you forgot dollar india handled exceptio...
\n","
[think you forgot dollar india handled excepti...
\n","
\n","
\n","
49
\n","
[-0.01967957802116871, 0.05570048466324806, -0...
\n","
negative
\n","
positive
\n","
49
\n","
tulsi gabbard rejected interviews with tyt but...
\n","
0.677169
\n","
tulsi gabbard rejected interviews with tyt but...
\n","
[tulsi gabbard rejected interviews with tyt bu...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_use ... sentence\n","0 [0.060062434524297714, -0.05557167902588844, -... ... [how narendra modi has almost killed the india...\n","1 [0.05362718552350998, -0.004547705873847008, -... ... [you think was modi behind that accident]\n","2 [0.07274721562862396, -0.061593908816576004, -... ... [kamal haasan takes chowkidar modi kamal haasa...\n","3 [0.06106054410338402, -0.060213156044483185, -... ... [connected name with surname not bcz religion ...\n","4 [0.0737471729516983, 0.006071774289011955, -0.... ... [anyone better than modi when nehruji expired ...\n","5 [0.05888386443257332, -0.0646616667509079, -0.... ... [modiji wont tired crying foul main chowkidar ...\n","6 [0.058948416262865067, -0.029682165011763573, ... ... [poor chap modi hasnâ given him anything can...\n","7 [0.051331546157598495, -0.06789953261613846, -... ... [green underwear missing ive been doubting isi...\n","8 [0.044129759073257446, -0.06111813709139824, -... ... [congress years wasnt able complete one rafale...\n","9 [0.03665374591946602, -0.03695330768823624, -0... ... [asked learn from how treat minority well does...\n","10 [0.07035735249519348, -0.06952506303787231, -0... ... [stop bull shitting worry about criminal vivek...\n","11 [0.013958276249468327, -0.030759528279304504, ... ... [drswamys timesnow last year debate nearly mil...\n","12 [0.026277026161551476, -0.06238812580704689, -... ... [asshole bahujan radical marxist grow brain kn...\n","13 [0.07457270473241806, -0.058670494705438614, -... ... [from selling dreams 2014 selling tshirts 2019...\n","14 [0.061704088002443314, -0.04553354158997536, -... ... [very true sir thats why they are against modi...\n","15 [0.053420260548591614, -0.0038897113408893347,... ... [they are giving jobs citizen india what you a...\n","16 [0.027197618037462234, -0.036435648798942566, ... ... [congress has always attempted empower people ...\n","17 [0.06601184606552124, -0.020045213401317596, -... ... [have never said that modi succeed yet even al...\n","18 [0.046943631023168564, -0.06800007820129395, -... ... [the foundation for new india 2022 has already...\n","19 [0.05615750327706337, -0.002462629694491625, -... ... [only rahul gandhis politics love can defeat t...\n","20 [0.030352214351296425, -0.06195472553372383, 0... ... [one step time navigating thru looteyns when e...\n","21 [0.07535804808139801, -0.05643236264586449, -0... ... [why sir mam shabana azami hate much that have...\n","22 [0.05986170098185539, -0.0674145296216011, -0.... ... [modi will remain for next 510 years and till ...\n","23 [0.023959942162036896, -0.01397246215492487, -... ... [pledge your first vote for modi]\n","24 [0.04451165348291397, -0.06473662704229355, -0... ... [why need modi lead bjp government again 2019 ...\n","25 [0.06561190634965897, -0.0614917054772377, -0.... ... [raghuram rajan sent list high profile bank fr...\n","26 [0.05217093229293823, -0.05785880237817764, -0... ... [modi govts slashing indias education budget c...\n","27 [0.04579754173755646, -0.051767487078905106, -... ... [why are you hell bent manoj tiwari just her p...\n","28 [0.047987841069698334, -0.050984784960746765, ... ... [know going into dirty details nehru family it...\n","29 [0.04509664326906204, -0.05019481107592583, -0... ... [momota begum will let her state become total ...\n","30 [0.04315190762281418, -0.04578147828578949, -0... ... [thanks anu sharma will vote and make sure peo...\n","31 [0.0144237345084548, -0.052222371101379395, -0... ... [those who themselves dont know how many fathe...\n","32 [0.02492097206413746, -0.0531931146979332, -0.... ... [the star campaigner myth bjp lost more than a...\n","33 [0.040389616042375565, -0.06375984847545624, -... ... [modi also live for few years only like you no...\n","34 [0.06742898374795914, -0.060488566756248474, -... ... [narendra modi more brainy than all the drswam...\n","35 [0.06360629200935364, -0.06786973774433136, -0... ... [have started calling chowkidaar narendra modi...\n","36 [0.024233123287558556, -0.05243394151329994, -... ... [this the difference confident leaders call up...\n","37 [0.039280060678720474, -0.05146652087569237, -... ... [jawans killed the border crimes against women...\n","38 [0.05051109194755554, -0.0660049319267273, 0.0... ... [tag this fast growing youtuber cared abt this...\n","39 [-0.010975896380841732, -0.059168506413698196,... ... [think hindus should back off and let them suf...\n","40 [0.02310813218355179, -0.027600247412919998, -... ... [yes cannot make any knee jerk moves drastic a...\n","41 [0.043231260031461716, -0.07101075351238251, -... ... [why picked chairman the devious aadhaar isnt ...\n","42 [0.04160398617386818, -0.06572042405605316, -0... ... [due automation and artificial intelligence fu...\n","43 [-0.00038854932063259184, -0.04599419981241226... ... [weak state capacity exacerbated excessive acc...\n","44 [-0.02063656784594059, -0.07548005133867264, -... ... [our narendra modi ordered indian air force ta...\n","45 [0.01779576577246189, -0.06789527088403702, -0... ... [why vote modi dynasty visionary 3no high leve...\n","46 [0.065566785633564, -0.04119298234581947, -0.0... ... [its modi chor corrupt maha thugbandhan janta ...\n","47 [0.03988223522901535, -0.04965453967452049, -0... ... [before modis arrival 2014 all supported him f...\n","48 [0.010842484422028065, 0.01363383699208498, -0... ... [think you forgot dollar india handled excepti...\n","49 [-0.01967957802116871, 0.05570048466324806, -0... ... [tulsi gabbard rejected interviews with tyt bu...\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"lVyOE2wV0fw_"},"source":["# Test the fitted pipe on new example"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":76},"id":"qdCUg2MR0PD2","executionInfo":{"status":"ok","timestamp":1620214646472,"user_tz":-120,"elapsed":260997,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"7fe8bbe8-421a-4231-971e-8527629b7bc8"},"source":["fitted_pipe.predict('the president of india just died')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_use
\n","
trained_sentiment
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
document
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.013345886953175068, -0.021778283640742302, ...
\n","
negative
\n","
0
\n","
0.722135
\n","
the president of india just died
\n","
[the president of india just died]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_use ... sentence\n","0 [0.013345886953175068, -0.021778283640742302, ... ... [the president of india just died]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620214646475,"user_tz":-120,"elapsed":260995,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"47f312f8-22c4-432e-a7e0-e152aedf6585"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['sentiment_dl'] has settable params:\n","pipe['sentiment_dl'].setMaxEpochs(1) | Info: Maximum number of epochs to train | Currently set to : 1\n","pipe['sentiment_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['sentiment_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['sentiment_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['sentiment_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n","pipe['sentiment_dl'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3e286d29) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3e286d29\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620214651490,"user_tz":-120,"elapsed":266005,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"d2862ef6-efc5-46b8-cf34-077b6ee3f4b5"},"source":["# Train longer!\n","trainable_pipe['sentiment_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.79 0.96 0.87 27\n"," neutral 0.00 0.00 0.00 0\n"," positive 1.00 0.09 0.16 23\n","\n"," accuracy 0.56 50\n"," macro avg 0.60 0.35 0.34 50\n","weighted avg 0.89 0.56 0.54 50\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\\r\\nthe foundation for new india 2022 has alre...
\n","
0.547697
\n","
the foundation for new india 2022 has already ...
\n","
[the foundation for new india 2022 has already...
\n","
\n","
\n","
19
\n","
[0.05615750327706337, -0.002462629694491625, -...
\n","
negative
\n","
negative
\n","
19
\n","
only rahul gandhis politics love can defeat th...
\n","
0.632572
\n","
only rahul gandhis politics love can defeat th...
\n","
[only rahul gandhis politics love can defeat t...
\n","
\n","
\n","
20
\n","
[0.030352214351296425, -0.06195472553372383, 0...
\n","
negative
\n","
negative
\n","
20
\n","
one step time navigating thru looteyns when ev...
\n","
0.635106
\n","
one step time navigating thru looteyns when ev...
\n","
[one step time navigating thru looteyns when e...
\n","
\n","
\n","
21
\n","
[0.07535804808139801, -0.05643236264586449, -0...
\n","
negative
\n","
negative
\n","
21
\n","
why sir mam shabana azami hate much that have ...
\n","
0.738669
\n","
why sir mam shabana azami hate much that have ...
\n","
[why sir mam shabana azami hate much that have...
\n","
\n","
\n","
22
\n","
[0.05986170098185539, -0.0674145296216011, -0....
\n","
negative
\n","
negative
\n","
22
\n","
modi will remain for next 510 years and till t...
\n","
0.659078
\n","
modi will remain for next 510 years and till t...
\n","
[modi will remain for next 510 years and till ...
\n","
\n","
\n","
23
\n","
[0.023959942162036896, -0.01397246215492487, -...
\n","
neutral
\n","
positive
\n","
23
\n","
pledge your first vote for modi
\n","
0.555447
\n","
pledge your first vote for modi
\n","
[pledge your first vote for modi]
\n","
\n","
\n","
24
\n","
[0.04451165348291397, -0.06473662704229355, -0...
\n","
neutral
\n","
positive
\n","
24
\n","
why need modi lead bjp government again 2019 j...
\n","
0.578395
\n","
why need modi lead bjp government again 2019 j...
\n","
[why need modi lead bjp government again 2019 ...
\n","
\n","
\n","
25
\n","
[0.06561190634965897, -0.0614917054772377, -0....
\n","
negative
\n","
negative
\n","
25
\n","
raghuram rajan sent list high profile bank fra...
\n","
0.706507
\n","
raghuram rajan sent list high profile bank fra...
\n","
[raghuram rajan sent list high profile bank fr...
\n","
\n","
\n","
26
\n","
[0.05217093229293823, -0.05785880237817764, -0...
\n","
negative
\n","
negative
\n","
26
\n","
modi govts slashing indias education budget cl...
\n","
0.607360
\n","
modi govts slashing indias education budget cl...
\n","
[modi govts slashing indias education budget c...
\n","
\n","
\n","
27
\n","
[0.04579754173755646, -0.051767487078905106, -...
\n","
neutral
\n","
positive
\n","
27
\n","
why are you hell bent manoj tiwari just her ph...
\n","
0.588993
\n","
why are you hell bent manoj tiwari just her ph...
\n","
[why are you hell bent manoj tiwari just her p...
\n","
\n","
\n","
28
\n","
[0.047987841069698334, -0.050984784960746765, ...
\n","
negative
\n","
negative
\n","
28
\n","
know going into dirty details nehru family its...
\n","
0.753084
\n","
know going into dirty details nehru family its...
\n","
[know going into dirty details nehru family it...
\n","
\n","
\n","
29
\n","
[0.04509664326906204, -0.05019481107592583, -0...
\n","
negative
\n","
negative
\n","
29
\n","
momota begum will let her state become total s...
\n","
0.615988
\n","
momota begum will let her state become total s...
\n","
[momota begum will let her state become total ...
\n","
\n","
\n","
30
\n","
[0.04315190762281418, -0.04578147828578949, -0...
\n","
neutral
\n","
positive
\n","
30
\n","
thanks anu sharma will vote and make sure peop...
\n","
0.555271
\n","
thanks anu sharma will vote and make sure peop...
\n","
[thanks anu sharma will vote and make sure peo...
\n","
\n","
\n","
31
\n","
[0.0144237345084548, -0.052222371101379395, -0...
\n","
negative
\n","
positive
\n","
31
\n","
those who themselves dont know how many father...
\n","
0.631877
\n","
those who themselves dont know how many father...
\n","
[those who themselves dont know how many fathe...
\n","
\n","
\n","
32
\n","
[0.02492097206413746, -0.0531931146979332, -0....
\n","
neutral
\n","
positive
\n","
32
\n","
the star campaigner myth bjp lost more than as...
\n","
0.586682
\n","
the star campaigner myth bjp lost more than as...
\n","
[the star campaigner myth bjp lost more than a...
\n","
\n","
\n","
33
\n","
[0.040389616042375565, -0.06375984847545624, -...
\n","
neutral
\n","
negative
\n","
33
\n","
modi also live for few years only like you not...
\n","
0.587196
\n","
modi also live for few years only like you not...
\n","
[modi also live for few years only like you no...
\n","
\n","
\n","
34
\n","
[0.06742898374795914, -0.060488566756248474, -...
\n","
neutral
\n","
positive
\n","
34
\n","
narendra modi more brainy than all the drswamy...
\n","
0.533663
\n","
narendra modi more brainy than all the drswamy...
\n","
[narendra modi more brainy than all the drswam...
\n","
\n","
\n","
35
\n","
[0.06360629200935364, -0.06786973774433136, -0...
\n","
negative
\n","
negative
\n","
35
\n","
have started calling chowkidaar narendra modi ...
\n","
0.672972
\n","
have started calling chowkidaar narendra modi ...
\n","
[have started calling chowkidaar narendra modi...
\n","
\n","
\n","
36
\n","
[0.024233123287558556, -0.05243394151329994, -...
\n","
neutral
\n","
positive
\n","
36
\n","
this the difference confident leaders call upo...
\n","
0.510922
\n","
this the difference confident leaders call upo...
\n","
[this the difference confident leaders call up...
\n","
\n","
\n","
37
\n","
[0.039280060678720474, -0.05146652087569237, -...
\n","
negative
\n","
negative
\n","
37
\n","
jawans killed the border\\r\\ncrimes against wom...
\n","
0.701794
\n","
jawans killed the border crimes against women ...
\n","
[jawans killed the border crimes against women...
\n","
\n","
\n","
38
\n","
[0.05051109194755554, -0.0660049319267273, 0.0...
\n","
negative
\n","
negative
\n","
38
\n","
tag this fast growing youtuber cared abt this ...
\n","
0.714883
\n","
tag this fast growing youtuber cared abt this ...
\n","
[tag this fast growing youtuber cared abt this...
\n","
\n","
\n","
39
\n","
[-0.010975896380841732, -0.059168506413698196,...
\n","
neutral
\n","
positive
\n","
39
\n","
think hindus should back off and let them suff...
\n","
0.553189
\n","
think hindus should back off and let them suff...
\n","
[think hindus should back off and let them suf...
\n","
\n","
\n","
40
\n","
[0.02310813218355179, -0.027600247412919998, -...
\n","
positive
\n","
positive
\n","
40
\n","
yes cannot make any knee jerk moves drastic ac...
\n","
0.671809
\n","
yes cannot make any knee jerk moves drastic ac...
\n","
[yes cannot make any knee jerk moves drastic a...
\n","
\n","
\n","
41
\n","
[0.043231260031461716, -0.07101075351238251, -...
\n","
negative
\n","
negative
\n","
41
\n","
why picked chairman the devious aadhaar isnt h...
\n","
0.709371
\n","
why picked chairman the devious aadhaar isnt h...
\n","
[why picked chairman the devious aadhaar isnt ...
\n","
\n","
\n","
42
\n","
[0.04160398617386818, -0.06572042405605316, -0...
\n","
neutral
\n","
positive
\n","
42
\n","
due automation and artificial intelligence fur...
\n","
0.553482
\n","
due automation and artificial intelligence fur...
\n","
[due automation and artificial intelligence fu...
\n","
\n","
\n","
43
\n","
[-0.00038854932063259184, -0.04599419981241226...
\n","
negative
\n","
positive
\n","
43
\n","
weak state capacity exacerbated excessive acco...
\n","
0.609747
\n","
weak state capacity exacerbated excessive acco...
\n","
[weak state capacity exacerbated excessive acc...
\n","
\n","
\n","
44
\n","
[-0.02063656784594059, -0.07548005133867264, -...
\n","
neutral
\n","
positive
\n","
44
\n","
our narendra modi ordered indian air force tak...
\n","
0.513191
\n","
our narendra modi ordered indian air force tak...
\n","
[our narendra modi ordered indian air force ta...
\n","
\n","
\n","
45
\n","
[0.01779576577246189, -0.06789527088403702, -0...
\n","
negative
\n","
negative
\n","
45
\n","
why vote modi dynasty visionary 3no high level...
\n","
0.635148
\n","
why vote modi dynasty visionary 3no high level...
\n","
[why vote modi dynasty visionary 3no high leve...
\n","
\n","
\n","
46
\n","
[0.065566785633564, -0.04119298234581947, -0.0...
\n","
negative
\n","
negative
\n","
46
\n","
its modi chor corrupt maha thugbandhan janta w...
\n","
0.687171
\n","
its modi chor corrupt maha thugbandhan janta w...
\n","
[its modi chor corrupt maha thugbandhan janta ...
\n","
\n","
\n","
47
\n","
[0.03988223522901535, -0.04965453967452049, -0...
\n","
neutral
\n","
positive
\n","
47
\n","
before modis arrival 2014 all supported him fo...
\n","
0.557571
\n","
before modis arrival 2014 all supported him fo...
\n","
[before modis arrival 2014 all supported him f...
\n","
\n","
\n","
48
\n","
[0.010842484422028065, 0.01363383699208498, -0...
\n","
negative
\n","
positive
\n","
48
\n","
think you forgot dollar india handled exceptio...
\n","
0.615532
\n","
think you forgot dollar india handled exceptio...
\n","
[think you forgot dollar india handled excepti...
\n","
\n","
\n","
49
\n","
[-0.01967957802116871, 0.05570048466324806, -0...
\n","
positive
\n","
positive
\n","
49
\n","
tulsi gabbard rejected interviews with tyt but...
\n","
0.604604
\n","
tulsi gabbard rejected interviews with tyt but...
\n","
[tulsi gabbard rejected interviews with tyt bu...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_use ... sentence\n","0 [0.060062434524297714, -0.05557167902588844, -... ... [how narendra modi has almost killed the india...\n","1 [0.05362718552350998, -0.004547705873847008, -... ... [you think was modi behind that accident]\n","2 [0.07274721562862396, -0.061593908816576004, -... ... [kamal haasan takes chowkidar modi kamal haasa...\n","3 [0.06106054410338402, -0.060213156044483185, -... ... [connected name with surname not bcz religion ...\n","4 [0.0737471729516983, 0.006071774289011955, -0.... ... [anyone better than modi when nehruji expired ...\n","5 [0.05888386443257332, -0.0646616667509079, -0.... ... [modiji wont tired crying foul main chowkidar ...\n","6 [0.058948416262865067, -0.029682165011763573, ... ... [poor chap modi hasnâ given him anything can...\n","7 [0.051331546157598495, -0.06789953261613846, -... ... [green underwear missing ive been doubting isi...\n","8 [0.044129759073257446, -0.06111813709139824, -... ... [congress years wasnt able complete one rafale...\n","9 [0.03665374591946602, -0.03695330768823624, -0... ... [asked learn from how treat minority well does...\n","10 [0.07035735249519348, -0.06952506303787231, -0... ... [stop bull shitting worry about criminal vivek...\n","11 [0.013958276249468327, -0.030759528279304504, ... ... [drswamys timesnow last year debate nearly mil...\n","12 [0.026277026161551476, -0.06238812580704689, -... ... [asshole bahujan radical marxist grow brain kn...\n","13 [0.07457270473241806, -0.058670494705438614, -... ... [from selling dreams 2014 selling tshirts 2019...\n","14 [0.061704088002443314, -0.04553354158997536, -... ... [very true sir thats why they are against modi...\n","15 [0.053420260548591614, -0.0038897113408893347,... ... [they are giving jobs citizen india what you a...\n","16 [0.027197618037462234, -0.036435648798942566, ... ... [congress has always attempted empower people ...\n","17 [0.06601184606552124, -0.020045213401317596, -... ... [have never said that modi succeed yet even al...\n","18 [0.046943631023168564, -0.06800007820129395, -... ... [the foundation for new india 2022 has already...\n","19 [0.05615750327706337, -0.002462629694491625, -... ... [only rahul gandhis politics love can defeat t...\n","20 [0.030352214351296425, -0.06195472553372383, 0... ... [one step time navigating thru looteyns when e...\n","21 [0.07535804808139801, -0.05643236264586449, -0... ... [why sir mam shabana azami hate much that have...\n","22 [0.05986170098185539, -0.0674145296216011, -0.... ... [modi will remain for next 510 years and till ...\n","23 [0.023959942162036896, -0.01397246215492487, -... ... [pledge your first vote for modi]\n","24 [0.04451165348291397, -0.06473662704229355, -0... ... [why need modi lead bjp government again 2019 ...\n","25 [0.06561190634965897, -0.0614917054772377, -0.... ... [raghuram rajan sent list high profile bank fr...\n","26 [0.05217093229293823, -0.05785880237817764, -0... ... [modi govts slashing indias education budget c...\n","27 [0.04579754173755646, -0.051767487078905106, -... ... [why are you hell bent manoj tiwari just her p...\n","28 [0.047987841069698334, -0.050984784960746765, ... ... [know going into dirty details nehru family it...\n","29 [0.04509664326906204, -0.05019481107592583, -0... ... [momota begum will let her state become total ...\n","30 [0.04315190762281418, -0.04578147828578949, -0... ... [thanks anu sharma will vote and make sure peo...\n","31 [0.0144237345084548, -0.052222371101379395, -0... ... [those who themselves dont know how many fathe...\n","32 [0.02492097206413746, -0.0531931146979332, -0.... ... [the star campaigner myth bjp lost more than a...\n","33 [0.040389616042375565, -0.06375984847545624, -... ... [modi also live for few years only like you no...\n","34 [0.06742898374795914, -0.060488566756248474, -... ... [narendra modi more brainy than all the drswam...\n","35 [0.06360629200935364, -0.06786973774433136, -0... ... [have started calling chowkidaar narendra modi...\n","36 [0.024233123287558556, -0.05243394151329994, -... ... [this the difference confident leaders call up...\n","37 [0.039280060678720474, -0.05146652087569237, -... ... [jawans killed the border crimes against women...\n","38 [0.05051109194755554, -0.0660049319267273, 0.0... ... [tag this fast growing youtuber cared abt this...\n","39 [-0.010975896380841732, -0.059168506413698196,... ... [think hindus should back off and let them suf...\n","40 [0.02310813218355179, -0.027600247412919998, -... ... [yes cannot make any knee jerk moves drastic a...\n","41 [0.043231260031461716, -0.07101075351238251, -... ... [why picked chairman the devious aadhaar isnt ...\n","42 [0.04160398617386818, -0.06572042405605316, -0... ... [due automation and artificial intelligence fu...\n","43 [-0.00038854932063259184, -0.04599419981241226... ... [weak state capacity exacerbated excessive acc...\n","44 [-0.02063656784594059, -0.07548005133867264, -... ... [our narendra modi ordered indian air force ta...\n","45 [0.01779576577246189, -0.06789527088403702, -0... ... [why vote modi dynasty visionary 3no high leve...\n","46 [0.065566785633564, -0.04119298234581947, -0.0... ... [its modi chor corrupt maha thugbandhan janta ...\n","47 [0.03988223522901535, -0.04965453967452049, -0... ... [before modis arrival 2014 all supported him f...\n","48 [0.010842484422028065, 0.01363383699208498, -0... ... [think you forgot dollar india handled excepti...\n","49 [-0.01967957802116871, 0.05570048466324806, -0... ... [tulsi gabbard rejected interviews with tyt bu...\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["# Try training with different Embeddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxWFzQOhjWC8","executionInfo":{"status":"ok","timestamp":1620214651491,"user_tz":-120,"elapsed":266002,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"36b0db66-250d-499a-acce-976bd9e188e9"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IKK_Ii_gjJfF","executionInfo":{"status":"ok","timestamp":1620215021948,"user_tz":-120,"elapsed":636454,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"8669ed6f-b02a-4b6a-b1f4-9dc850d2d384"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(100) \n","trainable_pipe['sentiment_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.78 0.67 0.72 300\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.86 0.56 0.67 300\n","\n"," accuracy 0.61 600\n"," macro avg 0.54 0.41 0.46 600\n","weighted avg 0.82 0.61 0.70 600\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620215202403,"user_tz":-120,"elapsed":816905,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"3b42a600-3686-4fe9-a430-93647db780af"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":76},"executionInfo":{"status":"ok","timestamp":1620215216214,"user_tz":-120,"elapsed":830714,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"fce06315-d677-491f-9a59-c276b6d23b62"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('the president of india just died')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentiment
\n","
sentence_embedding_from_disk
\n","
origin_index
\n","
text
\n","
sentiment_confidence
\n","
document
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[positive]
\n","
[[0.009460609406232834, -0.07943306118249893, ...
\n","
8589934592
\n","
the president of india just died
\n","
[0.8165338]
\n","
the president of india just died
\n","
[the president of india just died]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentiment ... sentence\n","0 [positive] ... [the president of india just died]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e0CVlkk9v6Qi","executionInfo":{"status":"ok","timestamp":1620215216215,"user_tz":-120,"elapsed":830711,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"d0978b3b-d8fd-4c14-e00b-7e5e34450ea7"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3c74a4cb) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3c74a4cb\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentiment_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo.ipynb b/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo.ipynb
deleted file mode 100644
index 926303c8..00000000
--- a/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_multi_class_text_classifier_demo.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo.ipynb)\n","\n","\n","\n","# Training a Deep Learning Classifier with NLU \n","## ClassifierDL (Multi-class Text Classification)\n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl-multi-class-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n"," \n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n","\n"," \n","\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188250696,"user_tz":-300,"elapsed":116328,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a6118c32-a060-48c5-c88d-a3570a26eaa3"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:15:34-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.108.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 04:15:35 (2.03 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 77kB/s \n","\u001b[K |████████████████████████████████| 153kB 57.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 23.0MB/s \n","\u001b[K |████████████████████████████████| 204kB 63.1MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download news classification dataset"]},{"cell_type":"code","metadata":{"id":"OrVb5ZMvvrQD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188253237,"user_tz":-300,"elapsed":118859,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"bd001162-846a-4a25-eb66-8509ad8d21f8"},"source":["! wget https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/classifier-dl/news_Category/news_category_train.csv\n","! wget https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/classifier-dl/news_Category/news_category_test.csv"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:17:30-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/classifier-dl/news_Category/news_category_train.csv\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.81.46\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.81.46|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 24032125 (23M) [text/csv]\n","Saving to: ‘news_category_train.csv’\n","\n","news_category_train 100%[===================>] 22.92M 17.6MB/s in 1.3s \n","\n","2021-05-05 04:17:31 (17.6 MB/s) - ‘news_category_train.csv’ saved [24032125/24032125]\n","\n","--2021-05-05 04:17:31-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/classifier-dl/news_Category/news_category_test.csv\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.26.150\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.26.150|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1504408 (1.4M) [text/csv]\n","Saving to: ‘news_category_test.csv’\n","\n","news_category_test. 100%[===================>] 1.43M 2.78MB/s in 0.5s \n","\n","2021-05-05 04:17:32 (2.78 MB/s) - ‘news_category_test.csv’ saved [1504408/1504408]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"y4xSRWIhwT28","colab":{"base_uri":"https://localhost:8080/","height":411},"executionInfo":{"status":"ok","timestamp":1620188254598,"user_tz":-300,"elapsed":120212,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c1fab4a6-6d04-4ec2-c333-028560260aa3"},"source":["import pandas as pd\n","test_path = '/content/news_category_test.csv'\n","train_df = pd.read_csv(test_path)\n","train_df.columns=['y','text']\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
\n"," \n"," \n","
\n","
2233
\n","
Business
\n","
Steven Woghin, the former general counsel of C...
\n","
\n","
\n","
5970
\n","
Sports
\n","
The US mens national team will look to extend ...
\n","
\n","
\n","
3706
\n","
Sci/Tech
\n","
The Federal Trade Commission formally announce...
\n","
\n","
\n","
6985
\n","
World
\n","
TOKYO (AP) Japan's economy barely grew duri...
\n","
\n","
\n","
750
\n","
World
\n","
Nervous Republicans are urging President Bush...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1537
\n","
World
\n","
The United States piled pressure on Sudan Wed...
\n","
\n","
\n","
2196
\n","
Sci/Tech
\n","
Ask Jeeves Search Engine Gets Slim and Persona...
\n","
\n","
\n","
553
\n","
Sports
\n","
Slumping Cleveland lost a three-run lead while...
\n","
\n","
\n","
3406
\n","
Business
\n","
Advanced Micro Devices Inc. reported a third ...
\n","
\n","
\n","
3888
\n","
Sci/Tech
\n","
A great white shark that was tagged with a da...
\n","
\n"," \n","
\n","
6080 rows × 2 columns
\n","
"],"text/plain":[" y text\n","2233 Business Steven Woghin, the former general counsel of C...\n","5970 Sports The US mens national team will look to extend ...\n","3706 Sci/Tech The Federal Trade Commission formally announce...\n","6985 World TOKYO (AP) Japan's economy barely grew duri...\n","750 World Nervous Republicans are urging President Bush...\n","... ... ...\n","1537 World The United States piled pressure on Sudan Wed...\n","2196 Sci/Tech Ask Jeeves Search Engine Gets Slim and Persona...\n","553 Sports Slumping Cleveland lost a three-run lead while...\n","3406 Business Advanced Micro Devices Inc. reported a third ...\n","3888 Sci/Tech A great white shark that was tagged with a da...\n","\n","[6080 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.classifier')\n","\n","By default, the Universal Sentence Encoder Embeddings (USE) are beeing downloaded to provide embeddings for the classifier. You can use any of the 50+ other sentence Emeddings in NLU tough!\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"id":"3ZIPkRkWftBG","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1620188442070,"user_tz":-300,"elapsed":307673,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"27f352ae-c42c-4685-ddab-34c897ad3a9b"},"source":["# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# Since there are no\n","fitted_pipe = nlu.load('train.classifier').fit(train_df)\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level = 'document')\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
text
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
sentence
\n","
y
\n","
trained_classifier
\n","
sentence_embedding_use
\n","
\n"," \n"," \n","
\n","
0
\n","
Steven Woghin, the former general counsel of C...
\n","
Steven Woghin, the former general counsel of C...
\n","
0.997040
\n","
2233
\n","
[Steven Woghin, the former general counsel of ...
\n","
Business
\n","
Business
\n","
[0.035635482519865036, -0.048168957233428955, ...
\n","
\n","
\n","
1
\n","
The US mens national team will look to extend ...
\n","
The US mens national team will look to extend ...
\n","
1.000000
\n","
5970
\n","
[The US mens national team will look to extend...
\n","
Sports
\n","
Sports
\n","
[0.0012551577528938651, -0.04456636682152748, ...
\n","
\n","
\n","
2
\n","
The Federal Trade Commission formally announce...
\n","
The Federal Trade Commission formally announce...
\n","
1.000000
\n","
3706
\n","
[The Federal Trade Commission formally announc...
\n","
Sci/Tech
\n","
Sci/Tech
\n","
[0.010981663130223751, -0.059497587382793427, ...
\n","
\n","
\n","
3
\n","
TOKYO (AP) Japan's economy barely grew during ...
\n","
TOKYO (AP) Japan's economy barely grew duri...
\n","
0.999996
\n","
6985
\n","
[TOKYO (AP) Japan's economy barely grew during...
\n","
World
\n","
Business
\n","
[0.0031178640201687813, -0.0026406561955809593...
\n","
\n","
\n","
4
\n","
Nervous Republicans are urging President Bush ...
\n","
Nervous Republicans are urging President Bush...
\n","
0.999992
\n","
750
\n","
[Nervous Republicans are urging President Bush...
\n","
World
\n","
World
\n","
[-0.018046000972390175, -0.010878069326281548,...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
6075
\n","
The United States piled pressure on Sudan Wedn...
\n","
The United States piled pressure on Sudan Wed...
\n","
0.999965
\n","
1537
\n","
[The United States piled pressure on Sudan Wed...
\n","
World
\n","
World
\n","
[-0.006876362022012472, 0.012149822898209095, ...
\n","
\n","
\n","
6076
\n","
Ask Jeeves Search Engine Gets Slim and Persona...
\n","
Ask Jeeves Search Engine Gets Slim and Persona...
\n","
1.000000
\n","
2196
\n","
[Ask Jeeves Search Engine Gets Slim and Person...
\n","
Sci/Tech
\n","
Sci/Tech
\n","
[-0.0036870003677904606, -0.04579205438494682,...
\n","
\n","
\n","
6077
\n","
Slumping Cleveland lost a three-run lead while...
\n","
Slumping Cleveland lost a three-run lead while...
\n","
1.000000
\n","
553
\n","
[Slumping Cleveland lost a three-run lead whil...
\n","
Sports
\n","
Sports
\n","
[0.03003542684018612, 0.016059285029768944, -0...
\n","
\n","
\n","
6078
\n","
Advanced Micro Devices Inc. reported a third q...
\n","
Advanced Micro Devices Inc. reported a third ...
\n","
0.980116
\n","
3406
\n","
[Advanced Micro Devices Inc. reported a third ...
\n","
Business
\n","
Business
\n","
[0.051615066826343536, -0.005852526053786278, ...
\n","
\n","
\n","
6079
\n","
A great white shark that was tagged with a dat...
\n","
A great white shark that was tagged with a da...
\n","
0.999983
\n","
3888
\n","
[A great white shark that was tagged with a da...
\n","
Sci/Tech
\n","
Sci/Tech
\n","
[-0.01544636394828558, -0.04387373477220535, -...
\n","
\n"," \n","
\n","
6080 rows × 8 columns
\n","
"],"text/plain":[" document ... sentence_embedding_use\n","0 Steven Woghin, the former general counsel of C... ... [0.035635482519865036, -0.048168957233428955, ...\n","1 The US mens national team will look to extend ... ... [0.0012551577528938651, -0.04456636682152748, ...\n","2 The Federal Trade Commission formally announce... ... [0.010981663130223751, -0.059497587382793427, ...\n","3 TOKYO (AP) Japan's economy barely grew during ... ... [0.0031178640201687813, -0.0026406561955809593...\n","4 Nervous Republicans are urging President Bush ... ... [-0.018046000972390175, -0.010878069326281548,...\n","... ... ... ...\n","6075 The United States piled pressure on Sudan Wedn... ... [-0.006876362022012472, 0.012149822898209095, ...\n","6076 Ask Jeeves Search Engine Gets Slim and Persona... ... [-0.0036870003677904606, -0.04579205438494682,...\n","6077 Slumping Cleveland lost a three-run lead while... ... [0.03003542684018612, 0.016059285029768944, -0...\n","6078 Advanced Micro Devices Inc. reported a third q... ... [0.051615066826343536, -0.005852526053786278, ...\n","6079 A great white shark that was tagged with a dat... ... [-0.01544636394828558, -0.04387373477220535, -...\n","\n","[6080 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"DL_5aY9b3jSd"},"source":["# 4. Evaluate the model"]},{"cell_type":"code","metadata":{"id":"djtoZVKBw2WU","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188442071,"user_tz":-300,"elapsed":307663,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"4dac293f-5bc3-41cf-dc55-c80fa608d3ff"},"source":["from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," Business 0.85 0.80 0.82 1488\n"," Sci/Tech 0.82 0.88 0.85 1540\n"," Sports 0.95 0.97 0.96 1504\n"," World 0.90 0.87 0.89 1548\n","\n"," accuracy 0.88 6080\n"," macro avg 0.88 0.88 0.88 6080\n","weighted avg 0.88 0.88 0.88 6080\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"mhFKVN93o1ZO"},"source":["# 5. Lets try different Sentence Emebddings"]},{"cell_type":"code","metadata":{"id":"CzJd8omao0gt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620188442073,"user_tz":-300,"elapsed":307659,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"6596a98c-94aa-486f-e7ec-df7dd3dfff1c"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ABHLgirmG1n9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191283243,"user_tz":-300,"elapsed":3148824,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"50149867-b85c-4f1a-ff9d-d0af0077524d"},"source":["# Load pipe with bert embeds\n","# using large embeddings can take a few hours..\n","# fitted_pipe = nlu.load('en.embed_sentence.bert_large_uncased train.classifier').fit(train_df)\n","fitted_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.classifier').fit(train_df)\n","\n","\n","# predict with the trained pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","print(classification_report(preds['y'], preds['trained_classifier']))\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," Business 0.84 0.86 0.85 1488\n"," Sci/Tech 0.87 0.85 0.86 1540\n"," Sports 0.94 0.98 0.96 1504\n"," World 0.92 0.88 0.90 1548\n","\n"," accuracy 0.89 6080\n"," macro avg 0.89 0.89 0.89 6080\n","weighted avg 0.89 0.89 0.89 6080\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 5 evaluate on Test Data"]},{"cell_type":"code","metadata":{"id":"Fxx4yNkNVGFl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191537907,"user_tz":-300,"elapsed":236219,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"645bfa57-4451-4878-dd85-b2f774a78092"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," Business 0.86 0.86 0.86 412\n"," Sci/Tech 0.87 0.86 0.87 360\n"," Sports 0.95 0.97 0.96 396\n"," World 0.90 0.89 0.90 352\n","\n"," accuracy 0.90 1520\n"," macro avg 0.90 0.90 0.90 1520\n","weighted avg 0.90 0.90 0.90 1520\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 6. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191735880,"user_tz":-300,"elapsed":434174,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"bf2fab86-94d6-49a1-f2ad-783bc56ca450"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 7. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":95},"executionInfo":{"status":"ok","timestamp":1620191750307,"user_tz":-300,"elapsed":448594,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"86286c2a-4eb4-46e0-ddf4-7088b1c2375f"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Tesla plans to invest 10M into the ML sector')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
from_disk
\n","
from_disk_confidence_confidence
\n","
text
\n","
origin_index
\n","
sentence_embedding_from_disk
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Tesla plans to invest 10M into the ML sector
\n","
[Business]
\n","
[0.6325806]
\n","
Tesla plans to invest 10M into the ML sector
\n","
8589934592
\n","
[[0.15737193822860718, 0.2598555386066437, 0.8...
\n","
[Tesla plans to invest 10M into the ML sector]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Tesla plans to invest 10M into the ML sector ... [Tesla plans to invest 10M into the ML sector]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191750314,"user_tz":-300,"elapsed":448025,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b5f147f2-0da0-4d4d-d53e-8d10a9be5c7e"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@6934a3e6) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@6934a3e6\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['classifier_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['classifier_dl@sent_small_bert_L12_768'].setClasses(['World', 'Sci/Tech', 'Sports', 'Business']) | Info: get the tags used to trained this ClassifierDLModel | Currently set to : ['World', 'Sci/Tech', 'Sports', 'Business']\n","pipe['classifier_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_amazon.ipynb b/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_amazon.ipynb
deleted file mode 100644
index 545db144..00000000
--- a/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_amazon.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_multi_class_text_classifier_demo_amazon.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_amazon.ipynb)\n","\n","\n","\n","# Training a Deep Learning Classifier with NLU \n","## ClassifierDL (Multi-class Text Classification)\n","## 3 class Amazon Phone review classifier training]\n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl-multi-class-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n","\n","\n"," \n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n","\n"," \n","\n","\n",""]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hFGnBCHavltY","executionInfo":{"status":"ok","timestamp":1620192280562,"user_tz":-300,"elapsed":32996,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"05a6bebd-406d-467d-c575-5c1d63ae192b"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:24:08-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 05:24:08 (1.82 MB/s) - written to stdout [1671/1671]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Amazon Unlocked mobile phones dataset \n","https://www.kaggle.com/PromptCloudHQ/amazon-reviews-unlocked-mobile-phones\n","\n","dataset with unlocked mobile phone reviews in 5 review classes\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620192281175,"user_tz":-300,"elapsed":31025,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"7d56a78e-05a5-45c8-8f5a-765834be24d2"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/01/Amazon_Unlocked_Mobile.csv"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:24:40-- http://ckl-it.de/wp-content/uploads/2021/01/Amazon_Unlocked_Mobile.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 452621 (442K) [text/csv]\n","Saving to: ‘Amazon_Unlocked_Mobile.csv.1’\n","\n","Amazon_Unlocked_Mob 100%[===================>] 442.01K 826KB/s in 0.5s \n","\n","2021-05-05 05:24:40 (826 KB/s) - ‘Amazon_Unlocked_Mobile.csv.1’ saved [452621/452621]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":411},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620192282089,"user_tz":-300,"elapsed":31580,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"612779a7-dcdc-420f-dda5-08842706f782"},"source":["import pandas as pd\n","test_path = '/content/Amazon_Unlocked_Mobile.csv'\n","train_df = pd.read_csv(test_path,sep=\",\")\n","cols = [\"y\",\"text\"]\n","train_df = train_df[cols]\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df\n","\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
\n"," \n"," \n","
\n","
42
\n","
poor
\n","
The product delivery was fast. I received it 4...
\n","
\n","
\n","
62
\n","
good
\n","
My wife loves it, works great and easy to use.
\n","
\n","
\n","
411
\n","
poor
\n","
loved the phone. I've had it a month and last ...
\n","
\n","
\n","
197
\n","
average
\n","
It has missing part: Sim holder. I dont like t...
\n","
\n","
\n","
645
\n","
average
\n","
Decent phone for the money, but if you are use...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1147
\n","
poor
\n","
the phone it is terrible ,got some pornovirus,...
\n","
\n","
\n","
277
\n","
average
\n","
Phone worked great! Only issue was that it wou...
\n","
\n","
\n","
891
\n","
poor
\n","
The phone worked very good the first month, th...
\n","
\n","
\n","
675
\n","
average
\n","
I never used the phone. I ordered it and did n...
\n","
\n","
\n","
930
\n","
good
\n","
Excellent phone functions recommend
\n","
\n"," \n","
\n","
1200 rows × 2 columns
\n","
"],"text/plain":[" y text\n","42 poor The product delivery was fast. I received it 4...\n","62 good My wife loves it, works great and easy to use.\n","411 poor loved the phone. I've had it a month and last ...\n","197 average It has missing part: Sim holder. I dont like t...\n","645 average Decent phone for the money, but if you are use...\n","... ... ...\n","1147 poor the phone it is terrible ,got some pornovirus,...\n","277 average Phone worked great! Only issue was that it wou...\n","891 poor The phone worked very good the first month, th...\n","675 average I never used the phone. I ordered it and did n...\n","930 good Excellent phone functions recommend\n","\n","[1200 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.classifier')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"elapsed":253326,"status":"ok","timestamp":1620190665634,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"49150117-7964-4577-bc0b-3aa9de2e8321"},"source":["# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# Since there are no\n","\n","trainable_pipe = nlu.load('train.classifier')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50] )\n","\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50] ,output_level='document')\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
"],"text/plain":[" origin_index ... trained_classifier_confidence_confidence\n","0 0 ... 0.746443\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## 5. Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"elapsed":253945,"status":"ok","timestamp":1620190666258,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"839a590f-91a6-4da7-e4cc-e88d11f19347"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['classifier_dl'] has settable params:\n","pipe['classifier_dl'].setMaxEpochs(3) | Info: Maximum number of epochs to train | Currently set to : 3\n","pipe['classifier_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['classifier_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['classifier_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['classifier_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@1632a50d) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@1632a50d\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## 6. Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":759},"id":"mptfvHx-MMMX","executionInfo":{"elapsed":266759,"status":"ok","timestamp":1620190679075,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"69f0c3de-c61d-45b5-fd3a-96c44d821f42"},"source":["# Train longer!\n","trainable_pipe['classifier_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:100])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," average 0.00 0.00 0.00 33\n"," good 0.65 0.97 0.78 36\n"," poor 0.57 0.84 0.68 31\n","\n"," accuracy 0.61 100\n"," macro avg 0.40 0.60 0.48 100\n","weighted avg 0.41 0.61 0.49 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
y
\n","
document
\n","
sentence
\n","
trained_classifier
\n","
text
\n","
sentence_embedding_use
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
1045
\n","
good
\n","
excelente
\n","
[excelente]
\n","
good
\n","
excelente
\n","
[0.032463133335113525, -0.01719777286052704, -...
\n","
0.981568
\n","
\n","
\n","
1
\n","
501
\n","
good
\n","
Good tanks
\n","
[Good tanks]
\n","
good
\n","
Good tanks
\n","
[-0.04523428902029991, -0.0027615062426775694,...
\n","
0.949588
\n","
\n","
\n","
2
\n","
539
\n","
poor
\n","
My charger does not work.I would like one that...
\n","
[My charger does not work., I would like one t...
\n","
poor
\n","
My charger does not work.I would like one that...
\n","
[0.05880051478743553, 0.07787849009037018, -0....
\n","
0.827465
\n","
\n","
\n","
3
\n","
1073
\n","
poor
\n","
Positive: Large screen is good for senior peop...
\n","
[Positive: Large screen is good for senior peo...
\n","
poor
\n","
Positive: Large screen is good for senior peop...
\n","
[0.06194938346743584, 0.046952128410339355, -0...
\n","
0.659105
\n","
\n","
\n","
4
\n","
191
\n","
good
\n","
works good for 3G and above. Simple to use. No...
\n","
[works good for 3G and above., Simple to use.,...
\n","
good
\n","
works good for 3G and above. Simple to use. No...
\n","
[0.07321183383464813, 0.023221753537654877, -0...
\n","
0.833691
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
276
\n","
average
\n","
Average phone with very buggy performance
\n","
[Average phone with very buggy performance]
\n","
poor
\n","
Average phone with very buggy performance
\n","
[0.02227046899497509, -0.027068903669714928, -...
\n","
0.652539
\n","
\n","
\n","
96
\n","
984
\n","
good
\n","
Great simple to use phone for my mother. Big b...
\n","
[Great simple to use phone for my mother., Big...
\n","
good
\n","
Great simple to use phone for my mother. Big b...
\n","
[0.05902329087257385, 0.039018064737319946, 0....
\n","
0.956777
\n","
\n","
\n","
97
\n","
1054
\n","
good
\n","
My husband loves it!! It is simple to use with...
\n","
[My husband loves it!!, It is simple to use wi...
\n","
good
\n","
My husband loves it!! It is simple to use with...
\n","
[0.05941574648022652, -0.00272793835029006, -0...
\n","
0.960578
\n","
\n","
\n","
98
\n","
1153
\n","
poor
\n","
This phone sucks the data is terrible if you h...
\n","
[This phone sucks the data is terrible if you ...
\n","
poor
\n","
This phone sucks the data is terrible if you h...
\n","
[0.05509388446807861, 0.005729800555855036, -0...
\n","
0.905695
\n","
\n","
\n","
99
\n","
732
\n","
average
\n","
The phone arrived on time as indicated (That i...
\n","
[The phone arrived on time as indicated (That ...
\n","
poor
\n","
The phone arrived on time as indicated (That i...
\n","
[0.022888457402586937, -0.02594594471156597, -...
\n","
0.832351
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" origin_index ... trained_classifier_confidence_confidence\n","0 1045 ... 0.981568\n","1 501 ... 0.949588\n","2 539 ... 0.827465\n","3 1073 ... 0.659105\n","4 191 ... 0.833691\n",".. ... ... ...\n","95 276 ... 0.652539\n","96 984 ... 0.956777\n","97 1054 ... 0.960578\n","98 1153 ... 0.905695\n","99 732 ... 0.832351\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["#7. Try training with different Embeddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxWFzQOhjWC8","executionInfo":{"elapsed":266753,"status":"ok","timestamp":1620190679077,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"5111811b-a8fa-4c3b-9e90-542712d069bd"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IKK_Ii_gjJfF","executionInfo":{"status":"ok","timestamp":1620196757175,"user_tz":-300,"elapsed":1376712,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"1edbc0da-2aee-41f9-f079-f4d124b2ba47"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.classifier')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['classifier_dl'].setMaxEpochs(90) \n","trainable_pipe['classifier_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","\n","#preds\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," average 0.69 0.66 0.68 393\n"," good 0.80 0.85 0.82 415\n"," poor 0.77 0.76 0.76 392\n","\n"," accuracy 0.76 1200\n"," macro avg 0.75 0.76 0.75 1200\n","weighted avg 0.76 0.76 0.76 1200\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 7.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fxx4yNkNVGFl","executionInfo":{"status":"ok","timestamp":1620196824778,"user_tz":-300,"elapsed":1440688,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a8ee67a1-bba9-457f-917d-3e9ccb968f2b"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," average 0.71 0.65 0.68 107\n"," good 0.77 0.86 0.81 85\n"," poor 0.77 0.76 0.77 108\n","\n"," accuracy 0.75 300\n"," macro avg 0.75 0.76 0.75 300\n","weighted avg 0.75 0.75 0.75 300\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 8. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620197096185,"user_tz":-300,"elapsed":248696,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"97597b39-5d93-4dfc-8b4d-852dc625cf0c"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./model/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 9. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620197114270,"user_tz":-300,"elapsed":257414,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"97c5da96-d94f-4d65-8f97-0074c3ba34fd"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('It worked perfectly.')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
from_disk_confidence_confidence
\n","
text
\n","
sentence_embedding_from_disk
\n","
from_disk
\n","
document
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
8589934592
\n","
[0.8648654]
\n","
It worked perfectly.
\n","
[[0.27597182989120483, 0.4924651086330414, 0.2...
\n","
[good]
\n","
It worked perfectly.
\n","
[It worked perfectly.]
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 8589934592 ... [It worked perfectly.]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620197114272,"user_tz":-300,"elapsed":257161,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"db2a6ebb-dc50-4d5e-b583-5b6fcde5b3f7"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@12a61521) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@12a61521\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['classifier_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['classifier_dl@sent_small_bert_L12_768'].setClasses(['average', 'poor', 'good']) | Info: get the tags used to trained this ClassifierDLModel | Currently set to : ['average', 'poor', 'good']\n","pipe['classifier_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SShSTNXA_AJ2"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_hotel_reviews.ipynb b/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_hotel_reviews.ipynb
deleted file mode 100644
index 611fdba3..00000000
--- a/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_hotel_reviews.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_multi_class_text_classifier_demo_hotel_reviews.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_hotel_reviews.ipynb)\n","\n","\n","\n","# Training a Deep Learning Classifier with NLU \n","## ClassifierDL (Multi-class Text Classification)\n","## 3 class Tripadvisor Hotel review classifier training\n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl-multi-class-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n"," \n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n"," \n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191227160,"user_tz":-300,"elapsed":116826,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"f2dc1e9c-3872-46ef-ccc3-9c6682cdf498"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:05:11-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 05:05:11 (1.65 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 72kB/s \n","\u001b[K |████████████████████████████████| 153kB 54.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 53.7MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download hotel reviews dataset \n","https://www.kaggle.com/andrewmvd/trip-advisor-hotel-reviews\n","\n","Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.\n","With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620191228830,"user_tz":-300,"elapsed":118485,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"c370ca94-56e6-4b3a-e205-d38162a54d13"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/01/tripadvisor_hotel_reviews.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:07:06-- http://ckl-it.de/wp-content/uploads/2021/01/tripadvisor_hotel_reviews.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 5160790 (4.9M) [text/csv]\n","Saving to: ‘tripadvisor_hotel_reviews.csv’\n","\n","tripadvisor_hotel_r 100%[===================>] 4.92M 4.01MB/s in 1.2s \n","\n","2021-05-05 05:07:08 (4.01 MB/s) - ‘tripadvisor_hotel_reviews.csv’ saved [5160790/5160790]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620191230311,"user_tz":-300,"elapsed":119954,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"94899131-dea3-434a-f4b9-5110a8a48653"},"source":["import pandas as pd\n","test_path = '/content/tripadvisor_hotel_reviews.csv'\n","train_df = pd.read_csv(test_path,sep=\",\")\n","cols = [\"y\",\"text\"]\n","train_df = train_df[cols]\n","from sklearn.model_selection import train_test_split\n","\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df\n","\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
\n"," \n"," \n","
\n","
577
\n","
average
\n","
decent hotel decent price stayed 5 nights delu...
\n","
\n","
\n","
5746
\n","
great
\n","
good said previous posts small tasteful renova...
\n","
\n","
\n","
675
\n","
great
\n","
gold floor best stayed gold floor club floor f...
\n","
\n","
\n","
4415
\n","
poor
\n","
truly awful, admit slightly sceptical arriving...
\n","
\n","
\n","
4099
\n","
great
\n","
union square jewel loved hotel ammenities loca...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
6318
\n","
average
\n","
average read travellers wrote hotel prior leav...
\n","
\n","
\n","
3855
\n","
great
\n","
plush comfortable stayed nights mark hopkins f...
\n","
\n","
\n","
1061
\n","
average
\n","
great potential just want special thanks anima...
\n","
\n","
\n","
3060
\n","
average
\n","
centrally located hotel enjoyable stay stayed ...
\n","
\n","
\n","
5239
\n","
average
\n","
distinctly average stayed short trip hong kong...
\n","
\n"," \n","
\n","
5241 rows × 2 columns
\n","
"],"text/plain":[" y text\n","577 average decent hotel decent price stayed 5 nights delu...\n","5746 great good said previous posts small tasteful renova...\n","675 great gold floor best stayed gold floor club floor f...\n","4415 poor truly awful, admit slightly sceptical arriving...\n","4099 great union square jewel loved hotel ammenities loca...\n","... ... ...\n","6318 average average read travellers wrote hotel prior leav...\n","3855 great plush comfortable stayed nights mark hopkins f...\n","1061 average great potential just want special thanks anima...\n","3060 average centrally located hotel enjoyable stay stayed ...\n","5239 average distinctly average stayed short trip hong kong...\n","\n","[5241 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.classifier')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620191368058,"user_tz":-300,"elapsed":257686,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"4e41c7cc-b230-4cc6-a819-2b540d620b2b"},"source":["# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# Since there are no\n","\n","trainable_pipe = nlu.load('train.classifier')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50] )\n","\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50] ,output_level='document')\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
"],"text/plain":[" trained_classifier_confidence_confidence ... origin_index\n","0 0.549657 ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"status":"ok","timestamp":1620191368985,"user_tz":-300,"elapsed":258592,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"316005d7-a943-4c86-8921-1c403304f5bd"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['classifier_dl'] has settable params:\n","pipe['classifier_dl'].setMaxEpochs(3) | Info: Maximum number of epochs to train | Currently set to : 3\n","pipe['classifier_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['classifier_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['classifier_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['classifier_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@113937db) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@113937db\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["##5. Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":759},"id":"mptfvHx-MMMX","executionInfo":{"status":"ok","timestamp":1620191380476,"user_tz":-300,"elapsed":270072,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"6d96e2b0-8c38-4495-9ad5-c8a0abeaa7b0"},"source":["# Train longer!\n","trainable_pipe['classifier_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:100])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," average 0.67 0.35 0.46 34\n"," great 0.73 0.94 0.82 34\n"," poor 0.66 0.78 0.71 32\n","\n"," accuracy 0.69 100\n"," macro avg 0.68 0.69 0.67 100\n","weighted avg 0.68 0.69 0.66 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_classifier_confidence_confidence
\n","
text
\n","
y
\n","
sentence
\n","
sentence_embedding_use
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
0.590834
\n","
decent hotel decent price stayed 5 nights delu...
\n","
average
\n","
[decent hotel decent price stayed 5 nights del...
\n","
[0.053218383342027664, 0.04507320374250412, 0....
\n","
decent hotel decent price stayed 5 nights delu...
\n","
poor
\n","
577
\n","
\n","
\n","
1
\n","
0.993803
\n","
good said previous posts small tasteful renova...
\n","
great
\n","
[good said previous posts small tasteful renov...
\n","
[0.039876967668533325, 0.06624795496463776, -0...
\n","
good said previous posts small tasteful renova...
\n","
great
\n","
5746
\n","
\n","
\n","
2
\n","
0.981204
\n","
gold floor best stayed gold floor club floor f...
\n","
great
\n","
[gold floor best stayed gold floor club floor ...
\n","
[0.0038577697705477476, 0.05996308475732803, -...
\n","
gold floor best stayed gold floor club floor f...
\n","
great
\n","
675
\n","
\n","
\n","
3
\n","
0.880106
\n","
truly awful, admit slightly sceptical arriving...
\n","
poor
\n","
[truly awful, admit slightly sceptical arrivin...
\n","
[0.06336931884288788, 0.0006446511833928525, -...
\n","
truly awful, admit slightly sceptical arriving...
\n","
poor
\n","
4415
\n","
\n","
\n","
4
\n","
0.989036
\n","
union square jewel loved hotel ammenities loca...
\n","
great
\n","
[union square jewel loved hotel ammenities loc...
\n","
[0.025744924321770668, 0.06057509407401085, 0....
\n","
union square jewel loved hotel ammenities loca...
\n","
great
\n","
4099
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
0.487027
\n","
nice hotel needs little updating, good price, ...
\n","
average
\n","
[nice hotel needs little updating, good price,...
\n","
[0.034561637789011, 0.05367675796151161, 0.012...
\n","
nice hotel needs little updating, good price, ...
\n","
great
\n","
310
\n","
\n","
\n","
96
\n","
0.602283
\n","
nice n't just returned stay macao feb 10-16/08...
\n","
average
\n","
[nice n't just returned stay macao feb 10-16/0...
\n","
[-0.027111494913697243, 0.05856683477759361, 0...
\n","
nice n't just returned stay macao feb 10-16/08...
\n","
average
\n","
5581
\n","
\n","
\n","
97
\n","
0.662033
\n","
15-22, fiance went club carabela 15 22. 23 fia...
\n","
average
\n","
[15-22, fiance went club carabela 15 22. 23 fi...
\n","
[-0.04782567545771599, 0.04917208105325699, 0....
\n","
15-22, fiance went club carabela 15 22. 23 fia...
\n","
average
\n","
4554
\n","
\n","
\n","
98
\n","
0.876396
\n","
quaint not rundown son decided celebrate gradu...
\n","
poor
\n","
[quaint not rundown son decided celebrate grad...
\n","
[0.03745331987738609, 0.04204617813229561, -0....
\n","
quaint not rundown son decided celebrate gradu...
\n","
poor
\n","
6498
\n","
\n","
\n","
99
\n","
0.740084
\n","
overwhelming stay oct 19 26thnegatives arrival...
\n","
average
\n","
[overwhelming stay oct 19 26thnegatives arriva...
\n","
[-0.0396517813205719, 0.017166994512081146, 0....
\n","
overwhelming stay oct 19 26thnegatives arrival...
\n","
poor
\n","
5651
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" trained_classifier_confidence_confidence ... origin_index\n","0 0.590834 ... 577\n","1 0.993803 ... 5746\n","2 0.981204 ... 675\n","3 0.880106 ... 4415\n","4 0.989036 ... 4099\n",".. ... ... ...\n","95 0.487027 ... 310\n","96 0.602283 ... 5581\n","97 0.662033 ... 4554\n","98 0.876396 ... 6498\n","99 0.740084 ... 5651\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["#6. Try training with different Embeddings"]},{"cell_type":"code","metadata":{"id":"nxWFzQOhjWC8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191380992,"user_tz":-300,"elapsed":270578,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"b7c7fdd6-b46c-4b82-9440-c7c2bcf87c64"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IKK_Ii_gjJfF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620201488264,"user_tz":-300,"elapsed":6142302,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"d071f07a-afcf-454f-87b5-9ce4c0e8bf86"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.classifier')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['classifier_dl'].setMaxEpochs(90) \n","trainable_pipe['classifier_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," average 0.67 0.61 0.64 1758\n"," great 0.77 0.84 0.80 1727\n"," poor 0.77 0.77 0.77 1756\n","\n"," accuracy 0.74 5241\n"," macro avg 0.74 0.74 0.74 5241\n","weighted avg 0.74 0.74 0.74 5241\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 7 evaluate on Test Data"]},{"cell_type":"code","metadata":{"id":"Fxx4yNkNVGFl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620201993037,"user_tz":-300,"elapsed":504888,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"8a4eedf0-5a5d-496a-c0fa-7968fa030f1c"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," average 0.61 0.65 0.63 426\n"," great 0.78 0.78 0.78 457\n"," poor 0.78 0.73 0.75 428\n","\n"," accuracy 0.72 1311\n"," macro avg 0.72 0.72 0.72 1311\n","weighted avg 0.72 0.72 0.72 1311\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 8. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620202238341,"user_tz":-300,"elapsed":245312,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"5d08a028-29d1-48db-858d-74c162d89b69"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 9. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620202254043,"user_tz":-300,"elapsed":15721,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"a0c1f0d3-e6a8-4dd6-f926-284545527afd"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('It was a good experince!')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
from_disk_confidence_confidence
\n","
text
\n","
sentence
\n","
sentence_embedding_from_disk
\n","
from_disk
\n","
document
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.89922845]
\n","
It was a good experince!
\n","
[It was a good experince!]
\n","
[[-0.1282019317150116, 0.30001381039619446, 0....
\n","
[great]
\n","
It was a good experince!
\n","
8589934592
\n","
\n"," \n","
\n","
"],"text/plain":[" from_disk_confidence_confidence ... origin_index\n","0 [0.89922845] ... 8589934592\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620202254063,"user_tz":-300,"elapsed":63,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"d39ac458-5cba-4e4d-b0f2-3b4b0c088c30"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@11f6140a) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@11f6140a\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['classifier_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['classifier_dl@sent_small_bert_L12_768'].setClasses(['average', 'great', 'poor']) | Info: get the tags used to trained this ClassifierDLModel | Currently set to : ['average', 'great', 'poor']\n","pipe['classifier_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_musical_instruments.ipynb b/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_musical_instruments.ipynb
deleted file mode 100644
index df5eabce..00000000
--- a/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_musical_instruments.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_multi_class_text_classifier_demo_musical_instruments.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_musical_instruments.ipynb)\n","\n","\n","\n","\n","# Training a Deep Learning Classifier with NLU \n","## ClassifierDL (Multi-class Text Classification)\n","## 4 class Amazon Musical Instruments review classifier training\n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl-multi-class-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n"," \n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n"," \n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hFGnBCHavltY","executionInfo":{"status":"ok","timestamp":1620190616877,"user_tz":-300,"elapsed":36961,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"54af3f90-c6c3-45ce-cb9f-ba8c06124414"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":1,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:56:20-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.108.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 04:56:20 (1.60 MB/s) - written to stdout [1671/1671]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download musical instruments classification dataset\n","\n","https://www.kaggle.com/eswarchandt/amazon-music-reviews\n","\n","dataset with products rated between 5 classes"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620190617767,"user_tz":-300,"elapsed":35618,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"5bd3e397-592e-4790-983a-b5e9449132f6"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/01/Musical_instruments_reviews.csv"],"execution_count":2,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:56:56-- http://ckl-it.de/wp-content/uploads/2021/01/Musical_instruments_reviews.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 51708 (50K) [text/csv]\n","Saving to: ‘Musical_instruments_reviews.csv.1’\n","\n","Musical_instruments 100%[===================>] 50.50K 220KB/s in 0.2s \n","\n","2021-05-05 04:56:57 (220 KB/s) - ‘Musical_instruments_reviews.csv.1’ saved [51708/51708]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620190618447,"user_tz":-300,"elapsed":33313,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"ce3a288d-579a-4cd6-910a-7da1b55e85ac"},"source":["import pandas as pd\n","test_path = '/content/Musical_instruments_reviews.csv'\n","train_df = pd.read_csv(test_path,sep=\",\")\n","cols = [\"y\",\"text\"]\n","train_df = train_df[cols]\n","from sklearn.model_selection import train_test_split\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df\n","\n"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
\n"," \n"," \n","
\n","
11
\n","
very poor
\n","
I know many people love the design, but I find...
\n","
\n","
\n","
10
\n","
good
\n","
Well made. Works as it should. However, seem t...
\n","
\n","
\n","
45
\n","
very good
\n","
The product does exactly as it should and is q...
\n","
\n","
\n","
25
\n","
average
\n","
This is a fine guitar, but it isn't amazing. ...
\n","
\n","
\n","
97
\n","
average
\n","
The problem with this pedal is that you have t...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
70
\n","
very poor
\n","
I might have done something wrong...I read in ...
\n","
\n","
\n","
31
\n","
good
\n","
Good product at a good price. Have used it mul...
\n","
\n","
\n","
48
\n","
average
\n","
Doe's not stay on to well, moves to much even ...
\n","
\n","
\n","
78
\n","
very poor
\n","
Go build your own. Build it to your specs and ...
\n","
\n","
\n","
39
\n","
good
\n","
I've been using these for about 3 weeks now - ...
\n","
\n"," \n","
\n","
96 rows × 2 columns
\n","
"],"text/plain":[" y text\n","11 very poor I know many people love the design, but I find...\n","10 good Well made. Works as it should. However, seem t...\n","45 very good The product does exactly as it should and is q...\n","25 average This is a fine guitar, but it isn't amazing. ...\n","97 average The problem with this pedal is that you have t...\n",".. ... ...\n","70 very poor I might have done something wrong...I read in ...\n","31 good Good product at a good price. Have used it mul...\n","48 average Doe's not stay on to well, moves to much even ...\n","78 very poor Go build your own. Build it to your specs and ...\n","39 good I've been using these for about 3 weeks now - ...\n","\n","[96 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.classifier')\n","\n","By default, the Universal Sentence Encoder Embeddings (USE) are beeing downloaded to provide embeddings for the classifier. You can use any of the 50+ other sentence Emeddings in NLU tough!\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"elapsed":278961,"status":"ok","timestamp":1620189909399,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"},"user_tz":-300},"outputId":"a72a6d7e-b133-43fc-a6ce-b49185a1cba5"},"source":["# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# Since there are no\n","\n","trainable_pipe = nlu.load('train.classifier')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50] )\n","\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50],output_level='document' )\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
document
\n","
text
\n","
y
\n","
sentence_embedding_use
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[Very good cable., Well made and it looks grea...
\n","
very poor
\n","
0.344603
\n","
Very good cable. Well made and it looks great ...
\n","
Very good cable. Well made and it looks great ...
\n","
good
\n","
[0.018388595432043076, -0.001244456390850246, ...
\n","
76
\n","
\n","
\n","
1
\n","
[I bought this hoping to use as intended, angl...
\n","
very poor
\n","
0.532565
\n","
I bought this hoping to use as intended, angle...
\n","
I bought this hoping to use as intended, angle...
\n","
very poor
\n","
[0.07512206584215164, 0.010702813044190407, -0...
\n","
53
\n","
\n","
\n","
2
\n","
[Hosa cable quality can be all over the place,...
\n","
good
\n","
0.413519
\n","
Hosa cable quality can be all over the place, ...
\n","
Hosa cable quality can be all over the place, ...
\n","
good
\n","
[0.05411579832434654, 0.04756820946931839, -0....
\n","
112
\n","
\n","
\n","
3
\n","
[wanted it just on looks alone., ., ., It is a...
\n","
good
\n","
0.350552
\n","
wanted it just on looks alone...It is a nice l...
\n","
wanted it just on looks alone...It is a nice l...
\n","
very good
\n","
[0.08269041031599045, 0.02812184765934944, -0....
\n","
79
\n","
\n","
\n","
4
\n","
[Well made., Works as it should., However, see...
\n","
good
\n","
0.453055
\n","
Well made. Works as it should. However, seem t...
\n","
Well made. Works as it should. However, seem t...
\n","
good
\n","
[0.07170691341161728, -0.012493303045630455, -...
\n","
10
\n","
\n","
\n","
5
\n","
[Nice solid cables, with excellent support at ...
\n","
good
\n","
0.435087
\n","
Nice solid cables, with excellent support at t...
\n","
Nice solid cables, with excellent support at t...
\n","
very good
\n","
[0.06005071476101875, 0.0279176477342844, -0.0...
\n","
20
\n","
\n","
\n","
6
\n","
[If you are not use to using a large sustainin...
\n","
average
\n","
0.627601
\n","
If you are not use to using a large sustaining...
\n","
If you are not use to using a large sustaining...
\n","
average
\n","
[0.06641201674938202, 0.07415173947811127, -0....
\n","
83
\n","
\n","
\n","
7
\n","
[The Hosa XLR cables are affordable and very h...
\n","
good
\n","
0.451387
\n","
The Hosa XLR cables are affordable and very he...
\n","
The Hosa XLR cables are affordable and very he...
\n","
good
\n","
[0.0650528222322464, -0.024019034579396248, -0...
\n","
117
\n","
\n","
\n","
8
\n","
[Not much to write about here, but it does exa...
\n","
good
\n","
0.375702
\n","
Not much to write about here, but it does exac...
\n","
Not much to write about here, but it does exac...
\n","
very good
\n","
[0.06030378118157387, 0.03140004724264145, -0....
\n","
89
\n","
\n","
\n","
9
\n","
[The primary job of this device is to block th...
\n","
average
\n","
0.445224
\n","
The primary job of this device is to block the...
\n","
The primary job of this device is to block the...
\n","
very good
\n","
[0.0768439993262291, 0.012965132482349873, -0....
\n","
116
\n","
\n","
\n","
10
\n","
[Only Lasted 2yr., I have a lot of cheaper cab...
\n","
good
\n","
0.370069
\n","
Only Lasted 2yr. I have a lot of cheaper cable...
\n","
Only Lasted 2yr. I have a lot of cheaper cabl...
\n","
very poor
\n","
[0.05348164588212967, 0.03492450341582298, -0....
\n","
55
\n","
\n","
\n","
11
\n","
[These cables are a little thin compared to ho...
\n","
average
\n","
0.581801
\n","
These cables are a little thin compared to hos...
\n","
These cables are a little thin compared to hos...
\n","
average
\n","
[0.05915072187781334, -0.03095736913383007, -0...
\n","
3
\n","
\n","
\n","
12
\n","
[This pedal has been around for a long time, a...
\n","
average
\n","
0.963964
\n","
This pedal has been around for a long time, an...
\n","
This pedal has been around for a long time, an...
\n","
average
\n","
[0.06582090258598328, 0.012904703617095947, -0...
\n","
102
\n","
\n","
\n","
13
\n","
[Fender cords look great and work just as well...
\n","
average
\n","
0.874885
\n","
Fender cords look great and work just as well....
\n","
Fender cords look great and work just as well....
\n","
very good
\n","
[0.018997572362422943, -0.012294900603592396, ...
\n","
95
\n","
\n","
\n","
14
\n","
[It just randomly pops off my bass, it's so sl...
\n","
very poor
\n","
0.731916
\n","
It just randomly pops off my bass, it's so sli...
\n","
It just randomly pops off my bass, it's so sli...
\n","
very poor
\n","
[0.018867170438170433, 0.05410968139767647, -0...
\n","
115
\n","
\n","
\n","
15
\n","
[the string work well but did not give the mel...
\n","
average
\n","
0.712628
\n","
the string work well but did not give the mell...
\n","
the string work well but did not give the mell...
\n","
average
\n","
[0.056391388177871704, 0.07034655660390854, -0...
\n","
36
\n","
\n","
\n","
16
\n","
[I've used a lot of cables and I always come b...
\n","
good
\n","
0.440745
\n","
I've used a lot of cables and I always come ba...
\n","
I've used a lot of cables and I always come ba...
\n","
very good
\n","
[0.07708822190761566, 0.016957569867372513, -0...
\n","
108
\n","
\n","
\n","
17
\n","
[Long story short, this string set has a stron...
\n","
average
\n","
0.767563
\n","
Long story short, this string set has a strong...
\n","
Long story short, this string set has a strong...
\n","
very poor
\n","
[0.06738117337226868, 0.05691230669617653, -0....
\n","
88
\n","
\n","
\n","
18
\n","
[Monster makes a wide array of cables, includi...
\n","
average
\n","
0.465245
\n","
Monster makes a wide array of cables, includin...
\n","
Monster makes a wide array of cables, includin...
\n","
very good
\n","
[0.06651592999696732, 0.0612233430147171, -0.0...
\n","
59
\n","
\n","
\n","
19
\n","
[...unbalanced guitar cable is notoriously noi...
\n","
average
\n","
0.511957
\n","
...unbalanced guitar cable is notoriously nois...
\n","
...unbalanced guitar cable is notoriously nois...
\n","
average
\n","
[0.07284880429506302, 0.024346861988306046, 0....
\n","
52
\n","
\n","
\n","
20
\n","
[Nice windscreen protects my MXL mic and preve...
\n","
average
\n","
0.410393
\n","
Nice windscreen protects my MXL mic and preven...
\n","
Nice windscreen protects my MXL mic and preven...
\n","
very good
\n","
[0.07302771508693695, -0.04292221739888191, -0...
\n","
14
\n","
\n","
\n","
21
\n","
[I bought 6 of these XLR to 1/4" adapters ...
\n","
good
\n","
0.436735
\n","
I bought 6 of these XLR to 1/4" adapters h...
\n","
I bought 6 of these XLR to 1/4" adapters h...
\n","
good
\n","
[0.049845140427351, -0.02525583654642105, -0.0...
\n","
16
\n","
\n","
\n","
22
\n","
[This Fender cable is the perfect length for m...
\n","
good
\n","
0.417587
\n","
This Fender cable is the perfect length for me...
\n","
This Fender cable is the perfect length for me...
\n","
good
\n","
[0.06648766994476318, 0.04505337029695511, -0....
\n","
57
\n","
\n","
\n","
23
\n","
[This L Jack is good enough to make connection...
\n","
average
\n","
0.330708
\n","
This L Jack is good enough to make connections...
\n","
This L Jack is good enough to make connections...
\n","
good
\n","
[0.08174190670251846, 0.07509452849626541, -0....
\n","
46
\n","
\n","
\n","
24
\n","
[These came stock on my Jackson Kelly KEXMG., ...
\n","
average
\n","
0.437556
\n","
These came stock on my Jackson Kelly KEXMG. Al...
\n","
These came stock on my Jackson Kelly KEXMG. Al...
\n","
very poor
\n","
[0.03714499622583389, 0.0533314123749733, -0.0...
\n","
18
\n","
\n","
\n","
25
\n","
[Good overall but pick ups are terrible - Migh...
\n","
average
\n","
0.922614
\n","
Good overall but pick ups are terrible - Might...
\n","
Good overall but pick ups are terrible - Might...
\n","
average
\n","
[0.07406474649906158, 0.007126895245164633, -0...
\n","
56
\n","
\n","
\n","
26
\n","
[These things are terrible., One wouldn't fit ...
\n","
good
\n","
0.348053
\n","
These things are terrible. One wouldn't fit in...
\n","
These things are terrible. One wouldn't fit in...
\n","
very poor
\n","
[0.05325164273381233, -0.036399971693754196, -...
\n","
101
\n","
\n","
\n","
27
\n","
[another item never received but i would not u...
\n","
very poor
\n","
0.638240
\n","
another item never received but i would not us...
\n","
another item never received but i would not us...
\n","
very poor
\n","
[0.024511804804205894, 0.055792637169361115, 0...
\n","
26
\n","
\n","
\n","
28
\n","
[wind screen is way too big its bulky and to m...
\n","
very poor
\n","
0.438548
\n","
wind screen is way too big its bulky and to me...
\n","
wind screen is way too big its bulky and to me...
\n","
very poor
\n","
[0.07763350009918213, 0.04169292375445366, -0....
\n","
63
\n","
\n","
\n","
29
\n","
[This is a fine guitar, but it isn't amazing.,...
\n","
average
\n","
0.988021
\n","
This is a fine guitar, but it isn't amazing. M...
\n","
This is a fine guitar, but it isn't amazing. ...
\n","
average
\n","
[0.05681991949677467, 0.055675093084573746, -0...
\n","
25
\n","
\n","
\n","
30
\n","
[I've been using these for about 3 weeks now -...
\n","
average
\n","
0.411459
\n","
I've been using these for about 3 weeks now - ...
\n","
I've been using these for about 3 weeks now - ...
\n","
good
\n","
[0.07338722050189972, 0.024855980649590492, 2....
\n","
39
\n","
\n","
\n","
31
\n","
[It's a good OD, but I thought it would be hig...
\n","
average
\n","
0.996302
\n","
It's a good OD, but I thought it would be high...
\n","
It's a good OD, but I thought it would be high...
\n","
average
\n","
[0.08331827074289322, -0.011826762929558754, -...
\n","
80
\n","
\n","
\n","
32
\n","
[This is a cheap piece of junk that does what ...
\n","
average
\n","
0.900313
\n","
This is a cheap piece of junk that does what i...
\n","
This is a cheap piece of junk that does what i...
\n","
very poor
\n","
[0.07090918719768524, -0.004987373482435942, 0...
\n","
75
\n","
\n","
\n","
33
\n","
[Works for practice ., .. it's a guitar instru...
\n","
average
\n","
0.785570
\n","
Works for practice ... it's a guitar instrumen...
\n","
Works for practice ... it's a guitar instrumen...
\n","
average
\n","
[0.08485165238380432, 0.0717940405011177, -0.0...
\n","
93
\n","
\n","
\n","
34
\n","
[Let me start by saying that I am a huge fan o...
\n","
average
\n","
0.950551
\n","
Let me start by saying that I am a huge fan of...
\n","
Let me start by saying that I am a huge fan of...
\n","
average
\n","
[0.06803068518638611, 0.03993965685367584, -0....
\n","
62
\n","
\n","
\n","
35
\n","
[Cant go wrong., Great quality on a budget pr...
\n","
very poor
\n","
0.331621
\n","
Cant go wrong. Great quality on a budget price...
\n","
Cant go wrong. Great quality on a budget price...
\n","
good
\n","
[0.07220637053251266, 0.020649369806051254, -0...
\n","
33
\n","
\n","
\n","
36
\n","
[this piece of plastic is just terrible., you'...
\n","
average
\n","
0.443282
\n","
this piece of plastic is just terrible. you'd ...
\n","
this piece of plastic is just terrible. you'd...
\n","
very poor
\n","
[0.07407913357019424, 0.07586841285228729, 0.0...
\n","
104
\n","
\n","
\n","
37
\n","
[It is a decent cable., It does its job, but i...
\n","
average
\n","
0.682894
\n","
It is a decent cable. It does its job, but it ...
\n","
It is a decent cable. It does its job, but it ...
\n","
average
\n","
[0.07582439482212067, 0.04041396453976631, -0....
\n","
4
\n","
\n","
\n","
38
\n","
[For the price, fantastic., They do feel light...
\n","
average
\n","
0.433290
\n","
For the price, fantastic.They do feel light an...
\n","
For the price, fantastic.They do feel light an...
\n","
good
\n","
[0.07524136453866959, 0.049453943967819214, -0...
\n","
99
\n","
\n","
\n","
39
\n","
[Just assembly for a try, only few rolls with ...
\n","
very poor
\n","
0.529660
\n","
Just assembly for a try, only few rolls with m...
\n","
Just assembly for a try, only few rolls with m...
\n","
very poor
\n","
[0.08081397414207458, 0.014145106077194214, -0...
\n","
98
\n","
\n","
\n","
40
\n","
[I know many people love the design, but I fin...
\n","
very poor
\n","
0.639893
\n","
I know many people love the design, but I find...
\n","
I know many people love the design, but I find...
\n","
very poor
\n","
[0.06185115873813629, 0.06986682116985321, -0....
\n","
11
\n","
\n","
\n","
41
\n","
[This cable seems like it will last me for a w...
\n","
good
\n","
0.449773
\n","
This cable seems like it will last me for a wh...
\n","
This cable seems like it will last me for a wh...
\n","
good
\n","
[0.08796018362045288, 0.015969790518283844, -0...
\n","
42
\n","
\n","
\n","
42
\n","
[So good that I bought another one., Love the ...
\n","
good
\n","
0.378669
\n","
So good that I bought another one. Love the he...
\n","
So good that I bought another one. Love the h...
\n","
very good
\n","
[0.05002278462052345, -0.013001631945371628, -...
\n","
74
\n","
\n","
\n","
43
\n","
[Good quality cable and sounds very good]
\n","
good
\n","
0.418967
\n","
Good quality cable and sounds very good
\n","
Good quality cable and sounds very good
\n","
very good
\n","
[0.06367859989404678, -0.0017134330701082945, ...
\n","
113
\n","
\n","
\n","
44
\n","
[Back in the 1980's I bought a can of deoxit(n...
\n","
average
\n","
0.983325
\n","
Back in the 1980's I bought a can of deoxit(no...
\n","
Back in the 1980's I bought a can of deoxit(no...
\n","
average
\n","
[0.06321641802787781, 0.04852892830967903, 0.0...
\n","
34
\n","
\n","
\n","
45
\n","
[I bought this cord after returning a cheap on...
\n","
good
\n","
0.438915
\n","
I bought this cord after returning a cheap one...
\n","
I bought this cord after returning a cheap one...
\n","
good
\n","
[0.06954371929168701, 0.016654161736369133, -0...
\n","
44
\n","
\n","
\n","
46
\n","
[It's a cable, no frills, tangles pretty easy ...
\n","
average
\n","
0.506431
\n","
It's a cable, no frills, tangles pretty easy a...
\n","
It's a cable, no frills, tangles pretty easy a...
\n","
average
\n","
[0.07703661173582077, 0.024149607867002487, -0...
\n","
118
\n","
\n","
\n","
47
\n","
[I acknowledge that this is a minority opinion...
\n","
average
\n","
0.410722
\n","
I acknowledge that this is a minority opinion ...
\n","
I acknowledge that this is a minority opinion ...
\n","
average
\n","
[0.06933587044477463, 0.03066118247807026, -0....
\n","
92
\n","
\n","
\n","
48
\n","
[I got one of these a while back and being lik...
\n","
average
\n","
0.972998
\n","
I got one of these a while back and being like...
\n","
I got one of these a while back and being like...
\n","
average
\n","
[0.06218786910176277, 0.019702719524502754, -0...
\n","
85
\n","
\n","
\n","
49
\n","
[If you're like me, you probably bought this t...
\n","
good
\n","
0.444040
\n","
If you're like me, you probably bought this to...
\n","
If you're like me, you probably bought this to...
\n","
good
\n","
[0.08035349100828171, -0.013505871407687664, -...
\n","
82
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... origin_index\n","0 [Very good cable., Well made and it looks grea... ... 76\n","1 [I bought this hoping to use as intended, angl... ... 53\n","2 [Hosa cable quality can be all over the place,... ... 112\n","3 [wanted it just on looks alone., ., ., It is a... ... 79\n","4 [Well made., Works as it should., However, see... ... 10\n","5 [Nice solid cables, with excellent support at ... ... 20\n","6 [If you are not use to using a large sustainin... ... 83\n","7 [The Hosa XLR cables are affordable and very h... ... 117\n","8 [Not much to write about here, but it does exa... ... 89\n","9 [The primary job of this device is to block th... ... 116\n","10 [Only Lasted 2yr., I have a lot of cheaper cab... ... 55\n","11 [These cables are a little thin compared to ho... ... 3\n","12 [This pedal has been around for a long time, a... ... 102\n","13 [Fender cords look great and work just as well... ... 95\n","14 [It just randomly pops off my bass, it's so sl... ... 115\n","15 [the string work well but did not give the mel... ... 36\n","16 [I've used a lot of cables and I always come b... ... 108\n","17 [Long story short, this string set has a stron... ... 88\n","18 [Monster makes a wide array of cables, includi... ... 59\n","19 [...unbalanced guitar cable is notoriously noi... ... 52\n","20 [Nice windscreen protects my MXL mic and preve... ... 14\n","21 [I bought 6 of these XLR to 1/4" adapters ... ... 16\n","22 [This Fender cable is the perfect length for m... ... 57\n","23 [This L Jack is good enough to make connection... ... 46\n","24 [These came stock on my Jackson Kelly KEXMG., ... ... 18\n","25 [Good overall but pick ups are terrible - Migh... ... 56\n","26 [These things are terrible., One wouldn't fit ... ... 101\n","27 [another item never received but i would not u... ... 26\n","28 [wind screen is way too big its bulky and to m... ... 63\n","29 [This is a fine guitar, but it isn't amazing.,... ... 25\n","30 [I've been using these for about 3 weeks now -... ... 39\n","31 [It's a good OD, but I thought it would be hig... ... 80\n","32 [This is a cheap piece of junk that does what ... ... 75\n","33 [Works for practice ., .. it's a guitar instru... ... 93\n","34 [Let me start by saying that I am a huge fan o... ... 62\n","35 [Cant go wrong., Great quality on a budget pr... ... 33\n","36 [this piece of plastic is just terrible., you'... ... 104\n","37 [It is a decent cable., It does its job, but i... ... 4\n","38 [For the price, fantastic., They do feel light... ... 99\n","39 [Just assembly for a try, only few rolls with ... ... 98\n","40 [I know many people love the design, but I fin... ... 11\n","41 [This cable seems like it will last me for a w... ... 42\n","42 [So good that I bought another one., Love the ... ... 74\n","43 [Good quality cable and sounds very good] ... 113\n","44 [Back in the 1980's I bought a can of deoxit(n... ... 34\n","45 [I bought this cord after returning a cheap on... ... 44\n","46 [It's a cable, no frills, tangles pretty easy ... ... 118\n","47 [I acknowledge that this is a minority opinion... ... 92\n","48 [I got one of these a while back and being lik... ... 85\n","49 [If you're like me, you probably bought this t... ... 82\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"DL_5aY9b3jSd"},"source":["# 4. Evaluate the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"djtoZVKBw2WU","executionInfo":{"elapsed":278954,"status":"ok","timestamp":1620189909402,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"},"user_tz":-300},"outputId":"c284dc4c-a2d8-4b4e-b493-e474f22175ff"},"source":["from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," average 0.58 1.00 0.73 15\n"," good 0.50 0.62 0.55 13\n"," very good 0.00 0.00 0.00 10\n"," very poor 0.75 0.50 0.60 12\n","\n"," accuracy 0.58 50\n"," macro avg 0.46 0.53 0.47 50\n","weighted avg 0.48 0.58 0.51 50\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"mhFKVN93o1ZO"},"source":["# 5. Lets try different Sentence Emebddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CzJd8omao0gt","executionInfo":{"elapsed":278946,"status":"ok","timestamp":1620189909403,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"},"user_tz":-300},"outputId":"691c5ce0-ce36-4082-9de9-070508bcbe9e"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ABHLgirmG1n9","executionInfo":{"elapsed":483071,"status":"ok","timestamp":1620190113536,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"},"user_tz":-300},"outputId":"2c9fbc78-da3b-4c61-f6b2-bb876789cd7a"},"source":["# Load pipe with bert embeds\n","# using large embeddings can take a few hours..\n","# fitted_pipe = nlu.load('en.embed_sentence.bert_large_uncased train.classifier').fit(train_df)\n","fitted_pipe = nlu.load('en.embed_sentence.bert train.classifier').fit(train_df.iloc[:100])\n","\n","\n","# predict with the trained pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:100],output_level='document')\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_bert_base_uncased download started this may take some time.\n","Approximate size to download 392.5 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," average 0.00 0.00 0.00 23\n"," good 0.00 0.00 0.00 25\n"," very good 0.00 0.00 0.00 22\n"," very poor 0.27 1.00 0.43 26\n","\n"," accuracy 0.27 96\n"," macro avg 0.07 0.25 0.11 96\n","weighted avg 0.07 0.27 0.12 96\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nbpdZGoZPslz","executionInfo":{"elapsed":507540,"status":"ok","timestamp":1620190138012,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"},"user_tz":-300},"outputId":"1680223b-0587-44b2-9d83-a1b87736129b"},"source":["# Load pipe with bert embeds\n","fitted_pipe = nlu.load('embed_sentence.bert train.classifier').fit(train_df.iloc[:100])\n","\n","# predict with the trained pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:100],output_level='document')\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L2_128 download started this may take some time.\n","Approximate size to download 16.1 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," average 0.24 1.00 0.39 23\n"," good 0.00 0.00 0.00 25\n"," very good 0.00 0.00 0.00 22\n"," very poor 0.00 0.00 0.00 26\n","\n"," accuracy 0.24 96\n"," macro avg 0.06 0.25 0.10 96\n","weighted avg 0.06 0.24 0.09 96\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wYV7ivdsQY8Z","executionInfo":{"status":"ok","timestamp":1620190883307,"user_tz":-300,"elapsed":291703,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"8ad7cc16-cfe2-4ebd-b16f-16755062ca0e"},"source":["from sklearn.metrics import classification_report\n","trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.classifier')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['classifier_dl'].setMaxEpochs(90) \n","trainable_pipe['classifier_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","\n","#preds"],"execution_count":4,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," average 0.26 1.00 0.41 25\n"," good 0.00 0.00 0.00 24\n"," very good 0.00 0.00 0.00 23\n"," very poor 0.00 0.00 0.00 24\n","\n"," accuracy 0.26 96\n"," macro avg 0.07 0.25 0.10 96\n","weighted avg 0.07 0.26 0.11 96\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 6. evaluate on Test Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fxx4yNkNVGFl","executionInfo":{"status":"ok","timestamp":1620190892875,"user_tz":-300,"elapsed":300897,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"5dbda6ed-5884-4d26-ed5b-9f8d774558da"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))"],"execution_count":5,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," average 0.22 1.00 0.36 5\n"," good 0.00 0.00 0.00 6\n"," very good 0.00 0.00 0.00 7\n"," very poor 1.00 0.17 0.29 6\n","\n"," accuracy 0.25 24\n"," macro avg 0.30 0.29 0.16 24\n","weighted avg 0.30 0.25 0.15 24\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 7. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620191279090,"user_tz":-300,"elapsed":262699,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"71745bf2-b5be-41e2-e1b4-d3683e113b57"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Stored model in ./model/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 8. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SO4uz45MoRgp","executionInfo":{"status":"ok","timestamp":1620191295930,"user_tz":-300,"elapsed":206694,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"84cd50b8-77b5-44e2-ca60-efc36a592891"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('It was really good ')\n","preds"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
origin_index
\n","
sentence_embedding_from_disk
\n","
document
\n","
from_disk_confidence_confidence
\n","
from_disk
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
It was really good
\n","
8589934592
\n","
[[-0.03466350957751274, 0.33072206377983093, 0...
\n","
It was really good
\n","
[0.53862494]
\n","
[very poor]
\n","
[It was really good]
\n","
\n"," \n","
\n","
"],"text/plain":[" text origin_index ... from_disk sentence\n","0 It was really good 8589934592 ... [very poor] [It was really good]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e0CVlkk9v6Qi","executionInfo":{"status":"ok","timestamp":1620191295931,"user_tz":-300,"elapsed":206555,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"c92becb6-6751-47de-a500-d2d8a371d545"},"source":["hdd_pipe.print_info()"],"execution_count":9,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@6ee9116a) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@6ee9116a\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['classifier_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['classifier_dl@sent_small_bert_L12_768'].setClasses(['very good', 'very poor', 'average', 'good']) | Info: get the tags used to trained this ClassifierDLModel | Currently set to : ['very good', 'very poor', 'average', 'good']\n","pipe['classifier_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"iJBrKFd94n8y","executionInfo":{"status":"ok","timestamp":1620191295932,"user_tz":-300,"elapsed":206488,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}}},"source":[""],"execution_count":9,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_wine.ipynb b/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_wine.ipynb
deleted file mode 100644
index a42c9f14..00000000
--- a/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_wine.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_multi_class_text_classifier_demo_wine.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_class_text_classification/NLU_training_multi_class_text_classifier_demo_wine.ipynb)\n","\n","\n","\n","# Training a Deep Learning Classifier with NLU \n","## ClassifierDL (Multi-class Text Classification)\n","## 4 class WineEnthusiast Wine review classifier training\n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl-multi-class-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","\n","You can achieve these results or even better on this dataset with training data:\n","\n"," \n","\n","\n","\n","You can achieve these results or even better on this dataset with test data:\n","\n"," \n","\n","\n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hFGnBCHavltY","executionInfo":{"elapsed":32398,"status":"ok","timestamp":1620188669127,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"be856a07-ea44-4bfe-c741-1e6674ae24d6"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:23:57-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 04:23:57 (2.60 MB/s) - written to stdout [1671/1671]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download wine review dataset \n","https://www.kaggle.com/zynicide/wine-reviews\n","dataset with products between 5 review classes"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"elapsed":33566,"status":"ok","timestamp":1620188670320,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"eb56146a-5ff2-4da7-da31-1fa504f3659c"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/01/winemag-data_first150k.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 04:24:28-- http://ckl-it.de/wp-content/uploads/2021/01/winemag-data_first150k.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1447273 (1.4M) [text/csv]\n","Saving to: ‘winemag-data_first150k.csv.2’\n","\n","winemag-data_first1 100%[===================>] 1.38M 1.91MB/s in 0.7s \n","\n","2021-05-05 04:24:29 (1.91 MB/s) - ‘winemag-data_first150k.csv.2’ saved [1447273/1447273]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"elapsed":34170,"status":"ok","timestamp":1620188670949,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"cc9da822-9e17-4a08-bd28-a82a42c1bd2d"},"source":["import pandas as pd\n","test_path = '/content/winemag-data_first150k.csv'\n","train_df = pd.read_csv(test_path,sep=\",\")\n","cols = [\"y\",\"text\"]\n","train_df = train_df[cols]\n","from sklearn.model_selection import train_test_split\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df\n","\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
\n"," \n"," \n","
\n","
1458
\n","
good
\n","
Full of yellow fruits, ripe apples and soft ac...
\n","
\n","
\n","
378
\n","
acceptable
\n","
Barnyard aromas atop berry scents make for a n...
\n","
\n","
\n","
17
\n","
very good
\n","
An aromatic twist of passion fruit plays on th...
\n","
\n","
\n","
2456
\n","
very good
\n","
Wood smoke and black pepper aromas start this ...
\n","
\n","
\n","
2103
\n","
best
\n","
Talk about magnetic aromas of bacon, tobacco a...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1464
\n","
acceptable
\n","
The flint soil of the vineyard shows in the st...
\n","
\n","
\n","
3942
\n","
acceptable
\n","
Fruit-forward and simple, with the sugared tas...
\n","
\n","
\n","
1871
\n","
very good
\n","
Bryan Babcock makes plenty of more expensive P...
\n","
\n","
\n","
1945
\n","
good
\n","
Berry and plum aromas are spicy and saucy, wit...
\n","
\n","
\n","
1219
\n","
good
\n","
A kitchen-sink blend of nine different varieti...
\n","
\n"," \n","
\n","
4048 rows × 2 columns
\n","
"],"text/plain":[" y text\n","1458 good Full of yellow fruits, ripe apples and soft ac...\n","378 acceptable Barnyard aromas atop berry scents make for a n...\n","17 very good An aromatic twist of passion fruit plays on th...\n","2456 very good Wood smoke and black pepper aromas start this ...\n","2103 best Talk about magnetic aromas of bacon, tobacco a...\n","... ... ...\n","1464 acceptable The flint soil of the vineyard shows in the st...\n","3942 acceptable Fruit-forward and simple, with the sugared tas...\n","1871 very good Bryan Babcock makes plenty of more expensive P...\n","1945 good Berry and plum aromas are spicy and saucy, wit...\n","1219 good A kitchen-sink blend of nine different varieti...\n","\n","[4048 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.classifier')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"elapsed":132178,"status":"ok","timestamp":1620188768985,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"bc0a38f8-d374-48da-98d0-196505e56e66"},"source":["# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# Since there are no\n","\n","trainable_pipe = nlu.load('train.classifier')\n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:50] )\n","\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:50] ,output_level='document')\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_use
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
document
\n","
y
\n","
sentence
\n","
trained_classifier
\n","
text
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.026885146275162697, -0.06771063804626465, 0...
\n","
1458
\n","
0.620346
\n","
Full of yellow fruits, ripe apples and soft ac...
\n","
good
\n","
[Full of yellow fruits, ripe apples and soft a...
\n","
good
\n","
Full of yellow fruits, ripe apples and soft ac...
\n","
\n","
\n","
1
\n","
[0.04962018504738808, 0.0652838945388794, -0.0...
\n","
378
\n","
0.543338
\n","
Barnyard aromas atop berry scents make for a n...
\n","
acceptable
\n","
[Barnyard aromas atop berry scents make for a ...
\n","
good
\n","
Barnyard aromas atop berry scents make for a n...
\n","
\n","
\n","
2
\n","
[0.017539022490382195, -0.010785154066979885, ...
\n","
17
\n","
0.607028
\n","
An aromatic twist of passion fruit plays on th...
\n","
very good
\n","
[An aromatic twist of passion fruit plays on t...
\n","
good
\n","
An aromatic twist of passion fruit plays on th...
\n","
\n","
\n","
3
\n","
[0.016984855756163597, -0.010578665882349014, ...
\n","
2456
\n","
0.602528
\n","
Wood smoke and black pepper aromas start this ...
\n","
very good
\n","
[Wood smoke and black pepper aromas start this...
\n","
good
\n","
Wood smoke and black pepper aromas start this ...
\n","
\n","
\n","
4
\n","
[0.02070983126759529, -0.05402781069278717, -0...
\n","
2103
\n","
0.591057
\n","
Talk about magnetic aromas of bacon, tobacco a...
\n","
best
\n","
[Talk about magnetic aromas of bacon, tobacco ...
\n","
good
\n","
Talk about magnetic aromas of bacon, tobacco a...
\n","
\n","
\n","
5
\n","
[-0.008546369150280952, -8.575373067287728e-05...
\n","
3448
\n","
0.611285
\n","
Smoky, oaky, charred flavors of savory plum an...
\n","
good
\n","
[Smoky, oaky, charred flavors of savory plum a...
\n","
good
\n","
Smoky, oaky, charred flavors of savory plum an...
\n","
\n","
\n","
6
\n","
[0.0020979971159249544, -0.059595026075839996,...
\n","
1556
\n","
0.600705
\n","
Made entirely with Nero d'Avola, this offers a...
\n","
good
\n","
[Made entirely with Nero d'Avola, this offers ...
\n","
good
\n","
Made entirely with Nero d'Avola, this offers a...
\n","
\n","
\n","
7
\n","
[0.03075247071683407, -0.05452873930335045, -0...
\n","
3453
\n","
0.614901
\n","
Remarkably strong cinnamon characterizes the n...
\n","
good
\n","
[Remarkably strong cinnamon characterizes the ...
\n","
good
\n","
Remarkably strong cinnamon characterizes the n...
\n","
\n","
\n","
8
\n","
[-0.005710848607122898, -0.054149989038705826,...
\n","
3729
\n","
0.568524
\n","
A sensational bottle at this price, it's ripe ...
\n","
very good
\n","
[A sensational bottle at this price, it's ripe...
\n","
good
\n","
A sensational bottle at this price, it's ripe ...
\n","
\n","
\n","
9
\n","
[0.028862619772553444, -0.05910215526819229, -...
\n","
996
\n","
0.605391
\n","
Produced from 25-year-old vines, this is a fre...
\n","
good
\n","
[Produced from 25-year-old vines, this is a fr...
\n","
good
\n","
Produced from 25-year-old vines, this is a fre...
\n","
\n","
\n","
10
\n","
[0.020464830100536346, -0.06415539979934692, -...
\n","
1867
\n","
0.566839
\n","
Despite its power, this is so elegant, showing...
\n","
best
\n","
[Despite its power, this is so elegant, showin...
\n","
good
\n","
Despite its power, this is so elegant, showing...
\n","
\n","
\n","
11
\n","
[-0.005847534164786339, -0.040369633585214615,...
\n","
1891
\n","
0.557577
\n","
So incredibly thick and sweet it's almost chew...
\n","
best
\n","
[So incredibly thick and sweet it's almost che...
\n","
good
\n","
So incredibly thick and sweet it's almost chew...
\n","
\n","
\n","
12
\n","
[0.005895282607525587, -0.02846178226172924, -...
\n","
3542
\n","
0.567143
\n","
By nature Albariño doesn't last long, and thi...
\n","
acceptable
\n","
[By nature Albariño doesn't last long, and th...
\n","
good
\n","
By nature Albariño doesn't last long, and thi...
\n","
\n","
\n","
13
\n","
[0.053103938698768616, -0.031248200684785843, ...
\n","
756
\n","
0.628370
\n","
Made entirely from Cabernet Franc, this opens ...
\n","
good
\n","
[Made entirely from Cabernet Franc, this opens...
\n","
good
\n","
Made entirely from Cabernet Franc, this opens ...
\n","
\n","
\n","
14
\n","
[0.04724583402276039, -0.05935594439506531, -0...
\n","
1456
\n","
0.584150
\n","
Peppery, herbal aromas are gritty and a touch ...
\n","
acceptable
\n","
[Peppery, herbal aromas are gritty and a touch...
\n","
good
\n","
Peppery, herbal aromas are gritty and a touch ...
\n","
\n","
\n","
15
\n","
[0.036762576550245285, -0.027270542457699776, ...
\n","
423
\n","
0.616689
\n","
Wild berries, from elderberry to salmonberry t...
\n","
good
\n","
[Wild berries, from elderberry to salmonberry ...
\n","
good
\n","
Wild berries, from elderberry to salmonberry t...
\n","
\n","
\n","
16
\n","
[-0.011491759680211544, -0.08527237921953201, ...
\n","
1760
\n","
0.551139
\n","
A serious, impressive wine produced from a sma...
\n","
best
\n","
[A serious, impressive wine produced from a sm...
\n","
good
\n","
A serious, impressive wine produced from a sma...
\n","
\n","
\n","
17
\n","
[-0.005252075847238302, -0.020571669563651085,...
\n","
1671
\n","
0.575260
\n","
Right out of the bottle, this 100% Syrah delig...
\n","
very good
\n","
[Right out of the bottle, this 100% Syrah deli...
\n","
good
\n","
Right out of the bottle, this 100% Syrah delig...
\n","
\n","
\n","
18
\n","
[-0.025395192205905914, -0.060151971876621246,...
\n","
4998
\n","
0.578186
\n","
Aromatic and lushly scented with rhubarb, stra...
\n","
very good
\n","
[Aromatic and lushly scented with rhubarb, str...
\n","
good
\n","
Aromatic and lushly scented with rhubarb, stra...
\n","
\n","
\n","
19
\n","
[0.026585254818201065, -0.03247511386871338, 0...
\n","
2561
\n","
0.602712
\n","
A clean nose of citrus paves the way for flavo...
\n","
acceptable
\n","
[A clean nose of citrus paves the way for flav...
\n","
good
\n","
A clean nose of citrus paves the way for flavo...
\n","
\n","
\n","
20
\n","
[0.020379165187478065, -0.01716090366244316, -...
\n","
3950
\n","
0.564268
\n","
Ripe blackberry aromas are a touch malty and j...
\n","
very good
\n","
[Ripe blackberry aromas are a touch malty and ...
\n","
good
\n","
Ripe blackberry aromas are a touch malty and j...
\n","
\n","
\n","
21
\n","
[-0.016609780490398407, -0.036731649190187454,...
\n","
2699
\n","
0.604877
\n","
Balanced toward the bold side, it's full-bodie...
\n","
good
\n","
[Balanced toward the bold side, it's full-bodi...
\n","
good
\n","
Balanced toward the bold side, it's full-bodie...
\n","
\n","
\n","
22
\n","
[-0.015338313765823841, -0.044104497879743576,...
\n","
3078
\n","
0.581559
\n","
Lifted notes of dried pear, dried chamomile fl...
\n","
very good
\n","
[Lifted notes of dried pear, dried chamomile f...
\n","
good
\n","
Lifted notes of dried pear, dried chamomile fl...
\n","
\n","
\n","
23
\n","
[-0.03071591444313526, -0.038018349558115005, ...
\n","
3333
\n","
0.570483
\n","
With its sophisticated mix of mineral, acid an...
\n","
very good
\n","
[With its sophisticated mix of mineral, acid a...
\n","
good
\n","
With its sophisticated mix of mineral, acid an...
\n","
\n","
\n","
24
\n","
[0.06536413729190826, -0.04731231555342674, -0...
\n","
550
\n","
0.503100
\n","
This is really a spectacular wine. It's hard t...
\n","
best
\n","
[This is really a spectacular wine., It's hard...
\n","
good
\n","
This is really a spectacular wine. It's hard t...
\n","
\n","
\n","
25
\n","
[0.03561289235949516, -0.03295084834098816, -0...
\n","
3615
\n","
0.576032
\n","
Bruce McGuire makes a strong case for the pote...
\n","
very good
\n","
[Bruce McGuire makes a strong case for the pot...
\n","
good
\n","
Bruce McGuire makes a strong case for the pote...
\n","
\n","
\n","
26
\n","
[0.017156174406409264, -0.06948413699865341, -...
\n","
1422
\n","
0.570740
\n","
This firm, tannic wine is made from fruit sour...
\n","
best
\n","
[This firm, tannic wine is made from fruit sou...
\n","
good
\n","
This firm, tannic wine is made from fruit sour...
\n","
\n","
\n","
27
\n","
[0.014733278192579746, 0.006120753940194845, -...
\n","
2824
\n","
0.553367
\n","
It's odd to find reductive elements on a white...
\n","
acceptable
\n","
[It's odd to find reductive elements on a whit...
\n","
good
\n","
It's odd to find reductive elements on a white...
\n","
\n","
\n","
28
\n","
[-0.02304786629974842, -0.058774229139089584, ...
\n","
4935
\n","
0.537677
\n","
With the 2009 vintage, De Loach is at the top ...
\n","
best
\n","
[With the 2009 vintage, De Loach is at the top...
\n","
good
\n","
With the 2009 vintage, De Loach is at the top ...
\n","
\n","
\n","
29
\n","
[0.06899003684520721, -0.043633416295051575, -...
\n","
2365
\n","
0.603090
\n","
This nose is rather closed but the firm palate...
\n","
good
\n","
[This nose is rather closed but the firm palat...
\n","
good
\n","
This nose is rather closed but the firm palate...
\n","
\n","
\n","
30
\n","
[-0.035565443336963654, -0.04602222889661789, ...
\n","
2754
\n","
0.619796
\n","
Enticing wildflower, chopped herb and ripe orc...
\n","
very good
\n","
[Enticing wildflower, chopped herb and ripe or...
\n","
good
\n","
Enticing wildflower, chopped herb and ripe orc...
\n","
\n","
\n","
31
\n","
[-0.021357573568820953, -0.07660140097141266, ...
\n","
955
\n","
0.600876
\n","
Fresh and juicy, this full-bodied while struct...
\n","
good
\n","
[Fresh and juicy, this full-bodied while struc...
\n","
good
\n","
Fresh and juicy, this full-bodied while struct...
\n","
\n","
\n","
32
\n","
[-0.008119497448205948, -0.022551201283931732,...
\n","
1632
\n","
0.606923
\n","
The best of the winery's 2011 block reserves, ...
\n","
good
\n","
[The best of the winery's 2011 block reserves,...
\n","
good
\n","
The best of the winery's 2011 block reserves, ...
\n","
\n","
\n","
33
\n","
[-0.010882342234253883, -0.06637204438447952, ...
\n","
3025
\n","
0.584699
\n","
A blend of fruit from three Rogue Valley sites...
\n","
good
\n","
[A blend of fruit from three Rogue Valley site...
\n","
good
\n","
A blend of fruit from three Rogue Valley sites...
\n","
\n","
\n","
34
\n","
[0.016116805374622345, -0.06279352307319641, -...
\n","
3763
\n","
0.549124
\n","
Exceeds even this producer's stunning beerenau...
\n","
best
\n","
[Exceeds even this producer's stunning beerena...
\n","
good
\n","
Exceeds even this producer's stunning beerenau...
\n","
\n","
\n","
35
\n","
[0.0413261242210865, -0.027950072661042213, -0...
\n","
695
\n","
0.532788
\n","
The palate opens slowly, offering an initial c...
\n","
best
\n","
[The palate opens slowly, offering an initial ...
\n","
good
\n","
The palate opens slowly, offering an initial c...
\n","
\n","
\n","
36
\n","
[-0.04491781070828438, -0.016601886600255966, ...
\n","
1196
\n","
0.597868
\n","
This is pretty pale for a Tavel, with a copper...
\n","
good
\n","
[This is pretty pale for a Tavel, with a coppe...
\n","
good
\n","
This is pretty pale for a Tavel, with a copper...
\n","
\n","
\n","
37
\n","
[0.04148593544960022, -0.005966056603938341, -...
\n","
4338
\n","
0.559302
\n","
From the producer's Yountville vineyard, as we...
\n","
acceptable
\n","
[From the producer's Yountville vineyard, as w...
\n","
good
\n","
From the producer's Yountville vineyard, as we...
\n","
\n","
\n","
38
\n","
[-0.044102396816015244, -0.021978359669446945,...
\n","
3514
\n","
0.587864
\n","
Fruit from the oldest blocks in this pioneerin...
\n","
very good
\n","
[Fruit from the oldest blocks in this pioneeri...
\n","
good
\n","
Fruit from the oldest blocks in this pioneerin...
\n","
\n","
\n","
39
\n","
[-0.01988844946026802, -0.07353201508522034, -...
\n","
2551
\n","
0.579697
\n","
More people would crave Mourvèdre if it were ...
\n","
very good
\n","
[More people would crave Mourvèdre if it were...
\n","
good
\n","
More people would crave Mourvèdre if it were ...
\n","
\n","
\n","
40
\n","
[0.03592630848288536, -0.0763244703412056, -0....
\n","
4793
\n","
0.586441
\n","
Seduction may be an odd word to use for a Malb...
\n","
good
\n","
[Seduction may be an odd word to use for a Mal...
\n","
good
\n","
Seduction may be an odd word to use for a Malb...
\n","
\n","
\n","
41
\n","
[-0.027629075571894646, -0.02934451773762703, ...
\n","
2996
\n","
0.625336
\n","
Oceanic aromas of grass, scallion, baby garlic...
\n","
good
\n","
[Oceanic aromas of grass, scallion, baby garli...
\n","
good
\n","
Oceanic aromas of grass, scallion, baby garlic...
\n","
\n","
\n","
42
\n","
[0.017115404829382896, -0.032828159630298615, ...
\n","
706
\n","
0.581963
\n","
As expected, the wine exhibits a dense black c...
\n","
best
\n","
[As expected, the wine exhibits a dense black ...
\n","
good
\n","
As expected, the wine exhibits a dense black c...
\n","
\n","
\n","
43
\n","
[0.030317923054099083, -0.07067029923200607, 0...
\n","
4033
\n","
0.606539
\n","
A mix of ripe black fruits and dense tannins c...
\n","
good
\n","
[A mix of ripe black fruits and dense tannins ...
\n","
good
\n","
A mix of ripe black fruits and dense tannins c...
\n","
\n","
\n","
44
\n","
[-0.018614105880260468, -0.05517773702740669, ...
\n","
1542
\n","
0.529212
\n","
This lightly wood-aged wine, from an estate th...
\n","
very good
\n","
[This lightly wood-aged wine, from an estate t...
\n","
good
\n","
This lightly wood-aged wine, from an estate th...
\n","
\n","
\n","
45
\n","
[0.01225570309907198, -0.03293222188949585, -0...
\n","
2927
\n","
0.590190
\n","
With tannins and new-wood flavors, this rich w...
\n","
good
\n","
[With tannins and new-wood flavors, this rich ...
\n","
good
\n","
With tannins and new-wood flavors, this rich w...
\n","
\n","
\n","
46
\n","
[0.023177076131105423, -0.040385812520980835, ...
\n","
3202
\n","
0.592817
\n","
Plummy chocolate stars in this densely texture...
\n","
good
\n","
[Plummy chocolate stars in this densely textur...
\n","
good
\n","
Plummy chocolate stars in this densely texture...
\n","
\n","
\n","
47
\n","
[-0.0009934792760759592, -0.0660802349448204, ...
\n","
3176
\n","
0.615645
\n","
Cidery aromas vie with bready notes to give th...
\n","
good
\n","
[Cidery aromas vie with bready notes to give t...
\n","
good
\n","
Cidery aromas vie with bready notes to give th...
\n","
\n","
\n","
48
\n","
[-0.055515553802251816, 0.007777589838951826, ...
\n","
646
\n","
0.594357
\n","
Salted apples and white rocks are marred by me...
\n","
acceptable
\n","
[Salted apples and white rocks are marred by m...
\n","
good
\n","
Salted apples and white rocks are marred by me...
\n","
\n","
\n","
49
\n","
[0.03407430648803711, -0.06791502982378006, -0...
\n","
3992
\n","
0.560114
\n","
The minty aromas indicate new wood aging as we...
\n","
very good
\n","
[The minty aromas indicate new wood aging as w...
\n","
good
\n","
The minty aromas indicate new wood aging as we...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_use ... text\n","0 [0.026885146275162697, -0.06771063804626465, 0... ... Full of yellow fruits, ripe apples and soft ac...\n","1 [0.04962018504738808, 0.0652838945388794, -0.0... ... Barnyard aromas atop berry scents make for a n...\n","2 [0.017539022490382195, -0.010785154066979885, ... ... An aromatic twist of passion fruit plays on th...\n","3 [0.016984855756163597, -0.010578665882349014, ... ... Wood smoke and black pepper aromas start this ...\n","4 [0.02070983126759529, -0.05402781069278717, -0... ... Talk about magnetic aromas of bacon, tobacco a...\n","5 [-0.008546369150280952, -8.575373067287728e-05... ... Smoky, oaky, charred flavors of savory plum an...\n","6 [0.0020979971159249544, -0.059595026075839996,... ... Made entirely with Nero d'Avola, this offers a...\n","7 [0.03075247071683407, -0.05452873930335045, -0... ... Remarkably strong cinnamon characterizes the n...\n","8 [-0.005710848607122898, -0.054149989038705826,... ... A sensational bottle at this price, it's ripe ...\n","9 [0.028862619772553444, -0.05910215526819229, -... ... Produced from 25-year-old vines, this is a fre...\n","10 [0.020464830100536346, -0.06415539979934692, -... ... Despite its power, this is so elegant, showing...\n","11 [-0.005847534164786339, -0.040369633585214615,... ... So incredibly thick and sweet it's almost chew...\n","12 [0.005895282607525587, -0.02846178226172924, -... ... By nature Albariño doesn't last long, and thi...\n","13 [0.053103938698768616, -0.031248200684785843, ... ... Made entirely from Cabernet Franc, this opens ...\n","14 [0.04724583402276039, -0.05935594439506531, -0... ... Peppery, herbal aromas are gritty and a touch ...\n","15 [0.036762576550245285, -0.027270542457699776, ... ... Wild berries, from elderberry to salmonberry t...\n","16 [-0.011491759680211544, -0.08527237921953201, ... ... A serious, impressive wine produced from a sma...\n","17 [-0.005252075847238302, -0.020571669563651085,... ... Right out of the bottle, this 100% Syrah delig...\n","18 [-0.025395192205905914, -0.060151971876621246,... ... Aromatic and lushly scented with rhubarb, stra...\n","19 [0.026585254818201065, -0.03247511386871338, 0... ... A clean nose of citrus paves the way for flavo...\n","20 [0.020379165187478065, -0.01716090366244316, -... ... Ripe blackberry aromas are a touch malty and j...\n","21 [-0.016609780490398407, -0.036731649190187454,... ... Balanced toward the bold side, it's full-bodie...\n","22 [-0.015338313765823841, -0.044104497879743576,... ... Lifted notes of dried pear, dried chamomile fl...\n","23 [-0.03071591444313526, -0.038018349558115005, ... ... With its sophisticated mix of mineral, acid an...\n","24 [0.06536413729190826, -0.04731231555342674, -0... ... This is really a spectacular wine. It's hard t...\n","25 [0.03561289235949516, -0.03295084834098816, -0... ... Bruce McGuire makes a strong case for the pote...\n","26 [0.017156174406409264, -0.06948413699865341, -... ... This firm, tannic wine is made from fruit sour...\n","27 [0.014733278192579746, 0.006120753940194845, -... ... It's odd to find reductive elements on a white...\n","28 [-0.02304786629974842, -0.058774229139089584, ... ... With the 2009 vintage, De Loach is at the top ...\n","29 [0.06899003684520721, -0.043633416295051575, -... ... This nose is rather closed but the firm palate...\n","30 [-0.035565443336963654, -0.04602222889661789, ... ... Enticing wildflower, chopped herb and ripe orc...\n","31 [-0.021357573568820953, -0.07660140097141266, ... ... Fresh and juicy, this full-bodied while struct...\n","32 [-0.008119497448205948, -0.022551201283931732,... ... The best of the winery's 2011 block reserves, ...\n","33 [-0.010882342234253883, -0.06637204438447952, ... ... A blend of fruit from three Rogue Valley sites...\n","34 [0.016116805374622345, -0.06279352307319641, -... ... Exceeds even this producer's stunning beerenau...\n","35 [0.0413261242210865, -0.027950072661042213, -0... ... The palate opens slowly, offering an initial c...\n","36 [-0.04491781070828438, -0.016601886600255966, ... ... This is pretty pale for a Tavel, with a copper...\n","37 [0.04148593544960022, -0.005966056603938341, -... ... From the producer's Yountville vineyard, as we...\n","38 [-0.044102396816015244, -0.021978359669446945,... ... Fruit from the oldest blocks in this pioneerin...\n","39 [-0.01988844946026802, -0.07353201508522034, -... ... More people would crave Mourvèdre if it were ...\n","40 [0.03592630848288536, -0.0763244703412056, -0.... ... Seduction may be an odd word to use for a Malb...\n","41 [-0.027629075571894646, -0.02934451773762703, ... ... Oceanic aromas of grass, scallion, baby garlic...\n","42 [0.017115404829382896, -0.032828159630298615, ... ... As expected, the wine exhibits a dense black c...\n","43 [0.030317923054099083, -0.07067029923200607, 0... ... A mix of ripe black fruits and dense tannins c...\n","44 [-0.018614105880260468, -0.05517773702740669, ... ... This lightly wood-aged wine, from an estate th...\n","45 [0.01225570309907198, -0.03293222188949585, -0... ... With tannins and new-wood flavors, this rich w...\n","46 [0.023177076131105423, -0.040385812520980835, ... ... Plummy chocolate stars in this densely texture...\n","47 [-0.0009934792760759592, -0.0660802349448204, ... ... Cidery aromas vie with bready notes to give th...\n","48 [-0.055515553802251816, 0.007777589838951826, ... ... Salted apples and white rocks are marred by me...\n","49 [0.03407430648803711, -0.06791502982378006, -0... ... The minty aromas indicate new wood aging as we...\n","\n","[50 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"lVyOE2wV0fw_"},"source":["# 4. Test the fitted pipe on new example"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"qdCUg2MR0PD2","executionInfo":{"elapsed":132735,"status":"ok","timestamp":1620188769564,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"c6e2d61a-5884-4739-cd8d-e47f51adee25"},"source":["fitted_pipe.predict('It was one of the best wines i ever tasted .')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_use
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
document
\n","
sentence
\n","
trained_classifier
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.0249565988779068, 0.02628515101969242, -0.0...
\n","
0
\n","
0.529498
\n","
It was one of the best wines i ever tasted .
\n","
[It was one of the best wines i ever tasted .]
\n","
good
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_use ... trained_classifier\n","0 [0.0249565988779068, 0.02628515101969242, -0.0... ... good\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"xflpwrVjjBVD"},"source":["## 5. Configure pipe training parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UtsAUGTmOTms","executionInfo":{"elapsed":133127,"status":"ok","timestamp":1620188769982,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"b6e8a835-2cec-4ed9-d3f5-3ed1707e861f"},"source":["trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['classifier_dl'] has settable params:\n","pipe['classifier_dl'].setMaxEpochs(3) | Info: Maximum number of epochs to train | Currently set to : 3\n","pipe['classifier_dl'].setLr(0.005) | Info: Learning Rate | Currently set to : 0.005\n","pipe['classifier_dl'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['classifier_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['classifier_dl'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@587bdb2f) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@587bdb2f\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2GJdDNV9jEIe"},"source":["## 6. Retrain with new parameters"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":793},"id":"mptfvHx-MMMX","executionInfo":{"elapsed":146378,"status":"ok","timestamp":1620188783252,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"94fb762e-f58f-47a5-f016-50d3f21e3c3a"},"source":["# Train longer!\n","trainable_pipe['classifier_dl'].setMaxEpochs(5) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:100])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," acceptable 0.00 0.00 0.00 20\n"," best 0.00 0.00 0.00 20\n"," good 0.00 0.00 0.00 31\n"," very good 0.29 1.00 0.45 29\n","\n"," accuracy 0.29 100\n"," macro avg 0.07 0.25 0.11 100\n","weighted avg 0.08 0.29 0.13 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_use
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
document
\n","
y
\n","
sentence
\n","
trained_classifier
\n","
text
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.026885146275162697, -0.06771063804626465, 0...
\n","
1458
\n","
0.393935
\n","
Full of yellow fruits, ripe apples and soft ac...
\n","
good
\n","
[Full of yellow fruits, ripe apples and soft a...
\n","
very good
\n","
Full of yellow fruits, ripe apples and soft ac...
\n","
\n","
\n","
1
\n","
[0.04962018504738808, 0.0652838945388794, -0.0...
\n","
378
\n","
0.395312
\n","
Barnyard aromas atop berry scents make for a n...
\n","
acceptable
\n","
[Barnyard aromas atop berry scents make for a ...
\n","
very good
\n","
Barnyard aromas atop berry scents make for a n...
\n","
\n","
\n","
2
\n","
[0.017539022490382195, -0.010785154066979885, ...
\n","
17
\n","
0.567956
\n","
An aromatic twist of passion fruit plays on th...
\n","
very good
\n","
[An aromatic twist of passion fruit plays on t...
\n","
very good
\n","
An aromatic twist of passion fruit plays on th...
\n","
\n","
\n","
3
\n","
[0.016984855756163597, -0.010578665882349014, ...
\n","
2456
\n","
0.577995
\n","
Wood smoke and black pepper aromas start this ...
\n","
very good
\n","
[Wood smoke and black pepper aromas start this...
\n","
very good
\n","
Wood smoke and black pepper aromas start this ...
\n","
\n","
\n","
4
\n","
[0.02070983126759529, -0.05402781069278717, -0...
\n","
2103
\n","
0.526734
\n","
Talk about magnetic aromas of bacon, tobacco a...
\n","
best
\n","
[Talk about magnetic aromas of bacon, tobacco ...
\n","
very good
\n","
Talk about magnetic aromas of bacon, tobacco a...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
[0.05499161034822464, -0.04508962109684944, -0...
\n","
392
\n","
0.437371
\n","
This lightly aromatic wine offers notes of her...
\n","
acceptable
\n","
[This lightly aromatic wine offers notes of he...
\n","
very good
\n","
This lightly aromatic wine offers notes of her...
\n","
\n","
\n","
96
\n","
[0.03258365020155907, -0.036026667803525925, -...
\n","
1376
\n","
0.384945
\n","
Brilliant aromatics here, just stupendously at...
\n","
best
\n","
[Brilliant aromatics here, just stupendously a...
\n","
very good
\n","
Brilliant aromatics here, just stupendously at...
\n","
\n","
\n","
97
\n","
[0.004543728660792112, -0.06775235384702682, -...
\n","
3182
\n","
0.405291
\n","
This sparkling wine is intense with startling ...
\n","
acceptable
\n","
[This sparkling wine is intense with startling...
\n","
very good
\n","
This sparkling wine is intense with startling ...
\n","
\n","
\n","
98
\n","
[0.0255692508071661, 0.04986872524023056, -0.0...
\n","
3709
\n","
0.456385
\n","
The nose is like a veil of Golden Delicious ap...
\n","
very good
\n","
[The nose is like a veil of Golden Delicious a...
\n","
very good
\n","
The nose is like a veil of Golden Delicious ap...
\n","
\n","
\n","
99
\n","
[-0.0047778417356312275, -0.05661110579967499,...
\n","
4389
\n","
0.399565
\n","
This wine is smooth and ripe, with soft tannin...
\n","
good
\n","
[This wine is smooth and ripe, with soft tanni...
\n","
very good
\n","
This wine is smooth and ripe, with soft tannin...
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" sentence_embedding_use ... text\n","0 [0.026885146275162697, -0.06771063804626465, 0... ... Full of yellow fruits, ripe apples and soft ac...\n","1 [0.04962018504738808, 0.0652838945388794, -0.0... ... Barnyard aromas atop berry scents make for a n...\n","2 [0.017539022490382195, -0.010785154066979885, ... ... An aromatic twist of passion fruit plays on th...\n","3 [0.016984855756163597, -0.010578665882349014, ... ... Wood smoke and black pepper aromas start this ...\n","4 [0.02070983126759529, -0.05402781069278717, -0... ... Talk about magnetic aromas of bacon, tobacco a...\n",".. ... ... ...\n","95 [0.05499161034822464, -0.04508962109684944, -0... ... This lightly aromatic wine offers notes of her...\n","96 [0.03258365020155907, -0.036026667803525925, -... ... Brilliant aromatics here, just stupendously at...\n","97 [0.004543728660792112, -0.06775235384702682, -... ... This sparkling wine is intense with startling ...\n","98 [0.0255692508071661, 0.04986872524023056, -0.0... ... The nose is like a veil of Golden Delicious ap...\n","99 [-0.0047778417356312275, -0.05661110579967499,... ... This wine is smooth and ripe, with soft tannin...\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"qFoT-s1MjTSS"},"source":["# 7. Try training with different Embeddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxWFzQOhjWC8","executionInfo":{"elapsed":146359,"status":"ok","timestamp":1620188783253,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"0991c8be-f22e-4d35-83be-6bcbe946d57e"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IKK_Ii_gjJfF","executionInfo":{"elapsed":4703913,"status":"ok","timestamp":1620193340823,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"},"user_tz":-300},"outputId":"feb3a543-d15e-4a21-d737-a6a33a18fdd3"},"source":["trainable_pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.classifier')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['classifier_dl'].setMaxEpochs(90) \n","trainable_pipe['classifier_dl'].setLr(0.0005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","\n","#preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," acceptable 0.88 0.51 0.65 1025\n"," best 0.64 0.97 0.77 1009\n"," good 0.47 0.30 0.37 1008\n"," very good 0.46 0.58 0.51 1006\n","\n"," accuracy 0.59 4048\n"," macro avg 0.61 0.59 0.57 4048\n","weighted avg 0.61 0.59 0.57 4048\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 8. evaluate on Test Data"]},{"cell_type":"code","metadata":{"colab":{"background_save":true},"id":"Fxx4yNkNVGFl","outputId":"88c09d44-b5d8-43fe-85fc-238bbaf593ab"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," acceptable 0.89 0.56 0.69 240\n"," best 0.65 0.95 0.77 256\n"," good 0.51 0.31 0.39 257\n"," very good 0.47 0.59 0.52 259\n","\n"," accuracy 0.60 1012\n"," macro avg 0.63 0.60 0.59 1012\n","weighted avg 0.62 0.60 0.59 1012\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 9. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620193806428,"user_tz":-300,"elapsed":74729,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"bb711e14-35b7-4773-f0d8-7211182d7b8b"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 10. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620193822974,"user_tz":-300,"elapsed":16558,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"76335160-560f-4c4d-8556-f3ef98622e23"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('It was one of the best wines i ever tasted .')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_from_disk
\n","
from_disk_confidence_confidence
\n","
origin_index
\n","
document
\n","
sentence
\n","
from_disk
\n","
text
\n","
\n"," \n"," \n","
\n","
0
\n","
[[-0.0787801593542099, 0.1528548002243042, 0.1...
\n","
[0.9994293]
\n","
8589934592
\n","
It was one of the best wines i ever tasted .
\n","
[It was one of the best wines i ever tasted .]
\n","
[best]
\n","
It was one of the best wines i ever tasted .
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_from_disk ... text\n","0 [[-0.0787801593542099, 0.1528548002243042, 0.1... ... It was one of the best wines i ever tasted .\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620193822977,"user_tz":-300,"elapsed":39,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"6752328e-cecd-4025-d11e-481e4b574ac3"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@5ea4fd4c) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@5ea4fd4c\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@sent_small_bert_L12_768'] has settable params:\n","pipe['bert_sentence@sent_small_bert_L12_768'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@sent_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@sent_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@sent_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['classifier_dl@sent_small_bert_L12_768'] has settable params:\n","pipe['classifier_dl@sent_small_bert_L12_768'].setClasses(['very good', 'acceptable', 'best', 'good']) | Info: get the tags used to trained this ClassifierDLModel | Currently set to : ['very good', 'acceptable', 'best', 'good']\n","pipe['classifier_dl@sent_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_label_text_classification/NLU_traing_multi_label_classifier_E2e.ipynb b/examples/colab/Training/multi_label_text_classification/NLU_traing_multi_label_classifier_E2e.ipynb
deleted file mode 100644
index 4da08e1d..00000000
--- a/examples/colab/Training/multi_label_text_classification/NLU_traing_multi_label_classifier_E2e.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_traing_multi_label_classifier_E2e.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_label_text_classification/NLU_traing_multi_label_classifier_E2e.ipynb)\n","\n","\n","\n","# Training a Deep Learning Classifier for multi label prediction\n","MultiClassifierDL is a Multi-label Text Classification. MultiClassifierDL uses a Bidirectional GRU with Convolution model that we have built inside TensorFlow and supports up to 100 classes. The input to MultiClassifierDL is Sentence Embeddings such as state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings\n","\n","\n","\n","### Multi ClassifierDL (Multi-class Text Classification with multiple classes per sentence)\n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#multiclassifierdl-multi-label-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY"},"source":["import os\n","! apt-get update -qq > /dev/null \n","# Install java\n","! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"PATH\"] = os.environ[\"JAVA_HOME\"] + \"/bin:\" + os.environ[\"PATH\"]\n","! pip install nlu pyspark==2.4.7 > /dev/null \n","import nlu"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download E2E Challenge multi token label classification dataset\n","\n","http://www.macs.hw.ac.uk/InteractionLab/E2E/"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":586},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1609529840956,"user_tz":-60,"elapsed":160088,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"39519c61-f3a4-4369-f72a-1f0590d9bb2e"},"source":["import pandas as pd\n","!wget http://ckl-it.de/wp-content/uploads/2020/12/e2e.csv\n","test_path = '/content/e2e.csv'\n","train_df = pd.read_csv(test_path)\n","train_df = train_df.iloc[:3000]\n","train_df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-01-01 19:37:17-- http://ckl-it.de/wp-content/uploads/2020/12/e2e.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1322591 (1.3M) [text/csv]\n","Saving to: ‘e2e.csv’\n","\n","e2e.csv 100%[===================>] 1.26M 715KB/s in 1.8s \n","\n","2021-01-01 19:37:20 (715 KB/s) - ‘e2e.csv’ saved [1322591/1322591]\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
Unnamed: 0
\n","
y
\n","
text
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
name[Blue Spice],eatType[coffee shop],area[cit...
\n","
A coffee shop in the city centre area called B...
\n","
0
\n","
\n","
\n","
1
\n","
1
\n","
name[Blue Spice],eatType[coffee shop],area[cit...
\n","
Blue Spice is a coffee shop in city centre.
\n","
1
\n","
\n","
\n","
2
\n","
2
\n","
name[Blue Spice],eatType[coffee shop],area[riv...
\n","
There is a coffee shop Blue Spice in the river...
\n","
2
\n","
\n","
\n","
3
\n","
3
\n","
name[Blue Spice],eatType[coffee shop],area[riv...
\n","
At the riverside, there is a coffee shop calle...
\n","
3
\n","
\n","
\n","
4
\n","
4
\n","
name[Blue Spice],eatType[coffee shop],customer...
\n","
The coffee shop Blue Spice is based near Crown...
\n","
4
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
2995
\n","
2995
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
Near Express by Holiday Inn, in the riverside ...
\n","
2995
\n","
\n","
\n","
2996
\n","
2996
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
In the riverside area, near Express by Holiday...
\n","
2996
\n","
\n","
\n","
2997
\n","
2997
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
The Punter is a restaurant with Indian food in...
\n","
2997
\n","
\n","
\n","
2998
\n","
2998
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
The Punter is a low rated restaurant that serv...
\n","
2998
\n","
\n","
\n","
2999
\n","
2999
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
The Punter is a restaurant providing Indian fo...
\n","
2999
\n","
\n"," \n","
\n","
3000 rows × 4 columns
\n","
"],"text/plain":[" Unnamed: 0 ... origin_index\n","0 0 ... 0\n","1 1 ... 1\n","2 2 ... 2\n","3 3 ... 3\n","4 4 ... 4\n","... ... ... ...\n","2995 2995 ... 2995\n","2996 2996 ... 2996\n","2997 2997 ... 2997\n","2998 2998 ... 2998\n","2999 2999 ... 2999\n","\n","[3000 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.multi_classifier')\n","\n","By default, the Universal Sentence Encoder Embeddings (USE) are beeing downloaded to provide embeddings for the classifier. You can use any of the 50+ other sentence Emeddings in NLU tough!\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":471},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1609522208492,"user_tz":-60,"elapsed":410284,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"bda58bd4-d56e-471c-deea-37fe6e06af5e"},"source":["import nlu\n","# load a trainable pipeline by specifying the train prefix \n","unfitted_pipe = nlu.load('train.multi_classifier')\n","#configure epochs\n","unfitted_pipe['multi_classifier'].setMaxEpochs(25)\n","# fit it on a datset with label='y' and text columns. Labels seperated by ','\n","fitted_pipe = unfitted_pipe.fit(train_df[['y','text']], label_seperator=',')\n","\n","# predict with the trained pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df[['y','text']])\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
multi_classifier_classes
\n","
multi_classifier_confidences
\n","
default_name_embeddings
\n","
y
\n","
sentence
\n","
text
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n","
\n","
\n","
\n","
\n"," \n"," \n","
\n","
0
\n","
[near[Café Rouge], name[Blue Spice], near[Rain...
\n","
[0.8555223, 0.99276984, 0.87128675, 0.9852337,...
\n","
[0.026563657447695732, -0.058662936091423035, ...
\n","
name[Blue Spice],eatType[coffee shop],area[cit...
\n","
A coffee shop in the city centre area called B...
\n","
A coffee shop in the city centre area called B...
\n","
\n","
\n","
1
\n","
[near[Café Rouge], name[Blue Spice], near[Rain...
\n","
[0.8142674, 0.99920505, 0.93413615, 0.98056525...
\n","
[0.040952689945697784, -0.04276810586452484, -...
\n","
name[Blue Spice],eatType[coffee shop],area[cit...
\n","
Blue Spice is a coffee shop in city centre.
\n","
Blue Spice is a coffee shop in city centre.
\n","
\n","
\n","
2
\n","
[name[Blue Spice], near[Rainbow Vegetarian Caf...
\n","
[0.9966337, 0.9044244, 0.904881, 0.56231284, 0...
\n","
[0.03141527622938156, -0.05154882371425629, 0....
\n","
name[Blue Spice],eatType[coffee shop],area[riv...
\n","
There is a coffee shop Blue Spice in the river...
\n","
There is a coffee shop Blue Spice in the river...
\n","
\n","
\n","
3
\n","
[near[Café Rouge], name[Blue Spice], near[Rain...
\n","
[0.5227911, 0.99917483, 0.9394022, 0.8839797, ...
\n","
[0.03584946319460869, -0.036898739635944366, -...
\n","
name[Blue Spice],eatType[coffee shop],area[riv...
\n","
At the riverside, there is a coffee shop calle...
\n","
At the riverside, there is a coffee shop calle...
\n","
\n","
\n","
4
\n","
[near[Café Rouge], name[Blue Spice], near[Crow...
\n","
[0.5985904, 0.7892299, 0.8222753, 0.9378743, 0...
\n","
[0.0405426099896431, -0.0243277158588171, 0.00...
\n","
name[Blue Spice],eatType[coffee shop],customer...
\n","
The coffee shop Blue Spice is based near Crown...
\n","
The coffee shop Blue Spice is based near Crown...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
2998
\n","
[near[Express by Holiday Inn], priceRange[high...
\n","
[0.9999982, 0.8146039, 0.99978125, 0.8511795, ...
\n","
[0.05956212058663368, 0.019028551876544952, -0...
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
The Punter has a price range of less than £20,...
\n","
The Punter is a low rated restaurant that serv...
\n","
\n","
\n","
2999
\n","
[near[Express by Holiday Inn], food[Indian], c...
\n","
[0.99992794, 0.99981034, 0.5099642, 0.9994041,...
\n","
[0.04296032711863518, -0.0015949805965647101, ...
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
The Punter is a restaurant providing Indian fo...
\n","
The Punter is a restaurant providing Indian fo...
\n","
\n","
\n","
2999
\n","
[near[Express by Holiday Inn], food[Indian], c...
\n","
[0.99992794, 0.99981034, 0.5099642, 0.9994041,...
\n","
[0.023289771750569344, 0.056861914694309235, -...
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
It is located in the riverside.
\n","
The Punter is a restaurant providing Indian fo...
\n","
\n","
\n","
2999
\n","
[near[Express by Holiday Inn], food[Indian], c...
\n","
[0.99992794, 0.99981034, 0.5099642, 0.9994041,...
\n","
[0.033101629465818405, 0.06402800232172012, 0....
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
It is near Express by Holiday Inn.
\n","
The Punter is a restaurant providing Indian fo...
\n","
\n","
\n","
2999
\n","
[near[Express by Holiday Inn], food[Indian], c...
\n","
[0.99992794, 0.99981034, 0.5099642, 0.9994041,...
\n","
[0.01677701249718666, 0.04876527190208435, -0....
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
Its customer rating is low.
\n","
The Punter is a restaurant providing Indian fo...
\n","
\n"," \n","
\n","
5266 rows × 6 columns
\n","
"],"text/plain":[" multi_classifier_classes ... text\n","origin_index ... \n","0 [near[Café Rouge], name[Blue Spice], near[Rain... ... A coffee shop in the city centre area called B...\n","1 [near[Café Rouge], name[Blue Spice], near[Rain... ... Blue Spice is a coffee shop in city centre.\n","2 [name[Blue Spice], near[Rainbow Vegetarian Caf... ... There is a coffee shop Blue Spice in the river...\n","3 [near[Café Rouge], name[Blue Spice], near[Rain... ... At the riverside, there is a coffee shop calle...\n","4 [near[Café Rouge], name[Blue Spice], near[Crow... ... The coffee shop Blue Spice is based near Crown...\n","... ... ... ...\n","2998 [near[Express by Holiday Inn], priceRange[high... ... The Punter is a low rated restaurant that serv...\n","2999 [near[Express by Holiday Inn], food[Indian], c... ... The Punter is a restaurant providing Indian fo...\n","2999 [near[Express by Holiday Inn], food[Indian], c... ... The Punter is a restaurant providing Indian fo...\n","2999 [near[Express by Holiday Inn], food[Indian], c... ... The Punter is a restaurant providing Indian fo...\n","2999 [near[Express by Holiday Inn], food[Indian], c... ... The Punter is a restaurant providing Indian fo...\n","\n","[5266 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"DL_5aY9b3jSd"},"source":["# 4. Evaluate the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0YDA2KunCeqQ","executionInfo":{"status":"ok","timestamp":1609522209572,"user_tz":-60,"elapsed":411343,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"37539c88-d18c-425d-a28d-4127dc9bbb99"},"source":["from sklearn.preprocessing import MultiLabelBinarizer\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import f1_score\n","from sklearn.metrics import roc_auc_score\n","mlb = MultiLabelBinarizer()\n","mlb = mlb.fit(preds.y.str.split(','))\n","y_true = mlb.transform(preds['y'].str.split(','))\n","y_pred = mlb.transform(preds.multi_classifier_classes.str.join(',').str.split(','))\n","print(\"Classification report: \\n\", (classification_report(y_true, y_pred)))\n","print(\"F1 micro averaging:\",(f1_score(y_true, y_pred, average='micro')))\n","print(\"ROC: \",(roc_auc_score(y_true, y_pred, average=\"micro\")))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Classification report: \n"," precision recall f1-score support\n","\n"," 0 0.78 0.97 0.86 1700\n"," 1 0.95 0.83 0.89 2914\n"," 2 0.56 0.64 0.60 576\n"," 3 0.33 0.28 0.30 367\n"," 4 0.38 0.55 0.45 455\n"," 5 0.30 0.76 0.42 599\n"," 6 0.37 0.77 0.50 550\n"," 7 0.69 0.44 0.54 457\n"," 8 0.99 0.72 0.84 337\n"," 9 0.91 0.98 0.95 2211\n"," 10 0.89 0.99 0.94 2718\n"," 11 0.53 0.89 0.67 1914\n"," 12 0.88 0.79 0.84 3154\n"," 13 0.79 0.98 0.87 1087\n"," 14 0.69 0.97 0.81 1118\n"," 15 0.98 0.64 0.78 1077\n"," 16 0.82 0.96 0.88 671\n"," 17 0.71 1.00 0.83 323\n"," 18 0.57 0.65 0.61 130\n"," 19 0.96 0.80 0.87 186\n"," 20 0.77 0.99 0.87 366\n"," 21 0.57 0.20 0.30 40\n"," 22 0.36 0.10 0.15 42\n"," 23 0.00 0.00 0.00 4\n"," 24 0.97 0.97 0.97 322\n"," 25 0.99 0.83 0.91 338\n"," 26 0.00 0.00 0.00 6\n"," 27 0.00 0.00 0.00 34\n"," 28 0.94 0.99 0.96 1273\n"," 29 0.96 1.00 0.98 987\n"," 30 0.90 0.99 0.95 1140\n"," 31 0.74 0.85 0.79 186\n"," 32 0.45 0.98 0.62 528\n"," 33 0.91 0.97 0.93 662\n"," 34 0.90 0.60 0.72 116\n"," 35 0.67 0.09 0.16 22\n"," 36 0.58 0.98 0.73 484\n"," 37 0.88 0.77 0.82 601\n"," 38 0.94 0.97 0.96 711\n"," 39 0.99 0.96 0.97 620\n"," 40 0.96 0.99 0.98 526\n"," 41 0.98 1.00 0.99 1410\n"," 42 1.00 0.28 0.43 72\n"," 43 0.00 0.00 0.00 8\n"," 44 0.00 0.00 0.00 8\n"," 45 0.00 0.00 0.00 4\n"," 46 0.35 0.42 0.38 595\n"," 47 0.34 0.66 0.45 849\n"," 48 0.57 0.44 0.50 627\n"," 49 0.69 0.53 0.60 767\n"," 50 0.31 0.32 0.32 347\n"," 51 0.25 0.53 0.34 453\n","\n"," micro avg 0.73 0.84 0.78 36692\n"," macro avg 0.64 0.65 0.62 36692\n","weighted avg 0.78 0.84 0.80 36692\n"," samples avg 0.76 0.84 0.79 36692\n","\n","F1 micro averaging: 0.7831856729396004\n","ROC: 0.8980818453315285\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"mhFKVN93o1ZO"},"source":["# 5. Lets try different Sentence Emebddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CzJd8omao0gt","executionInfo":{"status":"ok","timestamp":1609522209573,"user_tz":-60,"elapsed":411328,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"ce35ce12-fbc8-4e0f-c9a1-6feaf68da7b0"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0ofYHpu7sloS","executionInfo":{"status":"ok","timestamp":1609529895586,"user_tz":-60,"elapsed":54621,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"44154b28-c1db-4f58-bab1-7ac185fa40b8"},"source":["# You might need to restart your notebook to clear RAM, or you might run out of Memory when fitting\n","import nlu\n","pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.multi_classifier')\n","pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['en_embed_sentence_small_bert_L12_768'] has settable params:\n","pipe['en_embed_sentence_small_bert_L12_768'].setBatchSize(32) | Info: Batch size. Large values allows faster processing but requires more memory. | Currently set to : 32\n","pipe['en_embed_sentence_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['en_embed_sentence_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['en_embed_sentence_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['en_embed_sentence_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['en_embed_sentence_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['sentence_detector'] has settable params:\n","pipe['sentence_detector'].setUseAbbreviations(True) | Info: whether to apply abbreviations at sentence detection | Currently set to : True\n","pipe['sentence_detector'].setDetectLists(True) | Info: whether detect lists during sentence detection | Currently set to : True\n","pipe['sentence_detector'].setUseCustomBoundsOnly(False) | Info: Only utilize custom bounds in sentence detection | Currently set to : False\n","pipe['sentence_detector'].setCustomBounds([]) | Info: characters used to explicitly mark sentence bounds | Currently set to : []\n","pipe['sentence_detector'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector'].setMinLength(0) | Info: Set the minimum allowed length for each sentence. | Currently set to : 0\n","pipe['sentence_detector'].setMaxLength(99999) | Info: Set the maximum allowed length for each sentence | Currently set to : 99999\n",">>> pipe['default_tokenizer'] has settable params:\n","pipe['default_tokenizer'].setTargetPattern('\\S+') | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n","pipe['default_tokenizer'].setContextChars(['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]) | Info: character list used to separate from token boundaries | Currently set to : ['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]\n","pipe['default_tokenizer'].setCaseSensitiveExceptions(True) | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n","pipe['default_tokenizer'].setMinLength(0) | Info: Set the minimum allowed legth for each token | Currently set to : 0\n","pipe['default_tokenizer'].setMaxLength(99999) | Info: Set the maximum allowed legth for each token | Currently set to : 99999\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['multi_classifier'] has settable params:\n","pipe['multi_classifier'].setMaxEpochs(2) | Info: Maximum number of epochs to train | Currently set to : 2\n","pipe['multi_classifier'].setLr(0.001) | Info: Learning Rate | Currently set to : 0.001\n","pipe['multi_classifier'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['multi_classifier'].setValidationSplit(0.0) | Info: Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off. | Currently set to : 0.0\n","pipe['multi_classifier'].setThreshold(0.5) | Info: The minimum threshold for each label to be accepted. Default is 0.5 | Currently set to : 0.5\n","pipe['multi_classifier'].setRandomSeed(44) | Info: Random seed | Currently set to : 44\n","pipe['multi_classifier'].setShufflePerEpoch(False) | Info: whether to shuffle the training data on each Epoch | Currently set to : False\n","pipe['multi_classifier'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ABHLgirmG1n9","colab":{"base_uri":"https://localhost:8080/","height":417},"executionInfo":{"status":"ok","timestamp":1609531977887,"user_tz":-60,"elapsed":2136903,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"d312277d-3826-46e2-c67e-4a10a7116c4f"},"source":["\n","# Load pipe with bert embeds and configure hyper parameters\n","# using large embeddings can take a few hours..\n","pipe['multi_classifier'].setMaxEpochs(100) \n","pipe['multi_classifier'].setLr(0.0005) \n","fitted_pipe = pipe.fit(train_df[['y','text']],label_seperator=',')\n","preds = fitted_pipe.predict(train_df)\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
multi_classifier_classes
\n","
Unnamed: 0
\n","
document
\n","
y
\n","
multi_classifier_confidences
\n","
en_embed_sentence_small_bert_L12_768_embeddings
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n","
\n","
\n","
\n","
\n","
\n"," \n"," \n","
\n","
0
\n","
A coffee shop in the city centre area called B...
\n","
[name[Blue Spice], eatType[coffee shop], area[...
\n","
0
\n","
A coffee shop in the city centre area called B...
\n","
name[Blue Spice],eatType[coffee shop],area[cit...
\n","
[0.9740321, 0.99538183, 0.92562413]
\n","
[-0.1427491158246994, 0.5036071538925171, 0.07...
\n","
\n","
\n","
1
\n","
Blue Spice is a coffee shop in city centre.
\n","
[name[Blue Spice], eatType[coffee shop], area[...
\n","
1
\n","
Blue Spice is a coffee shop in city centre.
\n","
name[Blue Spice],eatType[coffee shop],area[cit...
\n","
[0.9950888, 0.9989519, 0.8684354]
\n","
[-0.20697341859340668, 0.5286431312561035, 0.2...
\n","
\n","
\n","
2
\n","
There is a coffee shop Blue Spice in the river...
\n","
[name[Blue Spice], eatType[coffee shop], area[...
\n","
2
\n","
There is a coffee shop Blue Spice in the river...
\n","
name[Blue Spice],eatType[coffee shop],area[riv...
\n","
[0.95310336, 0.9655487, 0.9785502]
\n","
[0.005826675333082676, 0.49930453300476074, -0...
\n","
\n","
\n","
3
\n","
At the riverside, there is a coffee shop calle...
\n","
[name[Blue Spice], eatType[coffee shop], area[...
\n","
3
\n","
At the riverside, there is a coffee shop calle...
\n","
name[Blue Spice],eatType[coffee shop],area[riv...
\n","
[0.8858954, 0.931189, 0.9990605]
\n","
[0.12191159278154373, 0.37966835498809814, 0.0...
\n","
\n","
\n","
4
\n","
The coffee shop Blue Spice is based near Crown...
\n","
[near[Crowne Plaza Hotel], customer rating[5 o...
\n","
4
\n","
The coffee shop Blue Spice is based near Crown...
\n","
name[Blue Spice],eatType[coffee shop],customer...
\n","
[0.99912286, 0.7930833, 0.9730882]
\n","
[-0.37350592017173767, 0.1885937601327896, 0.1...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
2995
\n","
Near Express by Holiday Inn, in the riverside ...
\n","
[near[Express by Holiday Inn], customer rating...
\n","
2995
\n","
Near Express by Holiday Inn, in the riverside ...
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
[0.9476669, 0.9914391, 0.8395983, 0.98047745, ...
\n","
[0.0485222227871418, 0.2381688505411148, 0.227...
\n","
\n","
\n","
2996
\n","
In the riverside area, near Express by Holiday...
\n","
[near[Express by Holiday Inn], food[Indian], c...
\n","
2996
\n","
In the riverside area, near Express by Holiday...
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
[0.94435394, 0.6119035, 0.7891044, 0.9885667, ...
\n","
[0.06879807263612747, 0.23580998182296753, 0.1...
\n","
\n","
\n","
2997
\n","
The Punter is a restaurant with Indian food in...
\n","
[near[Express by Holiday Inn], food[Indian], c...
\n","
2997
\n","
The Punter is a restaurant with Indian food in...
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
[0.99509084, 0.9424925, 0.7625178, 0.9907007, ...
\n","
[-0.12667560577392578, 0.22056235373020172, 0....
\n","
\n","
\n","
2998
\n","
The Punter is a low rated restaurant that serv...
\n","
[near[Express by Holiday Inn], food[Indian], c...
\n","
2998
\n","
The Punter is a low rated restaurant that serv...
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
[0.99541605, 0.9715836, 0.87202764, 0.99880993...
\n","
[-0.13057495653629303, 0.21937601268291473, 0....
\n","
\n","
\n","
2999
\n","
The Punter is a restaurant providing Indian fo...
\n","
[near[Express by Holiday Inn], food[Indian], c...
\n","
2999
\n","
The Punter is a restaurant providing Indian fo...
\n","
name[The Punter],eatType[restaurant],food[Indi...
\n","
[0.98941034, 0.99086845, 0.82358456, 0.985973,...
\n","
[-0.10767646133899689, 0.2529870569705963, 0.2...
\n","
\n"," \n","
\n","
3000 rows × 7 columns
\n","
"],"text/plain":[" text ... en_embed_sentence_small_bert_L12_768_embeddings\n","origin_index ... \n","0 A coffee shop in the city centre area called B... ... [-0.1427491158246994, 0.5036071538925171, 0.07...\n","1 Blue Spice is a coffee shop in city centre. ... [-0.20697341859340668, 0.5286431312561035, 0.2...\n","2 There is a coffee shop Blue Spice in the river... ... [0.005826675333082676, 0.49930453300476074, -0...\n","3 At the riverside, there is a coffee shop calle... ... [0.12191159278154373, 0.37966835498809814, 0.0...\n","4 The coffee shop Blue Spice is based near Crown... ... [-0.37350592017173767, 0.1885937601327896, 0.1...\n","... ... ... ...\n","2995 Near Express by Holiday Inn, in the riverside ... ... [0.0485222227871418, 0.2381688505411148, 0.227...\n","2996 In the riverside area, near Express by Holiday... ... [0.06879807263612747, 0.23580998182296753, 0.1...\n","2997 The Punter is a restaurant with Indian food in... ... [-0.12667560577392578, 0.22056235373020172, 0....\n","2998 The Punter is a low rated restaurant that serv... ... [-0.13057495653629303, 0.21937601268291473, 0....\n","2999 The Punter is a restaurant providing Indian fo... ... [-0.10767646133899689, 0.2529870569705963, 0.2...\n","\n","[3000 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"E7ah2LM6tIhG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1609531978935,"user_tz":-60,"elapsed":2137934,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2636e995-5ef1-4457-895e-adcdf34f40c1"},"source":["from sklearn.preprocessing import MultiLabelBinarizer\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import f1_score\n","from sklearn.metrics import roc_auc_score\n","mlb = MultiLabelBinarizer()\n","mlb = mlb.fit(preds.y.str.split(','))\n","y_true = mlb.transform(preds['y'].str.split(','))\n","y_pred = mlb.transform(preds.multi_classifier_classes.str.join(',').str.split(','))\n","print(\"Classification report: \\n\", (classification_report(y_true, y_pred)))\n","print(\"F1 micro averaging:\",(f1_score(y_true, y_pred, average='micro')))\n","print(\"ROC: \",(roc_auc_score(y_true, y_pred, average=\"micro\")))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Classification report: \n"," precision recall f1-score support\n","\n"," 0 0.97 0.98 0.97 846\n"," 1 0.99 0.98 0.98 1642\n"," 2 0.93 0.70 0.80 300\n"," 3 0.90 0.56 0.69 209\n"," 4 0.91 0.72 0.81 246\n"," 5 0.91 0.79 0.85 333\n"," 6 0.95 0.84 0.90 288\n"," 7 0.91 0.82 0.86 260\n"," 8 0.99 0.99 0.99 267\n"," 9 1.00 0.99 0.99 1275\n"," 10 0.99 0.99 0.99 1458\n"," 11 0.96 0.90 0.93 976\n"," 12 0.95 0.97 0.96 1844\n"," 13 1.00 0.99 0.99 492\n"," 14 0.99 0.98 0.99 613\n"," 15 0.97 0.98 0.98 632\n"," 16 0.99 0.97 0.98 365\n"," 17 1.00 0.97 0.99 145\n"," 18 1.00 0.93 0.96 83\n"," 19 1.00 0.98 0.99 136\n"," 20 1.00 0.99 0.99 228\n"," 21 1.00 0.69 0.82 36\n"," 22 1.00 0.95 0.97 38\n"," 23 1.00 0.50 0.67 4\n"," 24 1.00 1.00 1.00 222\n"," 25 0.99 1.00 0.99 240\n"," 26 1.00 0.67 0.80 6\n"," 27 1.00 0.94 0.97 32\n"," 28 0.99 1.00 0.99 703\n"," 29 1.00 1.00 1.00 524\n"," 30 1.00 1.00 1.00 612\n"," 31 1.00 0.94 0.97 88\n"," 32 1.00 0.97 0.98 267\n"," 33 1.00 1.00 1.00 297\n"," 34 1.00 0.98 0.99 82\n"," 35 1.00 0.89 0.94 18\n"," 36 1.00 0.97 0.98 251\n"," 37 1.00 1.00 1.00 348\n"," 38 1.00 1.00 1.00 393\n"," 39 1.00 0.99 1.00 390\n"," 40 1.00 0.98 0.99 333\n"," 41 1.00 1.00 1.00 794\n"," 42 1.00 0.98 0.99 52\n"," 43 1.00 0.50 0.67 8\n"," 44 1.00 0.88 0.93 8\n"," 45 0.00 0.00 0.00 4\n"," 46 0.90 0.78 0.83 303\n"," 47 0.89 0.70 0.78 425\n"," 48 0.89 0.78 0.83 349\n"," 49 0.93 0.80 0.86 373\n"," 50 0.82 0.42 0.56 170\n"," 51 0.95 0.67 0.79 220\n","\n"," micro avg 0.98 0.94 0.95 20228\n"," macro avg 0.96 0.86 0.90 20228\n","weighted avg 0.97 0.94 0.95 20228\n"," samples avg 0.98 0.94 0.96 20228\n","\n","F1 micro averaging: 0.9549113112810033\n","ROC: 0.9659676982287029\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1609535641300,"user_tz":-60,"elapsed":243837,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"458863e7-50f4-4cfe-dfdd-1b3edde4e8d8"},"source":["stored_model_path = './models/multi_classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/multi_classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":103},"executionInfo":{"status":"ok","timestamp":1609535674624,"user_tz":-60,"elapsed":274401,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"589912b1-32b5-4333-fe84-46cf40658451"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Tesla plans to invest 10M into the ML sector')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
multi_classifier_classes
\n","
document
\n","
multi_classifier_confidences
\n","
en_embed_sentence_small_bert_L12_768_embeddings
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n","
\n","
\n"," \n"," \n","
\n","
0
\n","
[customer rating[high], customer rating[low], ...
\n","
Tesla plans to invest 10M into the ML sector
\n","
[0.9597453, 0.6497742, 0.986845, 0.5315694, 0....
\n","
[0.15737222135066986, 0.2598555386066437, 0.85...
\n","
\n"," \n","
\n","
"],"text/plain":[" multi_classifier_classes ... en_embed_sentence_small_bert_L12_768_embeddings\n","origin_index ... \n","0 [customer rating[high], customer rating[low], ... ... [0.15737222135066986, 0.2598555386066437, 0.85...\n","\n","[1 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1609535674627,"user_tz":-60,"elapsed":273679,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"926c0a81-339a-49b8-e9ea-7f3ce049ca01"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['regex_tokenizer'] has settable params:\n","pipe['regex_tokenizer'].setCaseSensitiveExceptions(True) | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n","pipe['regex_tokenizer'].setTargetPattern('\\S+') | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n","pipe['regex_tokenizer'].setMaxLength(99999) | Info: Set the maximum allowed length for each token | Currently set to : 99999\n","pipe['regex_tokenizer'].setMinLength(0) | Info: Set the minimum allowed length for each token | Currently set to : 0\n",">>> pipe['sentence_detector'] has settable params:\n","pipe['sentence_detector'].setCustomBounds([]) | Info: characters used to explicitly mark sentence bounds | Currently set to : []\n","pipe['sentence_detector'].setDetectLists(True) | Info: whether detect lists during sentence detection | Currently set to : True\n","pipe['sentence_detector'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector'].setMaxLength(99999) | Info: Set the maximum allowed length for each sentence | Currently set to : 99999\n","pipe['sentence_detector'].setMinLength(0) | Info: Set the minimum allowed length for each sentence. | Currently set to : 0\n","pipe['sentence_detector'].setUseAbbreviations(True) | Info: whether to apply abbreviations at sentence detection | Currently set to : True\n","pipe['sentence_detector'].setUseCustomBoundsOnly(False) | Info: Only utilize custom bounds in sentence detection | Currently set to : False\n",">>> pipe['glove'] has settable params:\n","pipe['glove'].setBatchSize(32) | Info: Batch size. Large values allows faster processing but requires more memory. | Currently set to : 32\n","pipe['glove'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['glove'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['glove'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['glove'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['glove'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['multi_classifier'] has settable params:\n","pipe['multi_classifier'].setThreshold(0.5) | Info: The minimum threshold for each label to be accepted. Default is 0.5 | Currently set to : 0.5\n","pipe['multi_classifier'].setClasses(['name[Clowns]', 'name[Cotto]', 'near[Burger King]', 'near[Crowne Plaza Hotel]', 'customer rating[high]', 'near[Avalon]', 'near[The Bakers]', 'near[Ranch]', 'eatType[restaurant]', 'near[All Bar One]', 'customer rating[low]', 'near[Café Sicilia]', 'food[Indian]', 'eatType[pub]', 'name[Green Man]', 'name[Strada]', 'eatType[coffee shop]', 'name[Loch Fyne]', 'customer rating[5 out of 5]', 'near[Express by Holiday Inn]', 'food[French]', 'name[The Mill]', 'food[Japanese]', 'name[The Plough]', 'name[Cocum]', 'name[The Phoenix]', 'priceRange[cheap]', 'near[Rainbow Vegetarian Café]', 'near[The Rice Boat]', 'customer rating[3 out of 5]', 'customer rating[1 out of 5]', 'name[The Cricketers]', 'area[riverside]', 'name[Blue Spice]', 'priceRange[£20-25]', 'priceRange[less than £20]', 'priceRange[moderate]', 'priceRange[high]', 'name[Giraffe]', 'customer rating[average]', 'food[Fast food]', 'near[Café Rouge]', 'area[city centre]', 'familyFriendly[no]', 'food[Chinese]', 'food[Italian]', 'near[Raja Indian Cuisine]', 'priceRange[more than £30]', 'name[The Punter]', 'food[English]', 'near[The Sorrento]', 'familyFriendly[yes]']) | Info: get the tags used to trained this NerDLModel | Currently set to : ['name[Clowns]', 'name[Cotto]', 'near[Burger King]', 'near[Crowne Plaza Hotel]', 'customer rating[high]', 'near[Avalon]', 'near[The Bakers]', 'near[Ranch]', 'eatType[restaurant]', 'near[All Bar One]', 'customer rating[low]', 'near[Café Sicilia]', 'food[Indian]', 'eatType[pub]', 'name[Green Man]', 'name[Strada]', 'eatType[coffee shop]', 'name[Loch Fyne]', 'customer rating[5 out of 5]', 'near[Express by Holiday Inn]', 'food[French]', 'name[The Mill]', 'food[Japanese]', 'name[The Plough]', 'name[Cocum]', 'name[The Phoenix]', 'priceRange[cheap]', 'near[Rainbow Vegetarian Café]', 'near[The Rice Boat]', 'customer rating[3 out of 5]', 'customer rating[1 out of 5]', 'name[The Cricketers]', 'area[riverside]', 'name[Blue Spice]', 'priceRange[£20-25]', 'priceRange[less than £20]', 'priceRange[moderate]', 'priceRange[high]', 'name[Giraffe]', 'customer rating[average]', 'food[Fast food]', 'near[Café Rouge]', 'area[city centre]', 'familyFriendly[no]', 'food[Chinese]', 'food[Italian]', 'near[Raja Indian Cuisine]', 'priceRange[more than £30]', 'name[The Punter]', 'food[English]', 'near[The Sorrento]', 'familyFriendly[yes]']\n","pipe['multi_classifier'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"M1LjAwJVJxun"},"source":[" "],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_label_text_classification/NLU_training_multi_token_label_text_classifier_stackoverflow_tags.ipynb b/examples/colab/Training/multi_label_text_classification/NLU_training_multi_token_label_text_classifier_stackoverflow_tags.ipynb
deleted file mode 100644
index 0505a4cf..00000000
--- a/examples/colab/Training/multi_label_text_classification/NLU_training_multi_token_label_text_classifier_stackoverflow_tags.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_multi_token_label_text_classifier_stackoverflow_tags.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_label_text_classification/NLU_training_multi_token_label_text_classifier_stackoverflow_tags.ipynb)\n","\n","\n","\n","# Training a Deep Learning Classifier for sentences with multiple classes at the same time \n","MultiClassifierDL is a Multi-label Text Classification. MultiClassifierDL uses a Bidirectional GRU with Convolution model that we have built inside TensorFlow and supports up to 100 classes. The input to MultiClassifierDL is Sentence Embeddings such as state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings\n","\n","\n","\n","### Multi ClassifierDL (Multi-class Text Classification with multiple classes per sentence)\n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#multiclassifierdl-multi-label-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY"},"source":["import os\n","! apt-get update -qq > /dev/null \n","# Install java\n","! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"PATH\"] = os.environ[\"JAVA_HOME\"] + \"/bin:\" + os.environ[\"PATH\"]\n","! pip install nlu > /dev/null pyspark==2.4.7\n","import nlu"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2 Download sample dataset 60k Stack Overflow Questions with Quality Rating\n","\n","\n","https://www.kaggle.com/imoore/60k-stack-overflow-questions-with-quality-rate"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y4xSRWIhwT28","outputId":"f7ac934c-b18f-4ffd-d773-842c81b2a80a"},"source":["import pandas as pd\n","! wget -N https://ckl-it.de/wp-content/uploads/2020/11/60kstackoverflow.csv -P /tmp\n","test_path = '/tmp/60kstackoverflow.csv'\n","train_df = pd.read_csv(test_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-01-02 11:20:29-- https://ckl-it.de/wp-content/uploads/2020/11/60kstackoverflow.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50356825 (48M) [text/csv]\n","Saving to: ‘/tmp/60kstackoverflow.csv’\n","\n","60kstackoverflow.cs 100%[===================>] 48.02M 2.57MB/s in 21s \n","\n","2021-01-02 11:20:51 (2.32 MB/s) - ‘/tmp/60kstackoverflow.csv’ saved [50356825/50356825]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"gBxgVIB787wd"},"source":["# Split labels and clean them.\n","import pandas as pd\n","\n","train_df = pd.read_csv(test_path)\n","\n","f = lambda x : x.replace('<','').replace('>','')\n","g = lambda l : list(map(f,l))\n","train_df['y'] = train_df.Tags.str.split('><').map(g).str.join(',')\n","train_df['text'] = train_df['Title']\n","\n"," \n","# train_df = train_df.iloc[:50]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"OfMCrNk-L_pq","outputId":"6ce7798d-ff2f-4b02-a066-67497ba0bdfa"},"source":["counts = train_df.explode('y').y.value_counts()\n","counts.iloc[0:100].plot.bar(figsize=(40,8), title='Distribution of Label Tags in Dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":4},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":573},"id":"73UChGrePhr1","outputId":"af8b97e5-cec0-469e-c55d-433364ee31a5"},"source":["exp = train_df.y.str.split(',').explode().value_counts()\n","top_100_tags = list(exp[0:25].index)\n","# z = lambda r : True if r.split(',') in top_100_tags else False\n","z = lambda r : True if all(x in top_100_tags for x in r.split(',') ) else False\n","top_100_idx = train_df.y.map(z)\n","train_df = train_df[top_100_idx]\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
Id
\n","
Title
\n","
Body
\n","
Tags
\n","
CreationDate
\n","
Y
\n","
y
\n","
text
\n","
\n"," \n"," \n","
\n","
13
\n","
34556906
\n","
output FILE ,is this a fault?
\n","
\\r\\nmy code here\\r\\n\\r\\n #include <stdi...
\n","
<c++>
\n","
2016-01-01 14:20:01
\n","
LQ_EDIT
\n","
c++
\n","
output FILE ,is this a fault?
\n","
\n","
\n","
24
\n","
34560768
\n","
Can I throw from class init() in Swift with co...
\n","
<p>I'd like my class <em>init()</em> in Swift ...
\n","
<swift>
\n","
2016-01-01 22:42:24
\n","
HQ
\n","
swift
\n","
Can I throw from class init() in Swift with co...
\n","
\n","
\n","
25
\n","
34560942
\n","
C# - Count a specific word in richTextBox1 and...
\n","
<p>I'm not sure, if this question is unique, b...
\n","
<c#>
\n","
2016-01-01 23:06:53
\n","
LQ_CLOSE
\n","
c#
\n","
C# - Count a specific word in richTextBox1 and...
\n","
\n","
\n","
30
\n","
34562551
\n","
c++ vector type function implemetation
\n","
class City\\r\\n {\\r\\n private:\\r\\n...
\n","
<c++>
\n","
2016-01-02 04:17:27
\n","
LQ_EDIT
\n","
c++
\n","
c++ vector type function implemetation
\n","
\n","
\n","
48
\n","
34566364
\n","
japanese and portuguese language cannot support
\n","
My site Japanese supported. But Portuguese la...
\n","
<php>
\n","
2016-01-02 13:20:49
\n","
LQ_EDIT
\n","
php
\n","
japanese and portuguese language cannot support
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
44992
\n","
60458575
\n","
MySQL how to query five tables in one SELECT
\n","
<p>I have 5 tables as follows:</p>\\n\\n<ul>\\n<l...
\n","
<mysql>
\n","
2020-02-28 20:07:09
\n","
LQ_CLOSE
\n","
mysql
\n","
MySQL how to query five tables in one SELECT
\n","
\n","
\n","
44993
\n","
60460748
\n","
Copy value of list not reference
\n","
<p>I have a list that i want to compare to aft...
\n","
<python>
\n","
2020-02-28 23:54:33
\n","
LQ_CLOSE
\n","
python
\n","
Copy value of list not reference
\n","
\n","
\n","
44994
\n","
60461193
\n","
Weird question, but how do I make a python scr...
\n","
<p>Before you get confused, I am going to comp...
\n","
<python><python-3.x>
\n","
2020-02-29 01:25:40
\n","
LQ_CLOSE
\n","
python,python-3.x
\n","
Weird question, but how do I make a python scr...
\n","
\n","
\n","
44996
\n","
60461754
\n","
Does Python execute code from the top or botto...
\n","
<p>I am working on learning Python and was won...
\n","
<python>
\n","
2020-02-29 03:33:59
\n","
LQ_CLOSE
\n","
python
\n","
Does Python execute code from the top or botto...
\n","
\n","
\n","
44998
\n","
60465318
\n","
how to implement fill in the blank in Swift
\n","
<p>\"I _____ any questions.\"</p>\\n\\n<p>I want t...
\n","
<ios><swift>
\n","
2020-02-29 12:50:43
\n","
LQ_CLOSE
\n","
ios,swift
\n","
how to implement fill in the blank in Swift
\n","
\n"," \n","
\n","
9968 rows × 8 columns
\n","
"],"text/plain":[" Id ... text\n","13 34556906 ... output FILE ,is this a fault?\n","24 34560768 ... Can I throw from class init() in Swift with co...\n","25 34560942 ... C# - Count a specific word in richTextBox1 and...\n","30 34562551 ... c++ vector type function implemetation\n","48 34566364 ... japanese and portuguese language cannot support\n","... ... ... ...\n","44992 60458575 ... MySQL how to query five tables in one SELECT\n","44993 60460748 ... Copy value of list not reference\n","44994 60461193 ... Weird question, but how do I make a python scr...\n","44996 60461754 ... Does Python execute code from the top or botto...\n","44998 60465318 ... how to implement fill in the blank in Swift\n","\n","[9968 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":653},"id":"e_z1IU-XT0a0","outputId":"dc80c79e-11a0-4e63-bd40-8d933dbbb6aa"},"source":["import nlu\n","# load a trainable pipeline by specifying the train prefix \n","\n","unfitted_pipe = nlu.load('train.multi_classifier')\n","#configure epochs\n","unfitted_pipe['multi_classifier'].setMaxEpochs(100)\n","unfitted_pipe['multi_classifier'].setLr(0.005) \n","# fit it on a datset with label='y' and text columns. Labels seperated by ','\n","fitted_pipe = unfitted_pipe.fit(train_df[['y','text']], label_seperator=',')\n","\n","# predict with the trained pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df[['y','text']])\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
multi_classifier_confidences
\n","
sentence
\n","
default_name_embeddings
\n","
multi_classifier_classes
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n","
\n","
\n","
\n","
\n"," \n"," \n","
\n","
13
\n","
c++
\n","
output FILE ,is this a fault?
\n","
[]
\n","
output FILE ,is this a fault?
\n","
[0.04620636999607086, -0.04046135023236275, -0...
\n","
[]
\n","
\n","
\n","
24
\n","
swift
\n","
Can I throw from class init() in Swift with co...
\n","
[0.86285734, 0.98327714]
\n","
Can I throw from class init() in Swift with co...
\n","
[0.053270746022462845, -0.00784565694630146, -...
\n","
[swift, c]
\n","
\n","
\n","
25
\n","
c#
\n","
C# - Count a specific word in richTextBox1 and...
\n","
[0.64955217]
\n","
C# - Count a specific word in richTextBox1 and...
\n","
[-0.005682709161192179, -0.023547030985355377,...
\n","
[regex]
\n","
\n","
\n","
30
\n","
c++
\n","
c++ vector type function implemetation
\n","
[0.9755105, 0.77180904, 0.9789763]
\n","
c++ vector type function implemetation
\n","
[0.024628309532999992, -0.015623562969267368, ...
\n","
[c++, python-3.x, python]
\n","
\n","
\n","
48
\n","
php
\n","
japanese and portuguese language cannot support
\n","
[0.55255216]
\n","
japanese and portuguese language cannot support
\n","
[0.038325726985931396, -0.005848723463714123, ...
\n","
[php]
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
44992
\n","
mysql
\n","
MySQL how to query five tables in one SELECT
\n","
[0.6404308, 0.99544823]
\n","
MySQL how to query five tables in one SELECT
\n","
[0.006962132174521685, -0.03580842167139053, -...
\n","
[sql, mysql]
\n","
\n","
\n","
44993
\n","
python
\n","
Copy value of list not reference
\n","
[0.591653]
\n","
Copy value of list not reference
\n","
[0.025995030999183655, 0.001833591377362609, -...
\n","
[javascript]
\n","
\n","
\n","
44994
\n","
python,python-3.x
\n","
Weird question, but how do I make a python scr...
\n","
[0.7427199, 0.99999976, 0.70473063, 0.72811186...
\n","
Weird question, but how do I make a python scr...
\n","
[0.018493961542844772, -0.04660267382860184, -...
\n","
[html, python, javascript, node.js, php]
\n","
\n","
\n","
44996
\n","
python
\n","
Does Python execute code from the top or botto...
\n","
[0.9977689, 0.794142]
\n","
Does Python execute code from the top or botto...
\n","
[0.01413149293512106, -0.02844131551682949, -0...
\n","
[python, php]
\n","
\n","
\n","
44998
\n","
ios,swift
\n","
how to implement fill in the blank in Swift
\n","
[0.9999993]
\n","
how to implement fill in the blank in Swift
\n","
[0.019475314766168594, -0.022571099922060966, ...
\n","
[swift]
\n","
\n"," \n","
\n","
10944 rows × 6 columns
\n","
"],"text/plain":[" y ... multi_classifier_classes\n","origin_index ... \n","13 c++ ... []\n","24 swift ... [swift, c]\n","25 c# ... [regex]\n","30 c++ ... [c++, python-3.x, python]\n","48 php ... [php]\n","... ... ... ...\n","44992 mysql ... [sql, mysql]\n","44993 python ... [javascript]\n","44994 python,python-3.x ... [html, python, javascript, node.js, php]\n","44996 python ... [python, php]\n","44998 ios,swift ... [swift]\n","\n","[10944 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"DL_5aY9b3jSd"},"source":["# 4. Evaluate the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0YDA2KunCeqQ","outputId":"8f72b51d-8e4c-49e8-884e-af5b0fdfa1ac"},"source":["from sklearn.preprocessing import MultiLabelBinarizer\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import f1_score\n","from sklearn.metrics import roc_auc_score\n","mlb = MultiLabelBinarizer()\n","mlb = mlb.fit(preds.y.str.split(','))\n","y_true = mlb.transform(preds['y'].str.split(','))\n","y_pred = mlb.transform(preds.multi_classifier_classes.str.join(',').str.split(','))\n","print(\"Classification report: \\n\", (classification_report(y_true, y_pred)))\n","print(\"F1 micro averaging:\",(f1_score(y_true, y_pred, average='micro')))\n","print(\"ROC: \",(roc_auc_score(y_true, y_pred, average=\"micro\")))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Classification report: \n"," precision recall f1-score support\n","\n"," 0 0.67 0.80 0.73 840\n"," 1 0.22 0.62 0.32 237\n"," 2 0.37 0.47 0.41 467\n"," 3 0.38 0.67 0.49 561\n"," 4 0.48 0.54 0.51 831\n"," 5 0.54 0.58 0.56 697\n"," 6 0.49 0.73 0.59 792\n"," 7 0.58 0.39 0.47 1352\n"," 8 0.20 0.18 0.19 158\n"," 9 0.49 0.77 0.60 1431\n"," 10 0.57 0.75 0.65 2343\n"," 11 0.36 0.56 0.43 833\n"," 12 0.34 0.24 0.28 300\n"," 13 0.51 0.74 0.60 539\n"," 14 0.19 0.28 0.23 106\n"," 15 0.63 0.67 0.65 1283\n"," 16 0.61 0.74 0.67 1402\n"," 17 0.21 0.25 0.23 411\n"," 18 0.38 0.47 0.42 261\n"," 19 0.90 0.10 0.19 183\n"," 20 0.56 0.75 0.64 451\n"," 21 0.56 0.73 0.63 485\n"," 22 0.45 0.60 0.51 340\n"," 23 0.34 0.13 0.19 220\n"," 24 0.53 0.73 0.61 268\n","\n"," micro avg 0.50 0.63 0.56 16791\n"," macro avg 0.46 0.54 0.47 16791\n","weighted avg 0.51 0.63 0.55 16791\n"," samples avg 0.54 0.65 0.55 16791\n","\n","F1 micro averaging: 0.5556585043017869\n","ROC: 0.7920968190895907\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"mhFKVN93o1ZO"},"source":["# 5. Lets try different Sentence Emebddings"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CzJd8omao0gt","outputId":"c3903ffc-ee61-47c1-87cf-bb1876436e25"},"source":["# We can use nlu.print_components(action='embed_sentence') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0ofYHpu7sloS","outputId":"ea715585-daa2-433d-d281-02b9e61222a4"},"source":["pipe = nlu.load('en.embed_sentence.small_bert_L12_768 train.multi_classifier')\n","pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L12_768 download started this may take some time.\n","Approximate size to download 392.9 MB\n","[OK!]\n","The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['en_embed_sentence_small_bert_L12_768'] has settable params:\n","pipe['en_embed_sentence_small_bert_L12_768'].setBatchSize(32) | Info: Batch size. Large values allows faster processing but requires more memory. | Currently set to : 32\n","pipe['en_embed_sentence_small_bert_L12_768'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['en_embed_sentence_small_bert_L12_768'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['en_embed_sentence_small_bert_L12_768'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['en_embed_sentence_small_bert_L12_768'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['en_embed_sentence_small_bert_L12_768'].setStorageRef('sent_small_bert_L12_768') | Info: unique reference name for identification | Currently set to : sent_small_bert_L12_768\n",">>> pipe['default_tokenizer'] has settable params:\n","pipe['default_tokenizer'].setTargetPattern('\\S+') | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n","pipe['default_tokenizer'].setContextChars(['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]) | Info: character list used to separate from token boundaries | Currently set to : ['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]\n","pipe['default_tokenizer'].setCaseSensitiveExceptions(True) | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n","pipe['default_tokenizer'].setMinLength(0) | Info: Set the minimum allowed legth for each token | Currently set to : 0\n","pipe['default_tokenizer'].setMaxLength(99999) | Info: Set the maximum allowed legth for each token | Currently set to : 99999\n",">>> pipe['sentence_detector'] has settable params:\n","pipe['sentence_detector'].setUseAbbreviations(True) | Info: whether to apply abbreviations at sentence detection | Currently set to : True\n","pipe['sentence_detector'].setDetectLists(True) | Info: whether detect lists during sentence detection | Currently set to : True\n","pipe['sentence_detector'].setUseCustomBoundsOnly(False) | Info: Only utilize custom bounds in sentence detection | Currently set to : False\n","pipe['sentence_detector'].setCustomBounds([]) | Info: characters used to explicitly mark sentence bounds | Currently set to : []\n","pipe['sentence_detector'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector'].setMinLength(0) | Info: Set the minimum allowed length for each sentence. | Currently set to : 0\n","pipe['sentence_detector'].setMaxLength(99999) | Info: Set the maximum allowed length for each sentence | Currently set to : 99999\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['multi_classifier'] has settable params:\n","pipe['multi_classifier'].setMaxEpochs(2) | Info: Maximum number of epochs to train | Currently set to : 2\n","pipe['multi_classifier'].setLr(0.001) | Info: Learning Rate | Currently set to : 0.001\n","pipe['multi_classifier'].setBatchSize(64) | Info: Batch size | Currently set to : 64\n","pipe['multi_classifier'].setValidationSplit(0.0) | Info: Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off. | Currently set to : 0.0\n","pipe['multi_classifier'].setThreshold(0.5) | Info: The minimum threshold for each label to be accepted. Default is 0.5 | Currently set to : 0.5\n","pipe['multi_classifier'].setRandomSeed(44) | Info: Random seed | Currently set to : 44\n","pipe['multi_classifier'].setShufflePerEpoch(False) | Info: whether to shuffle the training data on each Epoch | Currently set to : False\n","pipe['multi_classifier'].setEnableOutputLogs(True) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : True\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":570},"id":"ABHLgirmG1n9","outputId":"60e9995e-080c-4213-cf03-c7baba89bd6a"},"source":["# Load pipe with bert embeds\n","# using large embeddings can take a few hours..\n","pipe['multi_classifier'].setMaxEpochs(120) \n","pipe['multi_classifier'].setLr(0.0005) \n","fitted_pipe = pipe.fit(train_df[['y','text']],label_seperator=',')\n","preds = fitted_pipe.predict(train_df[['y','text']])\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
multi_classifier_confidences
\n","
en_embed_sentence_small_bert_L12_768_embeddings
\n","
document
\n","
multi_classifier_classes
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n","
\n","
\n","
\n","
\n"," \n"," \n","
\n","
13
\n","
c++
\n","
output FILE ,is this a fault?
\n","
[]
\n","
[-0.0598912313580513, 0.429191917181015, -0.25...
\n","
output FILE ,is this a fault?
\n","
[]
\n","
\n","
\n","
24
\n","
swift
\n","
Can I throw from class init() in Swift with co...
\n","
[0.61310124]
\n","
[-0.45358699560165405, 0.1986018270254135, -0....
\n","
Can I throw from class init() in Swift with co...
\n","
[java]
\n","
\n","
\n","
25
\n","
c#
\n","
C# - Count a specific word in richTextBox1 and...
\n","
[0.8172003]
\n","
[-0.592096209526062, 0.0025841565802693367, -0...
\n","
C# - Count a specific word in richTextBox1 and...
\n","
[c#]
\n","
\n","
\n","
30
\n","
c++
\n","
c++ vector type function implemetation
\n","
[0.98100495]
\n","
[-0.6645137071609497, 0.34700289368629456, 0.1...
\n","
c++ vector type function implemetation
\n","
[c++]
\n","
\n","
\n","
48
\n","
php
\n","
japanese and portuguese language cannot support
\n","
[]
\n","
[-0.30820634961128235, 0.5732622742652893, 0.5...
\n","
japanese and portuguese language cannot support
\n","
[]
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
44992
\n","
mysql
\n","
MySQL how to query five tables in one SELECT
\n","
[0.94582915]
\n","
[-0.6759300231933594, 0.1323285549879074, 0.56...
\n","
MySQL how to query five tables in one SELECT
\n","
[mysql]
\n","
\n","
\n","
44993
\n","
python
\n","
Copy value of list not reference
\n","
[0.71518165]
\n","
[-0.7307966947555542, 0.3146328032016754, -0.5...
\n","
Copy value of list not reference
\n","
[python]
\n","
\n","
\n","
44994
\n","
python,python-3.x
\n","
Weird question, but how do I make a python scr...
\n","
[0.9938545]
\n","
[-0.478365957736969, -0.015336859039962292, 0....
\n","
Weird question, but how do I make a python scr...
\n","
[python]
\n","
\n","
\n","
44996
\n","
python
\n","
Does Python execute code from the top or botto...
\n","
[0.998447]
\n","
[-0.7976136803627014, -0.17537403106689453, 0....
\n","
Does Python execute code from the top or botto...
\n","
[python]
\n","
\n","
\n","
44998
\n","
ios,swift
\n","
how to implement fill in the blank in Swift
\n","
[0.6266076, 0.9772264]
\n","
[-0.4111633598804474, 0.04349775239825249, 0.2...
\n","
how to implement fill in the blank in Swift
\n","
[ios, swift]
\n","
\n"," \n","
\n","
9968 rows × 6 columns
\n","
"],"text/plain":[" y ... multi_classifier_classes\n","origin_index ... \n","13 c++ ... []\n","24 swift ... [java]\n","25 c# ... [c#]\n","30 c++ ... [c++]\n","48 php ... []\n","... ... ... ...\n","44992 mysql ... [mysql]\n","44993 python ... [python]\n","44994 python,python-3.x ... [python]\n","44996 python ... [python]\n","44998 ios,swift ... [ios, swift]\n","\n","[9968 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"E7ah2LM6tIhG","outputId":"edaa6235-c8d2-474a-9cc1-331e0967086c"},"source":["from sklearn.preprocessing import MultiLabelBinarizer\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import f1_score\n","from sklearn.metrics import roc_auc_score\n","mlb = MultiLabelBinarizer()\n","mlb = mlb.fit(preds.y.str.split(','))\n","y_true = mlb.transform(preds['y'].str.split(','))\n","y_pred = mlb.transform(preds.multi_classifier_classes.str.join(',').str.split(','))\n","print(\"Classification report: \\n\", (classification_report(y_true, y_pred)))\n","print(\"F1 micro averaging:\",(f1_score(y_true, y_pred, average='micro')))\n","print(\"ROC: \",(roc_auc_score(y_true, y_pred, average=\"micro\")))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Classification report: \n"," precision recall f1-score support\n","\n"," 0 0.96 0.67 0.79 738\n"," 1 0.95 0.71 0.82 228\n"," 2 0.70 0.53 0.60 440\n"," 3 0.91 0.63 0.75 508\n"," 4 0.95 0.57 0.71 733\n"," 5 0.91 0.58 0.71 621\n"," 6 0.88 0.70 0.78 736\n"," 7 0.81 0.65 0.72 1254\n"," 8 0.86 0.58 0.69 145\n"," 9 0.89 0.58 0.70 1288\n"," 10 0.87 0.73 0.80 2164\n"," 11 0.89 0.58 0.70 754\n"," 12 0.84 0.67 0.74 277\n"," 13 0.89 0.59 0.71 511\n"," 14 0.96 0.27 0.42 96\n"," 15 0.94 0.70 0.80 1193\n"," 16 0.93 0.70 0.80 1265\n"," 17 0.74 0.22 0.34 365\n"," 18 0.97 0.70 0.82 246\n"," 19 1.00 0.55 0.71 172\n"," 20 0.92 0.71 0.81 427\n"," 21 0.82 0.67 0.74 458\n"," 22 0.81 0.66 0.73 319\n"," 23 0.83 0.23 0.36 211\n"," 24 0.97 0.64 0.77 242\n","\n"," micro avg 0.89 0.64 0.74 15391\n"," macro avg 0.89 0.59 0.70 15391\n","weighted avg 0.89 0.64 0.73 15391\n"," samples avg 0.70 0.64 0.65 15391\n","\n","F1 micro averaging: 0.7401884721644023\n","ROC: 0.8150061228796474\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","outputId":"bbf99f56-d4b1-4440-ecb7-fe9d61935c62"},"source":["stored_model_path = './models/multi_classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/multi_classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Tesla plans to invest 10M into the ML sector')\n","preds"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"M1LjAwJVJxun"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_apple_twitter.ipynb b/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_apple_twitter.ipynb
deleted file mode 100644
index f43ef731..00000000
--- a/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_apple_twitter.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_multi_lingual_training_sentiment_classifier_demo_apple_twitter.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"RIV-9vEqxTBB"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples//colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_apple_twitter.ipynb)\n","\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 class Apple Tweets Sentiment Classifier Training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data : \n","\n"," \n","\n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data :\n","\n"," \n","\n",""]},{"cell_type":"code","metadata":{"id":"05-mAOF6ol-0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620195849229,"user_tz":-300,"elapsed":146589,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"05db6ea0-0d13-4f64-e84b-8039108710bd"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 06:21:47-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 06:21:47 (1.60 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 62kB/s \n","\u001b[K |████████████████████████████████| 153kB 33.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 16.6MB/s \n","\u001b[K |████████████████████████████████| 204kB 37.7MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download appple twitter Sentiment dataset \n","https://www.kaggle.com/seriousran/appletwittersentimenttexts\n","\n","this dataset contains tweets made towards apple and today we are going to train our model to predict whether the tweet contains sentiment!\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620195850046,"user_tz":-300,"elapsed":147289,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0f73a43b-1d20-4238-8c91-6f4f74c19efe"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/apple-twitter-sentiment-texts_multi_lingual.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 06:24:08-- http://ckl-it.de/wp-content/uploads/2021/02/apple-twitter-sentiment-texts_multi_lingual.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 48565 (47K) [text/csv]\n","Saving to: ‘apple-twitter-sentiment-texts_multi_lingual.csv’\n","\n","apple-twitter-senti 100%[===================>] 47.43K 237KB/s in 0.2s \n","\n","2021-05-05 06:24:09 (237 KB/s) - ‘apple-twitter-sentiment-texts_multi_lingual.csv’ saved [48565/48565]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620195851135,"user_tz":-300,"elapsed":148342,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"95f12040-2d70-4227-a925-4cb63206df77"},"source":["import pandas as pd\n","train_path = '/content/apple-twitter-sentiment-texts_multi_lingual.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","# the label column must have name 'y' name be of type str\n","columns=['text','y']\n","train_df = train_df[columns]\n","train_df = train_df[~train_df[\"y\"].isin([\"neuteral\"])]\n","from sklearn.model_selection import train_test_split\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
233
\n","
@Apple @iCloud Family in-app purchase approval...
\n","
negative
\n","
\n","
\n","
120
\n","
@apple#ipad #irig For the price to connect my...
\n","
negative
\n","
\n","
\n","
240
\n","
@Apple Stop liking man.
\n","
negative
\n","
\n","
\n","
28
\n","
fucking @apple are memer FAGGOTS http://t.co/w...
\n","
negative
\n","
\n","
\n","
236
\n","
RT @gxldblvnts: thanking @apple for the 'do no...
\n","
positive
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
51
\n","
I love @AIRMILES! Between @chaptersindigo &...
\n","
positive
\n","
\n","
\n","
210
\n","
Take the stress out of looking for Christmas p...
\n","
positive
\n","
\n","
\n","
98
\n","
It would be nice if I could type more than one...
\n","
negative
\n","
\n","
\n","
227
\n","
Always so impressed with the customer service ...
\n","
positive
\n","
\n","
\n","
104
\n","
Goodbye graphics problems. Thank you @apple fo...
\n","
positive
\n","
\n"," \n","
\n","
228 rows × 2 columns
\n","
"],"text/plain":[" text y\n","233 @Apple @iCloud Family in-app purchase approval... negative\n","120 @apple#ipad #irig For the price to connect my... negative\n","240 @Apple Stop liking man. negative\n","28 fucking @apple are memer FAGGOTS http://t.co/w... negative\n","236 RT @gxldblvnts: thanking @apple for the 'do no... positive\n",".. ... ...\n","51 I love @AIRMILES! Between @chaptersindigo &... positive\n","210 Take the stress out of looking for Christmas p... positive\n","98 It would be nice if I could type more than one... negative\n","227 Always so impressed with the customer service ... positive\n","104 Goodbye graphics problems. Thank you @apple fo... positive\n","\n","[228 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":844},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620196278269,"user_tz":-300,"elapsed":575445,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"741613cd-e446-4981-8925-c8bd58b1338a"},"source":["trainable_pipe = nlu.load('xx.embed_sentence.labse train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(60) \n","\n","trainable_pipe['sentiment_dl'].setLr(0.005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["labse download started this may take some time.\n","Approximate size to download 1.7 GB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.97 0.97 0.97 108\n"," positive 0.97 0.97 0.97 120\n","\n"," accuracy 0.97 228\n"," macro avg 0.97 0.97 0.97 228\n","weighted avg 0.97 0.97 0.97 228\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
y
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
@Apple @iCloud Family in-app purchase approval...
\n","
233
\n","
0.999632
\n","
[@Apple @iCloud Family in-app purchase approva...
\n","
[-0.005896878894418478, 0.00783093087375164, 0...
\n","
@Apple @iCloud Family in-app purchase approval...
\n","
negative
\n","
negative
\n","
\n","
\n","
1
\n","
@apple#ipad #irig For the price to connect my...
\n","
120
\n","
0.998172
\n","
[@apple#ipad #irig For the price to connect my...
\n","
[-0.03121807612478733, 0.0023986243177205324, ...
\n","
@apple#ipad #irig For the price to connect my ...
\n","
negative
\n","
negative
\n","
\n","
\n","
2
\n","
@Apple Stop liking man.
\n","
240
\n","
0.999984
\n","
[@Apple Stop liking man.]
\n","
[-0.042748626321554184, 0.02048809453845024, 0...
\n","
@Apple Stop liking man.
\n","
negative
\n","
negative
\n","
\n","
\n","
3
\n","
fucking @apple are memer FAGGOTS http://t.co/w...
\n","
28
\n","
0.999970
\n","
[fucking @apple are memer FAGGOTS http://t.co/...
\n","
[0.011834759265184402, -0.05850285664200783, 0...
\n","
fucking @apple are memer FAGGOTS http://t.co/w...
\n","
negative
\n","
negative
\n","
\n","
\n","
4
\n","
RT @gxldblvnts: thanking @apple for the 'do no...
\n","
236
\n","
0.999951
\n","
[RT @gxldblvnts: thanking @apple for the 'do n...
\n","
[-0.00759833725169301, -0.02228071726858616, 0...
\n","
RT @gxldblvnts: thanking @apple for the 'do no...
\n","
positive
\n","
positive
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
223
\n","
I love @AIRMILES! Between @chaptersindigo &...
\n","
51
\n","
0.999980
\n","
[I love @AIRMILES!, Between @chaptersindigo &a...
\n","
[-0.034336768090724945, -0.02440302073955536, ...
\n","
I love @AIRMILES! Between @chaptersindigo &...
\n","
positive
\n","
positive
\n","
\n","
\n","
224
\n","
Take the stress out of looking for Christmas p...
\n","
210
\n","
0.999983
\n","
[Take the stress out of looking for Christmas ...
\n","
[-0.03665369004011154, -0.04174784570932388, -...
\n","
Take the stress out of looking for Christmas p...
\n","
positive
\n","
positive
\n","
\n","
\n","
225
\n","
It would be nice if I could type more than one...
\n","
98
\n","
0.995613
\n","
[It would be nice if I could type more than on...
\n","
[0.0202105101197958, -0.010581637732684612, -0...
\n","
It would be nice if I could type more than one...
\n","
negative
\n","
negative
\n","
\n","
\n","
226
\n","
Always so impressed with the customer service ...
\n","
227
\n","
1.000000
\n","
[Always so impressed with the customer service...
\n","
[-0.05745420977473259, 0.03164859488606453, -0...
\n","
Always so impressed with the customer service ...
\n","
positive
\n","
positive
\n","
\n","
\n","
227
\n","
Goodbye graphics problems. Thank you @apple fo...
\n","
104
\n","
0.999992
\n","
[Goodbye graphics problems., Thank you @apple ...
\n","
[-0.019405337050557137, -0.0494435578584671, -...
\n","
Goodbye graphics problems. Thank you @apple fo...
\n","
positive
\n","
positive
\n","
\n"," \n","
\n","
228 rows × 8 columns
\n","
"],"text/plain":[" text ... trained_sentiment\n","0 @Apple @iCloud Family in-app purchase approval... ... negative\n","1 @apple#ipad #irig For the price to connect my... ... negative\n","2 @Apple Stop liking man. ... negative\n","3 fucking @apple are memer FAGGOTS http://t.co/w... ... negative\n","4 RT @gxldblvnts: thanking @apple for the 'do no... ... positive\n",".. ... ... ...\n","223 I love @AIRMILES! Between @chaptersindigo &... ... positive\n","224 Take the stress out of looking for Christmas p... ... positive\n","225 It would be nice if I could type more than one... ... negative\n","226 Always so impressed with the customer service ... ... positive\n","227 Goodbye graphics problems. Thank you @apple fo... ... positive\n","\n","[228 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 3.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fxx4yNkNVGFl","executionInfo":{"status":"ok","timestamp":1620196291463,"user_tz":-300,"elapsed":588621,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c93b24ba-3851-429b-d8a9-5dbdf5002c99"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.94 0.83 0.88 35\n"," positive 0.78 0.91 0.84 23\n","\n"," accuracy 0.86 58\n"," macro avg 0.86 0.87 0.86 58\n","weighted avg 0.87 0.86 0.86 58\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"BD5OKO4Umc5U"},"source":["#4. Test Model on 20 languages!"]},{"cell_type":"code","metadata":{"id":"OQ72hP9unML7","colab":{"base_uri":"https://localhost:8080/","height":742},"executionInfo":{"status":"ok","timestamp":1620196315254,"user_tz":-300,"elapsed":612397,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"45606261-50e8-4e3d-8089-795317569504"},"source":["import pandas as pd\n","\n","train_df = pd.read_csv('/content/apple-twitter-sentiment-texts_multi_lingual.csv')\n","columns=['test_sentences','y']\n","train_df = train_df[columns]\n","train_df = train_df[~train_df[\"y\"].isin([\"neuteral\"])]\n","train_df\n","preds = fitted_pipe.predict(train_df[[\"test_sentences\",\"y\"]].iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.93 0.89 0.91 47\n"," positive 0.91 0.94 0.93 53\n","\n"," accuracy 0.92 100\n"," macro avg 0.92 0.92 0.92 100\n","weighted avg 0.92 0.92 0.92 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
y
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
@Apple, du skal sortere dine telefoner.
\n","
0
\n","
0.808998
\n","
[@Apple, du skal sortere dine telefoner.]
\n","
[-0.061579205095767975, -0.006414646748453379,...
\n","
@Apple, du skal sortere dine telefoner.
\n","
negative
\n","
positive
\n","
\n","
\n","
1
\n","
వావ్. యాల్ నీడా స్టెప్ అప్ @ యాపిల్ ఆర్టి y హే...
\n","
1
\n","
0.949447
\n","
[వావ్. యాల్ నీడా స్టెప్ అప్ @ యాపిల్ ఆర్టి y హ...
\n","
[-0.06188163533806801, -0.07130676507949829, -...
\n","
వావ్. యాల్ నీడా స్టెప్ అప్ @ యాపిల్ ఆర్టి y హే...
\n","
negative
\n","
negative
\n","
\n","
\n","
2
\n","
আমি আশ্চর্য হয়েছি যে গতকাল # এএপএল-তে ফ্ল্যাশ...
\n","
2
\n","
0.817636
\n","
[আমি আশ্চর্য হয়েছি যে গতকাল # এএপএল-তে ফ্ল্যা...
\n","
[-0.009524044580757618, -0.022813592106103897,...
\n","
আমি আশ্চর্য হয়েছি যে গতকাল # এএপএল-তে ফ্ল্যাশ...
\n","
negative
\n","
negative
\n","
\n","
\n","
3
\n","
Uvědomili jsme si, že @apple vyrábí obrovské t...
\n","
3
\n","
0.987724
\n","
[Uvědomili jsme si, že @apple vyrábí obrovské ...
\n","
[-0.05080719664692879, 0.035508085042238235, 0...
\n","
Uvědomili jsme si, že @apple vyrábí obrovské t...
\n","
negative
\n","
positive
\n","
\n","
\n","
4
\n","
Apple Inc.'s administrerende direktør donerer ...
\n","
4
\n","
1.000000
\n","
[Apple Inc.'s administrerende direktør donerer...
\n","
[-0.04884444922208786, -0.02654154971241951, 0...
\n","
Apple Inc.'s administrerende direktør donerer ...
\n","
positive
\n","
positive
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
Təşəkkür edirəm @Apple İndi bir yerdə ünsiyyət...
\n","
95
\n","
0.999454
\n","
[Təşəkkür edirəm @Apple İndi bir yerdə ünsiyyə...
\n","
[-0.03029252029955387, -0.0471813827753067, -0...
\n","
Təşəkkür edirəm @Apple İndi bir yerdə ünsiyyət...
\n","
positive
\n","
positive
\n","
\n","
\n","
96
\n","
. @ tim_cook Die woede wanneer hulle @ Apple G...
\n","
96
\n","
0.990521
\n","
[. @ tim_cook Die woede wanneer hulle @ Apple ...
\n","
[0.01344149000942707, -0.05397084355354309, -0...
\n","
. @ tim_cook Die woede wanneer hulle @ Apple G...
\n","
negative
\n","
negative
\n","
\n","
\n","
97
\n","
ছদ্মবেশের ধরণটি হ'ল এই @ অ্যাপল @ অটোকোরেক্ট @...
\n","
97
\n","
0.993052
\n","
[ছদ্মবেশের ধরণটি হ'ল এই @ অ্যাপল @ অটোকোরেক্ট ...
\n","
[-0.028785958886146545, -0.06805533170700073, ...
\n","
ছদ্মবেশের ধরণটি হ'ল এই @ অ্যাপল @ অটোকোরেক্ট @...
\n","
negative
\n","
negative
\n","
\n","
\n","
98
\n","
Было бы неплохо, если бы я мог набрать более о...
\n","
98
\n","
0.815481
\n","
[Было бы неплохо, если бы я мог набрать более ...
\n","
[0.0006764091667719185, -0.0028165546245872974...
\n","
Было бы неплохо, если бы я мог набрать более о...
\n","
negative
\n","
positive
\n","
\n","
\n","
99
\n","
@OneRepublic @Apple Вы все готовы. #ColoradoLo...
\n","
99
\n","
0.999987
\n","
[@OneRepublic @Apple Вы все готовы., #Colorad...
\n","
[-0.006210405845195055, -0.05371640995144844, ...
\n","
@OneRepublic @Apple Вы все готовы. #ColoradoLo...
\n","
positive
\n","
positive
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" text ... trained_sentiment\n","0 @Apple, du skal sortere dine telefoner. ... positive\n","1 వావ్. యాల్ నీడా స్టెప్ అప్ @ యాపిల్ ఆర్టి y హే... ... negative\n","2 আমি আশ্চর্য হয়েছি যে গতকাল # এএপএল-তে ফ্ল্যাশ... ... negative\n","3 Uvědomili jsme si, že @apple vyrábí obrovské t... ... positive\n","4 Apple Inc.'s administrerende direktør donerer ... ... positive\n",".. ... ... ...\n","95 Təşəkkür edirəm @Apple İndi bir yerdə ünsiyyət... ... positive\n","96 . @ tim_cook Die woede wanneer hulle @ Apple G... ... negative\n","97 ছদ্মবেশের ধরণটি হ'ল এই @ অ্যাপল @ অটোকোরেক্ট @... ... negative\n","98 Было бы неплохо, если бы я мог набрать более о... ... positive\n","99 @OneRepublic @Apple Вы все готовы. #ColoradoLo... ... positive\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"RjtuNUcvuJTT"},"source":["# The Model understands Englsih\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"o0vu7PaWkcI7","executionInfo":{"status":"ok","timestamp":1620196316695,"user_tz":-300,"elapsed":613799,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"75a3fe2b-51e3-4626-deb0-1181785f03b7"},"source":["\n","fitted_pipe.predict(\"I hate the newest update!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999776
\n","
[I hate the newest update!, !]
\n","
[-0.03925566002726555, -0.020373696461319923, ...
\n","
I hate the newest update!!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"1ykjRQhCtQ4w","executionInfo":{"status":"ok","timestamp":1620196317752,"user_tz":-300,"elapsed":614830,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0e7918ec-70fb-4b18-88d4-8f819a8f9086"},"source":["fitted_pipe.predict(\"I love the newest update!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999902
\n","
[I love the newest update!, !]
\n","
[-0.03266981244087219, -0.03438195958733559, -...
\n","
I love the newest update!!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"vohym-XbuNHn"},"source":["# The Model understands German\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"BbhgTSBGtTtJ","executionInfo":{"status":"ok","timestamp":1620196319098,"user_tz":-300,"elapsed":616150,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b1658345-95fc-43a7-ebdf-c2543e90fdd5"},"source":["# german for: 'I love the newest update!!'\n","fitted_pipe.predict(\"Ich liebe das neueste Update !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999718
\n","
[Ich liebe das neueste Update !, !]
\n","
[-0.03806369751691818, -0.03677768632769585, -...
\n","
Ich liebe das neueste Update !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"ZaPYBxeL33pH","executionInfo":{"status":"ok","timestamp":1620196320112,"user_tz":-300,"elapsed":617139,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"deaf3626-a525-40d5-b169-86060a3aa708"},"source":["# german for: 'Apple is the worst company ever , I hate it !'\n","fitted_pipe.predict(\"Apple ist das schlechteste Unternehmen aller Zeiten, ich hasse es! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999974
\n","
[Apple ist das schlechteste Unternehmen aller ...
\n","
[-0.06440500915050507, 0.011019382625818253, -...
\n","
Apple ist das schlechteste Unternehmen aller Z...
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"a1JbtmWquQwj"},"source":["# The Model understands Chinese\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"kYSYqtoRtc-P","executionInfo":{"status":"ok","timestamp":1620196321594,"user_tz":-300,"elapsed":618596,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"046d9bb1-7c6b-491a-ea2b-42809da77ae0"},"source":["# Chinese for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"我讨厌最新的更新! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999468
\n","
[我讨厌最新的更新!]
\n","
[-0.0359969362616539, -0.03859588876366615, 0....
\n","
我讨厌最新的更新!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index trained_sentiment_confidence ... document trained_sentiment\n","0 0 0.999468 ... 我讨厌最新的更新! negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"06v9SD-QtlBU","executionInfo":{"status":"ok","timestamp":1620196322995,"user_tz":-300,"elapsed":619951,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"ca191395-4bd8-49dc-d1e5-0b9bbcf7a6e8"},"source":["# Chinese for: 'I love the newest update!!'\n","fitted_pipe.predict(\"我喜欢最新的更新! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999998
\n","
[我喜欢最新的更新!]
\n","
[-0.03270617872476578, -0.03804901987314224, -...
\n","
我喜欢最新的更新!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index trained_sentiment_confidence ... document trained_sentiment\n","0 0 0.999998 ... 我喜欢最新的更新! positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"9h7CvN4uu9Pb"},"source":["# Model understands Afrikaans\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"VMPhbgw9twtf","executionInfo":{"status":"ok","timestamp":1620196324051,"user_tz":-300,"elapsed":620975,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"219493f4-5ff4-4a1f-c552-c68fbf315276"},"source":["\t\t\n","# Afrikaans for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"Ek haat die nuutste opdatering !! \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999861
\n","
[Ek haat die nuutste opdatering !!]
\n","
[-0.043201908469200134, -0.023186631500720978,...
\n","
Ek haat die nuutste opdatering !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"zWgNTIdkumhX","executionInfo":{"status":"ok","timestamp":1620196325181,"user_tz":-300,"elapsed":622070,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"603bb80e-dd6f-4e2c-b961-a9ae77b2a769"},"source":["# Afrikaans for: 'I love the newest update!!'\n","fitted_pipe.predict(\"Ek is lief vir die nuutste opdatering !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999924
\n","
[Ek is lief vir die nuutste opdatering !!]
\n","
[-0.03175487741827965, -0.03518300876021385, -...
\n","
Ek is lief vir die nuutste opdatering !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"rSEPkC-Bwnpg"},"source":["# The model understands Vietnamese\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"M6giDPK-wm2G","executionInfo":{"status":"ok","timestamp":1620196326148,"user_tz":-300,"elapsed":622989,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"e453305f-6c5e-4d13-d100-27bd4d5ca198"},"source":["\n","# Vietnamese for: 'I love the newest update!!'\n","fitted_pipe.predict(\"Tôi yêu bản cập nhật mới nhất !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999969
\n","
[Tôi yêu bản cập nhật mới nhất !, !]
\n","
[-0.00781381968408823, -0.0691518485546112, -0...
\n","
Tôi yêu bản cập nhật mới nhất !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"benoJUN_4i3Q","executionInfo":{"status":"ok","timestamp":1620196326926,"user_tz":-300,"elapsed":623623,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"4ee9d849-b894-497c-89df-5807cb84d64f"},"source":["\n","# Vietnamese for: 'Apple is the worst company ever , I hate it !'\n","fitted_pipe.predict(\"Apple là công ty tồi tệ nhất từ trước đến nay, tôi ghét nó!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.99895
\n","
[Apple là công ty tồi tệ nhất từ trước đến n...
\n","
[-0.06147113814949989, 0.03707527741789818, 0....
\n","
Apple là công ty tồi tệ nhất từ trước đến na...
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"IlkmAaMoxTuy"},"source":["# The model understands Japanese\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"1IfJu3q8wwUt","executionInfo":{"status":"ok","timestamp":1620196328303,"user_tz":-300,"elapsed":624943,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"d5e3a13a-66ca-4132-e134-8a1e2a1d5150"},"source":["# Japanese for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"私は最新のアップデートが嫌いです! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999842
\n","
[私は最新のアップデートが嫌いです!]
\n","
[-0.030076516792178154, -0.002390763023868203,...
\n","
私は最新のアップデートが嫌いです!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"h3k7_PFhxOve","executionInfo":{"status":"ok","timestamp":1620196329400,"user_tz":-300,"elapsed":626002,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3e5656fe-a282-42fb-b2e2-42eeb280453e"},"source":["\t\t\n","# Japanese for: 'I love the newest update!!'\n","fitted_pipe.predict(\"私は最新のアップデートが大好きです! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999998
\n","
[私は最新のアップデートが大好きです!]
\n","
[-0.01563793420791626, -0.022478925064206123, ...
\n","
私は最新のアップデートが大好きです!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"GITfT7FK0CGv"},"source":["# The model understands Zulu\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"DKnrkkXzzpd5","executionInfo":{"status":"ok","timestamp":1620196330165,"user_tz":-300,"elapsed":626739,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c971b040-8789-4694-e026-4d4a8970f7ce"},"source":["# Zulu for: 'I love the newest update!!'\n","fitted_pipe.predict(\"Ngiyasithanda isibuyekezo esisha !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999417
\n","
[Ngiyasithanda isibuyekezo esisha !, !]
\n","
[-0.018757890909910202, -0.03155702352523804, ...
\n","
Ngiyasithanda isibuyekezo esisha !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"HInPIW9A4rg2","executionInfo":{"status":"ok","timestamp":1620196331872,"user_tz":-300,"elapsed":628382,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"423bc521-5ca5-4d19-ab40-318022deae11"},"source":["# Zulu for: Apple is the worst company ever , I hate it !'\n","fitted_pipe.predict(\"I-Apple iyinkampani embi kunazo zonke ezake, ngiyayizonda!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999329
\n","
[I-Apple iyinkampani embi kunazo zonke ezake, ...
\n","
[-0.06040400266647339, 0.025964893400669098, 0...
\n","
I-Apple iyinkampani embi kunazo zonke ezake, n...
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"VGVvzl_30a0T"},"source":["# The Model understands Turkish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"DRNnuEeQz2pd","executionInfo":{"status":"ok","timestamp":1620196332464,"user_tz":-300,"elapsed":628932,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"705430ec-7760-41b4-f58c-4893e717192c"},"source":["# Turkish for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"En yeni güncellemekten nefret ediyorum !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.99968
\n","
[En yeni güncellemekten nefret ediyorum !, !]
\n","
[-0.006802674848586321, -0.03453182056546211, ...
\n","
En yeni güncellemekten nefret ediyorum !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"aOSsiK6J0jWs","executionInfo":{"status":"ok","timestamp":1620196333251,"user_tz":-300,"elapsed":629686,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b1d51bd6-0fbc-40ef-e9b1-0870ff795109"},"source":["# Turkish for: 'I love the newest update!!'\n","fitted_pipe.predict(\"En yeni güncellemeyi seviyorum !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999978
\n","
[En yeni güncellemeyi seviyorum !, !]
\n","
[-0.013165195472538471, -0.04192955046892166, ...
\n","
En yeni güncellemeyi seviyorum !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"803qL2gt0vlb"},"source":["# The Model understands Hebrew\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"XQ5VCtxw0pc0","executionInfo":{"status":"ok","timestamp":1620196334671,"user_tz":-300,"elapsed":631080,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"141055ef-3e9c-47f5-ce8d-672e16a4e6b0"},"source":["# Hebrew for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"אני שונא את העדכון החדש ביותר! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999878
\n","
[אני שונא את העדכון החדש ביותר!]
\n","
[-0.026791388168931007, -0.024923792108893394,...
\n","
אני שונא את העדכון החדש ביותר!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"9w2ZHfns05A4","executionInfo":{"status":"ok","timestamp":1620196335414,"user_tz":-300,"elapsed":631792,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2c272b41-b214-4e0b-93ab-480c97e5782a"},"source":["# Hebrew for: 'I love the newest update!!'\n","fitted_pipe.predict(\"אני אוהב את העדכון החדש ביותר !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999993
\n","
[אני אוהב את העדכון החדש ביותר !!]
\n","
[-0.025626346468925476, -0.03748653084039688, ...
\n","
אני אוהב את העדכון החדש ביותר !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"SDlpd33H1HIX"},"source":["# The Model understands Telugu\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"-l-u6vrz1Obe","executionInfo":{"status":"ok","timestamp":1620196336805,"user_tz":-300,"elapsed":633162,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"4589a96d-d564-4d10-b405-4835f29bacb8"},"source":["# Telugu for: 'I love the newest update!!'\n","fitted_pipe.predict(\"నేను సరికొత్త నవీకరణను ప్రేమిస్తున్నాను !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999994
\n","
[నేను సరికొత్త నవీకరణను ప్రేమిస్తున్నాను !!]
\n","
[-0.035736482590436935, -0.04187411442399025, ...
\n","
నేను సరికొత్త నవీకరణను ప్రేమిస్తున్నాను !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"uuR3Reqc5JbT","executionInfo":{"status":"ok","timestamp":1620196337878,"user_tz":-300,"elapsed":634167,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"20abd3ca-41d4-41ee-eb24-81830508234d"},"source":["# Telugu for: 'Apple is the worst company ever , I hate it !'\n","fitted_pipe.predict(\" ఆపిల్ ఎప్పుడూ చెత్త సంస్థ, నేను దానిని ద్వేషిస్తున్నాను! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999924
\n","
[ఆపిల్ ఎప్పుడూ చెత్త సంస్థ, నేను దానిని ద్వేషి...
\n","
[-0.06062706932425499, 0.014408073388040066, -...
\n","
ఆపిల్ ఎప్పుడూ చెత్త సంస్థ, నేను దానిని ద్వేషిస...
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"nziBUe8t1Zwn"},"source":["# Model understands Russian\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Ckyjl3YQ1VFn","executionInfo":{"status":"ok","timestamp":1620196338258,"user_tz":-300,"elapsed":634526,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b481f186-de5c-4137-bf6c-a79c472ce75b"},"source":["# Russian for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"Я ненавижу новейшее обновление !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999845
\n","
[Я ненавижу новейшее обновление !!]
\n","
[-0.05251258984208107, -0.02137317694723606, -...
\n","
Я ненавижу новейшее обновление !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"GIdWkfGv1gFz","executionInfo":{"status":"ok","timestamp":1620196339448,"user_tz":-300,"elapsed":635693,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"99c3f083-3768-45eb-d610-76b174068470"},"source":["\n","\n","# Russian for: 'I love the newest update!!'\n","fitted_pipe.predict(\"Я люблю новейшее обновление !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999972
\n","
[Я люблю новейшее обновление !!]
\n","
[-0.04557504504919052, -0.0393301285803318, -0...
\n","
Я люблю новейшее обновление !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"8R1j9mwz2Cm4"},"source":["# Model understands Urdu\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"j4zwvRV11pcG","executionInfo":{"status":"ok","timestamp":1620196340361,"user_tz":-300,"elapsed":636446,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"305928c8-765f-4db1-b295-831753b31590"},"source":["# Urdu for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"مجھے تازہ ترین اپ ڈیٹ سے نفرت ہے !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999563
\n","
[مجھے تازہ ترین اپ ڈیٹ سے نفرت ہے !, !]
\n","
[-0.042040981352329254, -0.040164750069379807,...
\n","
مجھے تازہ ترین اپ ڈیٹ سے نفرت ہے !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SxzTuK4b2UKV","executionInfo":{"status":"ok","timestamp":1620196341167,"user_tz":-300,"elapsed":637211,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c0a94c88-1c03-4b72-bf52-b5873e7bd1c0"},"source":["# Urdu for: 'I love the newest update!!'\n","fitted_pipe.predict(\"مجھے تازہ ترین تازہ کاری پسند ہے !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999973
\n","
[مجھے تازہ ترین تازہ کاری پسند ہے !, !]
\n","
[-0.020344950258731842, -0.050028372555971146,...
\n","
مجھے تازہ ترین تازہ کاری پسند ہے !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"RoNg-C3k1qcX"},"source":["# Model understands Hindi\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"QZ9RT5Wv1r1n","executionInfo":{"status":"ok","timestamp":1620196342298,"user_tz":-300,"elapsed":638319,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"4e340e86-80a2-4a64-907f-e441ea3f47b8"},"source":["# hindi for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"मुझे नवीनतम अपडेट से नफरत है !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999722
\n","
[मुझे नवीनतम अपडेट से नफरत है !, !]
\n","
[-0.045306481420993805, -0.038678351789712906,...
\n","
मुझे नवीनतम अपडेट से नफरत है !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"quM-IL2i12-B","executionInfo":{"status":"ok","timestamp":1620196343281,"user_tz":-300,"elapsed":639281,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"f7a9482d-f5d9-466c-c097-362381e28e81"},"source":["# hindi for: 'I love the newest update!!'\n","fitted_pipe.predict(\"मैं नवीनतम अद्यतन प्यार करता हूँ !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999919
\n","
[मैं नवीनतम अद्यतन प्यार करता हूँ !!]
\n","
[-0.03802281618118286, -0.04779476672410965, -...
\n","
मैं नवीनतम अद्यतन प्यार करता हूँ !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"R4ByHOZn35Lc"},"source":["# The model understands Tartar\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"2JrzusSQ18F5","executionInfo":{"status":"ok","timestamp":1620196344487,"user_tz":-300,"elapsed":640464,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"bb6f1437-583f-4a0d-86a6-b88a9566744d"},"source":["# Tartar for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"Мин яңа яңартуны нәфрәт итәм !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999187
\n","
[Мин яңа яңартуны нәфрәт итәм !!]
\n","
[-0.038122836500406265, -0.03321803733706474, ...
\n","
Мин яңа яңартуны нәфрәт итәм !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"J06Xm_Ln4AYu","executionInfo":{"status":"ok","timestamp":1620196345259,"user_tz":-300,"elapsed":641162,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"955de65f-eccb-4833-e062-2986d9b447ab"},"source":["\n","# Tartar for: 'I love the newest update!!'\n","fitted_pipe.predict(\"Мин иң яңа яңартуны яратам !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999963
\n","
[Мин иң яңа яңартуны яратам !!]
\n","
[-0.022450288757681847, -0.04027746245265007, ...
\n","
Мин иң яңа яңартуны яратам !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"HKj5yWwwMplH"},"source":["# The Model understands French\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"CUHcJZfJMplL","executionInfo":{"status":"ok","timestamp":1620196346393,"user_tz":-300,"elapsed":642236,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"12505238-b8eb-4473-c0f8-45667e45e929"},"source":["# French for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"Je déteste la nouvelle mise à jour !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999315
\n","
[Je déteste la nouvelle mise à jour !!]
\n","
[-0.0480484776198864, -0.01981555111706257, -0...
\n","
Je déteste la nouvelle mise à jour !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"57NY2XoTMplM","executionInfo":{"status":"ok","timestamp":1620196346761,"user_tz":-300,"elapsed":642536,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3f99ecf2-d112-40db-cb45-7adf7214d524"},"source":["# French for: 'I love the newest update!!'\n","fitted_pipe.predict(\"J'adore la dernière mise à jour !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999959
\n","
[J'adore la dernière mise à jour !!]
\n","
[-0.03898211941123009, -0.04296712949872017, -...
\n","
J'adore la dernière mise à jour !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"jD2TBgT0Nq6F"},"source":["# The Model understands Thai\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"ICp_qoAhNq6Q","executionInfo":{"status":"ok","timestamp":1620196347725,"user_tz":-300,"elapsed":643302,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"fcffa441-5269-4b97-9b89-0c836fa4773d"},"source":["# Thai for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"ฉันเกลียดการอัปเดตใหม่ล่าสุด !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999806
\n","
[ฉันเกลียดการอัปเดตใหม่ล่าสุด !!]
\n","
[-0.05030808597803116, -0.03610166534781456, -...
\n","
ฉันเกลียดการอัปเดตใหม่ล่าสุด !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"gBp11S5GNq6S","executionInfo":{"status":"ok","timestamp":1620196348779,"user_tz":-300,"elapsed":644049,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c69d1e70-229c-460e-d4b5-38749d7e8a1d"},"source":["# Thai for: 'I love the newest update!!'\n","fitted_pipe.predict(\"โดนใจอัพเดทใหม่ล่าสุด !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999978
\n","
[โดนใจอัพเดทใหม่ล่าสุด !!]
\n","
[-0.046895772218704224, -0.04769491031765938, ...
\n","
โดนใจอัพเดทใหม่ล่าสุด !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"mLItI4KZOElB"},"source":["# The Model understands Khmer\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Fxh1gasROElC","executionInfo":{"status":"ok","timestamp":1620196349749,"user_tz":-300,"elapsed":645001,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"dce681df-3d99-4ac2-a64b-d69ac2b66d77"},"source":["# Khmer for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"ខ្ញុំស្អប់ការអាប់ដេតថ្មីបំផុត !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.99968
\n","
[ខ្ញុំស្អប់ការអាប់ដេតថ្មីបំផុត !!]
\n","
[-0.04094554856419563, -0.04082174971699715, -...
\n","
ខ្ញុំស្អប់ការអាប់ដេតថ្មីបំផុត !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SWbqMgAwOElC","executionInfo":{"status":"ok","timestamp":1620196350989,"user_tz":-300,"elapsed":646147,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b353b4a4-c82c-42fb-b0f8-59bd675c4f45"},"source":["# Khmer for: 'I love the newest update!!'\n","fitted_pipe.predict(\"ខ្ញុំចូលចិត្តការអាប់ដេតថ្មីបំផុត !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.99999
\n","
[ខ្ញុំចូលចិត្តការអាប់ដេតថ្មីបំផុត !!]
\n","
[-0.03578546270728111, -0.035129521042108536, ...
\n","
ខ្ញុំចូលចិត្តការអាប់ដេតថ្មីបំផុត !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"markdown","metadata":{"id":"lvE-LbNiPoBT"},"source":["# The Model understands Yiddish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"5h-pha_nPoBc","executionInfo":{"status":"ok","timestamp":1620196351778,"user_tz":-300,"elapsed":646791,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"e00f667c-f8e0-42f6-a505-f1eb3a8e51e4"},"source":["\n","# Yiddish for: 'I love the newest update!!'\n","fitted_pipe.predict(\"איך ליבע דער נואַסט דערהייַנטיקן !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999994
\n","
[איך ליבע דער נואַסט דערהייַנטיקן !!]
\n","
[-0.03009073995053768, -0.060998495668172836, ...
\n","
איך ליבע דער נואַסט דערהייַנטיקן !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"YcM5k6Ce5Vpo","executionInfo":{"status":"ok","timestamp":1620196352473,"user_tz":-300,"elapsed":647349,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2dcb88d7-3359-4c93-d335-dc53ba5cadf7"},"source":["\n","# Yiddish for: 'Apple is the worst company ever , I hate it !'\n","fitted_pipe.predict(\"עפּל איז די ערגסט פירמע טאָמיד, איך האַס עס!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.717363
\n","
[עפּל איז די ערגסט פירמע טאָמיד, איך האַס עס!]
\n","
[-0.0464082695543766, 0.007680143229663372, 0....
\n","
עפּל איז די ערגסט פירמע טאָמיד, איך האַס עס!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"XSz4WzScaAHj"},"source":["# The Model understands Kygrgyz\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"DXz6fhJSaAHu","executionInfo":{"status":"ok","timestamp":1620196353410,"user_tz":-300,"elapsed":648267,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"5505a0eb-a456-4642-aff0-e8dd2e522ecf"},"source":["\t\t\n","# Kygrgyz for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"Мен жаңы жаңыртууну жек көрөм !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999657
\n","
[Мен жаңы жаңыртууну жек көрөм !!]
\n","
[-0.04081014543771744, -0.037187058478593826, ...
\n","
Мен жаңы жаңыртууну жек көрөм !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"lh_ZSHlPaAHv","executionInfo":{"status":"ok","timestamp":1620196354442,"user_tz":-300,"elapsed":649139,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"35d7b00b-b6a1-4209-e2e0-ccde33d0d602"},"source":["\t\t\n","# Kygrgyz for: 'I love the newest update!!'\n","fitted_pipe.predict(\"Мен жаңы жаңыртууну жакшы көрөм !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999925
\n","
[Мен жаңы жаңыртууну жакшы көрөм !!]
\n","
[-0.02940315194427967, -0.0417410284280777, -0...
\n","
Мен жаңы жаңыртууну жакшы көрөм !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"DGMVMKaTdJFj"},"source":["# The Model understands Tamil\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"JWDr_LoCdJFn","executionInfo":{"status":"ok","timestamp":1620196355541,"user_tz":-300,"elapsed":650204,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"22b8e435-44b5-463f-8aa5-005568b91f2d"},"source":["\t\t\n","# Tamil for: 'I hate the newest update!!'\n","fitted_pipe.predict(\"நான் புதிய புதுப்பிப்பை வெறுக்கிறேன் !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999217
\n","
[நான் புதிய புதுப்பிப்பை வெறுக்கிறேன் !!]
\n","
[-0.04580855742096901, -0.03527894616127014, -...
\n","
நான் புதிய புதுப்பிப்பை வெறுக்கிறேன் !!
\n","
negative
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... negative\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Q6C0BmTtdJFp","executionInfo":{"status":"ok","timestamp":1620196356107,"user_tz":-300,"elapsed":650632,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"4700d21e-08a1-4f4b-936e-3d03f5b5548f"},"source":["\n","# Tamil for: 'I love the newest update!!'\n","fitted_pipe.predict(\"நான் புதிய புதுப்பிப்பை விரும்புகிறேன் !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
sentence
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
0.999528
\n","
[நான் புதிய புதுப்பிப்பை விரும்புகிறேன் !!]
\n","
[-0.0359167717397213, -0.05217977613210678, -0...
\n","
நான் புதிய புதுப்பிப்பை விரும்புகிறேன் !!
\n","
positive
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... trained_sentiment\n","0 0 ... positive\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"id":"bZZpObLOtqo8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620197205863,"user_tz":-300,"elapsed":1500368,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"39482b53-c621-44cd-aada-27e0e9fa5ab0"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197486850,"user_tz":-300,"elapsed":143939,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"9bd3f8e1-fcf7-4068-91d5-ef25eadd2ba6"},"source":["stored_model_path = './models/classifier_dl_trained' \n","hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('I hate the newest update')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
origin_index
\n","
text
\n","
sentiment
\n","
sentiment_confidence
\n","
sentence_embedding_from_disk
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
I hate the newest update
\n","
8589934592
\n","
I hate the newest update
\n","
[negative]
\n","
[0.9999902]
\n","
[[-0.04603002965450287, 0.03271652013063431, 0...
\n","
[I hate the newest update]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 I hate the newest update ... [I hate the newest update]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620197486852,"user_tz":-300,"elapsed":143923,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"6c1bbd08-44c4-4d23-d642-5976ae35e3d6"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@73da39db) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@73da39db\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@labse'] has settable params:\n","pipe['bert_sentence@labse'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@labse'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@labse'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@labse'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@labse'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n",">>> pipe['sentiment_dl@labse'] has settable params:\n","pipe['sentiment_dl@labse'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@labse'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@labse'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wFDFEaHHSXmY"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_covid_19.ipynb b/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_covid_19.ipynb
deleted file mode 100644
index d579a1ff..00000000
--- a/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_covid_19.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_multi_lingual_training_sentiment_classifier_demo_covid_19.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples//colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_covid_19.ipynb)\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 Class COVID19 Sentiment Classifier Training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data : \n","\n"," \n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data : \n","\n"," \n","\n",""]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620202126248,"user_tz":-300,"elapsed":120586,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c9a0f715-e9a3-4783-cecc-2fa2f488da33"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 08:06:46-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 08:06:46 (56.0 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 75kB/s \n","\u001b[K |████████████████████████████████| 153kB 39.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 21.6MB/s \n","\u001b[K |████████████████████████████████| 204kB 44.5MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Coivd19 NLP Text Sentiemnt Classifcation dataset \n","https://www.kaggle.com/datatattle/covid-19-nlp-text-classification\n","#Context\n","\n","This is a Dataset made of tweets about coivid 19 "]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620202127432,"user_tz":-300,"elapsed":121752,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"09d92b54-16bc-4492-cf7f-5e779b92a033"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/Corona_NLP_train_multi_lingual.csv"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 08:08:46-- http://ckl-it.de/wp-content/uploads/2021/02/Corona_NLP_train_multi_lingual.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 838005 (818K) [text/csv]\n","Saving to: ‘Corona_NLP_train_multi_lingual.csv’\n","\n","Corona_NLP_train_mu 100%[===================>] 818.36K 1.27MB/s in 0.6s \n","\n","2021-05-05 08:08:47 (1.27 MB/s) - ‘Corona_NLP_train_multi_lingual.csv’ saved [838005/838005]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620202128462,"user_tz":-300,"elapsed":122764,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"e58c6886-6a03-488f-c9c6-f3e371e43369"},"source":["import pandas as pd\n","train_path = '/content/Corona_NLP_train_multi_lingual.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","columns=['text','y']\n","train_df = train_df[columns]\n","from sklearn.model_selection import train_test_split\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
555
\n","
@udaygosalia2002 @FlyFrontier That's horrible ...
\n","
negative
\n","
\n","
\n","
444
\n","
ThatÂs such an awesome idea! Through the #cor...
\n","
positive
\n","
\n","
\n","
350
\n","
https://t.co/UcxIQz49vU: Buying Choices: Effic...
\n","
positive
\n","
\n","
\n","
383
\n","
Thought the period of 'hamstering' was over in...
\n","
positive
\n","
\n","
\n","
1053
\n","
Online shopping is booming, but warehouse work...
\n","
negative
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1493
\n","
Northland residents urged not to panic after s...
\n","
positive
\n","
\n","
\n","
1301
\n","
ÂWe can all now see that jobs that are never ...
\n","
negative
\n","
\n","
\n","
141
\n","
So many Canadians were extremely ill prepared ...
\n","
negative
\n","
\n","
\n","
773
\n","
The plummeting gas prices are due to a kind of...
\n","
positive
\n","
\n","
\n","
152
\n","
To me the unsung heroes of this #Covid_19 cris...
\n","
positive
\n","
\n"," \n","
\n","
1200 rows × 2 columns
\n","
"],"text/plain":[" text y\n","555 @udaygosalia2002 @FlyFrontier That's horrible ... negative\n","444 ThatÂs such an awesome idea! Through the #cor... positive\n","350 https://t.co/UcxIQz49vU: Buying Choices: Effic... positive\n","383 Thought the period of 'hamstering' was over in... positive\n","1053 Online shopping is booming, but warehouse work... negative\n","... ... ...\n","1493 Northland residents urged not to panic after s... positive\n","1301 ÂWe can all now see that jobs that are never ... negative\n","141 So many Canadians were extremely ill prepared ... negative\n","773 The plummeting gas prices are due to a kind of... positive\n","152 To me the unsung heroes of this #Covid_19 cris... positive\n","\n","[1200 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":861},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620203623271,"user_tz":-300,"elapsed":1617557,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"78f56da2-804a-4a74-b879-9d4d33afc333"},"source":["trainable_pipe = nlu.load('xx.embed_sentence.labse train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(60) \n","trainable_pipe['sentiment_dl'].setLr(0.005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","predsS"],"execution_count":null,"outputs":[{"output_type":"stream","text":["labse download started this may take some time.\n","Approximate size to download 1.7 GB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.94 0.94 0.94 591\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.95 0.93 0.94 609\n","\n"," accuracy 0.94 1200\n"," macro avg 0.63 0.63 0.63 1200\n","weighted avg 0.94 0.94 0.94 1200\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
y
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
@udaygosalia2002 @FlyFrontier That's horrible ...
\n","
[@udaygosalia2002 @FlyFrontier That's horrible...
\n","
negative
\n","
555
\n","
negative
\n","
[-0.009500819258391857, -0.0515713132917881, -...
\n","
1.000000
\n","
@udaygosalia2002 @FlyFrontier That's horrible ...
\n","
\n","
\n","
1
\n","
ThatÂs such an awesome idea! Through the #cor...
\n","
[ThatÂs such an awesome idea!, Through the #c...
\n","
positive
\n","
444
\n","
positive
\n","
[0.01753036119043827, -0.013980614952743053, 0...
\n","
1.000000
\n","
ThatÂs such an awesome idea! Through the #cor...
\n","
\n","
\n","
2
\n","
https://t.co/UcxIQz49vU: Buying Choices: Effic...
\n","
[https://t.co/UcxIQz49vU: Buying Choices: Effi...
\n","
positive
\n","
350
\n","
positive
\n","
[0.026550117880105972, -0.009575911797583103, ...
\n","
0.999927
\n","
https://t.co/UcxIQz49vU: Buying Choices: Effic...
\n","
\n","
\n","
3
\n","
Thought the period of 'hamstering' was over in...
\n","
[Thought the period of 'hamstering' was over i...
\n","
positive
\n","
383
\n","
positive
\n","
[0.049093276262283325, -0.05199237912893295, -...
\n","
0.967601
\n","
Thought the period of 'hamstering' was over in...
\n","
\n","
\n","
4
\n","
Online shopping is booming, but warehouse work...
\n","
[Online shopping is booming, but warehouse wor...
\n","
negative
\n","
1053
\n","
negative
\n","
[-0.044845301657915115, -0.0010483426740393043...
\n","
1.000000
\n","
Online shopping is booming, but warehouse work...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1195
\n","
Northland residents urged not to panic after s...
\n","
[Northland residents urged not to panic after ...
\n","
positive
\n","
1493
\n","
positive
\n","
[-0.0108554782345891, -0.005432534962892532, 0...
\n","
0.980916
\n","
Northland residents urged not to panic after s...
\n","
\n","
\n","
1196
\n","
ÂWe can all now see that jobs that are never ...
\n","
[ÂWe can all now see that jobs that are never...
\n","
negative
\n","
1301
\n","
negative
\n","
[-0.002810274949297309, 0.019298763945698738, ...
\n","
1.000000
\n","
ÂWe can all now see that jobs that are never ...
\n","
\n","
\n","
1197
\n","
So many Canadians were extremely ill prepared ...
\n","
[So many Canadians were extremely ill prepared...
\n","
negative
\n","
141
\n","
negative
\n","
[0.047991253435611725, -0.03192431479692459, -...
\n","
1.000000
\n","
So many Canadians were extremely ill prepared ...
\n","
\n","
\n","
1198
\n","
The plummeting gas prices are due to a kind of...
\n","
[The plummeting gas prices are due to a kind o...
\n","
negative
\n","
773
\n","
positive
\n","
[-0.009237667545676231, -0.05268627777695656, ...
\n","
1.000000
\n","
The plummeting gas prices are due to a kind of...
\n","
\n","
\n","
1199
\n","
To me the unsung heroes of this #Covid_19 cris...
\n","
[To me the unsung heroes of this #Covid_19 cri...
\n","
positive
\n","
152
\n","
positive
\n","
[-0.04374924674630165, 0.028920631855726242, 0...
\n","
0.999916
\n","
To me the unsung heroes of this #Covid_19 cris...
\n","
\n"," \n","
\n","
1200 rows × 8 columns
\n","
"],"text/plain":[" text ... document\n","0 @udaygosalia2002 @FlyFrontier That's horrible ... ... @udaygosalia2002 @FlyFrontier That's horrible ...\n","1 ThatÂs such an awesome idea! Through the #cor... ... ThatÂs such an awesome idea! Through the #cor...\n","2 https://t.co/UcxIQz49vU: Buying Choices: Effic... ... https://t.co/UcxIQz49vU: Buying Choices: Effic...\n","3 Thought the period of 'hamstering' was over in... ... Thought the period of 'hamstering' was over in...\n","4 Online shopping is booming, but warehouse work... ... Online shopping is booming, but warehouse work...\n","... ... ... ...\n","1195 Northland residents urged not to panic after s... ... Northland residents urged not to panic after s...\n","1196 ÂWe can all now see that jobs that are never ... ... ÂWe can all now see that jobs that are never ...\n","1197 So many Canadians were extremely ill prepared ... ... So many Canadians were extremely ill prepared ...\n","1198 The plummeting gas prices are due to a kind of... ... The plummeting gas prices are due to a kind of...\n","1199 To me the unsung heroes of this #Covid_19 cris... ... To me the unsung heroes of this #Covid_19 cris...\n","\n","[1200 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 3.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fxx4yNkNVGFl","executionInfo":{"status":"ok","timestamp":1620203699292,"user_tz":-300,"elapsed":1693565,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"76a1b9bd-8add-43f1-f48d-65d23314c352"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.92 0.88 0.90 154\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.88 0.90 0.89 146\n","\n"," accuracy 0.89 300\n"," macro avg 0.60 0.59 0.60 300\n","weighted avg 0.90 0.89 0.89 300\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"BD5OKO4Umc5U"},"source":["# 4. Test Model on 20 languages!"]},{"cell_type":"code","metadata":{"id":"OQ72hP9unML7","colab":{"base_uri":"https://localhost:8080/","height":793},"executionInfo":{"status":"ok","timestamp":1620203728331,"user_tz":-300,"elapsed":1722597,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"d9e9580d-02dd-4ad8-d004-89741f14c78b"},"source":["train_df = pd.read_csv(\"/content/Corona_NLP_train_multi_lingual.csv\")\n","preds = fitted_pipe.predict(train_df[[\"test_sentences\",\"y\"]].iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.93 0.86 0.89 44\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.91 0.93 0.92 56\n","\n"," accuracy 0.90 100\n"," macro avg 0.61 0.60 0.60 100\n","weighted avg 0.92 0.90 0.91 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
y
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
#Cheerios Hersteller @GeneralMills hat am Mitt...
\n","
[#Cheerios Hersteller @GeneralMills hat am Mit...
\n","
positive
\n","
0
\n","
positive
\n","
[0.009235107339918613, -0.05358916521072388, 0...
\n","
0.999428
\n","
#Cheerios Hersteller @GeneralMills hat am Mitt...
\n","
\n","
\n","
1
\n","
ต้องไปเยี่ยมชมเมื่อเช้านี้ซึ่งพวกเขาได้เปลี่ยน...
\n","
[ต้องไปเยี่ยมชมเมื่อเช้านี้ซึ่งพวกเขาได้เปลี่ย...
\n","
positive
\n","
1
\n","
positive
\n","
[-0.0758102685213089, 0.023602206259965897, -0...
\n","
1.000000
\n","
ต้องไปเยี่ยมชมเมื่อเช้านี้ซึ่งพวกเขาได้เปลี่ยน...
\n","
\n","
\n","
2
\n","
मुझे पहली बार Covid-19 के लक्षण होने के कारण ए...
\n","
[मुझे पहली बार Covid-19 के लक्षण होने के कारण ...
\n","
negative
\n","
2
\n","
negative
\n","
[0.0010585618438199162, 0.014147249981760979, ...
\n","
0.999945
\n","
मुझे पहली बार Covid-19 के लक्षण होने के कारण ए...
\n","
\n","
\n","
3
\n","
公元前2周的封锁期间更有可能死于饥饿,由于恐慌的买家,我们没有食物了。 #新冠病毒
\n","
[公元前2周的封锁期间更有可能死于饥饿,由于恐慌的买家,我们没有食物了。 #新冠病毒]
\n","
negative
\n","
3
\n","
negative
\n","
[-0.01255110278725624, 0.034000057727098465, 0...
\n","
1.000000
\n","
公元前2周的封锁期间更有可能死于饥饿,由于恐慌的买家,我们没有食物了。 #新冠病毒
\n","
\n","
\n","
4
\n","
Don't move around unnecessary \\r\\r\\r\\nStay at...
\n","
[Don't move around unnecessary Stay at home., ...
\n","
positive
\n","
4
\n","
positive
\n","
[-0.02680176869034767, -0.006846072152256966, ...
\n","
1.000000
\n","
Don't move around unnecessary Stay at home. Us...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
Киберкылмышты жөнөкөйлөштүрүү максатында COVID...
\n","
[Киберкылмышты жөнөкөйлөштүрүү максатында COVI...
\n","
negative
\n","
95
\n","
negative
\n","
[-0.016219787299633026, -0.07136883586645126, ...
\n","
0.999979
\n","
Киберкылмышты жөнөкөйлөштүрүү максатында COVID...
\n","
\n","
\n","
96
\n","
Ich bin alles dafür, die Kurve zu glätten und ...
\n","
[Ich bin alles dafür, die Kurve zu glätten und...
\n","
negative
\n","
96
\n","
negative
\n","
[-0.054645996540784836, -0.028411103412508965,...
\n","
1.000000
\n","
Ich bin alles dafür, die Kurve zu glätten und ...
\n","
\n","
\n","
97
\n","
איר זוכט צו רעדן צו עמעצער וואָס אַרבעט אין אַ...
\n","
[איר זוכט צו רעדן צו עמעצער וואָס אַרבעט אין א...
\n","
positive
\n","
97
\n","
positive
\n","
[-0.0533565990626812, -0.020032547414302826, -...
\n","
1.000000
\n","
איר זוכט צו רעדן צו עמעצער וואָס אַרבעט אין אַ...
\n","
\n","
\n","
98
\n","
Comment les bureaux de poste gèrent le boom de...
\n","
[Comment les bureaux de poste gèrent le boom d...
\n","
positive
\n","
98
\n","
positive
\n","
[-0.01647104136645794, -0.004591754637658596, ...
\n","
0.999770
\n","
Comment les bureaux de poste gèrent le boom de...
\n","
\n","
\n","
99
\n","
Acabo de ver un artículo que dice que las pers...
\n","
[Acabo de ver un artículo que dice que las per...
\n","
positive
\n","
99
\n","
positive
\n","
[-0.03019009903073311, 0.0009937069844454527, ...
\n","
0.999859
\n","
Acabo de ver un artículo que dice que las pers...
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" text ... document\n","0 #Cheerios Hersteller @GeneralMills hat am Mitt... ... #Cheerios Hersteller @GeneralMills hat am Mitt...\n","1 ต้องไปเยี่ยมชมเมื่อเช้านี้ซึ่งพวกเขาได้เปลี่ยน... ... ต้องไปเยี่ยมชมเมื่อเช้านี้ซึ่งพวกเขาได้เปลี่ยน...\n","2 मुझे पहली बार Covid-19 के लक्षण होने के कारण ए... ... मुझे पहली बार Covid-19 के लक्षण होने के कारण ए...\n","3 公元前2周的封锁期间更有可能死于饥饿,由于恐慌的买家,我们没有食物了。 #新冠病毒 ... 公元前2周的封锁期间更有可能死于饥饿,由于恐慌的买家,我们没有食物了。 #新冠病毒\n","4 Don't move around unnecessary \\r\\r\\r\\nStay at... ... Don't move around unnecessary Stay at home. Us...\n",".. ... ... ...\n","95 Киберкылмышты жөнөкөйлөштүрүү максатында COVID... ... Киберкылмышты жөнөкөйлөштүрүү максатында COVID...\n","96 Ich bin alles dafür, die Kurve zu glätten und ... ... Ich bin alles dafür, die Kurve zu glätten und ...\n","97 איר זוכט צו רעדן צו עמעצער וואָס אַרבעט אין אַ... ... איר זוכט צו רעדן צו עמעצער וואָס אַרבעט אין אַ...\n","98 Comment les bureaux de poste gèrent le boom de... ... Comment les bureaux de poste gèrent le boom de...\n","99 Acabo de ver un artículo que dice que las pers... ... Acabo de ver un artículo que dice que las pers...\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"vohym-XbuNHn"},"source":["# The Model understands German\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"dzaaZrI4tVWc","executionInfo":{"status":"ok","timestamp":1620203731681,"user_tz":-300,"elapsed":1725929,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"7a6737c3-fabb-4c41-cc28-353af7a1240e"},"source":["# german for: '5000 people died because of COVID19 Today!!'\n","fitted_pipe.predict(\"5000 Menschen starben heute an COVID 19 !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[5000 Menschen starben heute an COVID 19 !!]
\n","
negative
\n","
0
\n","
[-0.026059195399284363, -0.023131363093852997,...
\n","
0.999989
\n","
5000 Menschen starben heute an COVID 19 !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [5000 Menschen starben heute an COVID 19 !!] ... 5000 Menschen starben heute an COVID 19 !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"BbhgTSBGtTtJ","executionInfo":{"status":"ok","timestamp":1620203732295,"user_tz":-300,"elapsed":1726533,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"d267ccba-2928-49ce-cb09-02008bf0ec46"},"source":["# german for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"Wir haben endlich ein Heilmittel gegen COVID gefunden !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Wir haben endlich ein Heilmittel gegen COVID ...
\n","
positive
\n","
0
\n","
[-0.01908278465270996, -0.04215678572654724, -...
\n","
0.755457
\n","
Wir haben endlich ein Heilmittel gegen COVID g...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Wir haben endlich ein Heilmittel gegen COVID ... ... Wir haben endlich ein Heilmittel gegen COVID g...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"a1JbtmWquQwj"},"source":["# The Model understands Chinese\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"kYSYqtoRtc-P","executionInfo":{"status":"ok","timestamp":1620203733795,"user_tz":-300,"elapsed":1728022,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b36af752-e279-4999-832e-70a0a8844a76"},"source":["# Chinese for: '5000 people died because of COVID19 Today!!'\n","fitted_pipe.predict(\"5000人因今天的Covid 19人死了! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[5000人因今天的Covid 19人死了!]
\n","
negative
\n","
0
\n","
[-0.00861808005720377, -0.013505449518561363, ...
\n","
0.999903
\n","
5000人因今天的Covid 19人死了!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [5000人因今天的Covid 19人死了!] ... 5000人因今天的Covid 19人死了!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"06v9SD-QtlBU","executionInfo":{"status":"ok","timestamp":1620203734968,"user_tz":-300,"elapsed":1729187,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b5633db1-82b3-4109-8b7e-28f2b4f90718"},"source":["# Chinese for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"我们终于找到了治愈COVID的方法! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[我们终于找到了治愈COVID的方法!]
\n","
positive
\n","
0
\n","
[-0.01916874386370182, -0.0637722760438919, -0...
\n","
0.990226
\n","
我们终于找到了治愈COVID的方法!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [我们终于找到了治愈COVID的方法!] ... 我们终于找到了治愈COVID的方法!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"VGVvzl_30a0T"},"source":["# The Model understands Turkish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"DRNnuEeQz2pd","executionInfo":{"status":"ok","timestamp":1620203743114,"user_tz":-300,"elapsed":1737268,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0a57fdc4-1ce2-4408-dd24-fdab00fe945c"},"source":["# Turkish for: '5000 people died because of COVID19 Today!!'\n","fitted_pipe.predict(\"Bugün Covid 19 yüzünden 5000 kişi öldü !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bugün Covid 19 yüzünden 5000 kişi öldü !, !]
\n","
negative
\n","
0
\n","
[-0.019384179264307022, -0.01711968146264553, ...
\n","
0.999918
\n","
Bugün Covid 19 yüzünden 5000 kişi öldü !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bugün Covid 19 yüzünden 5000 kişi öldü !, !] ... Bugün Covid 19 yüzünden 5000 kişi öldü !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"aOSsiK6J0jWs","executionInfo":{"status":"ok","timestamp":1620203744278,"user_tz":-300,"elapsed":1738424,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"84ef5ca2-a6a8-4a5d-d2d0-b4d8c04fbc97"},"source":["# Turkish for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"Sonunda COVID'e bir tedavi bulduk !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Sonunda COVID'e bir tedavi bulduk !, !]
\n","
positive
\n","
0
\n","
[-0.02832956612110138, -0.05583051219582558, -...
\n","
0.83334
\n","
Sonunda COVID'e bir tedavi bulduk !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Sonunda COVID'e bir tedavi bulduk !, !] ... Sonunda COVID'e bir tedavi bulduk !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"803qL2gt0vlb"},"source":["# The Model understands Hebrew\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"XQ5VCtxw0pc0","executionInfo":{"status":"ok","timestamp":1620203744885,"user_tz":-300,"elapsed":1739025,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"4540851c-64fb-41a7-8df1-bc2f77500939"},"source":["# Hebrew for: '5000 people died because of COVID19 Today!!'\n","fitted_pipe.predict(\"Bugün Covid 19 yüzünden 5000 kişi öldü !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bugün Covid 19 yüzünden 5000 kişi öldü !, !]
\n","
negative
\n","
0
\n","
[-0.019384179264307022, -0.01711968146264553, ...
\n","
0.999918
\n","
Bugün Covid 19 yüzünden 5000 kişi öldü !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bugün Covid 19 yüzünden 5000 kişi öldü !, !] ... Bugün Covid 19 yüzünden 5000 kişi öldü !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"9w2ZHfns05A4","executionInfo":{"status":"ok","timestamp":1620203746112,"user_tz":-300,"elapsed":1740244,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0f1ff8a7-858a-49f9-9909-9de93a152a32"},"source":["# Hebrew for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"סוף סוף מצאנו תרופה ל- COVID !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[סוף סוף מצאנו תרופה ל- COVID !!]
\n","
positive
\n","
0
\n","
[-0.038195837289094925, -0.05654338747262955, ...
\n","
0.795063
\n","
סוף סוף מצאנו תרופה ל- COVID !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [סוף סוף מצאנו תרופה ל- COVID !!] ... סוף סוף מצאנו תרופה ל- COVID !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"RoNg-C3k1qcX"},"source":["# Model understands Hindi\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"QZ9RT5Wv1r1n","executionInfo":{"status":"ok","timestamp":1620203751826,"user_tz":-300,"elapsed":1745914,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"04be26bc-3931-4b14-ae91-770ae37ff01a"},"source":["# hindi for: '5000 people died because of COVID19 Today!!'\n","fitted_pipe.predict(\"COVID 19 की वजह से 5000 लोग मारे गए !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[COVID 19 की वजह से 5000 लोग मारे गए !, !]
\n","
negative
\n","
0
\n","
[-0.013016731478273869, -0.03924949839711189, ...
\n","
0.999977
\n","
COVID 19 की वजह से 5000 लोग मारे गए !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [COVID 19 की वजह से 5000 लोग मारे गए !, !] ... COVID 19 की वजह से 5000 लोग मारे गए !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"quM-IL2i12-B","executionInfo":{"status":"ok","timestamp":1620203752687,"user_tz":-300,"elapsed":1746770,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"cb1d359d-f516-4db4-abef-03781196b26f"},"source":["# hindi for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"हम अंत में कोविद को एक इलाज मिला !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[हम अंत में कोविद को एक इलाज मिला !!]
\n","
positive
\n","
0
\n","
[-0.04375814273953438, -0.05282781273126602, -...
\n","
0.992187
\n","
हम अंत में कोविद को एक इलाज मिला !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [हम अंत में कोविद को एक इलाज मिला !!] ... हम अंत में कोविद को एक इलाज मिला !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"R4ByHOZn35Lc"},"source":["# The model understands Tartar\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"2JrzusSQ18F5","executionInfo":{"status":"ok","timestamp":1620203753372,"user_tz":-300,"elapsed":1747450,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2a9f263e-9bf5-4cfb-c45e-66a0bb4b53a6"},"source":["# Tartar for: '5000 people died because of COVID19 Today!!'\n","fitted_pipe.predict(\"COVID 19 аркасында 5000 кеше үлде Бүген !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[COVID 19 аркасында 5000 кеше үлде Бүген !!]
\n","
negative
\n","
0
\n","
[-0.02741607092320919, -0.032239750027656555, ...
\n","
0.999982
\n","
COVID 19 аркасында 5000 кеше үлде Бүген !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [COVID 19 аркасында 5000 кеше үлде Бүген !!] ... COVID 19 аркасында 5000 кеше үлде Бүген !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"J06Xm_Ln4AYu","executionInfo":{"status":"ok","timestamp":1620203754331,"user_tz":-300,"elapsed":1748404,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"ed2b4351-7c88-41a5-bc62-59b9f7ec0fbc"},"source":["\t\n","# Tartar for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"Ниһаять, без COVID өчен дәвалау таптык !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Ниһаять, без COVID өчен дәвалау таптык !!]
\n","
positive
\n","
0
\n","
[-0.04030879586935043, -0.05435317009687424, -...
\n","
0.99665
\n","
Ниһаять, без COVID өчен дәвалау таптык !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Ниһаять, без COVID өчен дәвалау таптык !!] ... Ниһаять, без COVID өчен дәвалау таптык !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"HKj5yWwwMplH"},"source":["# The Model understands French\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"CUHcJZfJMplL","executionInfo":{"status":"ok","timestamp":1620203754900,"user_tz":-300,"elapsed":1748968,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"699b249a-39aa-45db-a9c1-8c97597a26c9"},"source":["\t\n","# French for: '5000 people died because of COVID19 Today!!'\n","fitted_pipe.predict(\"5000 personnes sont mortes à cause du COVID 19 aujourd'hui !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[5000 personnes sont mortes à cause du COVID 1...
\n","
negative
\n","
0
\n","
[-0.025422221049666405, -0.04044010490179062, ...
\n","
0.999976
\n","
5000 personnes sont mortes à cause du COVID 19...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [5000 personnes sont mortes à cause du COVID 1... ... 5000 personnes sont mortes à cause du COVID 19...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"57NY2XoTMplM","executionInfo":{"status":"ok","timestamp":1620203756242,"user_tz":-300,"elapsed":1750304,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"58429410-71cb-441a-e96f-f34d566b4230"},"source":["# French for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"Nous avons finalement trouvé un remède à Covid !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Nous avons finalement trouvé un remède à Covi...
\n","
positive
\n","
0
\n","
[-0.03685027360916138, -0.04665076732635498, -...
\n","
0.746119
\n","
Nous avons finalement trouvé un remède à Covid !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Nous avons finalement trouvé un remède à Covi... ... Nous avons finalement trouvé un remède à Covid !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"mLItI4KZOElB"},"source":["# The Model understands Khmer\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SWbqMgAwOElC","executionInfo":{"status":"ok","timestamp":1620203758297,"user_tz":-300,"elapsed":1752342,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c7fd53a1-d40a-48ea-d3aa-e684e2b398b2"},"source":["# Khmer for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"ទីបំផុតយើងបានរកឃើញការព្យាបាលដើម្បី covid !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[ទីបំផុតយើងបានរកឃើញការព្យាបាលដើម្បី covid !, !]
\n","
positive
\n","
0
\n","
[-0.029317181557416916, -0.054831378161907196,...
\n","
0.997803
\n","
ទីបំផុតយើងបានរកឃើញការព្យាបាលដើម្បី covid !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [ទីបំផុតយើងបានរកឃើញការព្យាបាលដើម្បី covid !, !] ... ទីបំផុតយើងបានរកឃើញការព្យាបាលដើម្បី covid !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"beoCtm4xQf2P","executionInfo":{"status":"ok","timestamp":1620203758861,"user_tz":-300,"elapsed":1752900,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c942e4f9-f308-47b6-a9f4-6e6f3feb028f"},"source":["# Khmer for: 'Many people faced depression because of the virus '\n","fitted_pipe.predict(\"មនុស្សជាច្រើនប្រឈមនឹងជំងឺធ្លាក់ទឹកចិត្តដោយសារតែវីរុស\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[មនុស្សជាច្រើនប្រឈមនឹងជំងឺធ្លាក់ទឹកចិត្តដោយសារ...
\n","
negative
\n","
0
\n","
[0.010295538231730461, -0.045677751302719116, ...
\n","
0.999991
\n","
មនុស្សជាច្រើនប្រឈមនឹងជំងឺធ្លាក់ទឹកចិត្តដោយសារត...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [មនុស្សជាច្រើនប្រឈមនឹងជំងឺធ្លាក់ទឹកចិត្តដោយសារ... ... មនុស្សជាច្រើនប្រឈមនឹងជំងឺធ្លាក់ទឹកចិត្តដោយសារត...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"markdown","metadata":{"id":"lvE-LbNiPoBT"},"source":["# The Model understands Yiddish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"sZlmLhajPoBb","executionInfo":{"status":"ok","timestamp":1620203759681,"user_tz":-300,"elapsed":1753715,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"e223c87a-de45-49ed-a445-1f2d5d713c30"},"source":["# Yiddish for: '5000 people died because of COVID19 Today!!'\n","fitted_pipe.predict(\"5000 מענטשן געשטארבן ווייַל פון COVID 19 הייַנט !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[5000 מענטשן געשטארבן ווייַל פון COVID 19 הייַ...
\n","
negative
\n","
0
\n","
[-0.026097219437360764, -0.03218626603484154, ...
\n","
0.999871
\n","
5000 מענטשן געשטארבן ווייַל פון COVID 19 הייַנ...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [5000 מענטשן געשטארבן ווייַל פון COVID 19 הייַ... ... 5000 מענטשן געשטארבן ווייַל פון COVID 19 הייַנ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"5h-pha_nPoBc","executionInfo":{"status":"ok","timestamp":1620203760283,"user_tz":-300,"elapsed":1754312,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3032b26d-1b27-418f-8a90-e555cd992901"},"source":["# Yiddish for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"מיר לעסאָף געפֿונען אַ היילונג צו קאָוויד !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[מיר לעסאָף געפֿונען אַ היילונג צו קאָוויד !!]
\n","
positive
\n","
0
\n","
[-0.04493892937898636, -0.054013777524232864, ...
\n","
0.999837
\n","
מיר לעסאָף געפֿונען אַ היילונג צו קאָוויד !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [מיר לעסאָף געפֿונען אַ היילונג צו קאָוויד !!] ... מיר לעסאָף געפֿונען אַ היילונג צו קאָוויד !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"XSz4WzScaAHj"},"source":["# The Model understands Kygrgyz\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"DXz6fhJSaAHu","executionInfo":{"status":"ok","timestamp":1620203761303,"user_tz":-300,"elapsed":1755327,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"cfd70733-c979-4355-e0d2-632ea230fe71"},"source":["\t\n","# Kygrgyz for: '5000 people died because of COVID19 Today!!'\n","fitted_pipe.predict(\"COVID 19дун айынан 5000 адам каза болду !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[COVID 19дун айынан 5000 адам каза болду !!]
\n","
negative
\n","
0
\n","
[-0.019092926755547523, -0.039794765412807465,...
\n","
0.99999
\n","
COVID 19дун айынан 5000 адам каза болду !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [COVID 19дун айынан 5000 адам каза болду !!] ... COVID 19дун айынан 5000 адам каза болду !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"lh_ZSHlPaAHv","executionInfo":{"status":"ok","timestamp":1620203761857,"user_tz":-300,"elapsed":1755876,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"5266f397-a6da-40f4-fe83-2e8b3b440984"},"source":["# Kygrgyz for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"Акыры, ковидди айыктырдык !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Акыры, ковидди айыктырдык !!]
\n","
positive
\n","
0
\n","
[-0.03051360882818699, -0.06864563375711441, -...
\n","
0.95901
\n","
Акыры, ковидди айыктырдык !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Акыры, ковидди айыктырдык !!] ... Акыры, ковидди айыктырдык !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"DGMVMKaTdJFj"},"source":["# The Model understands Tamil\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"JWDr_LoCdJFn","executionInfo":{"status":"ok","timestamp":1620203762948,"user_tz":-300,"elapsed":1756961,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2391eedc-ebdd-4cef-c3c6-b373fa181092"},"source":["# Tamil for: '5000 people died because of COVID19 Today!!'\n","fitted_pipe.predict(\"5000 பேர் இன்று Covid 19 இன்று இறந்தனர் !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[5000 பேர் இன்று Covid 19 இன்று இறந்தனர் !!]
\n","
negative
\n","
0
\n","
[-0.025186121463775635, -0.024766698479652405,...
\n","
0.999845
\n","
5000 பேர் இன்று Covid 19 இன்று இறந்தனர் !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [5000 பேர் இன்று Covid 19 இன்று இறந்தனர் !!] ... 5000 பேர் இன்று Covid 19 இன்று இறந்தனர் !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Q6C0BmTtdJFp","executionInfo":{"status":"ok","timestamp":1620203763681,"user_tz":-300,"elapsed":1757690,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a4e6ae24-3b97-484e-bb1a-aada7194d2f0"},"source":["# Tamil for: 'We finally found a cure to COVID!!'\n","fitted_pipe.predict(\"COVID க்கு ஒரு தீர்வைக் கண்டுபிடித்தோம் !! \")\n","\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
trained_sentiment
\n","
origin_index
\n","
sentence_embedding_labse
\n","
trained_sentiment_confidence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[COVID க்கு ஒரு தீர்வைக் கண்டுபிடித்தோம் !!]
\n","
positive
\n","
0
\n","
[-0.025669310241937637, -0.055228717625141144,...
\n","
0.938791
\n","
COVID க்கு ஒரு தீர்வைக் கண்டுபிடித்தோம் !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [COVID க்கு ஒரு தீர்வைக் கண்டுபிடித்தோம் !!] ... COVID க்கு ஒரு தீர்வைக் கண்டுபிடித்தோம் !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620204830508,"user_tz":-300,"elapsed":2824512,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c651a7e8-e5b1-4301-c8c0-27b3cb80e2ff"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SO4uz45MoRgp","executionInfo":{"status":"ok","timestamp":1620205118429,"user_tz":-300,"elapsed":143057,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0f59858c-1558-4fc2-a0e4-1ced092d19c6"},"source":["stored_model_path = './models/classifier_dl_trained' \n","hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Everything is under control !')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
text
\n","
sentence_embedding_from_disk
\n","
sentiment_confidence
\n","
origin_index
\n","
sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Everything is under control !
\n","
Everything is under control !
\n","
[[-0.028107844293117523, -0.06088888645172119,...
\n","
[0.99999535]
\n","
8589934592
\n","
[positive]
\n","
[Everything is under control !]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Everything is under control ! ... [Everything is under control !]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UqXHHH-TQTuu","executionInfo":{"status":"ok","timestamp":1620205246123,"user_tz":-300,"elapsed":1036,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c5c78a95-5c5a-4a68-b9a7-560dd63d27fc"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3651e736) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@3651e736\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@labse'] has settable params:\n","pipe['bert_sentence@labse'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@labse'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@labse'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@labse'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@labse'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n",">>> pipe['sentiment_dl@labse'] has settable params:\n","pipe['sentiment_dl@labse'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@labse'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@labse'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"SVEOEnXdwhIz"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_reddit.ipynb b/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_reddit.ipynb
deleted file mode 100644
index 76224b4c..00000000
--- a/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_reddit.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_multi_lingual_training_sentiment_classifier_demo_reddit.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples//colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_reddit.ipynb)\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 class Reddit comments sentiment classifier training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data : \n","\n"," \n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with training data : \n","\n"," \n","\n",""]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620202060181,"user_tz":-300,"elapsed":115170,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"99ce15aa-7eec-4c6c-8dd8-d6206ed07954"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 08:05:45-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 08:05:45 (31.4 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 66kB/s \n","\u001b[K |████████████████████████████████| 153kB 33.8MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 51.7MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Reddit Sentiment dataset \n","https://www.kaggle.com/cosmos98/twitter-and-reddit-sentimental-analysis-dataset\n","#Context\n","\n","This is was a Dataset Created as a part of the university Project On Sentimental Analysis On Multi-Source Social Media Platforms using PySpark."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620202061148,"user_tz":-300,"elapsed":116120,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"52a1546e-3754-4a78-f04e-6902510b4a56"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/Reddit_Data_multi_lingual.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 08:07:39-- http://ckl-it.de/wp-content/uploads/2021/02/Reddit_Data_multi_lingual.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 193200 (189K) [text/csv]\n","Saving to: ‘Reddit_Data_multi_lingual.csv’\n","\n","Reddit_Data_multi_l 100%[===================>] 188.67K 505KB/s in 0.4s \n","\n","2021-05-05 08:07:40 (505 KB/s) - ‘Reddit_Data_multi_lingual.csv’ saved [193200/193200]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620202061743,"user_tz":-300,"elapsed":116696,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"f7ee1f9f-309e-42a5-baad-254d471d27ee"},"source":["import pandas as pd\n","train_path = '/content/Reddit_Data_multi_lingual.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","columns=['text','y']\n","train_df = train_df[columns]\n","from sklearn.model_selection import train_test_split\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
363
\n","
regardless the opposition all girouds goals ha...
\n","
positive
\n","
\n","
\n","
346
\n","
lost count the amount times possible debris h...
\n","
negative
\n","
\n","
\n","
254
\n","
true india needs top universities and not fdi...
\n","
positive
\n","
\n","
\n","
102
\n","
hishammuddin sounding super tired today edit s...
\n","
negative
\n","
\n","
\n","
212
\n","
these two observations will help you understan...
\n","
negative
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
522
\n","
holy shit what you for living
\n","
negative
\n","
\n","
\n","
466
\n","
jizzed hard just reading
\n","
negative
\n","
\n","
\n","
378
\n","
who will moderate the debate and which channel...
\n","
positive
\n","
\n","
\n","
366
\n","
was pro congress long time ago long time befo...
\n","
negative
\n","
\n","
\n","
238
\n","
does that mean you guys are leaving india yay
\n","
negative
\n","
\n"," \n","
\n","
480 rows × 2 columns
\n","
"],"text/plain":[" text y\n","363 regardless the opposition all girouds goals ha... positive\n","346 lost count the amount times possible debris h... negative\n","254 true india needs top universities and not fdi... positive\n","102 hishammuddin sounding super tired today edit s... negative\n","212 these two observations will help you understan... negative\n",".. ... ...\n","522 holy shit what you for living negative\n","466 jizzed hard just reading negative\n","378 who will moderate the debate and which channel... positive\n","366 was pro congress long time ago long time befo... negative\n","238 does that mean you guys are leaving india yay negative\n","\n","[480 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":844},"id":"IKK_Ii_gjJfF","executionInfo":{"status":"ok","timestamp":1620202564226,"user_tz":-300,"elapsed":619161,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"ab472698-fd23-4188-f48c-cf83cb702cb5"},"source":["trainable_pipe = nlu.load('xx.embed_sentence.labse train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(60) \n","trainable_pipe['sentiment_dl'].setLr(0.005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["labse download started this may take some time.\n","Approximate size to download 1.7 GB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.97 0.98 0.98 240\n"," positive 0.98 0.97 0.97 240\n","\n"," accuracy 0.97 480\n"," macro avg 0.98 0.97 0.97 480\n","weighted avg 0.98 0.97 0.97 480\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
origin_index
\n","
sentence_embedding_labse
\n","
y
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
regardless the opposition all girouds goals ha...
\n","
363
\n","
[0.016946014016866684, -0.04248768463730812, 0...
\n","
positive
\n","
regardless the opposition all girouds goals ha...
\n","
0.999915
\n","
positive
\n","
[regardless the opposition all girouds goals h...
\n","
\n","
\n","
1
\n","
lost count the amount times possible debris h...
\n","
346
\n","
[-0.0054521397687494755, 0.033800169825553894,...
\n","
negative
\n","
lost count the amount times possible debris ha...
\n","
0.999966
\n","
negative
\n","
[lost count the amount times possible debris h...
\n","
\n","
\n","
2
\n","
true india needs top universities and not fdi...
\n","
254
\n","
[-0.026513056829571724, 0.022836964577436447, ...
\n","
positive
\n","
true india needs top universities and not fdi ...
\n","
0.999881
\n","
positive
\n","
[true india needs top universities and not fdi...
\n","
\n","
\n","
3
\n","
hishammuddin sounding super tired today edit s...
\n","
102
\n","
[-0.024292560294270515, 0.001604165299795568, ...
\n","
negative
\n","
hishammuddin sounding super tired today edit s...
\n","
0.999243
\n","
negative
\n","
[hishammuddin sounding super tired today edit ...
\n","
\n","
\n","
4
\n","
these two observations will help you understan...
\n","
212
\n","
[0.01662716642022133, 0.009799161925911903, 0....
\n","
negative
\n","
these two observations will help you understan...
\n","
0.992540
\n","
negative
\n","
[these two observations will help you understa...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
475
\n","
holy shit what you for living
\n","
522
\n","
[0.009813179261982441, -0.07502918690443039, 0...
\n","
negative
\n","
holy shit what you for living
\n","
0.999532
\n","
negative
\n","
[holy shit what you for living]
\n","
\n","
\n","
476
\n","
jizzed hard just reading
\n","
466
\n","
[0.021288076415657997, -0.032137248665094376, ...
\n","
negative
\n","
jizzed hard just reading
\n","
0.999912
\n","
negative
\n","
[jizzed hard just reading]
\n","
\n","
\n","
477
\n","
who will moderate the debate and which channel...
\n","
378
\n","
[0.02036549150943756, 0.0054930429905653, -0.0...
\n","
positive
\n","
who will moderate the debate and which channel...
\n","
0.998263
\n","
positive
\n","
[who will moderate the debate and which channe...
\n","
\n","
\n","
478
\n","
was pro congress long time ago long time befo...
\n","
366
\n","
[4.2188377847196534e-05, 0.005828899797052145,...
\n","
negative
\n","
was pro congress long time ago long time befor...
\n","
0.998717
\n","
negative
\n","
[was pro congress long time ago long time befo...
\n","
\n","
\n","
479
\n","
does that mean you guys are leaving india yay
\n","
238
\n","
[0.004341824445873499, -0.024586506187915802, ...
\n","
negative
\n","
does that mean you guys are leaving india yay
\n","
0.999851
\n","
negative
\n","
[does that mean you guys are leaving india yay]
\n","
\n"," \n","
\n","
480 rows × 8 columns
\n","
"],"text/plain":[" text ... sentence\n","0 regardless the opposition all girouds goals ha... ... [regardless the opposition all girouds goals h...\n","1 lost count the amount times possible debris h... ... [lost count the amount times possible debris h...\n","2 true india needs top universities and not fdi... ... [true india needs top universities and not fdi...\n","3 hishammuddin sounding super tired today edit s... ... [hishammuddin sounding super tired today edit ...\n","4 these two observations will help you understan... ... [these two observations will help you understa...\n",".. ... ... ...\n","475 holy shit what you for living ... [holy shit what you for living]\n","476 jizzed hard just reading ... [jizzed hard just reading]\n","477 who will moderate the debate and which channel... ... [who will moderate the debate and which channe...\n","478 was pro congress long time ago long time befo... ... [was pro congress long time ago long time befo...\n","479 does that mean you guys are leaving india yay ... [does that mean you guys are leaving india yay]\n","\n","[480 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 3.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"id":"Fxx4yNkNVGFl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620202588231,"user_tz":-300,"elapsed":643159,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"8c0c8d1d-add3-4c3b-cf49-3a4827e0a8f8"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.79 0.75 0.77 60\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.79 0.77 0.78 60\n","\n"," accuracy 0.76 120\n"," macro avg 0.53 0.51 0.52 120\n","weighted avg 0.79 0.76 0.77 120\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"BD5OKO4Umc5U"},"source":["#4. Test Model with 20 languages!"]},{"cell_type":"code","metadata":{"id":"OQ72hP9unML7","colab":{"base_uri":"https://localhost:8080/","height":759},"executionInfo":{"status":"ok","timestamp":1620202610653,"user_tz":-300,"elapsed":665574,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3c13a8e2-a7fa-42ff-f622-583ca3ea8b9d"},"source":["train_df = pd.read_csv(\"Reddit_Data_multi_lingual.csv\")\n","preds = fitted_pipe.predict(train_df[[\"test_sentences\",\"y\"]].iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.81 0.81 0.81 48\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.82 0.81 0.82 52\n","\n"," accuracy 0.81 100\n"," macro avg 0.55 0.54 0.54 100\n","weighted avg 0.82 0.81 0.81 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
origin_index
\n","
sentence_embedding_labse
\n","
y
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
je pravda, že přerušili moc, jaký kongres douc...
\n","
0
\n","
[0.047071367502212524, -0.023074660450220108, ...
\n","
positive
\n","
je pravda, že přerušili moc, jaký kongres douc...
\n","
0.999991
\n","
positive
\n","
[je pravda, že přerušili moc, jaký kongres dou...
\n","
\n","
\n","
1
\n","
今月のようにジルーをより良く仕上げる
\n","
1
\n","
[-0.01690051332116127, -0.02084467001259327, -...
\n","
positive
\n","
今月のようにジルーをより良く仕上げる
\n","
0.999995
\n","
positive
\n","
[今月のようにジルーをより良く仕上げる]
\n","
\n","
\n","
2
\n","
נראה חרא עכשיו אבל עדיין גאה
\n","
2
\n","
[-0.02598223276436329, -0.02113635465502739, -...
\n","
positive
\n","
נראה חרא עכשיו אבל עדיין גאה
\n","
0.978773
\n","
positive
\n","
[נראה חרא עכשיו אבל עדיין גאה]
\n","
\n","
\n","
3
\n","
פלור הבוער שונא את האל הרע הטוב ביותר
\n","
3
\n","
[-0.044415801763534546, -0.01080454234033823, ...
\n","
negative
\n","
פלור הבוער שונא את האל הרע הטוב ביותר
\n","
0.999930
\n","
negative
\n","
[פלור הבוער שונא את האל הרע הטוב ביותר]
\n","
\n","
\n","
4
\n","
पूछ सकते हैं कि आप इस शक्तिशाली चीज़ के साथ क्...
\n","
4
\n","
[0.040694039314985275, -0.02741238661110401, 0...
\n","
positive
\n","
पूछ सकते हैं कि आप इस शक्तिशाली चीज़ के साथ क्...
\n","
0.980074
\n","
positive
\n","
[पूछ सकते हैं कि आप इस शक्तिशाली चीज़ के साथ क...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
这并不奇怪
\n","
95
\n","
[0.026199784129858017, -0.0617312416434288, -0...
\n","
positive
\n","
这并不奇怪
\n","
0.997505
\n","
positive
\n","
[这并不奇怪]
\n","
\n","
\n","
96
\n","
এই পোস্টটি বিধি লঙ্ঘন করে সর্বশেষে আপনার ছাগল ...
\n","
96
\n","
[0.03437668830156326, -0.033291902393102646, -...
\n","
negative
\n","
এই পোস্টটি বিধি লঙ্ঘন করে সর্বশেষে আপনার ছাগল ...
\n","
0.999943
\n","
negative
\n","
[এই পোস্টটি বিধি লঙ্ঘন করে সর্বশেষে আপনার ছাগল...
\n","
\n","
\n","
97
\n","
سنا ہے براہ راست لائن نریندر مودی ہے جو کسی بھ...
\n","
97
\n","
[-0.0582120455801487, 0.05610273778438568, 0.0...
\n","
positive
\n","
سنا ہے براہ راست لائن نریندر مودی ہے جو کسی بھ...
\n","
0.999853
\n","
positive
\n","
[سنا ہے براہ راست لائن نریندر مودی ہے جو کسی ب...
\n","
\n","
\n","
98
\n","
allah lanet olsun bu şərhlərə hindistandan çox...
\n","
98
\n","
[-0.02142353542149067, 0.011710312217473984, -...
\n","
negative
\n","
allah lanet olsun bu şərhlərə hindistandan çox...
\n","
0.972011
\n","
positive
\n","
[allah lanet olsun bu şərhlərə hindistandan ço...
\n","
\n","
\n","
99
\n","
อัห์มดาบาดมีโอกาสที่จะกลายเป็นเมืองรถไฟใต้ดินท...
\n","
99
\n","
[0.003569604828953743, 0.017301399260759354, -...
\n","
positive
\n","
อัห์มดาบาดมีโอกาสที่จะกลายเป็นเมืองรถไฟใต้ดินท...
\n","
0.999819
\n","
positive
\n","
[อัห์มดาบาดมีโอกาสที่จะกลายเป็นเมืองรถไฟใต้ดิน...
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" text ... sentence\n","0 je pravda, že přerušili moc, jaký kongres douc... ... [je pravda, že přerušili moc, jaký kongres dou...\n","1 今月のようにジルーをより良く仕上げる ... [今月のようにジルーをより良く仕上げる]\n","2 נראה חרא עכשיו אבל עדיין גאה ... [נראה חרא עכשיו אבל עדיין גאה]\n","3 פלור הבוער שונא את האל הרע הטוב ביותר ... [פלור הבוער שונא את האל הרע הטוב ביותר]\n","4 पूछ सकते हैं कि आप इस शक्तिशाली चीज़ के साथ क्... ... [पूछ सकते हैं कि आप इस शक्तिशाली चीज़ के साथ क...\n",".. ... ... ...\n","95 这并不奇怪 ... [这并不奇怪]\n","96 এই পোস্টটি বিধি লঙ্ঘন করে সর্বশেষে আপনার ছাগল ... ... [এই পোস্টটি বিধি লঙ্ঘন করে সর্বশেষে আপনার ছাগল...\n","97 سنا ہے براہ راست لائن نریندر مودی ہے جو کسی بھ... ... [سنا ہے براہ راست لائن نریندر مودی ہے جو کسی ب...\n","98 allah lanet olsun bu şərhlərə hindistandan çox... ... [allah lanet olsun bu şərhlərə hindistandan ço...\n","99 อัห์มดาบาดมีโอกาสที่จะกลายเป็นเมืองรถไฟใต้ดินท... ... [อัห์มดาบาดมีโอกาสที่จะกลายเป็นเมืองรถไฟใต้ดิน...\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"RjtuNUcvuJTT"},"source":["# The Model understands Englsih\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"o0vu7PaWkcI7","executionInfo":{"status":"ok","timestamp":1620202612106,"user_tz":-300,"elapsed":667020,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"83b39dee-e5bf-4037-f355-c87b32d619ae"},"source":["fitted_pipe.predict(\"I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away... \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.023606013506650925, -0.02828284353017807, ...
\n","
I am depressed because of my recent break up a...
\n","
0.999837
\n","
negative
\n","
[I am depressed because of my recent break up ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [I am depressed because of my recent break up ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"1ykjRQhCtQ4w","executionInfo":{"status":"ok","timestamp":1620202613609,"user_tz":-300,"elapsed":668514,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b90ac718-5b5b-4334-8f8c-c06312b9f923"},"source":["fitted_pipe.predict(\"The love of my life proposed me , I feel like the happiest person alive!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.0050350213423371315, 0.022905258461833, -0...
\n","
The love of my life proposed me , I feel like ...
\n","
0.999997
\n","
positive
\n","
[The love of my life proposed me , I feel like...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [The love of my life proposed me , I feel like...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"vohym-XbuNHn"},"source":["# The Model understands German\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"dzaaZrI4tVWc","executionInfo":{"status":"ok","timestamp":1620202614500,"user_tz":-300,"elapsed":669398,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3b22dcbf-bd9e-416a-db54-a8862ba2dd9d"},"source":["# German for:'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict(\"Die Liebe meines Lebens schlug mich vor, ich fühle mich wie die glücklichste Person am Leben!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[0.006932601798325777, -0.008974265307188034, ...
\n","
Die Liebe meines Lebens schlug mich vor, ich f...
\n","
0.999992
\n","
positive
\n","
[Die Liebe meines Lebens schlug mich vor, ich ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Die Liebe meines Lebens schlug mich vor, ich ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"BbhgTSBGtTtJ","executionInfo":{"status":"ok","timestamp":1620202615621,"user_tz":-300,"elapsed":670514,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"62299b8b-83e7-4dc3-81a4-9f5007a47024"},"source":["# German for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"Ich bin wegen meiner kürzlichen Trennung depressiv und verbringe meine ganze Zeit damit zu weinen. Ich möchte, dass die Schmerzen verschwinden ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.04037578031420708, -0.019928034394979477, ...
\n","
Ich bin wegen meiner kürzlichen Trennung depre...
\n","
0.999968
\n","
negative
\n","
[Ich bin wegen meiner kürzlichen Trennung depr...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Ich bin wegen meiner kürzlichen Trennung depr...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"a1JbtmWquQwj"},"source":["# The Model understands Chinese\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"kYSYqtoRtc-P","executionInfo":{"status":"ok","timestamp":1620202616139,"user_tz":-300,"elapsed":671022,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"ac2f9602-4983-44f2-c91b-360938c72845"},"source":["\n","# Chinese for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"由于最近的分手,我感到沮丧,我花了所有的时间哭泣,我希望痛苦能够消失... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.04107382893562317, -0.04438788816332817, 0...
\n","
由于最近的分手,我感到沮丧,我花了所有的时间哭泣,我希望痛苦能够消失...
\n","
0.999859
\n","
negative
\n","
[由于最近的分手,我感到沮丧,我花了所有的时间哭泣,我希望痛苦能够消失., ..]
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [由于最近的分手,我感到沮丧,我花了所有的时间哭泣,我希望痛苦能够消失., ..]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"06v9SD-QtlBU","executionInfo":{"status":"ok","timestamp":1620202617533,"user_tz":-300,"elapsed":672409,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2fc34b26-a698-4526-a3d5-183ed34d17d2"},"source":["# Chinese for : \"The love of my life proposed me , I feel like the happiest person alive!\"\n","fitted_pipe.predict(\"我一生的爱向我提出了我,我感觉自己是最幸福的人!\")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.025317609310150146, -0.04454464837908745, ...
\n","
我一生的爱向我提出了我,我感觉自己是最幸福的人!
\n","
0.999999
\n","
positive
\n","
[我一生的爱向我提出了我,我感觉自己是最幸福的人!]
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [我一生的爱向我提出了我,我感觉自己是最幸福的人!]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"9h7CvN4uu9Pb"},"source":["# Model understands Afrikaans\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"VMPhbgw9twtf","executionInfo":{"status":"ok","timestamp":1620202618119,"user_tz":-300,"elapsed":672985,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3fd472f3-82f1-4d75-ed3d-a858600ddeca"},"source":["# Afrikaans for 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict(\"Die liefde van my lewe het my voorgestel, ek voel soos die gelukkigste persoon wat lewendig is!\")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.017393874004483223, 0.005962136201560497, ...
\n","
Die liefde van my lewe het my voorgestel, ek v...
\n","
1.0
\n","
positive
\n","
[Die liefde van my lewe het my voorgestel, ek ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Die liefde van my lewe het my voorgestel, ek ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"zWgNTIdkumhX","executionInfo":{"status":"ok","timestamp":1620202619781,"user_tz":-300,"elapsed":674637,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"5ad4ba58-3460-4af1-defe-80a0dfba2782"},"source":["# Afrikaans for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"Ek is depressief as gevolg van my onlangse breuk en ek spandeer al my tyd om te huil, ek wil hê dat die pyn moet verdwyn ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.042933885008096695, -0.0334482416510582, 0...
\n","
Ek is depressief as gevolg van my onlangse bre...
\n","
0.999771
\n","
negative
\n","
[Ek is depressief as gevolg van my onlangse br...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Ek is depressief as gevolg van my onlangse br...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"rSEPkC-Bwnpg"},"source":["# The model understands Vietnamese\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"wCcTS5gIu511","executionInfo":{"status":"ok","timestamp":1620202620213,"user_tz":-300,"elapsed":675061,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"71a93eee-b909-4c1e-f7d0-f1a496171bc1"},"source":["# Vietnamese for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict('Tình yêu của đời tôi đề xuất tôi, tôi cảm thấy như người hạnh phúc nhất còn sống!')\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.03576832264661789, -0.017268849536776543, ...
\n","
Tình yêu của đời tôi đề xuất tôi, tôi cảm thấy...
\n","
0.999889
\n","
positive
\n","
[Tình yêu của đời tôi đề xuất tôi, tôi cảm thấ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Tình yêu của đời tôi đề xuất tôi, tôi cảm thấ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"lpQmMRA59REb","executionInfo":{"status":"ok","timestamp":1620202621694,"user_tz":-300,"elapsed":676532,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"99eae0ce-ebc7-4709-efd8-c4d91cecb967"},"source":["# Vietnamese for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"Tôi chán nản vì cuộc chia tay gần đây và tôi dành toàn bộ thời gian để khóc, tôi muốn nỗi đau qua đi ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.009116670116782188, -0.007585291285067797,...
\n","
Tôi chán nản vì cuộc chia tay gần đây và tôi d...
\n","
0.99989
\n","
negative
\n","
[Tôi chán nản vì cuộc chia tay gần đây và tôi ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Tôi chán nản vì cuộc chia tay gần đây và tôi ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"IlkmAaMoxTuy"},"source":["# The model understands Japanese\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"1IfJu3q8wwUt","executionInfo":{"status":"ok","timestamp":1620202622270,"user_tz":-300,"elapsed":677098,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"6b526a54-20ce-42e0-fdcd-68cb2f52ef4d"},"source":["\n","# Japanese for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict('私の人生の愛は私を提案しました、私は生きている最も幸せな人のように感じます!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.011561010964214802, 0.00965394638478756, -...
\n","
私の人生の愛は私を提案しました、私は生きている最も幸せな人のように感じます!
\n","
1.0
\n","
positive
\n","
[私の人生の愛は私を提案しました、私は生きている最も幸せな人のように感じます!]
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [私の人生の愛は私を提案しました、私は生きている最も幸せな人のように感じます!]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"h3k7_PFhxOve","executionInfo":{"status":"ok","timestamp":1620202622854,"user_tz":-300,"elapsed":677668,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"55eca2a6-cacf-47f0-b078-60fedb82907a"},"source":["# Japanese for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"私は最近の別れのために落ち込んでいて、私はずっと泣いて過ごしています、私は痛みを取り除きたいです... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.03512014076113701, -0.03604918718338013, 0...
\n","
私は最近の別れのために落ち込んでいて、私はずっと泣いて過ごしています、私は痛みを取り除きたい...
\n","
0.999896
\n","
negative
\n","
[私は最近の別れのために落ち込んでいて、私はずっと泣いて過ごしています、私は痛みを取り除きた...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [私は最近の別れのために落ち込んでいて、私はずっと泣いて過ごしています、私は痛みを取り除きた...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"GITfT7FK0CGv"},"source":["# The model understands Zulu\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"6uelDwq4xdWv","executionInfo":{"status":"ok","timestamp":1620202624064,"user_tz":-300,"elapsed":678847,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"bcbaa257-b1de-4d12-c711-19a0b684dfc8"},"source":["# Zulu for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict('Uthando lwempilo yami lungihlongosile, ngizwa sengathi umuntu ojabule kunabo bonke ephila!')\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.011278740130364895, -0.005037062801420689,...
\n","
Uthando lwempilo yami lungihlongosile, ngizwa ...
\n","
0.999999
\n","
positive
\n","
[Uthando lwempilo yami lungihlongosile, ngizwa...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Uthando lwempilo yami lungihlongosile, ngizwa...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"VS5JE0tC9W-h","executionInfo":{"status":"ok","timestamp":1620202624980,"user_tz":-300,"elapsed":679742,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"8e15b20e-7cac-4b07-be83-8b2ed9e2cd1a"},"source":["# Zulu for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"Ngicindezelekile ngenxa yokuhlukana kwami kwakamuva futhi ngichitha sonke isikhathi sami ngikhala, ngifuna ubuhlungu buphele ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.013925889506936073, -0.032107651233673096,...
\n","
Ngicindezelekile ngenxa yokuhlukana kwami kwak...
\n","
0.999893
\n","
negative
\n","
[Ngicindezelekile ngenxa yokuhlukana kwami kwa...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Ngicindezelekile ngenxa yokuhlukana kwami kwa...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"VGVvzl_30a0T"},"source":["# The Model understands Turkish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"aOSsiK6J0jWs","executionInfo":{"status":"ok","timestamp":1620202625775,"user_tz":-300,"elapsed":680528,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c12cc91a-bf6b-42bf-ae32-a74b990482f2"},"source":["# Turkish for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict('Hayatımın aşkı bana teklif etti, yaşayan en mutlu insan gibi hissediyorum! ')\n","\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[0.0015444883611053228, 0.017139634117484093, ...
\n","
Hayatımın aşkı bana teklif etti, yaşayan en mu...
\n","
0.999999
\n","
positive
\n","
[Hayatımın aşkı bana teklif etti, yaşayan en m...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Hayatımın aşkı bana teklif etti, yaşayan en m...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"RFsJ9tZs9aCX","executionInfo":{"status":"ok","timestamp":1620202626744,"user_tz":-300,"elapsed":681490,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"da2a67ab-b755-4304-865d-cc33b5700dac"},"source":["# Turkish for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"Son ayrılığımdan dolayı depresyondayım ve tüm zamanımı ağlayarak geçiriyorum, acının gitmesini istiyorum ... \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.04846595600247383, -0.041023433208465576, ...
\n","
Son ayrılığımdan dolayı depresyondayım ve tüm ...
\n","
0.999948
\n","
negative
\n","
[Son ayrılığımdan dolayı depresyondayım ve tüm...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Son ayrılığımdan dolayı depresyondayım ve tüm...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"803qL2gt0vlb"},"source":["# The Model understands Hebrew\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"XQ5VCtxw0pc0","executionInfo":{"status":"ok","timestamp":1620202627823,"user_tz":-300,"elapsed":682559,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0a2dc574-968d-4aef-ebeb-e49fb6d0d84e"},"source":["# Hebrew for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"אני בדיכאון בגלל הפרידה האחרונה שלי ואני מבלה את כל זמני בבכי, אני רוצה שהכאב ייעלם ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.04138200357556343, -0.05670207366347313, 0...
\n","
אני בדיכאון בגלל הפרידה האחרונה שלי ואני מבלה ...
\n","
0.999874
\n","
negative
\n","
[אני בדיכאון בגלל הפרידה האחרונה שלי ואני מבלה...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [אני בדיכאון בגלל הפרידה האחרונה שלי ואני מבלה...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"9w2ZHfns05A4","executionInfo":{"status":"ok","timestamp":1620202628408,"user_tz":-300,"elapsed":683127,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c260eef9-11ad-46e1-bd89-000a3a1613c3"},"source":["# Hebrew for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict('אהבת חיי הציעה אותי, אני מרגישה כמו האדם המאושר ביותר בחיים!')\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.021271945908665657, -0.02133464813232422, ...
\n","
אהבת חיי הציעה אותי, אני מרגישה כמו האדם המאוש...
\n","
1.0
\n","
positive
\n","
[אהבת חיי הציעה אותי, אני מרגישה כמו האדם המאו...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [אהבת חיי הציעה אותי, אני מרגישה כמו האדם המאו...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"SDlpd33H1HIX"},"source":["# The Model understands Telugu\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Kc5n1bzv1BJT","executionInfo":{"status":"ok","timestamp":1620202628966,"user_tz":-300,"elapsed":683598,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"af2c9d3e-518b-4084-ceb5-f37cb8d2ada8"},"source":["# Telugu for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict('నా జీవితం యొక్క ప్రేమ నన్ను ప్రతిపాదించింది, సజీవంగా ఉన్న వ్యక్తిగా నేను భావిస్తున్నాను!' )"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.011076044291257858, -0.022865094244480133,...
\n","
నా జీవితం యొక్క ప్రేమ నన్ను ప్రతిపాదించింది, స...
\n","
0.999512
\n","
positive
\n","
[నా జీవితం యొక్క ప్రేమ నన్ను ప్రతిపాదించింది, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [నా జీవితం యొక్క ప్రేమ నన్ను ప్రతిపాదించింది, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"-l-u6vrz1Obe","executionInfo":{"status":"ok","timestamp":1620202630256,"user_tz":-300,"elapsed":684869,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"8c425592-091d-4f36-822c-b90fe76b847c"},"source":["\n","# Telugu for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"నా ఇటీవలి విడిపోవడం వల్ల నేను నిరాశకు గురయ్యాను మరియు నా సమయాన్ని ఏడుస్తూనే ఉన్నాను, నొప్పి పోవాలని నేను కోరుకుంటున్నాను ... \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.025107204914093018, -0.03688901290297508, ...
\n","
నా ఇటీవలి విడిపోవడం వల్ల నేను నిరాశకు గురయ్యాన...
\n","
0.999736
\n","
negative
\n","
[నా ఇటీవలి విడిపోవడం వల్ల నేను నిరాశకు గురయ్యా...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [నా ఇటీవలి విడిపోవడం వల్ల నేను నిరాశకు గురయ్యా...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"nziBUe8t1Zwn"},"source":["# Model understands Russian\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Ckyjl3YQ1VFn","executionInfo":{"status":"ok","timestamp":1620202630819,"user_tz":-300,"elapsed":685423,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"5878f2d1-006e-42df-8fc9-39d82474d560"},"source":["# Russian for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"Я в депрессии из-за моего недавнего разрыва, и я все время плачу, я хочу, чтобы боль ушла ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.042304884642362595, -0.020287370309233665,...
\n","
Я в депрессии из-за моего недавнего разрыва, и...
\n","
0.999906
\n","
negative
\n","
[Я в депрессии из-за моего недавнего разрыва, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Я в депрессии из-за моего недавнего разрыва, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"GIdWkfGv1gFz","executionInfo":{"status":"ok","timestamp":1620202631985,"user_tz":-300,"elapsed":686577,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"cb9c336c-84b8-4e70-b9af-21d88622e4d5"},"source":["# Russian for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict('Этот фильм был отличным!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.03249521926045418, -0.04056306555867195, -...
\n","
Этот фильм был отличным!
\n","
1.0
\n","
positive
\n","
[Этот фильм был отличным!]
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Этот фильм был отличным!]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"8R1j9mwz2Cm4"},"source":["# Model understands Urdu\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"j4zwvRV11pcG","executionInfo":{"status":"ok","timestamp":1620202632557,"user_tz":-300,"elapsed":687124,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"611124b9-5779-4bab-ad6e-b089823771a3"},"source":["# Urdu for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"میں حالیہ بریک اپ کی وجہ سے افسردہ ہوں اور میں اپنا سارا وقت روتے ہوئے گزارتا ہوں ، میں چاہتا ہوں کہ درد دور ہو ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.0319853238761425, -0.042503755539655685, 0...
\n","
میں حالیہ بریک اپ کی وجہ سے افسردہ ہوں اور میں...
\n","
0.999827
\n","
negative
\n","
[میں حالیہ بریک اپ کی وجہ سے افسردہ ہوں اور می...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [میں حالیہ بریک اپ کی وجہ سے افسردہ ہوں اور می...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SxzTuK4b2UKV","executionInfo":{"status":"ok","timestamp":1620202633073,"user_tz":-300,"elapsed":687625,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3aeceb9c-b387-4522-ac26-d44aa0f04b62"},"source":["# Urdu for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict('میری زندگی کی محبت نے مجھے پیش کیا، مجھے سب سے خوشگوار شخص زندہ لگتا ہے!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.014539830386638641, -0.022852826863527298,...
\n","
میری زندگی کی محبت نے مجھے پیش کیا، مجھے سب سے...
\n","
0.999996
\n","
positive
\n","
[میری زندگی کی محبت نے مجھے پیش کیا، مجھے سب س...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [میری زندگی کی محبت نے مجھے پیش کیا، مجھے سب س...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"RoNg-C3k1qcX"},"source":["# Model understands Hindi\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"QZ9RT5Wv1r1n","executionInfo":{"status":"ok","timestamp":1620202634150,"user_tz":-300,"elapsed":688689,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3d890e16-306f-4f17-a134-072702818df8"},"source":["# Hindi for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict('मेरे जीवन के प्यार ने मुझे प्रस्तावित किया, मुझे लगता है कि सबसे खुश व्यक्ति जीवित है!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.007552082184702158, 0.003644779557362199, ...
\n","
मेरे जीवन के प्यार ने मुझे प्रस्तावित किया, मु...
\n","
0.999995
\n","
positive
\n","
[मेरे जीवन के प्यार ने मुझे प्रस्तावित किया, म...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [मेरे जीवन के प्यार ने मुझे प्रस्तावित किया, म...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"quM-IL2i12-B","executionInfo":{"status":"ok","timestamp":1620202634732,"user_tz":-300,"elapsed":689259,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"4ee626e0-d134-49bf-9def-da2eba44ec98"},"source":["# Hindi for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"मेरे हालिया ब्रेक अप के कारण मैं उदास हूं और मैं अपना सारा समय रोने में बिताता हूं, मैं चाहता हूं कि दर्द दूर हो जाए ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.033875059336423874, -0.03928769379854202, ...
\n","
मेरे हालिया ब्रेक अप के कारण मैं उदास हूं और म...
\n","
0.999579
\n","
negative
\n","
[मेरे हालिया ब्रेक अप के कारण मैं उदास हूं और ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [मेरे हालिया ब्रेक अप के कारण मैं उदास हूं और ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"R4ByHOZn35Lc"},"source":["# The model understands Tartar\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"2JrzusSQ18F5","executionInfo":{"status":"ok","timestamp":1620202636130,"user_tz":-300,"elapsed":690641,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"9ca5745a-5d4d-491f-f901-f3ccae8318e4"},"source":["# Tartar for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"Соңгы аерылышуым аркасында мин депрессияләнәм һәм бөтен вакытымны елыйм, авыртуның китүен телим ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.04341091588139534, -0.04436178505420685, 0...
\n","
Соңгы аерылышуым аркасында мин депрессияләнәм ...
\n","
0.999768
\n","
negative
\n","
[Соңгы аерылышуым аркасында мин депрессияләнәм...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Соңгы аерылышуым аркасында мин депрессияләнәм...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"J06Xm_Ln4AYu","executionInfo":{"status":"ok","timestamp":1620202636718,"user_tz":-300,"elapsed":691220,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"11e6167e-2cde-4984-a3ee-3b932406a391"},"source":["# Tartar for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict('Тормышымның мәхәббәте миңа тәкъдим итте, мин үземне иң бәхетле кеше кебек хис итәм!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.0025619822554290295, -0.02031664550304413,...
\n","
Тормышымның мәхәббәте миңа тәкъдим итте, мин ү...
\n","
0.999999
\n","
positive
\n","
[Тормышымның мәхәббәте миңа тәкъдим итте, мин ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Тормышымның мәхәббәте миңа тәкъдим итте, мин ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"HKj5yWwwMplH"},"source":["# The Model understands French\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"CUHcJZfJMplL","executionInfo":{"status":"ok","timestamp":1620202637079,"user_tz":-300,"elapsed":691550,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a66b71f9-296a-4b4f-d45a-56bfd3e91711"},"source":["# French for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"Je suis déprimé à cause de ma récente rupture et je passe tout mon temps à pleurer, je veux que la douleur disparaisse ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.026194952428340912, -0.031189769506454468,...
\n","
Je suis déprimé à cause de ma récente rupture ...
\n","
0.999835
\n","
negative
\n","
[Je suis déprimé à cause de ma récente rupture...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Je suis déprimé à cause de ma récente rupture...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"57NY2XoTMplM","executionInfo":{"status":"ok","timestamp":1620202638450,"user_tz":-300,"elapsed":692915,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2d8c3012-02b6-4b1d-93dd-f93983c95f3a"},"source":["# French for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict(\"L'amour de ma vie m'a proposé, je me sens comme la personne la plus heureuse en vie!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.018955670297145844, -0.024394378066062927,...
\n","
L'amour de ma vie m'a proposé, je me sens comm...
\n","
1.0
\n","
positive
\n","
[L'amour de ma vie m'a proposé, je me sens com...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [L'amour de ma vie m'a proposé, je me sens com...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"jD2TBgT0Nq6F"},"source":["# The Model understands Thai\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"gBp11S5GNq6S","executionInfo":{"status":"ok","timestamp":1620202638827,"user_tz":-300,"elapsed":693282,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"151ef2f2-dfdc-495c-fc41-36b0af49dcf3"},"source":["# Thai for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict(\"ความรักในชีวิตของฉันเสนอให้ฉันฉันรู้สึกเหมือนคนที่มีความสุขที่สุดที่มีชีวิตอยู่!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.031056925654411316, -0.042199380695819855,...
\n","
ความรักในชีวิตของฉันเสนอให้ฉันฉันรู้สึกเหมือนค...
\n","
0.999998
\n","
positive
\n","
[ความรักในชีวิตของฉันเสนอให้ฉันฉันรู้สึกเหมือน...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [ความรักในชีวิตของฉันเสนอให้ฉันฉันรู้สึกเหมือน...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"uvlK8HEZ92qr","executionInfo":{"status":"ok","timestamp":1620202640192,"user_tz":-300,"elapsed":694642,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"54b6a661-f4b0-42f7-8a6a-650686b7050e"},"source":["# Thai for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"ฉันรู้สึกหดหู่ใจเพราะเพิ่งเลิกกันและฉันใช้เวลาร้องไห้ตลอดเวลาฉันอยากให้ความเจ็บปวดหายไป ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.01441363524645567, -0.05407707020640373, 0...
\n","
ฉันรู้สึกหดหู่ใจเพราะเพิ่งเลิกกันและฉันใช้เวลา...
\n","
0.999793
\n","
negative
\n","
[ฉันรู้สึกหดหู่ใจเพราะเพิ่งเลิกกันและฉันใช้เวล...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [ฉันรู้สึกหดหู่ใจเพราะเพิ่งเลิกกันและฉันใช้เวล...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"mLItI4KZOElB"},"source":["# The Model understands Khmer\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SWbqMgAwOElC","executionInfo":{"status":"ok","timestamp":1620202640791,"user_tz":-300,"elapsed":695235,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"cb1384e8-1ce8-4630-db91-a0e0250f4c71"},"source":["# Khmer for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict(\"សេចក្តីស្រឡាញ់នៃជីវិតរបស់ខ្ញុំបានស្នើខ្ញុំខ្ញុំមានអារម្មណ៍ថាដូចជាមនុស្សដែលសប្បាយរីករាយបំផុតនៅរស់! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[0.002843152964487672, -0.021134117618203163, ...
\n","
សេចក្តីស្រឡាញ់នៃជីវិតរបស់ខ្ញុំបានស្នើខ្ញុំខ្ញុ...
\n","
0.999999
\n","
positive
\n","
[សេចក្តីស្រឡាញ់នៃជីវិតរបស់ខ្ញុំបានស្នើខ្ញុំខ្ញ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [សេចក្តីស្រឡាញ់នៃជីវិតរបស់ខ្ញុំបានស្នើខ្ញុំខ្ញ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"YAKYMOJD95Ep","executionInfo":{"status":"ok","timestamp":1620202641730,"user_tz":-300,"elapsed":696166,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"dbf8b746-338b-4770-8ca8-00cede0282d1"},"source":["# Khmer for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"ខ្ញុំក្រៀមក្រំណាស់ដោយសារតែការបែកបាក់ថ្មីៗនេះហើយខ្ញុំចំណាយពេលវេលាយំអស់មួយជីវិតខ្ញុំចង់អោយការឈឺចាប់បាត់ទៅវិញ ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.024856086820364, -0.05315130203962326, -0....
\n","
ខ្ញុំក្រៀមក្រំណាស់ដោយសារតែការបែកបាក់ថ្មីៗនេះហើ...
\n","
0.999944
\n","
negative
\n","
[ខ្ញុំក្រៀមក្រំណាស់ដោយសារតែការបែកបាក់ថ្មីៗនេះហ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [ខ្ញុំក្រៀមក្រំណាស់ដោយសារតែការបែកបាក់ថ្មីៗនេះហ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"markdown","metadata":{"id":"lvE-LbNiPoBT"},"source":["# The Model understands Yiddish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"5h-pha_nPoBc","executionInfo":{"status":"ok","timestamp":1620202642173,"user_tz":-300,"elapsed":696601,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"e35a883e-3f0f-48d6-cbed-d4c3198ec0d1"},"source":["# Yiddish for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict(\"די ליבע פון מיין לעבן פארגעלייגט מיר, איך פילן ווי די כאַפּיאַסט מענטש לעבעדיק!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.019913051277399063, 0.015512099489569664, ...
\n","
די ליבע פון מיין לעבן פארגעלייגט מיר, איך פילן...
\n","
0.999998
\n","
positive
\n","
[די ליבע פון מיין לעבן פארגעלייגט מיר, איך פיל...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [די ליבע פון מיין לעבן פארגעלייגט מיר, איך פיל...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"yNRa1bWt97rg","executionInfo":{"status":"ok","timestamp":1620202643046,"user_tz":-300,"elapsed":697465,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c1be6a46-0f3c-404e-d284-daa0a244aa84"},"source":["# Yiddish for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"איך בין דערשלאָגן ווייַל פון מיין לעצטנס ברעכן זיך און איך פאַרברענגען אַלע מיין צייט וויינען, איך ווילן די ווייטיק וועט גיין אַוועק ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.02142149768769741, -0.04973936453461647, 0...
\n","
איך בין דערשלאָגן ווייַל פון מיין לעצטנס ברעכן...
\n","
0.999752
\n","
negative
\n","
[איך בין דערשלאָגן ווייַל פון מיין לעצטנס ברעכ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [איך בין דערשלאָגן ווייַל פון מיין לעצטנס ברעכ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"XSz4WzScaAHj"},"source":["# The Model understands Kygrgyz\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"lh_ZSHlPaAHv","executionInfo":{"status":"ok","timestamp":1620202643577,"user_tz":-300,"elapsed":697989,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"cd32af06-598a-416b-ceaa-571a5e8eeccc"},"source":["# Kygrgyz for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict(\"Менин жашоомдун сүйүүсү мени сунуш кылды, мен өзүмдү бактылуу адамдай сезип жатам!|\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.030747268348932266, -0.025966504588723183,...
\n","
Менин жашоомдун сүйүүсү мени сунуш кылды, мен ...
\n","
0.999741
\n","
positive
\n","
[Менин жашоомдун сүйүүсү мени сунуш кылды, мен...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Менин жашоомдун сүйүүсү мени сунуш кылды, мен...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"2kfUZ33P9-hX","executionInfo":{"status":"ok","timestamp":1620202644330,"user_tz":-300,"elapsed":698733,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"8f4bd029-27da-4e10-a797-f404ac30c448"},"source":["\n","# Kygrgyz for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"Менин акыркы ажырашуумдан улам депрессияга кабылып, бардык убактымды ыйлап өткөрөм, азаптын басылышын каалайм ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.04204171150922775, -0.022934677079319954, ...
\n","
Менин акыркы ажырашуумдан улам депрессияга каб...
\n","
0.999795
\n","
negative
\n","
[Менин акыркы ажырашуумдан улам депрессияга ка...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [Менин акыркы ажырашуумдан улам депрессияга ка...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"DGMVMKaTdJFj"},"source":["# The Model understands Tamil\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"JWDr_LoCdJFn","executionInfo":{"status":"ok","timestamp":1620202645336,"user_tz":-300,"elapsed":699734,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b75e1667-223b-4d7a-d22e-85e556c26af7"},"source":["# Tamil for: 'I am depressed because of my recent break up and I spend all my time crying, I want the pain to go away...'\n","fitted_pipe.predict(\"நான் சமீபத்தில் பிரிந்ததால் மனச்சோர்வடைந்து, என் நேரத்தை அழுதபடி செலவிடுகிறேன், வலி நீங்க வேண்டும் என்று நான் விரும்புகிறேன் ... \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.02321743592619896, -0.03598147630691528, 0...
\n","
நான் சமீபத்தில் பிரிந்ததால் மனச்சோர்வடைந்து, எ...
\n","
0.999127
\n","
negative
\n","
[நான் சமீபத்தில் பிரிந்ததால் மனச்சோர்வடைந்து, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [நான் சமீபத்தில் பிரிந்ததால் மனச்சோர்வடைந்து, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Q6C0BmTtdJFp","executionInfo":{"status":"ok","timestamp":1620202646105,"user_tz":-300,"elapsed":700498,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b9d5e5d9-454b-4e7a-b9ed-aa007d153ab8"},"source":["# Tamil for : 'The love of my life proposed me , I feel like the happiest person alive!'\n","fitted_pipe.predict(\"என் வாழ்க்கையின் அன்பு என்னை முன்மொழிந்தது, உயிருடன் இருக்கும் மகிழ்ச்சியான நபராக நான் உணர்கிறேன்! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_labse
\n","
document
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
[-0.0001591779146110639, -0.012185914441943169...
\n","
என் வாழ்க்கையின் அன்பு என்னை முன்மொழிந்தது, உய...
\n","
0.999979
\n","
positive
\n","
[என் வாழ்க்கையின் அன்பு என்னை முன்மொழிந்தது, உ...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... sentence\n","0 0 ... [என் வாழ்க்கையின் அன்பு என்னை முன்மொழிந்தது, உ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620203401449,"user_tz":-300,"elapsed":1455834,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"1c18be45-35a1-44fa-fe92-6f33966a145f"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SO4uz45MoRgp","executionInfo":{"status":"ok","timestamp":1620203683025,"user_tz":-300,"elapsed":128872,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c90432d6-9183-49c2-865a-8dd53bf11584"},"source":["import nlu\n","stored_model_path = './models/classifier_dl_trained' \n","hdd_pipe = nlu.load(path=stored_model_path)\n","preds = hdd_pipe.predict('It was one of the best films i have ever watched in my entire life !!')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
sentence
\n","
sentiment
\n","
document
\n","
origin_index
\n","
sentiment_confidence
\n","
sentence_embedding_from_disk
\n","
\n"," \n"," \n","
\n","
0
\n","
It was one of the best films i have ever watch...
\n","
[It was one of the best films i have ever watc...
\n","
[positive, positive]
\n","
It was one of the best films i have ever watch...
\n","
8589934592
\n","
[0.9999969, 0.9999969]
\n","
[[0.016216373071074486, 0.02273012138903141, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" text ... sentence_embedding_from_disk\n","0 It was one of the best films i have ever watch... ... [[0.016216373071074486, 0.02273012138903141, -...\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UqXHHH-TQTuu","executionInfo":{"status":"ok","timestamp":1620204016789,"user_tz":-300,"elapsed":959,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c45e062e-ff9e-4fb4-9bc2-53356dbeec89"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@1dde65a2) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@1dde65a2\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@labse'] has settable params:\n","pipe['bert_sentence@labse'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@labse'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@labse'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@labse'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@labse'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n",">>> pipe['sentiment_dl@labse'] has settable params:\n","pipe['sentiment_dl@labse'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@labse'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@labse'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xdgt0jiIr1A2"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_stock_market.ipynb b/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_stock_market.ipynb
deleted file mode 100644
index b98ed03c..00000000
--- a/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_stock_market.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_multi_lingual_training_sentiment_classifier_demo_stock_market.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples//colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_stock_market.ipynb)\n","\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 Class Stock Market Sentiment Classifier Training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data : \n","\n"," \n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data : \n","\n"," \n","\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620202089264,"user_tz":-300,"elapsed":114121,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"19e81539-28da-4236-ca10-e0c23e1253ea"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 08:06:15-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 08:06:15 (33.4 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 72kB/s \n","\u001b[K |████████████████████████████████| 153kB 41.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.8MB/s \n","\u001b[K |████████████████████████████████| 204kB 43.6MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Stock Market Sentiment dataset \n","https://www.kaggle.com/yash612/stockmarket-sentiment-dataset\n","#Context\n","\n","Gathered Stock news from Multiple twitter Handles regarding Economic news dividing into two parts : Negative and positive."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620202090504,"user_tz":-300,"elapsed":115338,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"f96cb8a6-5f89-4048-8694-546ca83c7f17"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/stock_data_multi_lingual.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 08:08:09-- http://ckl-it.de/wp-content/uploads/2021/02/stock_data_multi_lingual.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 564444 (551K) [text/csv]\n","Saving to: ‘stock_data_multi_lingual.csv’\n","\n","stock_data_multi_li 100%[===================>] 551.21K 879KB/s in 0.6s \n","\n","2021-05-05 08:08:10 (879 KB/s) - ‘stock_data_multi_lingual.csv’ saved [564444/564444]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620202091050,"user_tz":-300,"elapsed":115861,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"16e5bd58-4352-4439-f243-de1fb29c9213"},"source":["import pandas as pd\n","train_path = '/content/stock_data_multi_lingual.csv'\n","\n","train_df = pd.read_csv(train_path)\n","# the text data to use for classification should be in a column named 'text'\n","columns=['text','y']\n","train_df = train_df[columns]\n","from sklearn.model_selection import train_test_split\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
5571
\n","
Rupee Edges Higher To 75.51 Against Dollar\\nht...
\n","
negative
\n","
\n","
\n","
3475
\n","
AAP Is Still Cool
\n","
positive
\n","
\n","
\n","
4429
\n","
user: AKAM insiders buying heavily these leve...
\n","
positive
\n","
\n","
\n","
4681
\n","
well no complaints at this point regarding the...
\n","
positive
\n","
\n","
\n","
2582
\n","
SGY broke down its 50EMA & trendline support o...
\n","
negative
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
5704
\n","
Gold Futures Drop Over 2% To Rs 39,354 Per 10 ...
\n","
negative
\n","
\n","
\n","
2191
\n","
SQNM a Continuation - wil take it over the top...
\n","
positive
\n","
\n","
\n","
1980
\n","
BAC ot of buying on last minute !!!
\n","
positive
\n","
\n","
\n","
1582
\n","
Jeff Bezo's the wizard...it's all about Gross ...
\n","
negative
\n","
\n","
\n","
988
\n","
BAC anyone think this might slush and fall bel...
\n","
negative
\n","
\n"," \n","
\n","
4632 rows × 2 columns
\n","
"],"text/plain":[" text y\n","5571 Rupee Edges Higher To 75.51 Against Dollar\\nht... negative\n","3475 AAP Is Still Cool positive\n","4429 user: AKAM insiders buying heavily these leve... positive\n","4681 well no complaints at this point regarding the... positive\n","2582 SGY broke down its 50EMA & trendline support o... negative\n","... ... ...\n","5704 Gold Futures Drop Over 2% To Rs 39,354 Per 10 ... negative\n","2191 SQNM a Continuation - wil take it over the top... positive\n","1980 BAC ot of buying on last minute !!! positive\n","1582 Jeff Bezo's the wizard...it's all about Gross ... negative\n","988 BAC anyone think this might slush and fall bel... negative\n","\n","[4632 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":861},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620204658116,"user_tz":-300,"elapsed":2682905,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"47789158-bb85-4078-9ca2-64d4455b020c"},"source":["trainable_pipe = nlu.load('xx.embed_sentence.labse train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(60) \n","trainable_pipe['sentiment_dl'].setLr(0.005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["labse download started this may take some time.\n","Approximate size to download 1.7 GB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.92 0.82 0.87 1672\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.91 0.96 0.93 2960\n","\n"," accuracy 0.91 4632\n"," macro avg 0.61 0.59 0.60 4632\n","weighted avg 0.91 0.91 0.91 4632\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
y
\n","
text
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Rupee Edges Higher To 75.51 Against Dollar ht...
\n","
5571
\n","
negative
\n","
Rupee Edges Higher To 75.51 Against Dollar\\nht...
\n","
1.000000
\n","
negative
\n","
[-0.03053501807153225, 0.042389560490846634, -...
\n","
Rupee Edges Higher To 75.51 Against Dollar htt...
\n","
\n","
\n","
1
\n","
[AAP Is Still Cool]
\n","
3475
\n","
positive
\n","
AAP Is Still Cool
\n","
0.999996
\n","
positive
\n","
[-0.034850019961595535, -0.044849760830402374,...
\n","
AAP Is Still Cool
\n","
\n","
\n","
2
\n","
[user:, AKAM insiders buying heavily these le...
\n","
4429
\n","
positive
\n","
user: AKAM insiders buying heavily these leve...
\n","
1.000000
\n","
positive
\n","
[0.006040376145392656, -0.014217519201338291, ...
\n","
user: AKAM insiders buying heavily these levels
\n","
\n","
\n","
3
\n","
[well no complaints at this point regarding th...
\n","
4681
\n","
positive
\n","
well no complaints at this point regarding the...
\n","
1.000000
\n","
positive
\n","
[-0.02124304696917534, -0.01161095593124628, -...
\n","
well no complaints at this point regarding the...
\n","
\n","
\n","
4
\n","
[SGY broke down its 50EMA & trendline support ...
\n","
2582
\n","
negative
\n","
SGY broke down its 50EMA & trendline support o...
\n","
0.999996
\n","
negative
\n","
[-0.0251691285520792, 0.05739616975188255, -0....
\n","
SGY broke down its 50EMA & trendline support o...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
4627
\n","
[Gold Futures Drop Over 2% To Rs 39,354 Per 10...
\n","
5704
\n","
negative
\n","
Gold Futures Drop Over 2% To Rs 39,354 Per 10 ...
\n","
0.999668
\n","
negative
\n","
[-0.05454382300376892, -0.007509403396397829, ...
\n","
Gold Futures Drop Over 2% To Rs 39,354 Per 10 ...
\n","
\n","
\n","
4628
\n","
[SQNM a Continuation - wil take it over the to...
\n","
2191
\n","
positive
\n","
SQNM a Continuation - wil take it over the top...
\n","
1.000000
\n","
positive
\n","
[0.04926764965057373, 0.04899105057120323, 0.0...
\n","
SQNM a Continuation - wil take it over the top...
\n","
\n","
\n","
4629
\n","
[BAC ot of buying on last minute !, !!]
\n","
1980
\n","
positive
\n","
BAC ot of buying on last minute !!!
\n","
1.000000
\n","
positive
\n","
[-0.02965172566473484, -0.02375778928399086, 0...
\n","
BAC ot of buying on last minute !!!
\n","
\n","
\n","
4630
\n","
[Jeff Bezo's the wizard., ..it's all about Gro...
\n","
1582
\n","
negative
\n","
Jeff Bezo's the wizard...it's all about Gross ...
\n","
0.999998
\n","
negative
\n","
[-0.04103326052427292, -0.013734265230596066, ...
\n","
Jeff Bezo's the wizard...it's all about Gross ...
\n","
\n","
\n","
4631
\n","
[BAC anyone think this might slush and fall be...
\n","
988
\n","
negative
\n","
BAC anyone think this might slush and fall bel...
\n","
0.999999
\n","
negative
\n","
[-0.05278955399990082, 0.015668092295527458, 0...
\n","
BAC anyone think this might slush and fall bel...
\n","
\n"," \n","
\n","
4632 rows × 8 columns
\n","
"],"text/plain":[" sentence ... document\n","0 [Rupee Edges Higher To 75.51 Against Dollar ht... ... Rupee Edges Higher To 75.51 Against Dollar htt...\n","1 [AAP Is Still Cool] ... AAP Is Still Cool\n","2 [user:, AKAM insiders buying heavily these le... ... user: AKAM insiders buying heavily these levels\n","3 [well no complaints at this point regarding th... ... well no complaints at this point regarding the...\n","4 [SGY broke down its 50EMA & trendline support ... ... SGY broke down its 50EMA & trendline support o...\n","... ... ... ...\n","4627 [Gold Futures Drop Over 2% To Rs 39,354 Per 10... ... Gold Futures Drop Over 2% To Rs 39,354 Per 10 ...\n","4628 [SQNM a Continuation - wil take it over the to... ... SQNM a Continuation - wil take it over the top...\n","4629 [BAC ot of buying on last minute !, !!] ... BAC ot of buying on last minute !!!\n","4630 [Jeff Bezo's the wizard., ..it's all about Gro... ... Jeff Bezo's the wizard...it's all about Gross ...\n","4631 [BAC anyone think this might slush and fall be... ... BAC anyone think this might slush and fall bel...\n","\n","[4632 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 3.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"id":"Fxx4yNkNVGFl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620204828990,"user_tz":-300,"elapsed":2853766,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"6cd168da-21da-485d-bbe0-8929ae5b33a4"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.72 0.59 0.65 434\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.78 0.85 0.81 725\n","\n"," accuracy 0.75 1159\n"," macro avg 0.50 0.48 0.49 1159\n","weighted avg 0.76 0.75 0.75 1159\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"BD5OKO4Umc5U"},"source":["# 4. Test Model on 20 languages!"]},{"cell_type":"code","metadata":{"id":"OQ72hP9unML7","colab":{"base_uri":"https://localhost:8080/","height":759},"executionInfo":{"status":"ok","timestamp":1620204845711,"user_tz":-300,"elapsed":2870481,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3ce6fc97-2617-4a4b-84b0-f694bb704a3c"},"source":["train_df = pd.read_csv(\"/content/stock_data_multi_lingual.csv\")\n","preds = fitted_pipe.predict(train_df[[\"test_sentences\",\"y\"]].iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.35 0.62 0.44 13\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.95 0.80 0.87 87\n","\n"," accuracy 0.78 100\n"," macro avg 0.43 0.47 0.44 100\n","weighted avg 0.87 0.78 0.81 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
y
\n","
text
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[İzləmə siyahımdakı Kickers XIDE TIT SOQ PNK C...
\n","
0
\n","
positive
\n","
İzləmə siyahımdakı Kickers XIDE TIT SOQ PNK CP...
\n","
0.586977
\n","
neutral
\n","
[-0.0005844525876455009, 0.012249358929693699,...
\n","
İzləmə siyahımdakı Kickers XIDE TIT SOQ PNK CP...
\n","
\n","
\n","
1
\n","
[उपयोगकर्ता: AAP MOVIE। वर्ष के लिए FEA / GEED...
\n","
1
\n","
positive
\n","
उपयोगकर्ता: AAP MOVIE। वर्ष के लिए FEA / GEED ...
\n","
1.000000
\n","
positive
\n","
[-0.027744855731725693, -0.034640125930309296,...
\n","
उपयोगकर्ता: AAP MOVIE। वर्ष के लिए FEA / GEED ...
\n","
\n","
\n","
2
\n","
[משתמש אני מפחד לקצר את AMZN - הם נראים כמו מו...
\n","
2
\n","
positive
\n","
משתמש אני מפחד לקצר את AMZN - הם נראים כמו מונ...
\n","
0.999755
\n","
negative
\n","
[-0.029554689303040504, -0.0026017462369054556...
\n","
משתמש אני מפחד לקצר את AMZN - הם נראים כמו מונ...
\n","
\n","
\n","
3
\n","
[12.00 से अधिक MNTA]
\n","
3
\n","
positive
\n","
12.00 से अधिक MNTA
\n","
1.000000
\n","
positive
\n","
[-0.03708072751760483, 0.054913319647312164, 0...
\n","
12.00 से अधिक MNTA
\n","
\n","
\n","
4
\n","
[OI 21.37ден жогору]
\n","
4
\n","
positive
\n","
OI 21.37ден жогору
\n","
1.000000
\n","
positive
\n","
[-0.043793801218271255, 0.03190923109650612, -...
\n","
OI 21.37ден жогору
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
[NG nhod - kyk na die weeklikse - teiken vorig...
\n","
95
\n","
positive
\n","
NG nhod - kyk na die weeklikse - teiken vorige...
\n","
0.999998
\n","
positive
\n","
[-0.03982162103056908, -0.07269033044576645, -...
\n","
NG nhod - kyk na die weeklikse - teiken vorige...
\n","
\n","
\n","
96
\n","
[NG nhod - ¿qué ves? consulte el semanario - o...
\n","
96
\n","
positive
\n","
NG nhod - ¿qué ves? consulte el semanario - ob...
\n","
1.000000
\n","
positive
\n","
[-0.04516294226050377, -0.04459897428750992, 0...
\n","
NG nhod - ¿qué ves? consulte el semanario - ob...
\n","
\n","
\n","
97
\n","
[এআইজি আমেরিকান ইন্টারন্যাশনাল গ্রুপ অপশন ট্রে...
\n","
97
\n","
negative
\n","
এআইজি আমেরিকান ইন্টারন্যাশনাল গ্রুপ অপশন ট্রেড...
\n","
0.932880
\n","
negative
\n","
[-0.0716203823685646, -0.00996206421405077, -0...
\n","
এআইজি আমেরিকান ইন্টারন্যাশনাল গ্রুপ অপশন ট্রেড...
\n","
\n","
\n","
98
\n","
[P out balance +.32]
\n","
98
\n","
positive
\n","
P out balance +.32
\n","
1.000000
\n","
positive
\n","
[-0.0412631593644619, -0.0414082333445549, -0....
\n","
P out balance +.32
\n","
\n","
\n","
99
\n","
[VNG ซื้อเทียบกับขาย?]
\n","
99
\n","
positive
\n","
VNG ซื้อเทียบกับขาย?
\n","
1.000000
\n","
positive
\n","
[-0.038816627115011215, 0.04581355303525925, -...
\n","
VNG ซื้อเทียบกับขาย?
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" sentence ... document\n","0 [İzləmə siyahımdakı Kickers XIDE TIT SOQ PNK C... ... İzləmə siyahımdakı Kickers XIDE TIT SOQ PNK CP...\n","1 [उपयोगकर्ता: AAP MOVIE। वर्ष के लिए FEA / GEED... ... उपयोगकर्ता: AAP MOVIE। वर्ष के लिए FEA / GEED ...\n","2 [משתמש אני מפחד לקצר את AMZN - הם נראים כמו מו... ... משתמש אני מפחד לקצר את AMZN - הם נראים כמו מונ...\n","3 [12.00 से अधिक MNTA] ... 12.00 से अधिक MNTA\n","4 [OI 21.37ден жогору] ... OI 21.37ден жогору\n",".. ... ... ...\n","95 [NG nhod - kyk na die weeklikse - teiken vorig... ... NG nhod - kyk na die weeklikse - teiken vorige...\n","96 [NG nhod - ¿qué ves? consulte el semanario - o... ... NG nhod - ¿qué ves? consulte el semanario - ob...\n","97 [এআইজি আমেরিকান ইন্টারন্যাশনাল গ্রুপ অপশন ট্রে... ... এআইজি আমেরিকান ইন্টারন্যাশনাল গ্রুপ অপশন ট্রেড...\n","98 [P out balance +.32] ... P out balance +.32\n","99 [VNG ซื้อเทียบกับขาย?] ... VNG ซื้อเทียบกับขาย?\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"RjtuNUcvuJTT"},"source":["# The Model understands Englsih\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"o0vu7PaWkcI7","executionInfo":{"status":"ok","timestamp":1620204846415,"user_tz":-300,"elapsed":2871176,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"7b6b47f7-39bf-4af7-f5a5-c6c592d3fd5e"},"source":["fitted_pipe.predict(\"Bitcoin dropped by 50 percent !!!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin dropped by 50 percent !, !!]
\n","
0
\n","
0.999995
\n","
negative
\n","
[-0.05163608863949776, -0.029772670939564705, ...
\n","
Bitcoin dropped by 50 percent !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin dropped by 50 percent !, !!] ... Bitcoin dropped by 50 percent !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"1ykjRQhCtQ4w","executionInfo":{"status":"ok","timestamp":1620204847415,"user_tz":-300,"elapsed":2872169,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"fb8e35dd-2dcb-4d68-fb92-213fc1bde95d"},"source":["fitted_pipe.predict(\"Bitcoin went up by 50 percent !!!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin went up by 50 percent !, !!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.028688747435808182, -0.026630541309714317,...
\n","
Bitcoin went up by 50 percent !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin went up by 50 percent !, !!] ... Bitcoin went up by 50 percent !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"vohym-XbuNHn"},"source":["# The Model understands German\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"dzaaZrI4tVWc","executionInfo":{"status":"ok","timestamp":1620204848517,"user_tz":-300,"elapsed":2873266,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"884204ac-af7c-41ad-caba-6cc4ab0daa78"},"source":["# German for:'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict(\"Bitcoin stieg um 50 Prozent auf !!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin stieg um 50 Prozent auf !!!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.038156550377607346, -0.024729670956730843,...
\n","
Bitcoin stieg um 50 Prozent auf !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin stieg um 50 Prozent auf !!!] ... Bitcoin stieg um 50 Prozent auf !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"BbhgTSBGtTtJ","executionInfo":{"status":"ok","timestamp":1620204849519,"user_tz":-300,"elapsed":2874263,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"6ea6ee4f-27d7-4282-99cb-b68207577558"},"source":["# German for: 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict(\"Bitcoin fiel um 50 Prozent !!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin fiel um 50 Prozent !, !!]
\n","
0
\n","
0.999989
\n","
negative
\n","
[-0.05688922852277756, -0.02544567361474037, -...
\n","
Bitcoin fiel um 50 Prozent !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin fiel um 50 Prozent !, !!] ... Bitcoin fiel um 50 Prozent !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"a1JbtmWquQwj"},"source":["# The Model understands Chinese\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"kYSYqtoRtc-P","executionInfo":{"status":"ok","timestamp":1620204850369,"user_tz":-300,"elapsed":2875107,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"bc3edc3e-4295-462b-83b5-4850d5d8a751"},"source":["# Chinese for: \"Bitcoin dropped by 50 percent !!!\"\n","fitted_pipe.predict(\"比特币下跌了50%!!!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[比特币下跌了50%!, !!]
\n","
0
\n","
1.0
\n","
negative
\n","
[-0.07537588477134705, -0.027679994702339172, ...
\n","
比特币下跌了50%!!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [比特币下跌了50%!, !!] ... 比特币下跌了50%!!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"06v9SD-QtlBU","executionInfo":{"status":"ok","timestamp":1620204851535,"user_tz":-300,"elapsed":2876267,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"636b4438-8a71-4ba2-b6e3-25490282c4e2"},"source":["# Chinese for : \"Bitcoin went up by 50 percent !!!\"\n","fitted_pipe.predict(\"比特币上涨了50%!\")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[比特币上涨了50%!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.04113336279988289, -0.00941209401935339, -...
\n","
比特币上涨了50%!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [比特币上涨了50%!] ... 比特币上涨了50%!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"9h7CvN4uu9Pb"},"source":["# Model understands Afrikaans\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"VMPhbgw9twtf","executionInfo":{"status":"ok","timestamp":1620204851985,"user_tz":-300,"elapsed":2876712,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a14811bb-3acc-4264-9c5c-5cf561721d29"},"source":["# Afrikaans for 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict(\"Bitcoin het met 50 persent toegeneem !!!\")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin het met 50 persent toegeneem !, !!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.03586146607995033, -0.03901935741305351, -...
\n","
Bitcoin het met 50 persent toegeneem !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin het met 50 persent toegeneem !, !!] ... Bitcoin het met 50 persent toegeneem !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"zWgNTIdkumhX","executionInfo":{"status":"ok","timestamp":1620204853421,"user_tz":-300,"elapsed":2878142,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b5a7dccc-8e18-40f3-b772-b328093fe6ec"},"source":["# Afrikaans for :'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict('Bitcoin het met 50 persent gedaal !!! |')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin het met 50 persent gedaal !!! |]
\n","
0
\n","
0.930014
\n","
negative
\n","
[-0.05142543464899063, -0.03921075910329819, -...
\n","
Bitcoin het met 50 persent gedaal !!! |
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin het met 50 persent gedaal !!! |] ... Bitcoin het met 50 persent gedaal !!! |\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"rSEPkC-Bwnpg"},"source":["# The model understands Vietnamese\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"wCcTS5gIu511","executionInfo":{"status":"ok","timestamp":1620204853991,"user_tz":-300,"elapsed":2878701,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"dd406ee5-9431-4151-cb25-eea4a7cb5967"},"source":["# Vietnamese for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict('Bitcoin đã tăng 50% !!! ')\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin đã tăng 50% !, !!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.035115890204906464, -0.053469520062208176,...
\n","
Bitcoin đã tăng 50% !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin đã tăng 50% !, !!] ... Bitcoin đã tăng 50% !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"3Y-kLeGp5uc0","executionInfo":{"status":"ok","timestamp":1620204854787,"user_tz":-300,"elapsed":2879490,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"d8d97343-2368-4e50-9f87-754ad79eff72"},"source":["# Vietnamese for : 'Bitcoin droppedy by 50 percent !!!'\n","fitted_pipe.predict('Bitcoin giảm 50% !!! ')\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin giảm 50% !, !!]
\n","
0
\n","
0.912477
\n","
positive
\n","
[-0.013879671692848206, -0.05431622639298439, ...
\n","
Bitcoin giảm 50% !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin giảm 50% !, !!] ... Bitcoin giảm 50% !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"IlkmAaMoxTuy"},"source":["# The model understands Japanese\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"1IfJu3q8wwUt","executionInfo":{"status":"ok","timestamp":1620204855820,"user_tz":-300,"elapsed":2880518,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c71a8ddd-26df-4b28-9b94-d79285aca4d0"},"source":["\n","# Japanese for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict('ビットコインは50%上昇しました!!! ')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[ビットコインは50%上昇しました!!!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.04407081380486488, -0.013696372509002686, ...
\n","
ビットコインは50%上昇しました!!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [ビットコインは50%上昇しました!!!] ... ビットコインは50%上昇しました!!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"h3k7_PFhxOve","executionInfo":{"status":"ok","timestamp":1620204856723,"user_tz":-300,"elapsed":2881415,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"4420c8bc-01aa-4bb7-bfc5-4218478f828d"},"source":["\n","# Japanese for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict('Bitcoinは50%減少しました!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoinは50%減少しました!]
\n","
0
\n","
0.999988
\n","
negative
\n","
[-0.05689822509884834, -0.04127806797623634, -...
\n","
Bitcoinは50%減少しました!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoinは50%減少しました!] ... Bitcoinは50%減少しました!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"GITfT7FK0CGv"},"source":["# The model understands Zulu\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"6uelDwq4xdWv","executionInfo":{"status":"ok","timestamp":1620204857545,"user_tz":-300,"elapsed":2882230,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"8a968f5d-0e42-4c26-8981-28b8edcd7198"},"source":["# Zulu for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict('I-Bitcoin inyuke ngamaphesenti ama-50 !!!')\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[I-Bitcoin inyuke ngamaphesenti ama-50 !!!]
\n","
0
\n","
0.999993
\n","
positive
\n","
[-0.04226835444569588, -0.029177606105804443, ...
\n","
I-Bitcoin inyuke ngamaphesenti ama-50 !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [I-Bitcoin inyuke ngamaphesenti ama-50 !!!] ... I-Bitcoin inyuke ngamaphesenti ama-50 !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"NY_D-USEBeFe","executionInfo":{"status":"ok","timestamp":1620204858547,"user_tz":-300,"elapsed":2883226,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"83f5b821-fc09-43cd-a2e2-e9df34b78352"},"source":["# Zulu for : 'The whole crypto system crashed!!! '\n","fitted_pipe.predict('Lonke uhlelo lwe-crypto luphahlazeka !!!')\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Lonke uhlelo lwe-crypto luphahlazeka !, !!]
\n","
0
\n","
0.994109
\n","
negative
\n","
[0.014095810241997242, -0.0629298985004425, -0...
\n","
Lonke uhlelo lwe-crypto luphahlazeka !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Lonke uhlelo lwe-crypto luphahlazeka !, !!] ... Lonke uhlelo lwe-crypto luphahlazeka !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"VGVvzl_30a0T"},"source":["# The Model understands Turkish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"DRNnuEeQz2pd","executionInfo":{"status":"ok","timestamp":1620204859432,"user_tz":-300,"elapsed":2884106,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"539d80f1-61e1-4d26-b411-0a5319d5869e"},"source":["# Turkish for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict('Bitcoin yüzde 50 düştü !!! ')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin yüzde 50 düştü !, !, !]
\n","
0
\n","
0.984235
\n","
positive
\n","
[-0.02441188506782055, -0.04112468659877777, -...
\n","
Bitcoin yüzde 50 düştü !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin yüzde 50 düştü !, !, !] ... Bitcoin yüzde 50 düştü !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"aOSsiK6J0jWs","executionInfo":{"status":"ok","timestamp":1620204859976,"user_tz":-300,"elapsed":2884642,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a7982174-5286-494c-b5a0-89095facf623"},"source":["# Turkish for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict('Bitcoin yüzde 50 arttı !!!')\n","\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin yüzde 50 arttı !, !!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.02950628288090229, -0.022814499214291573, ...
\n","
Bitcoin yüzde 50 arttı !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin yüzde 50 arttı !, !!] ... Bitcoin yüzde 50 arttı !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"803qL2gt0vlb"},"source":["# The Model understands Hebrew\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"XQ5VCtxw0pc0","executionInfo":{"status":"ok","timestamp":1620204861244,"user_tz":-300,"elapsed":2885906,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"529a0ee9-19b3-4e86-a5fc-695941b6e13f"},"source":["# Hebrew for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict('Bitcoin ירד ב -50% !!! ')\n","\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin ירד ב -50% !, !!]
\n","
0
\n","
0.999994
\n","
negative
\n","
[-0.058318834751844406, -0.04578540101647377, ...
\n","
Bitcoin ירד ב -50% !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin ירד ב -50% !, !!] ... Bitcoin ירד ב -50% !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"9w2ZHfns05A4","executionInfo":{"status":"ok","timestamp":1620204861767,"user_tz":-300,"elapsed":2886424,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c539033f-24a3-4e34-c127-d137658f1315"},"source":["# Hebrew for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict('Bitcoin עלה ב -50% !!! ')\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin עלה ב -50% !, !!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.03875327855348587, -0.04096429422497749, -...
\n","
Bitcoin עלה ב -50% !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin עלה ב -50% !, !!] ... Bitcoin עלה ב -50% !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"SDlpd33H1HIX"},"source":["# The Model understands Telugu\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Kc5n1bzv1BJT","executionInfo":{"status":"ok","timestamp":1620204862983,"user_tz":-300,"elapsed":2887633,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"1cbcaf6e-dbda-48d9-d58a-3bf888b4bcac"},"source":["# Telugu for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict('బిట్\\u200cకాయిన్ 50 శాతం పెరిగింది !!!' )"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[బిట్కాయిన్ 50 శాతం పెరిగింది !!!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.04653506726026535, 0.020184172317385674, -...
\n","
బిట్కాయిన్ 50 శాతం పెరిగింది !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [బిట్కాయిన్ 50 శాతం పెరిగింది !!!] ... బిట్కాయిన్ 50 శాతం పెరిగింది !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"-l-u6vrz1Obe","executionInfo":{"status":"ok","timestamp":1620204863397,"user_tz":-300,"elapsed":2888040,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0c8c1acf-3752-43c2-b9bb-53ed598b9d0d"},"source":["# Telgu for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict('బిట్\\u200cకాయిన్ 50 శాతం పడిపోయింది !!! ')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[బిట్కాయిన్ 50 శాతం పడిపోయింది !!!]
\n","
0
\n","
0.999995
\n","
negative
\n","
[-0.07144764810800552, 0.00431971438229084, -0...
\n","
బిట్కాయిన్ 50 శాతం పడిపోయింది !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [బిట్కాయిన్ 50 శాతం పడిపోయింది !!!] ... బిట్కాయిన్ 50 శాతం పడిపోయింది !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"nziBUe8t1Zwn"},"source":["# Model understands Russian\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Ckyjl3YQ1VFn","executionInfo":{"status":"ok","timestamp":1620204863722,"user_tz":-300,"elapsed":2888355,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"5a33f302-995c-4357-f80f-c3adec65f23b"},"source":["# Russian for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict('Биткойн упал на 50 процентов !!! ')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Биткойн упал на 50 процентов !!!]
\n","
0
\n","
0.999988
\n","
negative
\n","
[-0.05514693260192871, -0.025273755192756653, ...
\n","
Биткойн упал на 50 процентов !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Биткойн упал на 50 процентов !!!] ... Биткойн упал на 50 процентов !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"GIdWkfGv1gFz","executionInfo":{"status":"ok","timestamp":1620204864709,"user_tz":-300,"elapsed":2889336,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"04798c4e-1524-4219-a424-d8a6aef7ca2a"},"source":["# Russian for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict('Биткойн поднялся на 50 процентов !!!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Биткойн поднялся на 50 процентов !!!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.0347013957798481, -0.01663934625685215, -0...
\n","
Биткойн поднялся на 50 процентов !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Биткойн поднялся на 50 процентов !!!] ... Биткойн поднялся на 50 процентов !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"8R1j9mwz2Cm4"},"source":["# Model understands Urdu\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"j4zwvRV11pcG","executionInfo":{"status":"ok","timestamp":1620204865516,"user_tz":-300,"elapsed":2890134,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b5809ecd-d35e-40c9-c5a9-38b707db8a04"},"source":["# Urdu for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict('Bitcoin 50 فیصد کی طرف سے گرا دیا !!!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin 50 فیصد کی طرف سے گرا دیا !!!]
\n","
0
\n","
0.999564
\n","
negative
\n","
[-0.042979832738637924, -0.042187534272670746,...
\n","
Bitcoin 50 فیصد کی طرف سے گرا دیا !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin 50 فیصد کی طرف سے گرا دیا !!!] ... Bitcoin 50 فیصد کی طرف سے گرا دیا !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"_EwpYn_hBpzt","executionInfo":{"status":"ok","timestamp":1620204865962,"user_tz":-300,"elapsed":2890572,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"4e79bc75-5aa6-4f31-b0c6-8e23caa7a92d"},"source":["# Urdu for : 'TDollar rates skyrocketed!!'\n","fitted_pipe.predict('ڈالر کے نرخ آسمان چھائے ہوئے ہیں !!!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[ڈالر کے نرخ آسمان چھائے ہوئے ہیں !!!]
\n","
0
\n","
0.615615
\n","
positive
\n","
[-0.06762911379337311, -0.02299044094979763, -...
\n","
ڈالر کے نرخ آسمان چھائے ہوئے ہیں !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [ڈالر کے نرخ آسمان چھائے ہوئے ہیں !!!] ... ڈالر کے نرخ آسمان چھائے ہوئے ہیں !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"RoNg-C3k1qcX"},"source":["# Model understands Hindi\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"QZ9RT5Wv1r1n","executionInfo":{"status":"ok","timestamp":1620204867391,"user_tz":-300,"elapsed":2891993,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"f189db28-78b8-4860-9aff-c5c463d3683f"},"source":["# Hindi for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict('बिटकॉइन 50 प्रतिशत चढ़ गया !!! ')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[बिटकॉइन 50 प्रतिशत चढ़ गया !!!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.03370286896824837, -0.027637438848614693, ...
\n","
बिटकॉइन 50 प्रतिशत चढ़ गया !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [बिटकॉइन 50 प्रतिशत चढ़ गया !!!] ... बिटकॉइन 50 प्रतिशत चढ़ गया !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"quM-IL2i12-B","executionInfo":{"status":"ok","timestamp":1620204868130,"user_tz":-300,"elapsed":2892727,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"1efc705b-2631-4038-a539-9342296987d0"},"source":["# Hindi for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict('बिटकॉइन में 50 प्रतिशत की गिरावट !!!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[बिटकॉइन में 50 प्रतिशत की गिरावट !!!]
\n","
0
\n","
0.999983
\n","
negative
\n","
[-0.056788038462400436, -0.045364461839199066,...
\n","
बिटकॉइन में 50 प्रतिशत की गिरावट !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [बिटकॉइन में 50 प्रतिशत की गिरावट !!!] ... बिटकॉइन में 50 प्रतिशत की गिरावट !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"R4ByHOZn35Lc"},"source":["# The model understands Tartar\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"2JrzusSQ18F5","executionInfo":{"status":"ok","timestamp":1620204869238,"user_tz":-300,"elapsed":2893830,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"46084c37-33f9-47c2-e2da-46abf957bedf"},"source":["# Tartar for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict('Bitcoin 50 процентка төште !!!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin 50 процентка төште !!!]
\n","
0
\n","
0.99712
\n","
negative
\n","
[-0.046695344150066376, -0.045206207782030106,...
\n","
Bitcoin 50 процентка төште !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin 50 процентка төште !!!] ... Bitcoin 50 процентка төште !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"J06Xm_Ln4AYu","executionInfo":{"status":"ok","timestamp":1620204870025,"user_tz":-300,"elapsed":2894612,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"32c93cbb-cf58-45ca-b911-594ec7639b25"},"source":["# Tartar for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict('Биткойн 50 процентка артты !!!')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Биткойн 50 процентка артты !!!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.015944570302963257, -0.024691367521882057,...
\n","
Биткойн 50 процентка артты !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Биткойн 50 процентка артты !!!] ... Биткойн 50 процентка артты !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"HKj5yWwwMplH"},"source":["# The Model understands French\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"CUHcJZfJMplL","executionInfo":{"status":"ok","timestamp":1620204870028,"user_tz":-300,"elapsed":2894605,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"f741335f-56eb-4083-9f74-a0f5c7ec177e"},"source":["# French for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict(\"Bitcoin a chuté de 50% !!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin a chuté de 50% !, !!]
\n","
0
\n","
0.999993
\n","
negative
\n","
[-0.05751340091228485, -0.055390965193510056, ...
\n","
Bitcoin a chuté de 50% !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin a chuté de 50% !, !!] ... Bitcoin a chuté de 50% !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"57NY2XoTMplM","executionInfo":{"status":"ok","timestamp":1620204870622,"user_tz":-300,"elapsed":2895194,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2f15d469-5c73-4b89-a8e6-164f8018e2a0"},"source":["# French for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict(\"Le Bitcoin a augmenté de 50% !!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Le Bitcoin a augmenté de 50% !, !!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.036176975816488266, -0.03910718113183975, ...
\n","
Le Bitcoin a augmenté de 50% !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Le Bitcoin a augmenté de 50% !, !!] ... Le Bitcoin a augmenté de 50% !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"jD2TBgT0Nq6F"},"source":["# The Model understands Thai\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"gBp11S5GNq6S","executionInfo":{"status":"ok","timestamp":1620204871161,"user_tz":-300,"elapsed":2895726,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0921469b-ddb9-4f90-84b8-2166499c38eb"},"source":["# Thai for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict(\"Bitcoin เพิ่มขึ้น 50 เปอร์เซ็นต์ !!!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
"],"text/plain":[" sentence ... document\n","0 [Bitcoin ลดลง 50 เปอร์เซ็นต์ !!!] ... Bitcoin ลดลง 50 เปอร์เซ็นต์ !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"mLItI4KZOElB"},"source":["# The Model understands Khmer\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Mv0KkUxo7Lh_","executionInfo":{"status":"ok","timestamp":1620204872543,"user_tz":-300,"elapsed":2897098,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"928cecc5-9e0a-4438-ffb7-1f88cb6dd210"},"source":["# Khmer for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict(\"Bitcoin បានធ្លាក់ចុះ 50 ភាគរយ !!!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin បានធ្លាក់ចុះ 50 ភាគរយ !!!]
\n","
0
\n","
0.999976
\n","
negative
\n","
[-0.057029254734516144, -0.042525384575128555,...
\n","
Bitcoin បានធ្លាក់ចុះ 50 ភាគរយ !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin បានធ្លាក់ចុះ 50 ភាគរយ !!!] ... Bitcoin បានធ្លាក់ចុះ 50 ភាគរយ !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SWbqMgAwOElC","executionInfo":{"status":"ok","timestamp":1620204873221,"user_tz":-300,"elapsed":2897769,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"1aa07a23-f0cd-40d9-a9b2-e34a32af3f4b"},"source":["# Khmer for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict(\"Bitcoin បានកើនឡើង 50 ភាគរយ !!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin បានកើនឡើង 50 ភាគរយ !!!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.0398690328001976, -0.03344428539276123, -0...
\n","
Bitcoin បានកើនឡើង 50 ភាគរយ !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin បានកើនឡើង 50 ភាគរយ !!!] ... Bitcoin បានកើនឡើង 50 ភាគរយ !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"markdown","metadata":{"id":"lvE-LbNiPoBT"},"source":["# The Model understands Yiddish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"sZlmLhajPoBb","executionInfo":{"status":"ok","timestamp":1620204873753,"user_tz":-300,"elapsed":2898296,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b379f5c5-f85b-4fcf-8769-7d41c81135b8"},"source":["# Yiddish for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict(\"ביטקאָין דראַפּט דורך 50 פּראָצענט !!!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[ביטקאָין דראַפּט דורך 50 פּראָצענט !!!]
\n","
0
\n","
0.999989
\n","
negative
\n","
[-0.05685275420546532, -0.04757662117481232, -...
\n","
ביטקאָין דראַפּט דורך 50 פּראָצענט !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [ביטקאָין דראַפּט דורך 50 פּראָצענט !!!] ... ביטקאָין דראַפּט דורך 50 פּראָצענט !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"5h-pha_nPoBc","executionInfo":{"status":"ok","timestamp":1620204875147,"user_tz":-300,"elapsed":2899687,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a8aad03f-19fa-430f-b1aa-4053990641af"},"source":["# Yiddish for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict(\"ביטקאָין איז אַרויף מיט 50 פּראָצענט !!!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[ביטקאָין איז אַרויף מיט 50 פּראָצענט !!!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.04943440482020378, -0.05143021047115326, -...
\n","
ביטקאָין איז אַרויף מיט 50 פּראָצענט !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [ביטקאָין איז אַרויף מיט 50 פּראָצענט !!!] ... ביטקאָין איז אַרויף מיט 50 פּראָצענט !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"XSz4WzScaAHj"},"source":["# The Model understands Kygrgyz\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"DXz6fhJSaAHu","executionInfo":{"status":"ok","timestamp":1620204875696,"user_tz":-300,"elapsed":2900229,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"7399213b-dac2-4025-945b-8d8ee778c266"},"source":["# Kygrgyz for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict(\"Bitcoin 50 пайызга төмөндөдү !!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin 50 пайызга төмөндөдү !!!]
\n","
0
\n","
0.99996
\n","
negative
\n","
[-0.0606391467154026, -0.020774153992533684, -...
\n","
Bitcoin 50 пайызга төмөндөдү !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin 50 пайызга төмөндөдү !!!] ... Bitcoin 50 пайызга төмөндөдү !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"lh_ZSHlPaAHv","executionInfo":{"status":"ok","timestamp":1620204876020,"user_tz":-300,"elapsed":2900548,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"7748fda1-aac9-47df-a24c-2248eb102336"},"source":["# Kygrgyz for : 'Bitcoin went up by 50 percent !!!'\n","fitted_pipe.predict(\"Bitcoin 50 пайызга көтөрүлдү !!!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bitcoin 50 пайызга көтөрүлдү !!!]
\n","
0
\n","
0.999998
\n","
positive
\n","
[-0.031883276998996735, -0.009198011830449104,...
\n","
Bitcoin 50 пайызга көтөрүлдү !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [Bitcoin 50 пайызга көтөрүлдү !!!] ... Bitcoin 50 пайызга көтөрүлдү !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"DGMVMKaTdJFj"},"source":["# The Model understands Tamil\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"JWDr_LoCdJFn","executionInfo":{"status":"ok","timestamp":1620204877114,"user_tz":-300,"elapsed":2901637,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"ffbaa262-cb4c-4ac8-a87b-dc8776100a5a"},"source":["# Tamil for : 'Bitcoin dropped by 50 percent !!!'\n","fitted_pipe.predict(\"பிட்காயின் 50 சதவீதம் குறைந்தது !!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[பிட்காயின் 50 சதவீதம் குறைந்தது !!!]
\n","
0
\n","
0.999997
\n","
negative
\n","
[-0.06173097714781761, -0.034972332417964935, ...
\n","
பிட்காயின் 50 சதவீதம் குறைந்தது !!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [பிட்காயின் 50 சதவீதம் குறைந்தது !!!] ... பிட்காயின் 50 சதவீதம் குறைந்தது !!!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"J8dGo9wLCFSa","executionInfo":{"status":"ok","timestamp":1620204877495,"user_tz":-300,"elapsed":2902012,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"09ecd623-1fb3-4f79-a784-feb110037c61"},"source":["# Tamil for : 'Dollar rates skyrocketed!!'\n","fitted_pipe.predict(\"ڈالر کے نرخ آسمان چھائے ہوئے ہیں !!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
trained_sentiment_confidence
\n","
trained_sentiment
\n","
sentence_embedding_labse
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[ڈالر کے نرخ آسمان چھائے ہوئے ہیں !!]
\n","
0
\n","
1.0
\n","
positive
\n","
[-0.06327779591083527, -0.029234550893306732, ...
\n","
ڈالر کے نرخ آسمان چھائے ہوئے ہیں !!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... document\n","0 [ڈالر کے نرخ آسمان چھائے ہوئے ہیں !!] ... ڈالر کے نرخ آسمان چھائے ہوئے ہیں !!\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620205654008,"user_tz":-300,"elapsed":3678520,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"f76c419d-e144-44d7-a9c5-2dbc67b69d2c"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SO4uz45MoRgp","executionInfo":{"status":"ok","timestamp":1620206095082,"user_tz":-300,"elapsed":136895,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"5f0bacad-9c51-4ca0-90d3-614260033d6e"},"source":["stored_model_path = './models/classifier_dl_trained' \n","\n","hdd_pipe = nlu.load(path=\"./models/classifier_dl_trained\")\n","\n","preds = hdd_pipe.predict('Bitcoin dropped by 50 percent!!')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentiment
\n","
sentence
\n","
origin_index
\n","
sentiment_confidence
\n","
text
\n","
sentence_embedding_from_disk
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[negative, negative]
\n","
[Bitcoin dropped by 50 percent!, !]
\n","
8589934592
\n","
[0.9999988, 0.9999988]
\n","
Bitcoin dropped by 50 percent!!
\n","
[[-0.049401186406612396, -0.02522580698132515,...
\n","
Bitcoin dropped by 50 percent!!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentiment ... document\n","0 [negative, negative] ... Bitcoin dropped by 50 percent!!\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e0CVlkk9v6Qi","executionInfo":{"status":"ok","timestamp":1620206095084,"user_tz":-300,"elapsed":136873,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"e5d12349-cbe9-4f02-8266-490dd9ff9344"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@1b8112d8) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@1b8112d8\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@labse'] has settable params:\n","pipe['bert_sentence@labse'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@labse'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@labse'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@labse'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@labse'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n",">>> pipe['sentiment_dl@labse'] has settable params:\n","pipe['sentiment_dl@labse'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@labse'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@labse'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"dxr5nnv8zPLz"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_twitter.ipynb b/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_twitter.ipynb
deleted file mode 100644
index 394348dc..00000000
--- a/examples/colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_twitter.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_multi_lingual_training_sentiment_classifier_demo_twitter.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples//colab/Training/multi_lingual/binary_text_classification/NLU_multi_lingual_training_sentiment_classifier_demo_twitter.ipynb)\n","\n","# Training a Sentiment Analysis Classifier with NLU \n","## 2 Class Twitter Sentiment Classifier Training\n","With the [SentimentDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#sentimentdl-multi-class-sentiment-analysis-annotator) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","\n","* List item\n","* List item\n","\n","\n","You can achieve these results or even better on this dataset with training data : \n","\n"," \n","\n","\n","\n","You can achieve these results or even better on this dataset with test data : \n","\n"," \n","\n",""]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hFGnBCHavltY","executionInfo":{"status":"ok","timestamp":1620202068188,"user_tz":-300,"elapsed":106986,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"54e36062-3758-4478-9353-614909af18c6"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 08:06:01-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 08:06:02 (1.81 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 76kB/s \n","\u001b[K |████████████████████████████████| 153kB 48.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 21.6MB/s \n","\u001b[K |████████████████████████████████| 204kB 49.6MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download twitter Sentiment dataset \n","https://www.kaggle.com/cosmos98/twitter-and-reddit-sentimental-analysis-dataset\n","#Context\n","\n","This is was a Dataset Created as a part of the university Project On Sentimental Analysis On Multi-Source Social Media Platforms using PySpark."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620202068746,"user_tz":-300,"elapsed":107458,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"4598d263-965a-47f3-9fc6-a3869e7b4318"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/twitter_data_multi_lang.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 08:07:47-- http://ckl-it.de/wp-content/uploads/2021/02/twitter_data_multi_lang.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 125908 (123K) [text/csv]\n","Saving to: ‘twitter_data_multi_lang.csv’\n","\n","twitter_data_multi_ 100%[===================>] 122.96K 357KB/s in 0.3s \n","\n","2021-05-05 08:07:48 (357 KB/s) - ‘twitter_data_multi_lang.csv’ saved [125908/125908]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620202069157,"user_tz":-300,"elapsed":107840,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"b98f130f-2334-4472-e9d5-585b3e4f0417"},"source":["import pandas as pd\n","train_path = '/content/twitter_data_multi_lang.csv'\n","\n","train_df = pd.read_csv(train_path)\n","train_df.test_sentences = train_df.test_sentences.astype(str)\n","# the text data to use for classification should be in a column named 'text'\n","# the label column must have name 'y' name be of type str\n","train_df= train_df[[\"text\",\"y\"]]\n","from sklearn.model_selection import train_test_split\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
y
\n","
\n"," \n"," \n","
\n","
261
\n","
narender modi think the candidate who standing...
\n","
negative
\n","
\n","
\n","
250
\n","
you forgot one more cruel incident happened ka...
\n","
negative
\n","
\n","
\n","
55
\n","
modi the countryhe worked chaiwala not chowkid...
\n","
negative
\n","
\n","
\n","
105
\n","
india wants leader who has vision success buil...
\n","
positive
\n","
\n","
\n","
532
\n","
wrong assumptions made letter\\r\\nbeing modi fa...
\n","
negative
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
168
\n","
this hindutva terrorism enjoys the tacit suppo...
\n","
positive
\n","
\n","
\n","
80
\n","
modis opposition trying defame him they not wa...
\n","
negative
\n","
\n","
\n","
560
\n","
modi has crippled economy destroyed jobs far f...
\n","
positive
\n","
\n","
\n","
383
\n","
and hope hindustan will over take all other co...
\n","
negative
\n","
\n","
\n","
133
\n","
why not ask them vote for modern india modi wh...
\n","
negative
\n","
\n"," \n","
\n","
480 rows × 2 columns
\n","
"],"text/plain":[" text y\n","261 narender modi think the candidate who standing... negative\n","250 you forgot one more cruel incident happened ka... negative\n","55 modi the countryhe worked chaiwala not chowkid... negative\n","105 india wants leader who has vision success buil... positive\n","532 wrong assumptions made letter\\r\\nbeing modi fa... negative\n",".. ... ...\n","168 this hindutva terrorism enjoys the tacit suppo... positive\n","80 modis opposition trying defame him they not wa... negative\n","560 modi has crippled economy destroyed jobs far f... positive\n","383 and hope hindustan will over take all other co... negative\n","133 why not ask them vote for modern india modi wh... negative\n","\n","[480 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.sentiment')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":844},"id":"IKK_Ii_gjJfF","executionInfo":{"status":"ok","timestamp":1620202522330,"user_tz":-300,"elapsed":560988,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"5db3cc9e-7808-46d0-9f4e-934e1a3650ee"},"source":["trainable_pipe = nlu.load('xx.embed_sentence.labse train.sentiment')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['sentiment_dl'].setMaxEpochs(60) \n","trainable_pipe['sentiment_dl'].setLr(0.005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["labse download started this may take some time.\n","Approximate size to download 1.7 GB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," negative 0.93 0.97 0.95 233\n"," positive 0.97 0.94 0.95 247\n","\n"," accuracy 0.95 480\n"," macro avg 0.95 0.95 0.95 480\n","weighted avg 0.95 0.95 0.95 480\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
y
\n","
trained_sentiment
\n","
text
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.997279
\n","
narender modi think the candidate who standing...
\n","
261
\n","
negative
\n","
negative
\n","
narender modi think the candidate who standing...
\n","
[narender modi think the candidate who standin...
\n","
[-0.07268679887056351, 0.06004006788134575, 0....
\n","
\n","
\n","
1
\n","
0.999994
\n","
you forgot one more cruel incident happened ka...
\n","
250
\n","
negative
\n","
negative
\n","
you forgot one more cruel incident happened ka...
\n","
[you forgot one more cruel incident happened k...
\n","
[0.07269874960184097, -0.027332717552781105, -...
\n","
\n","
\n","
2
\n","
0.999994
\n","
modi the countryhe worked chaiwala not chowkid...
\n","
55
\n","
negative
\n","
negative
\n","
modi the countryhe worked chaiwala not chowkid...
\n","
[modi the countryhe worked chaiwala not chowki...
\n","
[0.016793066635727882, -0.021795757114887238, ...
\n","
\n","
\n","
3
\n","
1.000000
\n","
india wants leader who has vision success buil...
\n","
105
\n","
positive
\n","
positive
\n","
india wants leader who has vision success buil...
\n","
[india wants leader who has vision success bui...
\n","
[-0.049463558942079544, 0.04899046570062637, 0...
\n","
\n","
\n","
4
\n","
0.999807
\n","
wrong assumptions made letter being modi fan d...
\n","
532
\n","
negative
\n","
negative
\n","
wrong assumptions made letter\\r\\nbeing modi fa...
\n","
[wrong assumptions made letter being modi fan ...
\n","
[0.02427481859922409, 0.03929490968585014, -0....
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
475
\n","
0.999200
\n","
this hindutva terrorism enjoys the tacit suppo...
\n","
168
\n","
positive
\n","
positive
\n","
this hindutva terrorism enjoys the tacit suppo...
\n","
[this hindutva terrorism enjoys the tacit supp...
\n","
[0.05460929870605469, -0.00930434837937355, 0....
\n","
\n","
\n","
476
\n","
0.999818
\n","
modis opposition trying defame him they not wa...
\n","
80
\n","
negative
\n","
negative
\n","
modis opposition trying defame him they not wa...
\n","
[modis opposition trying defame him they not w...
\n","
[-0.06659215688705444, 0.01988210715353489, 0....
\n","
\n","
\n","
477
\n","
0.999971
\n","
modi has crippled economy destroyed jobs far f...
\n","
560
\n","
positive
\n","
negative
\n","
modi has crippled economy destroyed jobs far f...
\n","
[modi has crippled economy destroyed jobs far ...
\n","
[-0.0314302034676075, 0.027095980942249298, -0...
\n","
\n","
\n","
478
\n","
1.000000
\n","
and hope hindustan will over take all other co...
\n","
383
\n","
negative
\n","
positive
\n","
and hope hindustan will over take all other co...
\n","
[and hope hindustan will over take all other c...
\n","
[0.048234350979328156, -0.037638068199157715, ...
\n","
\n","
\n","
479
\n","
0.998324
\n","
why not ask them vote for modern india modi wh...
\n","
133
\n","
negative
\n","
negative
\n","
why not ask them vote for modern india modi wh...
\n","
[why not ask them vote for modern india modi w...
\n","
[-0.03812406584620476, 0.07693956047296524, -0...
\n","
\n"," \n","
\n","
480 rows × 8 columns
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.997279 ... [-0.07268679887056351, 0.06004006788134575, 0....\n","1 0.999994 ... [0.07269874960184097, -0.027332717552781105, -...\n","2 0.999994 ... [0.016793066635727882, -0.021795757114887238, ...\n","3 1.000000 ... [-0.049463558942079544, 0.04899046570062637, 0...\n","4 0.999807 ... [0.02427481859922409, 0.03929490968585014, -0....\n",".. ... ... ...\n","475 0.999200 ... [0.05460929870605469, -0.00930434837937355, 0....\n","476 0.999818 ... [-0.06659215688705444, 0.01988210715353489, 0....\n","477 0.999971 ... [-0.0314302034676075, 0.027095980942249298, -0...\n","478 1.000000 ... [0.048234350979328156, -0.037638068199157715, ...\n","479 0.998324 ... [-0.03812406584620476, 0.07693956047296524, -0...\n","\n","[480 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 3.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fxx4yNkNVGFl","executionInfo":{"status":"ok","timestamp":1620202543579,"user_tz":-300,"elapsed":582223,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"9f534cb3-736b-4d2f-f465-df0ccec6716b"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.75 0.70 0.72 67\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.65 0.68 0.67 53\n","\n"," accuracy 0.69 120\n"," macro avg 0.47 0.46 0.46 120\n","weighted avg 0.71 0.69 0.70 120\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"BD5OKO4Umc5U"},"source":["# 4. Test Model on 20 languages!"]},{"cell_type":"code","metadata":{"id":"OQ72hP9unML7","colab":{"base_uri":"https://localhost:8080/","height":776},"executionInfo":{"status":"ok","timestamp":1620202562769,"user_tz":-300,"elapsed":601405,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"409a40af-dff1-4910-8136-c74f09527aa5"},"source":["train_df = pd.read_csv(\"/content/twitter_data_multi_lang.csv\")\n","preds = fitted_pipe.predict(train_df[[\"test_sentences\",\"y\"]].iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_sentiment']))\n","\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," negative 0.83 0.82 0.82 49\n"," neutral 0.00 0.00 0.00 0\n"," positive 0.85 0.80 0.83 51\n","\n"," accuracy 0.81 100\n"," macro avg 0.56 0.54 0.55 100\n","weighted avg 0.84 0.81 0.83 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
y
\n","
trained_sentiment
\n","
text
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999401
\n","
how narendra modi has almost killed the indian...
\n","
0
\n","
negative
\n","
negative
\n","
how narendra modi has almost killed the indian...
\n","
[how narendra modi has almost killed the india...
\n","
[-0.05593567714095116, 0.050420816987752914, -...
\n","
\n","
\n","
1
\n","
0.995770
\n","
تعتقد أنه كان مودي وراء هذا الحادث
\n","
1
\n","
negative
\n","
negative
\n","
تعتقد أنه كان مودي وراء هذا الحادث
\n","
[تعتقد أنه كان مودي وراء هذا الحادث]
\n","
[0.007358793169260025, -0.0520767867565155, 0....
\n","
\n","
\n","
2
\n","
0.998129
\n","
カマル・ハサーンがチョウキダール・モディを連れて行くカマル・ハサーン・モディの金持ちが貧しい...
\n","
2
\n","
negative
\n","
negative
\n","
カマル・ハサーンがチョウキダール・モディを連れて行くカマル・ハサーン・モディの金持ちが貧しい...
\n","
[カマル・ハサーンがチョウキダール・モディを連れて行くカマル・ハサーン・モディの金持ちが貧し...
\n","
[-0.012155424803495407, -0.02065391279757023, ...
\n","
\n","
\n","
3
\n","
0.998547
\n","
связанное имя с фамилией, а не bcz религия, св...
\n","
3
\n","
negative
\n","
negative
\n","
связанное имя с фамилией, а не bcz религия, св...
\n","
[связанное имя с фамилией, а не bcz религия, с...
\n","
[-0.006620907690376043, 0.02574392780661583, -...
\n","
\n","
\n","
4
\n","
1.000000
\n","
kdokoli lepší než modi, když nehruji vypršela,...
\n","
4
\n","
positive
\n","
positive
\n","
kdokoli lepší než modi, když nehruji vypršela,...
\n","
[kdokoli lepší než modi, když nehruji vypršela...
\n","
[-0.04917769134044647, 0.017523039132356644, -...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
0.999987
\n","
lol qui va épouser son hippopotame tous les ho...
\n","
95
\n","
positive
\n","
positive
\n","
lol qui va épouser son hippopotame tous les ho...
\n","
[lol qui va épouser son hippopotame tous les h...
\n","
[-0.01001912634819746, -0.031715311110019684, ...
\n","
\n","
\n","
96
\n","
0.999994
\n","
拉贾斯坦邦州长卡莉安·辛格·阿里加3月23日全都是bjp工人,希望bjp胜利,希望莫迪再次成...
\n","
96
\n","
positive
\n","
positive
\n","
拉贾斯坦邦州长卡莉安·辛格·阿里加3月23日全都是bjp工人,希望bjp胜利,希望莫迪再次成...
\n","
[拉贾斯坦邦州长卡莉安·辛格·阿里加3月23日全都是bjp工人,希望bjp胜利,希望莫迪再次...
\n","
[0.009000560268759727, -0.021888386458158493, ...
\n","
\n","
\n","
97
\n","
0.979382
\n","
మోడీ భక్తులు రాహుల్ గురించి అబద్ధాలు చెబుతున్న...
\n","
97
\n","
positive
\n","
positive
\n","
మోడీ భక్తులు రాహుల్ గురించి అబద్ధాలు చెబుతున్న...
\n","
[మోడీ భక్తులు రాహుల్ గురించి అబద్ధాలు చెబుతున్...
\n","
[-0.05518203601241112, -0.0041709886863827705,...
\n","
\n","
\n","
98
\n","
0.882261
\n","
lol neha, je to jako dát hlavu zabít těm, kteř...
\n","
98
\n","
positive
\n","
positive
\n","
lol neha, je to jako dát hlavu zabít těm, kteř...
\n","
[lol neha, je to jako dát hlavu zabít těm, kte...
\n","
[-0.019701892510056496, -0.01936856471002102, ...
\n","
\n","
\n","
99
\n","
0.999997
\n","
por favor venda nuestro bosque por favor haga ...
\n","
99
\n","
positive
\n","
positive
\n","
por favor venda nuestro bosque por favor haga ...
\n","
[por favor venda nuestro bosque por favor haga...
\n","
[-0.039666254073381424, -0.0194801464676857, -...
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999401 ... [-0.05593567714095116, 0.050420816987752914, -...\n","1 0.995770 ... [0.007358793169260025, -0.0520767867565155, 0....\n","2 0.998129 ... [-0.012155424803495407, -0.02065391279757023, ...\n","3 0.998547 ... [-0.006620907690376043, 0.02574392780661583, -...\n","4 1.000000 ... [-0.04917769134044647, 0.017523039132356644, -...\n",".. ... ... ...\n","95 0.999987 ... [-0.01001912634819746, -0.031715311110019684, ...\n","96 0.999994 ... [0.009000560268759727, -0.021888386458158493, ...\n","97 0.979382 ... [-0.05518203601241112, -0.0041709886863827705,...\n","98 0.882261 ... [-0.019701892510056496, -0.01936856471002102, ...\n","99 0.999997 ... [-0.039666254073381424, -0.0194801464676857, -...\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"RjtuNUcvuJTT"},"source":["# The Model understands Englsih\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"o0vu7PaWkcI7","executionInfo":{"status":"ok","timestamp":1620202564814,"user_tz":-300,"elapsed":603442,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"c5dd75ba-92bc-45b9-f6da-e44f6a90b35e"},"source":["fitted_pipe.predict(\"Congress's new policies made many people sad \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.971534
\n","
Congress's new policies made many people sad
\n","
0
\n","
negative
\n","
[Congress's new policies made many people sad]
\n","
[0.004380704369395971, -0.00210917298682034, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.971534 ... [0.004380704369395971, -0.00210917298682034, -...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"1ykjRQhCtQ4w","executionInfo":{"status":"ok","timestamp":1620202564816,"user_tz":-300,"elapsed":603426,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"1bfa4d52-54a2-43e3-99ed-b054129c8b41"},"source":["fitted_pipe.predict(\"Congress's new policies made many people happy \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999984
\n","
Congress's new policies made many people happy
\n","
0
\n","
positive
\n","
[Congress's new policies made many people happy]
\n","
[0.025979558005928993, -0.007445275783538818, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999984 ... [0.025979558005928993, -0.007445275783538818, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"vohym-XbuNHn"},"source":["# The Model understands German\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"dzaaZrI4tVWc","executionInfo":{"status":"ok","timestamp":1620202566199,"user_tz":-300,"elapsed":604799,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"7a0eeb2c-0d28-4bfa-fe8b-a5827afe89f1"},"source":["# German for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"Die neue Politik des Kongresses machte viele Menschen arm, traurig und depressiv \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.779711
\n","
Die neue Politik des Kongresses machte viele M...
\n","
0
\n","
negative
\n","
[Die neue Politik des Kongresses machte viele ...
\n","
[-0.02746928110718727, 0.015148899517953396, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.779711 ... [-0.02746928110718727, 0.015148899517953396, -...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"BbhgTSBGtTtJ","executionInfo":{"status":"ok","timestamp":1620202566201,"user_tz":-300,"elapsed":604792,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"9f0c244c-834a-4b01-8d60-df278e60de56"},"source":["# German for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"Die neue Politik des Kongresses machte viele Menschen glücklich \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999974
\n","
Die neue Politik des Kongresses machte viele M...
\n","
0
\n","
positive
\n","
[Die neue Politik des Kongresses machte viele ...
\n","
[0.008141197264194489, -0.009829358197748661, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999974 ... [0.008141197264194489, -0.009829358197748661, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"a1JbtmWquQwj"},"source":["# The Model understands Chinese\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"kYSYqtoRtc-P","executionInfo":{"status":"ok","timestamp":1620202567220,"user_tz":-300,"elapsed":605804,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"45ea8add-578f-4c7a-bf50-f5649f2b3bbe"},"source":["# Chinese for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"国会的新政策使许多人感到高兴 \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999998
\n","
国会的新政策使许多人感到高兴
\n","
0
\n","
positive
\n","
[国会的新政策使许多人感到高兴]
\n","
[0.009464389644563198, -0.012016313150525093, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999998 ... [0.009464389644563198, -0.012016313150525093, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"06v9SD-QtlBU","executionInfo":{"status":"ok","timestamp":1620202567221,"user_tz":-300,"elapsed":605796,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"0e1f7d04-90d5-4765-a4d4-09d88e7ff62b"},"source":["# Chinese for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"国会的新政策使许多人变得贫穷,悲伤和沮丧 \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.998916
\n","
国会的新政策使许多人变得贫穷,悲伤和沮丧
\n","
0
\n","
negative
\n","
[国会的新政策使许多人变得贫穷,悲伤和沮丧]
\n","
[-0.05506608635187149, -0.002640362363308668, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.998916 ... [-0.05506608635187149, -0.002640362363308668, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"9h7CvN4uu9Pb"},"source":["# Model understands Afrikaans\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"VMPhbgw9twtf","executionInfo":{"status":"ok","timestamp":1620202568514,"user_tz":-300,"elapsed":607077,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"54ce4a9a-52dc-477c-8195-3fb28a321074"},"source":["# Afrikaans for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"Die Kongres se nuwe beleid het baie mense arm, hartseer en depressief gemaak \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.720446
\n","
Die Kongres se nuwe beleid het baie mense arm,...
\n","
0
\n","
negative
\n","
[Die Kongres se nuwe beleid het baie mense arm...
\n","
[-0.023684442043304443, 0.0034083062782883644,...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.720446 ... [-0.023684442043304443, 0.0034083062782883644,...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"zWgNTIdkumhX","executionInfo":{"status":"ok","timestamp":1620202569943,"user_tz":-300,"elapsed":608497,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"7f7ccf28-2d4d-487d-9205-1c469d23275b"},"source":["# Afrikaans for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"Die Kongres se nuwe beleid het baie mense gelukkig gemaak \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999999
\n","
Die Kongres se nuwe beleid het baie mense gelu...
\n","
0
\n","
positive
\n","
[Die Kongres se nuwe beleid het baie mense gel...
\n","
[0.005836560856550932, -0.02982638217508793, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999999 ... [0.005836560856550932, -0.02982638217508793, -...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"IlkmAaMoxTuy"},"source":["# The model understands Japanese\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"1IfJu3q8wwUt","executionInfo":{"status":"ok","timestamp":1620202569947,"user_tz":-300,"elapsed":608494,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"e1be5a26-606f-4bda-8494-ce0f58aabf14"},"source":["# Japanese for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"議会の新しい政策は多くの人々を貧しく、悲しくそして落ち込んだものにしました \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.993153
\n","
議会の新しい政策は多くの人々を貧しく、悲しくそして落ち込んだものにしました
\n","
0
\n","
negative
\n","
[議会の新しい政策は多くの人々を貧しく、悲しくそして落ち込んだものにしました]
\n","
[-0.04006955772638321, 0.0033476136159151793, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.993153 ... [-0.04006955772638321, 0.0033476136159151793, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"h3k7_PFhxOve","executionInfo":{"status":"ok","timestamp":1620202570964,"user_tz":-300,"elapsed":609500,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"76551d94-8d73-4904-9636-e1495852e33d"},"source":["\n","\t\t\n","# Japanese for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"議会の新しい政策は多くの人々を幸せにしました \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999996
\n","
議会の新しい政策は多くの人々を幸せにしました
\n","
0
\n","
positive
\n","
[議会の新しい政策は多くの人々を幸せにしました]
\n","
[-0.017957264557480812, -0.015919487923383713,...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999996 ... [-0.017957264557480812, -0.015919487923383713,...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"VGVvzl_30a0T"},"source":["# The Model understands Turkish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"DRNnuEeQz2pd","executionInfo":{"status":"ok","timestamp":1620202571532,"user_tz":-300,"elapsed":610061,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"515337da-dcec-4345-f112-cd35e644c79e"},"source":["# Turkish for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"Kongrenin yeni politikaları birçok insanı fakir, hüzünlü ve depresif hale getirdi \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.995125
\n","
Kongrenin yeni politikaları birçok insanı faki...
\n","
0
\n","
negative
\n","
[Kongrenin yeni politikaları birçok insanı fak...
\n","
[-0.02755207195878029, 0.012688503600656986, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.995125 ... [-0.02755207195878029, 0.012688503600656986, -...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"aOSsiK6J0jWs","executionInfo":{"status":"ok","timestamp":1620202572302,"user_tz":-300,"elapsed":610810,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"45f525b8-617b-4469-d61e-df03469b8734"},"source":["# Turkish for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"Kongrenin yeni politikaları birçok insanı mutlu etti \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999997
\n","
Kongrenin yeni politikaları birçok insanı mutl...
\n","
0
\n","
positive
\n","
[Kongrenin yeni politikaları birçok insanı mut...
\n","
[0.019367843866348267, -0.0063224597834050655,...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999997 ... [0.019367843866348267, -0.0063224597834050655,...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"803qL2gt0vlb"},"source":["# The Model understands Hebrew\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"XQ5VCtxw0pc0","executionInfo":{"status":"ok","timestamp":1620202573308,"user_tz":-300,"elapsed":611804,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"e9a8ce71-dbad-46b6-e556-f51b2c7ef208"},"source":["# Hebrew for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"המדיניות החדשה של הקונגרס גרמה לאנשים רבים להיות עניים, עצובים ומדוכאים \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.994727
\n","
המדיניות החדשה של הקונגרס גרמה לאנשים רבים להי...
\n","
0
\n","
negative
\n","
[המדיניות החדשה של הקונגרס גרמה לאנשים רבים לה...
\n","
[-0.03273192420601845, -0.016592275351285934, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.994727 ... [-0.03273192420601845, -0.016592275351285934, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"9w2ZHfns05A4","executionInfo":{"status":"ok","timestamp":1620202574066,"user_tz":-300,"elapsed":612547,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"cb13aabf-b93c-4fa1-e729-af42f241366b"},"source":["# Hebrew for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"המדיניות החדשה של הקונגרס שימחה אנשים רבים \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999977
\n","
המדיניות החדשה של הקונגרס שימחה אנשים רבים
\n","
0
\n","
positive
\n","
[המדיניות החדשה של הקונגרס שימחה אנשים רבים]
\n","
[0.0014839837094768882, -0.01997891254723072, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999977 ... [0.0014839837094768882, -0.01997891254723072, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"SDlpd33H1HIX"},"source":["# The Model understands Telugu\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Kc5n1bzv1BJT","executionInfo":{"status":"ok","timestamp":1620202574942,"user_tz":-300,"elapsed":613408,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"3a47d3cd-1748-4725-d6b2-329c6b3b8664"},"source":["# Telugu for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"కాంగ్రెస్ కొత్త విధానాలు చాలా మందిని పేదలుగా, విచారంగా, నిరాశకు గురి చేశాయి \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.994245
\n","
కాంగ్రెస్ కొత్త విధానాలు చాలా మందిని పేదలుగా, ...
\n","
0
\n","
negative
\n","
[కాంగ్రెస్ కొత్త విధానాలు చాలా మందిని పేదలుగా,...
\n","
[-0.02907465025782585, -0.02225475199520588, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.994245 ... [-0.02907465025782585, -0.02225475199520588, -...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"-l-u6vrz1Obe","executionInfo":{"status":"ok","timestamp":1620202575499,"user_tz":-300,"elapsed":613956,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"24fcaacc-949f-4243-d9ba-02537d895c71"},"source":["# Telugu for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"కాంగ్రెస్ కొత్త విధానాలు చాలా మందికి సంతోషాన్నిచ్చాయి \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
1.0
\n","
కాంగ్రెస్ కొత్త విధానాలు చాలా మందికి సంతోషాన్న...
\n","
0
\n","
positive
\n","
[కాంగ్రెస్ కొత్త విధానాలు చాలా మందికి సంతోషాన్...
\n","
[0.003831395646557212, -0.034895412623882294, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 1.0 ... [0.003831395646557212, -0.034895412623882294, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"nziBUe8t1Zwn"},"source":["# Model understands Russian\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Ckyjl3YQ1VFn","executionInfo":{"status":"ok","timestamp":1620202576540,"user_tz":-300,"elapsed":614990,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"45003fc1-6c5e-4ca2-9ffa-762379c6e612"},"source":["# Russian for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"Новая политика Конгресса сделала многих людей бедными, грустными и подавленными \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.997476
\n","
Новая политика Конгресса сделала многих людей ...
\n","
0
\n","
negative
\n","
[Новая политика Конгресса сделала многих людей...
\n","
[-0.029941784217953682, 0.016272399574518204, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.997476 ... [-0.029941784217953682, 0.016272399574518204, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"GIdWkfGv1gFz","executionInfo":{"status":"ok","timestamp":1620202577054,"user_tz":-300,"elapsed":615494,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"ab04d8b1-3682-4a20-87bb-e0cb1ff12446"},"source":["\n","\t\t\n","# Russian for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"Новая политика Конгресса порадовала многих людей \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999979
\n","
Новая политика Конгресса порадовала многих людей
\n","
0
\n","
positive
\n","
[Новая политика Конгресса порадовала многих лю...
\n","
[-0.002074694959446788, 0.014204198494553566, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999979 ... [-0.002074694959446788, 0.014204198494553566, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"8R1j9mwz2Cm4"},"source":["# Model understands Urdu\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"j4zwvRV11pcG","executionInfo":{"status":"ok","timestamp":1620202577597,"user_tz":-300,"elapsed":616025,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"9e08445b-75e1-4d68-b34b-98a6c90d7201"},"source":["\n","\t\t\n","# Urdu for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"کانگریس کی نئی پالیسیوں نے بہت سارے لوگوں کو غریب ، افسردہ اور افسردہ کردیا \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999625
\n","
کانگریس کی نئی پالیسیوں نے بہت سارے لوگوں کو غ...
\n","
0
\n","
negative
\n","
[کانگریس کی نئی پالیسیوں نے بہت سارے لوگوں کو ...
\n","
[-0.032778408378362656, -0.01915016397833824, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999625 ... [-0.032778408378362656, -0.01915016397833824, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SxzTuK4b2UKV","executionInfo":{"status":"ok","timestamp":1620202578836,"user_tz":-300,"elapsed":617254,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"cb327854-4b6d-4179-964b-db657342db20"},"source":["# Urdu for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"کانگریس کی نئی پالیسیوں نے بہت سارے لوگوں کو خوش کیا \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.995908
\n","
کانگریس کی نئی پالیسیوں نے بہت سارے لوگوں کو خ...
\n","
0
\n","
positive
\n","
[کانگریس کی نئی پالیسیوں نے بہت سارے لوگوں کو ...
\n","
[0.0033543300814926624, -0.0338786281645298, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.995908 ... [0.0033543300814926624, -0.0338786281645298, -...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"RoNg-C3k1qcX"},"source":["# Model understands Hindi\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"QZ9RT5Wv1r1n","executionInfo":{"status":"ok","timestamp":1620202579420,"user_tz":-300,"elapsed":617832,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"17e2dba5-3371-40dd-b831-9148fa4c7007"},"source":["# hindi for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"कांग्रेस की नई नीतियों ने कई लोगों को गरीब, दुखी और उदास बना दिया \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.995045
\n","
कांग्रेस की नई नीतियों ने कई लोगों को गरीब, दु...
\n","
0
\n","
negative
\n","
[कांग्रेस की नई नीतियों ने कई लोगों को गरीब, द...
\n","
[-0.030935177579522133, -0.011918646283447742,...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.995045 ... [-0.030935177579522133, -0.011918646283447742,...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"quM-IL2i12-B","executionInfo":{"status":"ok","timestamp":1620202580321,"user_tz":-300,"elapsed":618728,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"4ef407db-efe4-4ef8-e2b6-2eeb63ab775e"},"source":["# hindi for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"कांग्रेस की नई नीतियों ने कई लोगों को खुश किया \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999983
\n","
कांग्रेस की नई नीतियों ने कई लोगों को खुश किया
\n","
0
\n","
positive
\n","
[कांग्रेस की नई नीतियों ने कई लोगों को खुश किया]
\n","
[0.005392681807279587, -0.024082284420728683, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999983 ... [0.005392681807279587, -0.024082284420728683, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"HKj5yWwwMplH"},"source":["# The Model understands French\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"CUHcJZfJMplL","executionInfo":{"status":"ok","timestamp":1620202582298,"user_tz":-300,"elapsed":620676,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"41c1b052-da38-40be-860a-b006f2f9df6e"},"source":["\t\t\n","# French for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"Les nouvelles politiques du Congrès ont rendu de nombreuses personnes pauvres, tristes et déprimées \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.995686
\n","
Les nouvelles politiques du Congrès ont rendu ...
\n","
0
\n","
negative
\n","
[Les nouvelles politiques du Congrès ont rendu...
\n","
[-0.017834002152085304, 0.011118772439658642, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.995686 ... [-0.017834002152085304, 0.011118772439658642, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"57NY2XoTMplM","executionInfo":{"status":"ok","timestamp":1620202582866,"user_tz":-300,"elapsed":621235,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"4fa2fd7c-9697-4b60-c270-b0c2e644b020"},"source":["# French for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"Les nouvelles politiques du Congrès ont rendu de nombreuses personnes heureuses \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999999
\n","
Les nouvelles politiques du Congrès ont rendu ...
\n","
0
\n","
positive
\n","
[Les nouvelles politiques du Congrès ont rendu...
\n","
[0.019515689462423325, -0.010051749646663666, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999999 ... [0.019515689462423325, -0.010051749646663666, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"jD2TBgT0Nq6F"},"source":["# The Model understands Thai\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"gBp11S5GNq6S","executionInfo":{"status":"ok","timestamp":1620202584197,"user_tz":-300,"elapsed":622559,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"878c5149-214e-427a-d206-325ad89d6e2c"},"source":["\t\t\n","# Thai for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"นโยบายใหม่ของสภาคองเกรสทำให้หลายคนยากจนเศร้าและหดหู่ \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.727099
\n","
นโยบายใหม่ของสภาคองเกรสทำให้หลายคนยากจนเศร้าแล...
\n","
0
\n","
positive
\n","
[นโยบายใหม่ของสภาคองเกรสทำให้หลายคนยากจนเศร้าแ...
\n","
[4.369320549812983e-07, -0.002880594925954938,...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.727099 ... [4.369320549812983e-07, -0.002880594925954938,...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"KgatiiyuZumz","executionInfo":{"status":"ok","timestamp":1620202584199,"user_tz":-300,"elapsed":622549,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"89c4d1ab-b36b-4e97-a414-1d07866c3304"},"source":["# Thai for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"นโยบายใหม่ของสภาคองเกรสทำให้หลายคนพอใจ \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999999
\n","
นโยบายใหม่ของสภาคองเกรสทำให้หลายคนพอใจ
\n","
0
\n","
positive
\n","
[นโยบายใหม่ของสภาคองเกรสทำให้หลายคนพอใจ]
\n","
[0.012098133563995361, 0.006513903383165598, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999999 ... [0.012098133563995361, 0.006513903383165598, -...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"mLItI4KZOElB"},"source":["# The Model understands Khmer\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Fxh1gasROElC","executionInfo":{"status":"ok","timestamp":1620202585353,"user_tz":-300,"elapsed":623695,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"9c517c18-1b44-45a5-8941-a1c21c21a454"},"source":["# Khmer for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"គោលនយោបាយថ្មីរបស់សភាបានធ្វើឱ្យប្រជាជនជាច្រើនក្រីក្រក្រៀមក្រំនិងធ្លាក់ទឹកចិត្ត \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.987534
\n","
គោលនយោបាយថ្មីរបស់សភាបានធ្វើឱ្យប្រជាជនជាច្រើនក្...
\n","
0
\n","
negative
\n","
[គោលនយោបាយថ្មីរបស់សភាបានធ្វើឱ្យប្រជាជនជាច្រើនក...
\n","
[-0.045212410390377045, 0.010355290956795216, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.987534 ... [-0.045212410390377045, 0.010355290956795216, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SWbqMgAwOElC","executionInfo":{"status":"ok","timestamp":1620202586047,"user_tz":-300,"elapsed":624382,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"922c462d-f20b-4501-dbcf-6368801b889f"},"source":["# Khmer for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"គោលនយោបាយថ្មីរបស់សភាបានធ្វើឱ្យមនុស្សជាច្រើនសប្បាយរីករាយ \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999997
\n","
គោលនយោបាយថ្មីរបស់សភាបានធ្វើឱ្យមនុស្សជាច្រើនសប្...
\n","
0
\n","
positive
\n","
[គោលនយោបាយថ្មីរបស់សភាបានធ្វើឱ្យមនុស្សជាច្រើនសប...
\n","
[-0.025576695799827576, -0.020313698798418045,...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999997 ... [-0.025576695799827576, -0.020313698798418045,...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"lvE-LbNiPoBT"},"source":["# The Model understands Yiddish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"sZlmLhajPoBb","executionInfo":{"status":"ok","timestamp":1620202586621,"user_tz":-300,"elapsed":624922,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"3715ec9a-21a3-4375-a8cb-7ea846674419"},"source":["\t\t\n","# Yiddish for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"קאָנגרעס ס נייַ פּאַלאַסיז געמאכט פילע מענטשן נעבעך, טרויעריק און דערשלאָגן \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.972501
\n","
קאָנגרעס ס נייַ פּאַלאַסיז געמאכט פילע מענטשן ...
\n","
0
\n","
positive
\n","
[קאָנגרעס ס נייַ פּאַלאַסיז געמאכט פילע מענטשן...
\n","
[-0.007056341972202063, -0.0033368950244039297...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.972501 ... [-0.007056341972202063, -0.0033368950244039297...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"5h-pha_nPoBc","executionInfo":{"status":"ok","timestamp":1620202587337,"user_tz":-300,"elapsed":625626,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"a8a486c3-0557-4be3-b6a0-57d6daf6a218"},"source":["# Yiddish for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"קאָנגרעס ס נייַ פּאַלאַסיז געמאכט פילע מענטשן צופרידן \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999997
\n","
קאָנגרעס ס נייַ פּאַלאַסיז געמאכט פילע מענטשן ...
\n","
0
\n","
positive
\n","
[קאָנגרעס ס נייַ פּאַלאַסיז געמאכט פילע מענטשן...
\n","
[0.002619848819449544, -0.018449869006872177, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999997 ... [0.002619848819449544, -0.018449869006872177, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"XSz4WzScaAHj"},"source":["# The Model understands Kygrgyz\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"DXz6fhJSaAHu","executionInfo":{"status":"ok","timestamp":1620202587920,"user_tz":-300,"elapsed":626067,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"7b409dc7-7ec0-47c4-b42a-24e24b67c830"},"source":["# Kygrgyz for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"Конгресстин жаңы саясаты көптөгөн адамдарды жакыр, кайгыга чөгүп, көңүл чөгөттү \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.987505
\n","
Конгресстин жаңы саясаты көптөгөн адамдарды жа...
\n","
0
\n","
negative
\n","
[Конгресстин жаңы саясаты көптөгөн адамдарды ж...
\n","
[-0.0002846009156201035, -0.002948872279375791...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.987505 ... [-0.0002846009156201035, -0.002948872279375791...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"lh_ZSHlPaAHv","executionInfo":{"status":"ok","timestamp":1620202588449,"user_tz":-300,"elapsed":626583,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"5fc6a691-eb12-4cd2-e24c-6391ce070614"},"source":["# Kygrgyz for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"Конгресстин жаңы саясаты көпчүлүктү кубандырды \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999999
\n","
Конгресстин жаңы саясаты көпчүлүктү кубандырды
\n","
0
\n","
positive
\n","
[Конгресстин жаңы саясаты көпчүлүктү кубандырды]
\n","
[0.018544858321547508, -0.003260228084400296, ...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999999 ... [0.018544858321547508, -0.003260228084400296, ...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"markdown","metadata":{"id":"DGMVMKaTdJFj"},"source":["# The Model understands Tamil\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"JWDr_LoCdJFn","executionInfo":{"status":"ok","timestamp":1620202589857,"user_tz":-300,"elapsed":627966,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"9eb05327-e956-4fb5-a5ee-62dc31e5b372"},"source":["# Tamil for: 'Congress's newest polices made many people poor, sad and depressed '\n","fitted_pipe.predict(\"காங்கிரசின் புதிய கொள்கைகள் பலரை ஏழைகளாகவும், சோகமாகவும், மனச்சோர்வடையச் செய்தன \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.974183
\n","
காங்கிரசின் புதிய கொள்கைகள் பலரை ஏழைகளாகவும், ...
\n","
0
\n","
negative
\n","
[காங்கிரசின் புதிய கொள்கைகள் பலரை ஏழைகளாகவும்,...
\n","
[-0.006572885904461145, -0.0003982966300100088...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.974183 ... [-0.006572885904461145, -0.0003982966300100088...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"Q6C0BmTtdJFp","executionInfo":{"status":"ok","timestamp":1620202590214,"user_tz":-300,"elapsed":628312,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"02b09df5-298a-48f4-af0a-ba7bbf241971"},"source":["# Tamil for: 'Congress's newest polices made many people happy '\n","fitted_pipe.predict(\"காங்கிரசின் புதிய கொள்கைகள் பலரை மகிழ்ச்சியடையச் செய்தன \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
trained_sentiment_confidence
\n","
document
\n","
origin_index
\n","
trained_sentiment
\n","
sentence
\n","
sentence_embedding_labse
\n","
\n"," \n"," \n","
\n","
0
\n","
0.999997
\n","
காங்கிரசின் புதிய கொள்கைகள் பலரை மகிழ்ச்சியடைய...
\n","
0
\n","
positive
\n","
[காங்கிரசின் புதிய கொள்கைகள் பலரை மகிழ்ச்சியடை...
\n","
[0.018834874033927917, -0.01959705911576748, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" trained_sentiment_confidence ... sentence_embedding_labse\n","0 0.999997 ... [0.018834874033927917, -0.01959705911576748, -...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eLex095goHwm","executionInfo":{"status":"ok","timestamp":1620203583379,"user_tz":-300,"elapsed":1621468,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"bcc02088-1c38-4c16-d115-80708a73636c"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SO4uz45MoRgp","executionInfo":{"status":"ok","timestamp":1620204133344,"user_tz":-300,"elapsed":124905,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"2f75322c-c669-4956-e4ed-0388960ada0b"},"source":["stored_model_path = './models/classifier_dl_trained' \n","hdd_pipe = nlu.load(path=stored_model_path)\n","preds = hdd_pipe.predict('I am extremly depressed and down cause of school and just feel like ending my life...')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentiment_confidence
\n","
sentence
\n","
sentence_embedding_from_disk
\n","
document
\n","
sentiment
\n","
text
\n","
\n"," \n"," \n","
\n","
0
\n","
8589934592
\n","
[0.9989802, 0.9989802]
\n","
[I am extremly depressed and down cause of sch...
\n","
[[-0.029913833364844322, -0.03314201161265373,...
\n","
I am extremly depressed and down cause of scho...
\n","
[negative, negative]
\n","
I am extremly depressed and down cause of scho...
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... text\n","0 8589934592 ... I am extremly depressed and down cause of scho...\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"btTWUdsDNhfx","executionInfo":{"status":"ok","timestamp":1620204215401,"user_tz":-300,"elapsed":823,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"108b339c-4dea-4586-de63-8e424e41e9f6"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@2a835f76) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@2a835f76\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@labse'] has settable params:\n","pipe['bert_sentence@labse'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@labse'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@labse'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@labse'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@labse'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n",">>> pipe['sentiment_dl@labse'] has settable params:\n","pipe['sentiment_dl@labse'].setThreshold(0.6) | Info: The minimum threshold for the final result otheriwse it will be neutral | Currently set to : 0.6\n","pipe['sentiment_dl@labse'].setThresholdLabel('neutral') | Info: In case the score is less than threshold, what should be the label. Default is neutral. | Currently set to : neutral\n","pipe['sentiment_dl@labse'].setClasses(['positive', 'negative']) | Info: get the tags used to trained this SentimentDLModel | Currently set to : ['positive', 'negative']\n","pipe['sentiment_dl@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"l_SDhKMysljL"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo.ipynb b/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo.ipynb
deleted file mode 100644
index e44c13dd..00000000
--- a/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_multi_lingual_multi_class_text_classifier_demo.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo.ipynb)\n","\n","\n","\n","\n","# Training a Deep Learning Classifier with NLU \n","## ClassifierDL (Multi-class Text Classification)\n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl-multi-class-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data :\n","\n","\n","\n"," \n","\n","\n","You can achieve these results or even better on this dataset with test data :\n","\n"," \n","\n",""]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620195847576,"user_tz":-300,"elapsed":127308,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"ed595633-2079-469a-f097-9ca9183fc24b"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 06:22:01-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 06:22:02 (1.63 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 68kB/s \n","\u001b[K |████████████████████████████████| 153kB 49.5MB/s \n","\u001b[K |████████████████████████████████| 204kB 15.4MB/s \n","\u001b[K |████████████████████████████████| 204kB 54.7MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download news classification dataset"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620195849627,"user_tz":-300,"elapsed":129273,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"2b74892b-d5da-4393-e5e9-276107260486"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/news_category_test_multi_lingual.csv"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 06:24:07-- http://ckl-it.de/wp-content/uploads/2021/02/news_category_test_multi_lingual.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1592801 (1.5M) [text/csv]\n","Saving to: ‘news_category_test_multi_lingual.csv’\n","\n","news_category_test_ 100%[===================>] 1.52M 1.45MB/s in 1.0s \n","\n","2021-05-05 06:24:08 (1.45 MB/s) - ‘news_category_test_multi_lingual.csv’ saved [1592801/1592801]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620195850528,"user_tz":-300,"elapsed":130140,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"80ace5e3-deb4-4da1-dd2a-34a4ff9b7c98"},"source":["import pandas as pd\n","test_path = '/content/news_category_test_multi_lingual.csv'\n","train_df = pd.read_csv(test_path)\n","from sklearn.model_selection import train_test_split\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
Unnamed: 0
\n","
y
\n","
text
\n","
test_sentences
\n","
\n"," \n"," \n","
\n","
6171
\n","
6171
\n","
Sports
\n","
LeBron James scored 25 points, Jeff McInnis a...
\n","
\n","
\n","
\n","
4540
\n","
4540
\n","
Sports
\n","
year old Miss Peru has been crowned Miss World...
\n","
\n","
\n","
\n","
1776
\n","
1776
\n","
Sports
\n","
The message board in Canada #39;s dressing roo...
\n","
\n","
\n","
\n","
7173
\n","
7173
\n","
Business
\n","
Mumbai: Singapore Technologies Telemedia and T...
\n","
\n","
\n","
\n","
6939
\n","
6939
\n","
Sports
\n","
Syracuse coach Jim Boeheim, while watching tap...
\n","
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
2870
\n","
2870
\n","
Sports
\n","
CLEVELAND Indians righthander Kyle Denney was ...
\n","
\n","
\n","
\n","
5610
\n","
5610
\n","
World
\n","
An Italian prosecutor asked a court on Frida...
\n","
\n","
\n","
\n","
6838
\n","
6838
\n","
Sci/Tech
\n","
One of the hottest holiday gifts this year is ...
\n","
\n","
\n","
\n","
2226
\n","
2226
\n","
World
\n","
President Bush went before a skeptical hall of...
\n","
\n","
\n","
\n","
2559
\n","
2559
\n","
World
\n","
Pakistan says it has dealt a major blow to al-...
\n","
\n","
\n"," \n","
\n","
6080 rows × 4 columns
\n","
"],"text/plain":[" Unnamed: 0 ... test_sentences\n","6171 6171 ... \n","4540 4540 ... \n","1776 1776 ... \n","7173 7173 ... \n","6939 6939 ... \n","... ... ... ...\n","2870 2870 ... \n","5610 5610 ... \n","6838 6838 ... \n","2226 2226 ... \n","2559 2559 ... \n","\n","[6080 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.classifier')\n","\n","By default, the Universal Sentence Encoder Embeddings (USE) are beeing downloaded to provide embeddings for the classifier. You can use any of the 50+ other sentence Emeddings in NLU tough!\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"id":"3ZIPkRkWftBG","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1620197164105,"user_tz":-300,"elapsed":1443608,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"c4004705-d784-4d1e-d608-ab87e99ec308"},"source":["trainable_pipe = nlu.load('xx.embed_sentence.labse train.classifier')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['classifier_dl'].setMaxEpochs(60) \n","trainable_pipe['classifier_dl'].setLr(0.005) \n","fitted_pipe = trainable_pipe.fit(train_df.iloc[:1500])\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df.iloc[:1500],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["labse download started this may take some time.\n","Approximate size to download 1.7 GB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," Business 0.87 0.90 0.89 384\n"," Sci/Tech 0.90 0.91 0.90 351\n"," Sports 0.95 0.97 0.96 376\n"," World 0.96 0.90 0.93 389\n","\n"," accuracy 0.92 1500\n"," macro avg 0.92 0.92 0.92 1500\n","weighted avg 0.92 0.92 0.92 1500\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
trained_classifier
\n","
test_sentences
\n","
text
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
Unnamed: 0
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
Sports
\n","
Sports
\n","
\n","
LeBron James scored 25 points, Jeff McInnis a...
\n","
[LeBron James scored 25 points, Jeff McInnis a...
\n","
6171
\n","
LeBron James scored 25 points, Jeff McInnis ad...
\n","
[-0.028670351952314377, 0.002140851691365242, ...
\n","
6171
\n","
1.000000
\n","
\n","
\n","
1
\n","
Sports
\n","
Sports
\n","
\n","
year old Miss Peru has been crowned Miss World...
\n","
[year old Miss Peru has been crowned Miss Worl...
\n","
4540
\n","
year old Miss Peru has been crowned Miss World...
\n","
[0.024964714422822, -0.005680068861693144, 0.0...
\n","
4540
\n","
0.994070
\n","
\n","
\n","
2
\n","
Sports
\n","
Sports
\n","
\n","
The message board in Canada #39;s dressing roo...
\n","
[The message board in Canada #39;s dressing ro...
\n","
1776
\n","
The message board in Canada #39;s dressing roo...
\n","
[0.036584075540304184, 0.04450026899576187, -0...
\n","
1776
\n","
1.000000
\n","
\n","
\n","
3
\n","
Business
\n","
Business
\n","
\n","
Mumbai: Singapore Technologies Telemedia and T...
\n","
[Mumbai: Singapore Technologies Telemedia and ...
\n","
7173
\n","
Mumbai: Singapore Technologies Telemedia and T...
\n","
[-0.04297986626625061, -0.0017465378623455763,...
\n","
7173
\n","
0.986490
\n","
\n","
\n","
4
\n","
Sports
\n","
Sports
\n","
\n","
Syracuse coach Jim Boeheim, while watching tap...
\n","
[Syracuse coach Jim Boeheim, while watching ta...
\n","
6939
\n","
Syracuse coach Jim Boeheim, while watching tap...
\n","
[-0.020442135632038116, 0.004873048048466444, ...
\n","
6939
\n","
1.000000
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1495
\n","
Sci/Tech
\n","
Business
\n","
\n","
definition TV yet. Competition may force the p...
\n","
[definition TV yet., Competition may force the...
\n","
6539
\n","
definition TV yet. Competition may force the p...
\n","
[-0.019764604046940804, 0.004894972778856754, ...
\n","
6539
\n","
0.994347
\n","
\n","
\n","
1496
\n","
Sci/Tech
\n","
Sci/Tech
\n","
\n","
Webshots users offer their photos of Bill Gate...
\n","
[Webshots users offer their photos of Bill Gat...
\n","
4257
\n","
Webshots users offer their photos of Bill Gate...
\n","
[0.029549693688750267, 0.0014347410760819912, ...
\n","
4257
\n","
0.998830
\n","
\n","
\n","
1497
\n","
Sci/Tech
\n","
Sci/Tech
\n","
\n","
The two companies say they will jointly develo...
\n","
[The two companies say they will jointly devel...
\n","
2910
\n","
The two companies say they will jointly develo...
\n","
[-0.035613108426332474, -0.029767965897917747,...
\n","
2910
\n","
1.000000
\n","
\n","
\n","
1498
\n","
World
\n","
World
\n","
\n","
Peruvian authorities on Monday launched an o...
\n","
[Peruvian authorities on Monday launched an of...
\n","
6626
\n","
Peruvian authorities on Monday launched an off...
\n","
[0.030554521828889847, 0.014035936444997787, 0...
\n","
6626
\n","
1.000000
\n","
\n","
\n","
1499
\n","
World
\n","
World
\n","
\n","
Britain's Tony Blair flew to Khartoum on Wedn...
\n","
[Britain's Tony Blair flew to Khartoum on Wedn...
\n","
3233
\n","
Britain's Tony Blair flew to Khartoum on Wedne...
\n","
[0.011163398623466492, -0.03577205538749695, 0...
\n","
3233
\n","
1.000000
\n","
\n"," \n","
\n","
1500 rows × 10 columns
\n","
"],"text/plain":[" y ... trained_classifier_confidence_confidence\n","0 Sports ... 1.000000\n","1 Sports ... 0.994070\n","2 Sports ... 1.000000\n","3 Business ... 0.986490\n","4 Sports ... 1.000000\n","... ... ... ...\n","1495 Sci/Tech ... 0.994347\n","1496 Sci/Tech ... 0.998830\n","1497 Sci/Tech ... 1.000000\n","1498 World ... 1.000000\n","1499 World ... 1.000000\n","\n","[1500 rows x 10 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 3.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"id":"Fxx4yNkNVGFl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620197483940,"user_tz":-300,"elapsed":1763436,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"60b586d0-51db-434a-a84f-195443d6a9a5"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," Business 0.80 0.79 0.80 381\n"," Sci/Tech 0.80 0.83 0.81 384\n"," Sports 0.89 0.95 0.92 375\n"," World 0.89 0.81 0.85 380\n","\n"," accuracy 0.84 1520\n"," macro avg 0.85 0.85 0.84 1520\n","weighted avg 0.85 0.84 0.84 1520\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"BD5OKO4Umc5U"},"source":["# 4. Test Model with 20 languages!"]},{"cell_type":"code","metadata":{"id":"OQ72hP9unML7","colab":{"base_uri":"https://localhost:8080/","height":776},"executionInfo":{"status":"ok","timestamp":1620197513218,"user_tz":-300,"elapsed":1792703,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"2f12e36a-dd91-4a01-d85c-bee5004193dc"},"source":["train_df = pd.read_csv(\"news_category_test_multi_lingual.csv\")\n","preds = fitted_pipe.predict(train_df[[\"test_sentences\",\"y\"]].iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," Business 0.62 0.83 0.71 12\n"," Sci/Tech 0.91 0.78 0.84 37\n"," Sports 0.71 0.95 0.82 21\n"," World 0.88 0.70 0.78 30\n","\n"," accuracy 0.80 100\n"," macro avg 0.78 0.82 0.79 100\n","weighted avg 0.82 0.80 0.80 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
y
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
text
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[టర్నర్ నెవాల్ వద్ద కార్మికులకు ప్రాతినిధ్యం వ...
\n","
0
\n","
Business
\n","
టర్నర్ నెవాల్ వద్ద కార్మికులకు ప్రాతినిధ్యం వహ...
\n","
[-0.05777166411280632, -0.011031205765902996, ...
\n","
Business
\n","
టర్నర్ నెవాల్ వద్ద కార్మికులకు ప్రాతినిధ్యం వహ...
\n","
0.995227
\n","
\n","
\n","
1
\n","
[Торонто, Канада # 36; 10 миллион Ансари X пре...
\n","
1
\n","
Sci/Tech
\n","
Торонто, Канада # 36; 10 миллион Ансари X прем...
\n","
[-0.03927089646458626, -0.059984903782606125, ...
\n","
Sports
\n","
Торонто, Канада # 36; 10 миллион Ансари X прем...
\n","
0.965024
\n","
\n","
\n","
2
\n","
[Une société fondée par un chercheur en chimie...
\n","
2
\n","
Sci/Tech
\n","
Une société fondée par un chercheur en chimie ...
\n","
[-0.04712514951825142, -0.025509435683488846, ...
\n","
Sci/Tech
\n","
Une société fondée par un chercheur en chimie ...
\n","
0.999993
\n","
\n","
\n","
3
\n","
[সবেমাত্র ভোর যখন মাইক ফিৎসপ্যাট্রিক রঙিন মানচ...
\n","
3
\n","
Sci/Tech
\n","
সবেমাত্র ভোর যখন মাইক ফিৎসপ্যাট্রিক রঙিন মানচি...
\n","
[-0.046090301126241684, -0.05127095431089401, ...
\n","
Sci/Tech
\n","
সবেমাত্র ভোর যখন মাইক ফিৎসপ্যাট্রিক রঙিন মানচি...
\n","
0.999484
\n","
\n","
\n","
4
\n","
[Көньяк Калифорниянең томанга каршы көрәш аген...
\n","
4
\n","
Sci/Tech
\n","
Көньяк Калифорниянең томанга каршы көрәш агент...
\n","
[-0.02939724549651146, -0.04042039066553116, -...
\n","
Sci/Tech
\n","
Көньяк Калифорниянең томанга каршы көрәш агент...
\n","
0.682823
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
[ఫుట్బాల్ అసోసియేషన్ ప్రతిష్టను దెబ్బతీసిన కు...
\n","
95
\n","
Sports
\n","
ఫుట్బాల్ అసోసియేషన్ ప్రతిష్టను దెబ్బతీసిన కుం...
\n","
[0.025159751996397972, -0.026320766657590866, ...
\n","
Sports
\n","
ఫుట్బాల్ అసోసియేషన్ ప్రతిష్టను దెబ్బతీసిన కుం...
\n","
1.000000
\n","
\n","
\n","
96
\n","
[Hücumçu Emile Heskey, Çərşənbə # 39-un Çərşən...
\n","
96
\n","
Sports
\n","
Hücumçu Emile Heskey, Çərşənbə # 39-un Çərşənb...
\n","
[0.04458563029766083, 0.03187406063079834, -0....
\n","
Sports
\n","
Hücumçu Emile Heskey, Çərşənbə # 39-un Çərşənb...
\n","
1.000000
\n","
\n","
\n","
97
\n","
[Staples Inc. & lt; A HREF = \"http://www., inv...
\n","
97
\n","
Business
\n","
Staples Inc. & lt; A HREF = \"http://www.invest...
\n","
[-0.016342531889677048, -0.004877157974988222,...
\n","
Business
\n","
Staples Inc. & lt; A HREF = \"http://www.invest...
\n","
1.000000
\n","
\n","
\n","
98
\n","
[គណៈប្រតិភូនៃប្រទេសអ៊ីរ៉ាក់ត្រូវបានពន្យារពេលដោ...
\n","
98
\n","
World
\n","
គណៈប្រតិភូនៃប្រទេសអ៊ីរ៉ាក់ត្រូវបានពន្យារពេលដោយ...
\n","
[0.030007336288690567, -0.002715253969654441, ...
\n","
World
\n","
គណៈប្រតិភូនៃប្រទេសអ៊ីរ៉ាក់ត្រូវបានពន្យារពេលដោយ...
\n","
0.999755
\n","
\n","
\n","
99
\n","
[امریکی صارفین کی قیمتوں میں جولائی میں پہلی ب...
\n","
99
\n","
Business
\n","
امریکی صارفین کی قیمتوں میں جولائی میں پہلی با...
\n","
[-0.04715615138411522, -0.04999866336584091, -...
\n","
Business
\n","
امریکی صارفین کی قیمتوں میں جولائی میں پہلی با...
\n","
0.999998
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [టర్నర్ నెవాల్ వద్ద కార్మికులకు ప్రాతినిధ్యం వ... ... 0.995227\n","1 [Торонто, Канада # 36; 10 миллион Ансари X пре... ... 0.965024\n","2 [Une société fondée par un chercheur en chimie... ... 0.999993\n","3 [সবেমাত্র ভোর যখন মাইক ফিৎসপ্যাট্রিক রঙিন মানচ... ... 0.999484\n","4 [Көньяк Калифорниянең томанга каршы көрәш аген... ... 0.682823\n",".. ... ... ...\n","95 [ఫుట్బాల్ అసోసియేషన్ ప్రతిష్టను దెబ్బతీసిన కు... ... 1.000000\n","96 [Hücumçu Emile Heskey, Çərşənbə # 39-un Çərşən... ... 1.000000\n","97 [Staples Inc. & lt; A HREF = \"http://www., inv... ... 1.000000\n","98 [គណៈប្រតិភូនៃប្រទេសអ៊ីរ៉ាក់ត្រូវបានពន្យារពេលដោ... ... 0.999755\n","99 [امریکی صارفین کی قیمتوں میں جولائی میں پہلی ب... ... 0.999998\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"RjtuNUcvuJTT"},"source":["# The Model understands Englsih\n",""]},{"cell_type":"code","metadata":{"id":"o0vu7PaWkcI7","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197514386,"user_tz":-300,"elapsed":1793858,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"71269801-f7f5-475e-84c7-09cd471c6b18"},"source":["fitted_pipe.predict(\"There have been a great increase in businesses over the last decade \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[There have been a great increase in businesse...
\n","
0
\n","
There have been a great increase in businesses...
\n","
[0.012169234454631805, -0.002660348080098629, ...
\n","
Business
\n","
0.999809
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [There have been a great increase in businesse... ... 0.999809\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"1ykjRQhCtQ4w","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197515551,"user_tz":-300,"elapsed":1795019,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"9b6314ff-1ff8-4912-8e0c-6d257bde5633"},"source":["fitted_pipe.predict(\"Science has advanced rapidly over the last century \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Science has advanced rapidly over the last ce...
\n","
0
\n","
Science has advanced rapidly over the last cen...
\n","
[0.022739632055163383, -0.034671563655138016, ...
\n","
Sci/Tech
\n","
0.999993
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Science has advanced rapidly over the last ce... ... 0.999993\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"vohym-XbuNHn"},"source":["# The Model understands German\n",""]},{"cell_type":"code","metadata":{"id":"dzaaZrI4tVWc","colab":{"base_uri":"https://localhost:8080/","height":97},"executionInfo":{"status":"ok","timestamp":1620197516557,"user_tz":-300,"elapsed":1795978,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"d848eab5-e763-46c6-8a8a-584960fca3f2"},"source":["# German for: 'Businesses are the best way of making profit'\n","fitted_pipe.predict(\"Unternehmen sind der beste Weg, um Gewinn zu erzielen\")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Unternehmen sind der beste Weg, um Gewinn zu ...
\n","
0
\n","
Unternehmen sind der beste Weg, um Gewinn zu e...
\n","
[-0.048822492361068726, -0.007162907160818577,...
\n","
Business
\n","
0.999662
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Unternehmen sind der beste Weg, um Gewinn zu ... ... 0.999662\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"BbhgTSBGtTtJ","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197517522,"user_tz":-300,"elapsed":1796939,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"d144c6e7-2949-4f82-97c1-713c9d4986cc"},"source":["# German for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"Die Wissenschaft hat im letzten Jahrhundert rasante Fortschritte gemacht \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Die Wissenschaft hat im letzten Jahrhundert r...
\n","
0
\n","
Die Wissenschaft hat im letzten Jahrhundert ra...
\n","
[0.035708051174879074, -0.04514779895544052, -...
\n","
Sci/Tech
\n","
0.999872
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Die Wissenschaft hat im letzten Jahrhundert r... ... 0.999872\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"a1JbtmWquQwj"},"source":["# The Model understands Chinese\n",""]},{"cell_type":"code","metadata":{"id":"kYSYqtoRtc-P","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197518854,"user_tz":-300,"elapsed":1798198,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"0ad35189-387e-4a9b-9e3a-c10632d3e491"},"source":["# Chinese for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"在过去的十年中,业务有了很大的增长 \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[在过去的十年中,业务有了很大的增长]
\n","
0
\n","
在过去的十年中,业务有了很大的增长
\n","
[0.0071435291320085526, -0.0031970362178981304...
\n","
Business
\n","
0.998403
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [在过去的十年中,业务有了很大的增长] ... 0.998403\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"06v9SD-QtlBU","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197519426,"user_tz":-300,"elapsed":1798767,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"ea98f01b-6d68-47ac-94fd-33bdc3611f7e"},"source":["# Chinese for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"在上个世纪,科学发展迅速 \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[在上个世纪,科学发展迅速]
\n","
0
\n","
在上个世纪,科学发展迅速
\n","
[0.018992088735103607, -0.05363348498940468, -...
\n","
Sci/Tech
\n","
0.999965
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [在上个世纪,科学发展迅速] ... 0.999965\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"9h7CvN4uu9Pb"},"source":["# Model understands Afrikaans\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"VMPhbgw9twtf","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197520330,"user_tz":-300,"elapsed":1799668,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"2aee9017-96a9-4343-fa95-a9dc396baa93"},"source":["# Afrikaans for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"Daar het die afgelope dekade 'n groot toename in besighede plaasgevind \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Daar het die afgelope dekade 'n groot toename...
\n","
0
\n","
Daar het die afgelope dekade 'n groot toename ...
\n","
[0.028091425076127052, -0.01651562750339508, -...
\n","
Business
\n","
0.999667
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Daar het die afgelope dekade 'n groot toename... ... 0.999667\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"zWgNTIdkumhX","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197521279,"user_tz":-300,"elapsed":1800610,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"52f6d92e-ddfc-40ec-a98b-a1d8bd25f5e2"},"source":["# Afrikaans for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"Die wetenskap het die afgelope eeu vinnig gevorder \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Die wetenskap het die afgelope eeu vinnig gev...
\n","
0
\n","
Die wetenskap het die afgelope eeu vinnig gevo...
\n","
[0.026470882818102837, -0.04339250922203064, -...
\n","
Sci/Tech
\n","
0.999957
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Die wetenskap het die afgelope eeu vinnig gev... ... 0.999957\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"rSEPkC-Bwnpg"},"source":["# The model understands Vietnamese\n",""]},{"cell_type":"code","metadata":{"id":"7ksJosuTOYpE","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197522133,"user_tz":-300,"elapsed":1801460,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"3d7881e4-3a07-4d98-90d2-bcffc2813cd2"},"source":["# Vietnamese for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"Đã có sự gia tăng đáng kể trong các doanh nghiệp trong thập kỷ qua \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Đã có sự gia tăng đáng kể trong các doanh ngh...
\n","
0
\n","
Đã có sự gia tăng đáng kể trong các doanh nghi...
\n","
[0.0025938497856259346, -0.03647598996758461, ...
\n","
Business
\n","
0.990353
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Đã có sự gia tăng đáng kể trong các doanh ngh... ... 0.990353\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"VfG3UaCTEZB_","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197523269,"user_tz":-300,"elapsed":1802591,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"38e3a860-2b5f-4892-9d1f-7b4fc4db775f"},"source":["# Vietnamese for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"Khoa học đã phát triển nhanh chóng trong thế kỷ qua \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Khoa học đã phát triển nhanh chóng trong thế ...
\n","
0
\n","
Khoa học đã phát triển nhanh chóng trong thế k...
\n","
[0.006926487199962139, -0.05958796292543411, -...
\n","
Sci/Tech
\n","
0.999156
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Khoa học đã phát triển nhanh chóng trong thế ... ... 0.999156\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"IlkmAaMoxTuy"},"source":["# The model understands Japanese\n","\n"]},{"cell_type":"code","metadata":{"id":"1IfJu3q8wwUt","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197523837,"user_tz":-300,"elapsed":1803156,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"2e3d3d37-f53a-4ea4-881e-b4006a7576a2"},"source":["# Japanese for: 'Businesses are the best way of making profit'\n","fitted_pipe.predict(\"ビジネスは利益を上げるための最良の方法です\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[ビジネスは利益を上げるための最良の方法です]
\n","
0
\n","
ビジネスは利益を上げるための最良の方法です
\n","
[-0.029112379997968674, -0.022607864812016487,...
\n","
Business
\n","
0.999007
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [ビジネスは利益を上げるための最良の方法です] ... 0.999007\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"-RjXWbFIPvIs","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197524793,"user_tz":-300,"elapsed":1804105,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"f53d6d52-e8af-4198-87aa-552b04b19c5d"},"source":["# Japanese for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"科学は前世紀にわたって急速に進歩しました \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[科学は前世紀にわたって急速に進歩しました]
\n","
0
\n","
科学は前世紀にわたって急速に進歩しました
\n","
[0.019697299227118492, -0.043922919780015945, ...
\n","
Sci/Tech
\n","
0.999981
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [科学は前世紀にわたって急速に進歩しました] ... 0.999981\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"GITfT7FK0CGv"},"source":["# The model understands Zulu\n",""]},{"cell_type":"code","metadata":{"id":"ifRhs6e7OcR3","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197525357,"user_tz":-300,"elapsed":1804645,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"638310b0-2976-4ec5-f4c2-1331569e3d4e"},"source":["# Zulu for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"Kube nokwanda okukhulu emabhizinisini kule minyaka eyishumi edlule \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Kube nokwanda okukhulu emabhizinisini kule mi...
\n","
0
\n","
Kube nokwanda okukhulu emabhizinisini kule min...
\n","
[0.011455180123448372, -0.01975909061729908, -...
\n","
Business
\n","
0.998063
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Kube nokwanda okukhulu emabhizinisini kule mi... ... 0.998063\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"6uelDwq4xdWv","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197526413,"user_tz":-300,"elapsed":1805695,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"57ec65a4-41e7-4c6c-c126-733915f52c38"},"source":["# Zulu for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"Isayensi ithuthuke ngokushesha ngekhulu leminyaka elidlule \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Isayensi ithuthuke ngokushesha ngekhulu lemin...
\n","
0
\n","
Isayensi ithuthuke ngokushesha ngekhulu leminy...
\n","
[0.0330704040825367, -0.044426657259464264, -0...
\n","
Sci/Tech
\n","
0.999993
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Isayensi ithuthuke ngokushesha ngekhulu lemin... ... 0.999993\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"VGVvzl_30a0T"},"source":["# The Model understands Turkish\n",""]},{"cell_type":"code","metadata":{"id":"DRNnuEeQz2pd","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197526996,"user_tz":-300,"elapsed":1806273,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"5f77024b-daac-404b-c8e6-86ff343a162b"},"source":["# Turkish for: 'Businesses are the best way of making profit'\n","fitted_pipe.predict(\"İşletmeler kar elde etmenin en iyi yoludur \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[İşletmeler kar elde etmenin en iyi yoludur]
\n","
0
\n","
İşletmeler kar elde etmenin en iyi yoludur
\n","
[-0.02334517240524292, 0.000546906900126487, -...
\n","
World
\n","
0.778869
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [İşletmeler kar elde etmenin en iyi yoludur] ... 0.778869\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"aOSsiK6J0jWs","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197528015,"user_tz":-300,"elapsed":1807289,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"351e95ec-5b7b-4d02-d4c4-f69612e5e171"},"source":["# Turkish for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"Bilim, geçen yüzyılda hızla ilerledi \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Bilim, geçen yüzyılda hızla ilerledi]
\n","
0
\n","
Bilim, geçen yüzyılda hızla ilerledi
\n","
[0.01670285128057003, -0.050043195486068726, -...
\n","
Sci/Tech
\n","
0.999952
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Bilim, geçen yüzyılda hızla ilerledi] ... 0.999952\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"803qL2gt0vlb"},"source":["# The Model understands Hebrew\n",""]},{"cell_type":"code","metadata":{"id":"XQ5VCtxw0pc0","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197528577,"user_tz":-300,"elapsed":1807847,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"c4ebb377-dc49-47d3-cf7a-95cbf1f51bdb"},"source":["# Hebrew for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"חלה עלייה גדולה בעסקים בעשור האחרון \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[חלה עלייה גדולה בעסקים בעשור האחרון]
\n","
0
\n","
חלה עלייה גדולה בעסקים בעשור האחרון
\n","
[0.03062829189002514, -0.02228061482310295, -0...
\n","
Business
\n","
0.99995
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [חלה עלייה גדולה בעסקים בעשור האחרון] ... 0.99995\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"9w2ZHfns05A4","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197529362,"user_tz":-300,"elapsed":1808629,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"44abcf8e-e408-4f40-9406-ede613c24ed6"},"source":["# Hebrew for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"המדע התקדם במהירות במהלך המאה האחרונה \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[המדע התקדם במהירות במהלך המאה האחרונה]
\n","
0
\n","
המדע התקדם במהירות במהלך המאה האחרונה
\n","
[-0.0030932666268199682, -0.05540183186531067,...
\n","
Sci/Tech
\n","
0.999996
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [המדע התקדם במהירות במהלך המאה האחרונה] ... 0.999996\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"SDlpd33H1HIX"},"source":["# The Model understands Telugu\n","\n"]},{"cell_type":"code","metadata":{"id":"Kc5n1bzv1BJT","colab":{"base_uri":"https://localhost:8080/","height":97},"executionInfo":{"status":"ok","timestamp":1620197530739,"user_tz":-300,"elapsed":1810001,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"156cd14f-54ce-4bca-edb7-63b6ef6bb781"},"source":["# Telugu for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"గత దశాబ్దంలో వ్యాపారాలలో గొప్ప పెరుగుదల ఉంది \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[గత దశాబ్దంలో వ్యాపారాలలో గొప్ప పెరుగుదల ఉంది]
\n","
0
\n","
గత దశాబ్దంలో వ్యాపారాలలో గొప్ప పెరుగుదల ఉంది
\n","
[0.005267495755106211, -0.022807631641626358, ...
\n","
Business
\n","
0.999976
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [గత దశాబ్దంలో వ్యాపారాలలో గొప్ప పెరుగుదల ఉంది] ... 0.999976\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"-l-u6vrz1Obe","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197530740,"user_tz":-300,"elapsed":1809994,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"2b33d38c-62df-42d2-b8ce-a1fb09ee0d16"},"source":["# Telugu for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"గత శతాబ్దంలో సైన్స్ వేగంగా అభివృద్ధి చెందింది \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[గత శతాబ్దంలో సైన్స్ వేగంగా అభివృద్ధి చెందింది]
\n","
0
\n","
గత శతాబ్దంలో సైన్స్ వేగంగా అభివృద్ధి చెందింది
\n","
[-0.015292854979634285, -0.03326154127717018, ...
\n","
Sci/Tech
\n","
0.999914
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [గత శతాబ్దంలో సైన్స్ వేగంగా అభివృద్ధి చెందింది] ... 0.999914\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"nziBUe8t1Zwn"},"source":["# Model understands Russian\n","\n"]},{"cell_type":"code","metadata":{"id":"Ckyjl3YQ1VFn","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197532046,"user_tz":-300,"elapsed":1811291,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"490fc79d-f508-446b-ad11-51e47e604fb4"},"source":["# Russian for: 'Businesses are the best way of making profit'\n","fitted_pipe.predict(\"Бизнес - лучший способ получения прибыли\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Бизнес - лучший способ получения прибыли]
\n","
0
\n","
Бизнес - лучший способ получения прибыли
\n","
[-0.016973992809653282, -0.024397604167461395,...
\n","
Business
\n","
0.999864
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Бизнес - лучший способ получения прибыли] ... 0.999864\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"GIdWkfGv1gFz","colab":{"base_uri":"https://localhost:8080/","height":97},"executionInfo":{"status":"ok","timestamp":1620197532616,"user_tz":-300,"elapsed":1811857,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"c028edae-42b5-40d2-dd69-099b202016ec"},"source":["# Russian for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"Наука стремительно развивалась за последнее столетие \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Наука стремительно развивалась за последнее с...
\n","
0
\n","
Наука стремительно развивалась за последнее ст...
\n","
[0.013989578001201153, -0.0456346794962883, -0...
\n","
Sci/Tech
\n","
0.999994
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Наука стремительно развивалась за последнее с... ... 0.999994\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"8R1j9mwz2Cm4"},"source":["# Model understands Urdu\n",""]},{"cell_type":"code","metadata":{"id":"j4zwvRV11pcG","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197533481,"user_tz":-300,"elapsed":1812710,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"335470f5-4a73-4391-ac4d-32668d723ea0"},"source":["# Urdu for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"پچھلے ایک دہائی کے دوران کاروباروں میں زبردست اضافہ ہوا ہے \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[پچھلے ایک دہائی کے دوران کاروباروں میں زبردست...
\n","
0
\n","
پچھلے ایک دہائی کے دوران کاروباروں میں زبردست ...
\n","
[-0.004565518815070391, -0.008193258196115494,...
\n","
Business
\n","
0.999983
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [پچھلے ایک دہائی کے دوران کاروباروں میں زبردست... ... 0.999983\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"code","metadata":{"id":"SxzTuK4b2UKV","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197534080,"user_tz":-300,"elapsed":1813306,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"6ecb7a1c-4c42-415f-a0f0-63e7fece5b8f"},"source":["# Urdu for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"سائنس گذشتہ صدی کے دوران تیزی سے ترقی کرچکی ہے \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[سائنس گذشتہ صدی کے دوران تیزی سے ترقی کرچکی ہے]
\n","
0
\n","
سائنس گذشتہ صدی کے دوران تیزی سے ترقی کرچکی ہے
\n","
[-0.013339939527213573, -0.026210565119981766,...
\n","
Sci/Tech
\n","
0.984679
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [سائنس گذشتہ صدی کے دوران تیزی سے ترقی کرچکی ہے] ... 0.984679\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"RoNg-C3k1qcX"},"source":["# Model understands Hindi\n","\n"]},{"cell_type":"code","metadata":{"id":"QZ9RT5Wv1r1n","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197534954,"user_tz":-300,"elapsed":1814173,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"e2b82257-d3dc-4c25-8360-299f76a2c927"},"source":["# hindi for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"पिछले दशक में व्यवसायों में बहुत वृद्धि हुई है \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[पिछले दशक में व्यवसायों में बहुत वृद्धि हुई है]
\n","
0
\n","
पिछले दशक में व्यवसायों में बहुत वृद्धि हुई है
\n","
[-0.003939628601074219, -0.029372189193964005,...
\n","
Business
\n","
0.999962
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [पिछले दशक में व्यवसायों में बहुत वृद्धि हुई है] ... 0.999962\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"quM-IL2i12-B","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197536135,"user_tz":-300,"elapsed":1815347,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"4471f6ea-7286-4c7c-b9ce-ba545039c9d4"},"source":["\t\t\n","# hindi for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"विज्ञान पिछली सदी में तेजी से आगे बढ़ा है \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[विज्ञान पिछली सदी में तेजी से आगे बढ़ा है]
\n","
0
\n","
विज्ञान पिछली सदी में तेजी से आगे बढ़ा है
\n","
[-0.0006327558076009154, -0.04775548726320267,...
\n","
Sci/Tech
\n","
0.999993
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [विज्ञान पिछली सदी में तेजी से आगे बढ़ा है] ... 0.999993\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"R4ByHOZn35Lc"},"source":["# The model understands Tartar\n",""]},{"cell_type":"code","metadata":{"id":"2JrzusSQ18F5","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197536699,"user_tz":-300,"elapsed":1815907,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"03b0d1d4-c29d-4015-d0c2-bed8d6ecd862"},"source":["# Tartar for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"Соңгы ун елда бизнеста зур үсеш булды \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Соңгы ун елда бизнеста зур үсеш булды]
\n","
0
\n","
Соңгы ун елда бизнеста зур үсеш булды
\n","
[0.023730726912617683, -0.02879853919148445, -...
\n","
Business
\n","
0.934704
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Соңгы ун елда бизнеста зур үсеш булды] ... 0.934704\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"id":"J06Xm_Ln4AYu","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197537548,"user_tz":-300,"elapsed":1816753,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"ec4be7d1-e6b2-40a4-8b88-f9d8021277cb"},"source":["# Tartar for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"Соңгы гасырда фән тиз үсә \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Соңгы гасырда фән тиз үсә]
\n","
0
\n","
Соңгы гасырда фән тиз үсә
\n","
[0.021184425801038742, -0.046850692480802536, ...
\n","
Sci/Tech
\n","
0.999991
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Соңгы гасырда фән тиз үсә] ... 0.999991\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"HKj5yWwwMplH"},"source":["# The Model understands French\n",""]},{"cell_type":"code","metadata":{"id":"CUHcJZfJMplL","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197538136,"user_tz":-300,"elapsed":1817338,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"6247ab60-e61c-490c-faaf-a44ec7127f3e"},"source":["# French for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"Il y a eu une forte augmentation des entreprises au cours de la dernière décennie \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Il y a eu une forte augmentation des entrepri...
\n","
0
\n","
Il y a eu une forte augmentation des entrepris...
\n","
[0.007794354110956192, -0.012789416126906872, ...
\n","
Business
\n","
0.999989
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Il y a eu une forte augmentation des entrepri... ... 0.999989\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"id":"57NY2XoTMplM","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197539065,"user_tz":-300,"elapsed":1818260,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"6f131b62-8a18-4757-fe98-e4be5df19a3e"},"source":["# French for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"La science a progressé rapidement au cours du siècle dernier \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[La science a progressé rapidement au cours du...
\n","
0
\n","
La science a progressé rapidement au cours du ...
\n","
[0.012393303215503693, -0.04608025774359703, -...
\n","
Sci/Tech
\n","
0.999996
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [La science a progressé rapidement au cours du... ... 0.999996\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"jD2TBgT0Nq6F"},"source":["# The Model understands Thai\n",""]},{"cell_type":"code","metadata":{"id":"gBp11S5GNq6S","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197539659,"user_tz":-300,"elapsed":1818850,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"79cee46e-1920-4c15-c14c-cc175f24f8e4"},"source":["\t\t\n","# Thai for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"มีธุรกิจเพิ่มขึ้นอย่างมากในช่วงทศวรรษที่ผ่านมา \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[มีธุรกิจเพิ่มขึ้นอย่างมากในช่วงทศวรรษที่ผ่านมา]
\n","
0
\n","
มีธุรกิจเพิ่มขึ้นอย่างมากในช่วงทศวรรษที่ผ่านมา
\n","
[0.008413499221205711, -0.024852054193615913, ...
\n","
Business
\n","
0.991779
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [มีธุรกิจเพิ่มขึ้นอย่างมากในช่วงทศวรรษที่ผ่านมา] ... 0.991779\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"R6nKI7C3QKa3","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197540533,"user_tz":-300,"elapsed":1819712,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"21a225d5-c5a4-46ea-d8f7-17e7c391d173"},"source":["# Thai for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"วิทยาศาสตร์ก้าวหน้าอย่างรวดเร็วในช่วงศตวรรษที่ผ่านมา \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[วิทยาศาสตร์ก้าวหน้าอย่างรวดเร็วในช่วงศตวรรษที...
\n","
0
\n","
วิทยาศาสตร์ก้าวหน้าอย่างรวดเร็วในช่วงศตวรรษที่...
\n","
[0.007343569304794073, -0.04965794086456299, -...
\n","
Sci/Tech
\n","
0.999949
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [วิทยาศาสตร์ก้าวหน้าอย่างรวดเร็วในช่วงศตวรรษที... ... 0.999949\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"mLItI4KZOElB"},"source":["# The Model understands Khmer\n",""]},{"cell_type":"code","metadata":{"id":"SWbqMgAwOElC","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197541542,"user_tz":-300,"elapsed":1820712,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"a8376e8f-1d80-43ac-db55-4afe447cdbf5"},"source":["# Khmer for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"មានការរីកចម្រើនយ៉ាងខ្លាំងនៅក្នុងអាជីវកម្មក្នុងរយៈពេលមួយទសវត្សចុងក្រោយនេះ \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[មានការរីកចម្រើនយ៉ាងខ្លាំងនៅក្នុងអាជីវកម្មក្នុ...
\n","
0
\n","
មានការរីកចម្រើនយ៉ាងខ្លាំងនៅក្នុងអាជីវកម្មក្នុង...
\n","
[0.025004420429468155, -0.037305913865566254, ...
\n","
Business
\n","
0.967588
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [មានការរីកចម្រើនយ៉ាងខ្លាំងនៅក្នុងអាជីវកម្មក្នុ... ... 0.967588\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"id":"beoCtm4xQf2P","colab":{"base_uri":"https://localhost:8080/","height":97},"executionInfo":{"status":"ok","timestamp":1620197541894,"user_tz":-300,"elapsed":1821049,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"5e03f1bb-e2e2-4df8-d835-7b131a931aa2"},"source":["\t\t\n","# Khmer for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"វិទ្យាសាស្ត្របានជឿនលឿនយ៉ាងលឿនក្នុងរយៈពេលមួយសតវត្សចុងក្រោយនេះ \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[វិទ្យាសាស្ត្របានជឿនលឿនយ៉ាងលឿនក្នុងរយៈពេលមួយសត...
\n","
0
\n","
វិទ្យាសាស្ត្របានជឿនលឿនយ៉ាងលឿនក្នុងរយៈពេលមួយសតវ...
\n","
[0.00846723560243845, -0.05188147351145744, -0...
\n","
Sci/Tech
\n","
0.999939
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [វិទ្យាសាស្ត្របានជឿនលឿនយ៉ាងលឿនក្នុងរយៈពេលមួយសត... ... 0.999939\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"markdown","metadata":{"id":"lvE-LbNiPoBT"},"source":["# The Model understands Yiddish\n",""]},{"cell_type":"code","metadata":{"id":"sZlmLhajPoBb","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197542690,"user_tz":-300,"elapsed":1821827,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"d781e4d8-ae66-4e51-b9f8-bd28ee03ec7c"},"source":["\n","# Yiddish for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"די לעצטע יאָרצענדלינג איז געווען אַ גרויס פאַרגרעסערן אין געשעפטן \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[די לעצטע יאָרצענדלינג איז געווען אַ גרויס פאַ...
\n","
0
\n","
די לעצטע יאָרצענדלינג איז געווען אַ גרויס פאַר...
\n","
[0.0017608355265110731, -0.03173188120126724, ...
\n","
Business
\n","
0.999986
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [די לעצטע יאָרצענדלינג איז געווען אַ גרויס פאַ... ... 0.999986\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"id":"5h-pha_nPoBc","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197543635,"user_tz":-300,"elapsed":1822744,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"44adf300-29ba-49e9-d279-a402c16622ef"},"source":["# Yiddish for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"וויסנשאַפֿט איז ראַפּאַדלי אַוואַנסירטע איבער די לעצטע יאָרהונדערט \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[וויסנשאַפֿט איז ראַפּאַדלי אַוואַנסירטע איבער...
\n","
0
\n","
וויסנשאַפֿט איז ראַפּאַדלי אַוואַנסירטע איבער ...
\n","
[-0.020669342949986458, -0.055476754903793335,...
\n","
Sci/Tech
\n","
0.99999
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [וויסנשאַפֿט איז ראַפּאַדלי אַוואַנסירטע איבער... ... 0.99999\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"XSz4WzScaAHj"},"source":["# The Model understands Kygrgyz\n",""]},{"cell_type":"code","metadata":{"id":"DXz6fhJSaAHu","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197544172,"user_tz":-300,"elapsed":1823265,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"e04b33fd-e642-4af1-ba93-a4720e47f744"},"source":["# Kygrgyz for: 'Businesses are the best way of making profit'\n","fitted_pipe.predict(\"Бизнес - бул киреше табуунун эң мыкты жолу \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Бизнес - бул киреше табуунун эң мыкты жолу]
\n","
0
\n","
Бизнес - бул киреше табуунун эң мыкты жолу
\n","
[-0.02840232476592064, -0.02759084478020668, -...
\n","
Business
\n","
0.99997
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Бизнес - бул киреше табуунун эң мыкты жолу] ... 0.99997\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"id":"lh_ZSHlPaAHv","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620197544517,"user_tz":-300,"elapsed":1823598,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"bdd76b58-7246-4de0-f7b6-bdeb2dad47bd"},"source":["# Kygrgyz for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"Илим акыркы кылымда тездик менен өнүккөн \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[Илим акыркы кылымда тездик менен өнүккөн]
\n","
0
\n","
Илим акыркы кылымда тездик менен өнүккөн
\n","
[0.025420306250452995, -0.044107209891080856, ...
\n","
Sci/Tech
\n","
0.999989
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [Илим акыркы кылымда тездик менен өнүккөн] ... 0.999989\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"DGMVMKaTdJFj"},"source":["# The Model understands Tamil\n",""]},{"cell_type":"code","metadata":{"id":"JWDr_LoCdJFn","colab":{"base_uri":"https://localhost:8080/","height":97},"executionInfo":{"status":"ok","timestamp":1620197545323,"user_tz":-300,"elapsed":1824394,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"daf468d4-1e74-427a-918c-d526a0570e1e"},"source":["# Tamil for: 'There have been a great increase in businesses over the last decade'\n","fitted_pipe.predict(\"கடந்த தசாப்தத்தில் வணிகங்களில் பெரும் அதிகரிப்பு ஏற்பட்டுள்ளது \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[கடந்த தசாப்தத்தில் வணிகங்களில் பெரும் அதிகரிப...
\n","
0
\n","
கடந்த தசாப்தத்தில் வணிகங்களில் பெரும் அதிகரிப்...
\n","
[0.00573153980076313, -0.03077314794063568, -0...
\n","
Business
\n","
0.99997
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [கடந்த தசாப்தத்தில் வணிகங்களில் பெரும் அதிகரிப... ... 0.99997\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"code","metadata":{"id":"Q6C0BmTtdJFp","colab":{"base_uri":"https://localhost:8080/","height":97},"executionInfo":{"status":"ok","timestamp":1620197546099,"user_tz":-300,"elapsed":1825152,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"cf5ca785-5493-448a-d6f3-39b425ad95ff"},"source":["\t\t\n","# Tamil for: 'Science has advanced rapidly over the last century'\n","fitted_pipe.predict(\"கடந்த நூற்றாண்டில் அறிவியல் வேகமாக முன்னேறியுள்ளது \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
origin_index
\n","
document
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
trained_classifier_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[கடந்த நூற்றாண்டில் அறிவியல் வேகமாக முன்னேறியு...
\n","
0
\n","
கடந்த நூற்றாண்டில் அறிவியல் வேகமாக முன்னேறியுள...
\n","
[0.00972939282655716, -0.04586024209856987, -0...
\n","
Sci/Tech
\n","
0.999998
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... trained_classifier_confidence_confidence\n","0 [கடந்த நூற்றாண்டில் அறிவியல் வேகமாக முன்னேறியு... ... 0.999998\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620198319579,"user_tz":-300,"elapsed":2598620,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"69c875c5-a49b-456e-e11d-fe897b214ab0"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":97},"executionInfo":{"status":"ok","timestamp":1620198578228,"user_tz":-300,"elapsed":137070,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"d356f0ea-880f-4dd6-e8a7-2cb80cc356ff"},"source":["stored_model_path = './models/classifier_dl_trained'\n","hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Tesla plans to invest 10M into the ML sector')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
from_disk
\n","
origin_index
\n","
sentence_embedding_from_disk
\n","
document
\n","
from_disk_confidence_confidence
\n","
sentence
\n","
text
\n","
\n"," \n"," \n","
\n","
0
\n","
[Business]
\n","
8589934592
\n","
[[0.02070710062980652, -0.031539998948574066, ...
\n","
Tesla plans to invest 10M into the ML sector
\n","
[0.93137294]
\n","
[Tesla plans to invest 10M into the ML sector]
\n","
Tesla plans to invest 10M into the ML sector
\n","
\n"," \n","
\n","
"],"text/plain":[" from_disk ... text\n","0 [Business] ... Tesla plans to invest 10M into the ML sector\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo_amazon.ipynb b/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo_amazon.ipynb
deleted file mode 100644
index 0b5185ad..00000000
--- a/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo_amazon.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_multi_lingual_multi_class_text_classifier_demo_amazon.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo_amazon.ipynb)\n","\n","\n","\n","\n","# Training a Deep Learning Classifier with NLU \n","## ClassifierDL (Multi-class Text Classification)\n","## 3 class Amazon Phone review classifier training]\n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl-multi-class-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","\n","You can achieve these results or even better on this dataset with training data :\n","\n"," \n","\n","\n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data :\n","\n"," \n","\n",""]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hFGnBCHavltY","executionInfo":{"status":"ok","timestamp":1620206587860,"user_tz":-300,"elapsed":34820,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"f6325653-98eb-43e7-8621-b624faaca59b"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":3,"outputs":[{"output_type":"stream","text":["--2021-05-05 09:22:33-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 09:22:33 (28.5 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download Amazon Unlocked mobile phones dataset \n","https://www.kaggle.com/PromptCloudHQ/amazon-reviews-unlocked-mobile-phones\n","\n","dataset with unlocked mobile phone reviews in 5 review classes\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620206589024,"user_tz":-300,"elapsed":35970,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"094fa652-e925-4360-80c2-fd54b806284e"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/Amazon_Unlocked_Mobile_multi_lingual.csv"],"execution_count":4,"outputs":[{"output_type":"stream","text":["--2021-05-05 09:23:07-- http://ckl-it.de/wp-content/uploads/2021/02/Amazon_Unlocked_Mobile_multi_lingual.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 511871 (500K) [text/csv]\n","Saving to: ‘Amazon_Unlocked_Mobile_multi_lingual.csv.1’\n","\n","Amazon_Unlocked_Mob 100%[===================>] 499.87K 810KB/s in 0.6s \n","\n","2021-05-05 09:23:08 (810 KB/s) - ‘Amazon_Unlocked_Mobile_multi_lingual.csv.1’ saved [511871/511871]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620206589583,"user_tz":-300,"elapsed":36515,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"e7079afb-8f0a-41d4-8a77-560b175ca7e6"},"source":["import pandas as pd\n","test_path = '/content/Amazon_Unlocked_Mobile_multi_lingual.csv'\n","train_df = pd.read_csv(test_path,sep=\",\")\n","cols = [\"y\",\"text\"]\n","train_df = train_df[cols]\n","from sklearn.model_selection import train_test_split\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df\n","\n"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
\n"," \n"," \n","
\n","
1266
\n","
good
\n","
This is like the 5th cellphone for my dad in l...
\n","
\n","
\n","
121
\n","
poor
\n","
I bought this phone to give as a gift to a fri...
\n","
\n","
\n","
528
\n","
good
\n","
nice phone, nice up grade from my pantach revu...
\n","
\n","
\n","
151
\n","
good
\n","
Ver Good!
\n","
\n","
\n","
892
\n","
good
\n","
excellent product in perfect condition
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1490
\n","
average
\n","
So far so good. Needed a stopgap for my old Ne...
\n","
\n","
\n","
894
\n","
average
\n","
Good phone overall. Excellent speakers and ver...
\n","
\n","
\n","
314
\n","
good
\n","
I love it, the only problem is the camera its ...
\n","
\n","
\n","
53
\n","
average
\n","
The battery goes down to quick, takes a while ...
\n","
\n","
\n","
739
\n","
average
\n","
I\"m giving this phone a 3 because there seems ...
\n","
\n"," \n","
\n","
1200 rows × 2 columns
\n","
"],"text/plain":[" y text\n","1266 good This is like the 5th cellphone for my dad in l...\n","121 poor I bought this phone to give as a gift to a fri...\n","528 good nice phone, nice up grade from my pantach revu...\n","151 good Ver Good!\n","892 good excellent product in perfect condition\n","... ... ...\n","1490 average So far so good. Needed a stopgap for my old Ne...\n","894 average Good phone overall. Excellent speakers and ver...\n","314 good I love it, the only problem is the camera its ...\n","53 average The battery goes down to quick, takes a while ...\n","739 average I\"m giving this phone a 3 because there seems ...\n","\n","[1200 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.classifier')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":861},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620208183255,"user_tz":-300,"elapsed":41782,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"1d50fa2f-bd42-41f8-dd24-5e170ec8366c"},"source":["trainable_pipe = nlu.load('xx.embed_sentence.labse train.classifier')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['classifier_dl'].setMaxEpochs(60) \n","trainable_pipe['classifier_dl'].setLr(0.005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","\n","preds"],"execution_count":6,"outputs":[{"output_type":"stream","text":["labse download started this may take some time.\n","Approximate size to download 1.7 GB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," average 0.78 0.84 0.81 390\n"," good 0.87 0.91 0.89 414\n"," poor 0.92 0.81 0.86 396\n","\n"," accuracy 0.85 1200\n"," macro avg 0.86 0.85 0.85 1200\n","weighted avg 0.86 0.85 0.85 1200\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
text
\n","
sentence
\n","
y
\n","
\n"," \n"," \n","
\n","
0
\n","
This is like the 5th cellphone for my dad in l...
\n","
good
\n","
1266
\n","
0.990780
\n","
[-0.04503230005502701, -0.0068597435019910336,...
\n","
This is like the 5th cellphone for my dad in l...
\n","
[This is like the 5th cellphone for my dad in ...
\n","
good
\n","
\n","
\n","
1
\n","
I bought this phone to give as a gift to a fri...
\n","
poor
\n","
121
\n","
0.995079
\n","
[-0.053808409720659256, 0.043268490582704544, ...
\n","
I bought this phone to give as a gift to a fri...
\n","
[I bought this phone to give as a gift to a fr...
\n","
poor
\n","
\n","
\n","
2
\n","
nice phone, nice up grade from my pantach revu...
\n","
good
\n","
528
\n","
1.000000
\n","
[-0.031718023121356964, 0.05311667174100876, -...
\n","
nice phone, nice up grade from my pantach revu...
\n","
[nice phone, nice up grade from my pantach rev...
\n","
good
\n","
\n","
\n","
3
\n","
Ver Good!
\n","
good
\n","
151
\n","
0.999916
\n","
[0.004338219296187162, -0.05601995438337326, -...
\n","
Ver Good!
\n","
[Ver Good!]
\n","
good
\n","
\n","
\n","
4
\n","
excellent product in perfect condition
\n","
good
\n","
892
\n","
0.999986
\n","
[-0.045638032257556915, 0.013802768662571907, ...
\n","
excellent product in perfect condition
\n","
[excellent product in perfect condition]
\n","
good
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1195
\n","
So far so good. Needed a stopgap for my old Ne...
\n","
average
\n","
1490
\n","
0.997774
\n","
[-0.03902469947934151, 0.03762187063694, -0.01...
\n","
So far so good. Needed a stopgap for my old Ne...
\n","
[So far so good., Needed a stopgap for my old ...
\n","
average
\n","
\n","
\n","
1196
\n","
Good phone overall. Excellent speakers and ver...
\n","
average
\n","
894
\n","
0.999868
\n","
[-0.0373789444565773, -0.011103338561952114, -...
\n","
Good phone overall. Excellent speakers and ver...
\n","
[Good phone overall., Excellent speakers and v...
\n","
average
\n","
\n","
\n","
1197
\n","
I love it, the only problem is the camera its ...
\n","
average
\n","
314
\n","
0.999975
\n","
[0.0009155190782621503, -0.04911276325583458, ...
\n","
I love it, the only problem is the camera its ...
\n","
[I love it, the only problem is the camera its...
\n","
good
\n","
\n","
\n","
1198
\n","
The battery goes down to quick, takes a while ...
\n","
poor
\n","
53
\n","
0.535710
\n","
[-0.06607282906770706, 0.012522447854280472, -...
\n","
The battery goes down to quick, takes a while ...
\n","
[The battery goes down to quick, takes a while...
\n","
average
\n","
\n","
\n","
1199
\n","
I\"m giving this phone a 3 because there seems ...
\n","
average
\n","
739
\n","
1.000000
\n","
[-0.04678669199347496, -0.02481876127421856, -...
\n","
I\"m giving this phone a 3 because there seems ...
\n","
[I\"m giving this phone a 3 because there seems...
\n","
average
\n","
\n"," \n","
\n","
1200 rows × 8 columns
\n","
"],"text/plain":[" document ... y\n","0 This is like the 5th cellphone for my dad in l... ... good\n","1 I bought this phone to give as a gift to a fri... ... poor\n","2 nice phone, nice up grade from my pantach revu... ... good\n","3 Ver Good! ... good\n","4 excellent product in perfect condition ... good\n","... ... ... ...\n","1195 So far so good. Needed a stopgap for my old Ne... ... average\n","1196 Good phone overall. Excellent speakers and ver... ... average\n","1197 I love it, the only problem is the camera its ... ... good\n","1198 The battery goes down to quick, takes a while ... ... average\n","1199 I\"m giving this phone a 3 because there seems ... ... average\n","\n","[1200 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 3.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fxx4yNkNVGFl","executionInfo":{"status":"ok","timestamp":1620208254188,"user_tz":-300,"elapsed":70962,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"5188c09f-0fdc-417a-fb64-0edfb8b57b37"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))"],"execution_count":7,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," average 0.68 0.75 0.71 110\n"," good 0.76 0.80 0.78 86\n"," poor 0.79 0.67 0.73 104\n","\n"," accuracy 0.74 300\n"," macro avg 0.74 0.74 0.74 300\n","weighted avg 0.74 0.74 0.74 300\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yUkk_L8MGcRg"},"source":["#4. Test Model with 20 languages!"]},{"cell_type":"code","metadata":{"id":"q2s6nsZZGcRm","colab":{"base_uri":"https://localhost:8080/","height":759},"executionInfo":{"status":"ok","timestamp":1620208282713,"user_tz":-300,"elapsed":28530,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"e797e94f-d83c-494e-ba0d-d7887543b42b"},"source":["train_df = pd.read_csv(\"Amazon_Unlocked_Mobile_multi_lingual.csv\")\n","preds = fitted_pipe.predict(train_df[[\"test_sentences\",\"y\"]].iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","\n","preds"],"execution_count":8,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," average 0.65 0.90 0.75 29\n"," good 0.85 0.88 0.86 32\n"," poor 1.00 0.69 0.82 39\n","\n"," accuracy 0.81 100\n"," macro avg 0.83 0.82 0.81 100\n","weighted avg 0.85 0.81 0.81 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
text
\n","
sentence
\n","
y
\n","
\n"," \n"," \n","
\n","
0
\n","
Alındı, onu yandırdı, işləmədi. Geri açıldı, b...
\n","
poor
\n","
0
\n","
0.999992
\n","
[0.023933352902531624, 0.03197602182626724, -0...
\n","
Alındı, onu yandırdı, işləmədi. Geri açıldı, b...
\n","
[Alındı, onu yandırdı, işləmədi., Geri açıldı,...
\n","
poor
\n","
\n","
\n","
1
\n","
דערווייַל עס איז 2014, די 3gs איז דיסקאַנטיניו...
\n","
average
\n","
1
\n","
1.000000
\n","
[-0.019562795758247375, -0.03646012768149376, ...
\n","
דערווייַל עס איז 2014, די 3gs איז דיסקאַנטיניו...
\n","
[דערווייַל עס איז 2014, די 3gs איז דיסקאַנטיני...
\n","
average
\n","
\n","
\n","
2
\n","
100% recommended
\n","
good
\n","
2
\n","
0.999967
\n","
[0.022297078743577003, -0.038920555263757706, ...
\n","
100% recommended
\n","
[100% recommended]
\n","
good
\n","
\n","
\n","
3
\n","
Đó là một chiếc điện thoại tốt nhưng nếu bạn s...
\n","
good
\n","
3
\n","
0.999701
\n","
[0.023473074659705162, -0.056649111211299896, ...
\n","
Đó là một chiếc điện thoại tốt nhưng nếu bạn s...
\n","
[Đó là một chiếc điện thoại tốt nhưng nếu bạn ...
\n","
average
\n","
\n","
\n","
4
\n","
វាល្អដែលទូរស័ព្ទនេះមានប្រព័ន្ធ LTE ហើយវាដំណើរក...
\n","
average
\n","
4
\n","
1.000000
\n","
[-0.04908803477883339, 0.006140733137726784, -...
\n","
វាល្អដែលទូរស័ព្ទនេះមានប្រព័ន្ធ LTE ហើយវាដំណើរក...
\n","
[វាល្អដែលទូរស័ព្ទនេះមានប្រព័ន្ធ LTE ហើយវាដំណើរ...
\n","
average
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
העלא, מיר געקויפט צוויי פאָנעס און זיי געקומען...
\n","
average
\n","
95
\n","
0.994167
\n","
[-0.05593854561448097, 0.04983929917216301, -0...
\n","
העלא, מיר געקויפט צוויי פאָנעס און זיי געקומען...
\n","
[העלא, מיר געקויפט צוויי פאָנעס און זיי געקומע...
\n","
poor
\n","
\n","
\n","
96
\n","
Uitstekend
\n","
good
\n","
96
\n","
1.000000
\n","
[0.017209608107805252, 0.013231031596660614, -...
\n","
Uitstekend
\n","
[Uitstekend]
\n","
good
\n","
\n","
\n","
97
\n","
پروڈکٹ اچھی ہے لیکن انگریزی زبان میں اب بھی چی...
\n","
average
\n","
97
\n","
0.999992
\n","
[-0.0447855070233345, 0.03711877763271332, -0....
\n","
پروڈکٹ اچھی ہے لیکن انگریزی زبان میں اب بھی چی...
\n","
[پروڈکٹ اچھی ہے لیکن انگریزی زبان میں اب بھی چ...
\n","
poor
\n","
\n","
\n","
98
\n","
Veronderstel om 'n splinternuwe ontsluitfoon t...
\n","
poor
\n","
98
\n","
0.999984
\n","
[-0.0475904643535614, 0.04630507901310921, -0....
\n","
Veronderstel om 'n splinternuwe ontsluitfoon t...
\n","
[Veronderstel om 'n splinternuwe ontsluitfoon ...
\n","
poor
\n","
\n","
\n","
99
\n","
خلل بسيط ومزعج للغاية عند إرسال الرسائل النصية...
\n","
average
\n","
99
\n","
0.943725
\n","
[-0.027637170627713203, 0.0048340680077672005,...
\n","
خلل بسيط ومزعج للغاية عند إرسال الرسائل النصية...
\n","
[خلل بسيط ومزعج للغاية عند إرسال الرسائل النصي...
\n","
average
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" document ... y\n","0 Alındı, onu yandırdı, işləmədi. Geri açıldı, b... ... poor\n","1 דערווייַל עס איז 2014, די 3gs איז דיסקאַנטיניו... ... average\n","2 100% recommended ... good\n","3 Đó là một chiếc điện thoại tốt nhưng nếu bạn s... ... average\n","4 វាល្អដែលទូរស័ព្ទនេះមានប្រព័ន្ធ LTE ហើយវាដំណើរក... ... average\n",".. ... ... ...\n","95 העלא, מיר געקויפט צוויי פאָנעס און זיי געקומען... ... poor\n","96 Uitstekend ... good\n","97 پروڈکٹ اچھی ہے لیکن انگریزی زبان میں اب بھی چی... ... poor\n","98 Veronderstel om 'n splinternuwe ontsluitfoon t... ... poor\n","99 خلل بسيط ومزعج للغاية عند إرسال الرسائل النصية... ... average\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"RjtuNUcvuJTT"},"source":["# The Model understands Englsih\n",""]},{"cell_type":"code","metadata":{"id":"o0vu7PaWkcI7","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208284226,"user_tz":-300,"elapsed":1537,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"f2326b4b-a210-4515-d10e-cf965b17af12"},"source":["fitted_pipe.predict(\"It was like brand new \")"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
It was like brand new
\n","
good
\n","
0
\n","
0.971137
\n","
[0.02449253760278225, -0.003671379294246435, -...
\n","
[It was like brand new]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 It was like brand new ... [It was like brand new]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"1ykjRQhCtQ4w","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208285058,"user_tz":-300,"elapsed":843,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"99fc430e-7de4-44e5-d002-5b1f60d8137c"},"source":["fitted_pipe.predict(\"It stopped working on the first day \")\n"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
It stopped working on the first day
\n","
poor
\n","
0
\n","
0.999895
\n","
[-0.0048237149603664875, 0.020508447661995888,...
\n","
[It stopped working on the first day]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 It stopped working on the first day ... [It stopped working on the first day]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"vohym-XbuNHn"},"source":["# The Model understands German\n",""]},{"cell_type":"code","metadata":{"id":"dzaaZrI4tVWc","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208286338,"user_tz":-300,"elapsed":1290,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a122f086-37e9-4273-9f36-d5742d05b42e"},"source":["# German for: 'It worked perfectly '\n","fitted_pipe.predict(\"Es hat perfekt funktioniert\")"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Es hat perfekt funktioniert
\n","
good
\n","
0
\n","
0.998511
\n","
[-0.005111832171678543, -0.048203449696302414,...
\n","
[Es hat perfekt funktioniert]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Es hat perfekt funktioniert ... [Es hat perfekt funktioniert]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"BbhgTSBGtTtJ","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208287189,"user_tz":-300,"elapsed":862,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2454f205-eb8c-488a-9883-d555e4ab9849"},"source":["# German for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"Am ersten Tag hörte es auf zu arbeiten \")"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Am ersten Tag hörte es auf zu arbeiten
\n","
poor
\n","
0
\n","
0.999722
\n","
[0.02086009830236435, -0.011390610598027706, 0...
\n","
[Am ersten Tag hörte es auf zu arbeiten]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Am ersten Tag hörte es auf zu arbeiten ... [Am ersten Tag hörte es auf zu arbeiten]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"a1JbtmWquQwj"},"source":["# The Model understands Chinese\n",""]},{"cell_type":"code","metadata":{"id":"kYSYqtoRtc-P","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208288045,"user_tz":-300,"elapsed":867,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"df22eb72-fe4f-4c33-e42b-355393886cb7"},"source":["# Chinese for: 'It was like brand new'\n","fitted_pipe.predict(\"就像全新 \")"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
就像全新
\n","
good
\n","
0
\n","
0.999759
\n","
[-0.018629543483257294, -0.023574186488986015,...
\n","
[就像全新]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 就像全新 ... [就像全新]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"06v9SD-QtlBU","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208288891,"user_tz":-300,"elapsed":853,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"40291b56-9ab4-4283-a895-f561cda66d88"},"source":["# Chinese for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"第一天停止工作 \")\n","\t\t"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
第一天停止工作
\n","
poor
\n","
0
\n","
0.99838
\n","
[-0.0022839070297777653, 0.01226264052093029, ...
\n","
[第一天停止工作]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 第一天停止工作 ... [第一天停止工作]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"9h7CvN4uu9Pb"},"source":["# Model understands Afrikaans\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"VMPhbgw9twtf","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208290261,"user_tz":-300,"elapsed":1378,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0c85cc35-f3f0-4406-be49-220f754de617"},"source":["\n","# Afrikaans for: 'It worked perfectly '\n","fitted_pipe.predict(\"Dit het perfek gewerk\")"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Dit het perfek gewerk
\n","
good
\n","
0
\n","
0.997483
\n","
[-0.001879673101939261, -0.043611448258161545,...
\n","
[Dit het perfek gewerk]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Dit het perfek gewerk ... [Dit het perfek gewerk]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"zWgNTIdkumhX","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208290606,"user_tz":-300,"elapsed":353,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"89c5e830-650e-47a6-a2a6-f31ccea2a77f"},"source":["# Afrikaans for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"Dit het op die eerste dag opgehou werk \")"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Dit het op die eerste dag opgehou werk
\n","
poor
\n","
0
\n","
0.999799
\n","
[0.00801782961934805, -0.01342733483761549, -0...
\n","
[Dit het op die eerste dag opgehou werk]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Dit het op die eerste dag opgehou werk ... [Dit het op die eerste dag opgehou werk]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"GITfT7FK0CGv"},"source":["# The model understands Zulu\n",""]},{"cell_type":"code","metadata":{"id":"ifRhs6e7OcR3","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208291786,"user_tz":-300,"elapsed":1194,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2282c562-dd97-4784-f2ee-8114ad955dae"},"source":["# Zulu for: 'It worked perfectly '\n","fitted_pipe.predict(\"Kusebenze ngokuphelele\")"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Kusebenze ngokuphelele
\n","
good
\n","
0
\n","
0.675412
\n","
[0.010045904666185379, -0.05170843377709389, -...
\n","
[Kusebenze ngokuphelele]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Kusebenze ngokuphelele ... [Kusebenze ngokuphelele]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"6uelDwq4xdWv","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208292579,"user_tz":-300,"elapsed":801,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"4ed1017b-42b9-4635-eb61-52aa29987da0"},"source":["# Zulu for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"Iyeke ukusebenza ngosuku lokuqala \")"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Iyeke ukusebenza ngosuku lokuqala
\n","
poor
\n","
0
\n","
0.9992
\n","
[0.004491243977099657, 0.018703386187553406, 0...
\n","
[Iyeke ukusebenza ngosuku lokuqala]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Iyeke ukusebenza ngosuku lokuqala ... [Iyeke ukusebenza ngosuku lokuqala]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"VGVvzl_30a0T"},"source":["# The Model understands Turkish\n",""]},{"cell_type":"code","metadata":{"id":"DRNnuEeQz2pd","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208293097,"user_tz":-300,"elapsed":528,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0d9ba936-54c4-41d8-a9b2-4f4cd18a6b7d"},"source":["\n","# Turkish for: 'It It worked perfectly '\n","fitted_pipe.predict(\"Mükemmel çalıştı\")\n","\t\t"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Mükemmel çalıştı
\n","
good
\n","
0
\n","
0.999996
\n","
[0.06036874279379845, 0.0009111528052017093, -...
\n","
[Mükemmel çalıştı]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Mükemmel çalıştı ... [Mükemmel çalıştı]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"aOSsiK6J0jWs","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208294444,"user_tz":-300,"elapsed":790,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"20619d0e-c9ba-4862-c953-7247b747d87a"},"source":["\n","# Turkish for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"İlk gün çalışmayı bıraktı \")"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
İlk gün çalışmayı bıraktı
\n","
poor
\n","
0
\n","
0.998341
\n","
[0.050941046327352524, 0.020712943747639656, 0...
\n","
[İlk gün çalışmayı bıraktı]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 İlk gün çalışmayı bıraktı ... [İlk gün çalışmayı bıraktı]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"803qL2gt0vlb"},"source":["# The Model understands Hebrew\n",""]},{"cell_type":"code","metadata":{"id":"XQ5VCtxw0pc0","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208295262,"user_tz":-300,"elapsed":830,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c12a5cd4-2c49-4807-bfcb-56232c7c7c90"},"source":["# Hebrew for: 'It worked perfectly '\n","fitted_pipe.predict(\"זה עבד בצורה מושלמת\")"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
זה עבד בצורה מושלמת
\n","
good
\n","
0
\n","
0.888842
\n","
[-0.01338683720678091, -0.054987359791994095, ...
\n","
[זה עבד בצורה מושלמת]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 זה עבד בצורה מושלמת ... [זה עבד בצורה מושלמת]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"9w2ZHfns05A4","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208296285,"user_tz":-300,"elapsed":1030,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"229daf7b-d6bc-4819-c228-fb75068e6239"},"source":["\t\t\n","# Hebrew for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"זה הפסיק לעבוד ביום הראשון \")"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
זה הפסיק לעבוד ביום הראשון
\n","
poor
\n","
0
\n","
0.999894
\n","
[-0.013081019744277, -0.02689044550061226, -0....
\n","
[זה הפסיק לעבוד ביום הראשון]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 זה הפסיק לעבוד ביום הראשון ... [זה הפסיק לעבוד ביום הראשון]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"SDlpd33H1HIX"},"source":["# The Model understands Telugu\n","\n"]},{"cell_type":"code","metadata":{"id":"Kc5n1bzv1BJT","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208297143,"user_tz":-300,"elapsed":865,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"6e51fe15-e745-4c7a-f996-f38b04f993e7"},"source":["\t\t\n","# Telugu for: 'It was like brand new'\n","fitted_pipe.predict(\"ఇది సరికొత్తది \")\n","\t\t"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
ఇది సరికొత్తది
\n","
good
\n","
0
\n","
0.99935
\n","
[0.020253609865903854, -0.045859843492507935, ...
\n","
[ఇది సరికొత్తది]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 ఇది సరికొత్తది ... [ఇది సరికొత్తది]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"-l-u6vrz1Obe","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208297504,"user_tz":-300,"elapsed":374,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"1119d43e-fb8a-4588-b515-6e57690ba3cb"},"source":["\n","# Telugu for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"ఇది మొదటి రోజు పనిచేయడం మానేసింది \")"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
ఇది మొదటి రోజు పనిచేయడం మానేసింది
\n","
poor
\n","
0
\n","
0.999886
\n","
[0.00022219969832804054, -0.01876474916934967,...
\n","
[ఇది మొదటి రోజు పనిచేయడం మానేసింది]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 ఇది మొదటి రోజు పనిచేయడం మానేసింది ... [ఇది మొదటి రోజు పనిచేయడం మానేసింది]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"nziBUe8t1Zwn"},"source":["# Model understands Russian\n","\n"]},{"cell_type":"code","metadata":{"id":"Ckyjl3YQ1VFn","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208298684,"user_tz":-300,"elapsed":1191,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"1c3b0bde-17c4-483b-9ee6-54f20af2ee9f"},"source":["\t\t\n","# Russian for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"Перестал работать в первый же день \")\n","\t\t"],"execution_count":25,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Перестал работать в первый же день
\n","
poor
\n","
0
\n","
0.999956
\n","
[-0.040550969541072845, 0.023711256682872772, ...
\n","
[Перестал работать в первый же день]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Перестал работать в первый же день ... [Перестал работать в первый же день]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"GIdWkfGv1gFz","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208299374,"user_tz":-300,"elapsed":706,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"5276251f-bd2d-4503-9af9-bddf41340558"},"source":["# Russian for: 'It worked perfectly '\n","fitted_pipe.predict(\"Это сработало отлично\")\n"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Это сработало отлично
\n","
good
\n","
0
\n","
0.992138
\n","
[0.008218108676373959, -0.05058329924941063, -...
\n","
[Это сработало отлично]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Это сработало отлично ... [Это сработало отлично]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"HKj5yWwwMplH"},"source":["# The Model understands French\n",""]},{"cell_type":"code","metadata":{"id":"CUHcJZfJMplL","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208304798,"user_tz":-300,"elapsed":562,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"e3b7057e-3ee0-4c1f-9a71-58f2caab31bf"},"source":["# French for: 'It was the best ever !!'\n","fitted_pipe.predict(\"C'était la meilleure chose que je n'ai jamais faite !!\")"],"execution_count":33,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
C'était la meilleure chose que je n'ai jamais ...
\n","
good
\n","
0
\n","
0.789434
\n","
[0.027519920840859413, -0.04782726615667343, -...
\n","
[C'était la meilleure chose que je n'ai jamais...
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 C'était la meilleure chose que je n'ai jamais ... ... [C'était la meilleure chose que je n'ai jamais...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"id":"57NY2XoTMplM","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208305800,"user_tz":-300,"elapsed":1014,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"5865f7da-7bfc-4f4e-ca1e-8366f10441bf"},"source":["\t\t\n","# French for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"Il a cessé de fonctionner le premier jour \")"],"execution_count":34,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Il a cessé de fonctionner le premier jour
\n","
poor
\n","
0
\n","
0.99996
\n","
[-0.02747691236436367, -0.006572246551513672, ...
\n","
[Il a cessé de fonctionner le premier jour]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Il a cessé de fonctionner le premier jour ... [Il a cessé de fonctionner le premier jour]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"jD2TBgT0Nq6F"},"source":["# The Model understands Thai\n",""]},{"cell_type":"code","metadata":{"id":"gBp11S5GNq6S","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208306839,"user_tz":-300,"elapsed":1046,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2f8d6182-9714-4b8e-8290-7751654f680f"},"source":["# Thai for: 'It was the best ever !!'\n","fitted_pipe.predict(\"มันดีที่สุดเท่าที่เคยมีมา !!\")"],"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
มันดีที่สุดเท่าที่เคยมีมา !!
\n","
good
\n","
0
\n","
0.9783
\n","
[-0.00953330472111702, -0.05253228917717934, -...
\n","
[มันดีที่สุดเท่าที่เคยมีมา !!]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 มันดีที่สุดเท่าที่เคยมีมา !! ... [มันดีที่สุดเท่าที่เคยมีมา !!]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"id":"R6nKI7C3QKa3","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208307547,"user_tz":-300,"elapsed":714,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"89d73606-66a5-4a1c-ea7d-f5dfcf93d8be"},"source":["# Thai for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"มันหยุดทำงานในวันแรก \")\n","\t\t"],"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
มันหยุดทำงานในวันแรก
\n","
poor
\n","
0
\n","
0.999704
\n","
[-0.02828541025519371, -0.025081545114517212, ...
\n","
[มันหยุดทำงานในวันแรก]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 มันหยุดทำงานในวันแรก ... [มันหยุดทำงานในวันแรก]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"mLItI4KZOElB"},"source":["# The Model understands Khmer\n",""]},{"cell_type":"code","metadata":{"id":"SWbqMgAwOElC","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208307858,"user_tz":-300,"elapsed":357,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2b9cb7bf-7280-4e50-946f-7d27d735249b"},"source":["\n","# Khmer for: 'It was like brand new'\n","fitted_pipe.predict(\"វាដូចជាម៉ាកថ្មី \")"],"execution_count":37,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
វាដូចជាម៉ាកថ្មី
\n","
good
\n","
0
\n","
0.998938
\n","
[-0.013914491981267929, 0.01159849762916565, -...
\n","
[វាដូចជាម៉ាកថ្មី]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 វាដូចជាម៉ាកថ្មី ... [វាដូចជាម៉ាកថ្មី]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"beoCtm4xQf2P","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208308862,"user_tz":-300,"elapsed":1021,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c2c9f684-ef2d-4bb3-bc16-7afdda19fe30"},"source":["\t\t\n","# Khmer for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"វាឈប់ធ្វើការនៅថ្ងៃដំបូង \")"],"execution_count":38,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
វាឈប់ធ្វើការនៅថ្ងៃដំបូង
\n","
poor
\n","
0
\n","
0.999504
\n","
[-0.012111755087971687, -0.02565937303006649, ...
\n","
[វាឈប់ធ្វើការនៅថ្ងៃដំបូង]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 វាឈប់ធ្វើការនៅថ្ងៃដំបូង ... [វាឈប់ធ្វើការនៅថ្ងៃដំបូង]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"lvE-LbNiPoBT"},"source":["# The Model understands Yiddish\n",""]},{"cell_type":"code","metadata":{"id":"sZlmLhajPoBb","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208309622,"user_tz":-300,"elapsed":769,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b2b134e1-906c-4595-8015-c793c8132b77"},"source":["# Yiddish for: 'It was the best ever !!'\n","fitted_pipe.predict(\"עס איז געווען דער בעסטער טאָמיד !!\")\n","\t\t"],"execution_count":39,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
עס איז געווען דער בעסטער טאָמיד !!
\n","
good
\n","
0
\n","
0.958143
\n","
[0.01722853071987629, -0.04829197749495506, -0...
\n","
[עס איז געווען דער בעסטער טאָמיד !!]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 עס איז געווען דער בעסטער טאָמיד !! ... [עס איז געווען דער בעסטער טאָמיד !!]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"id":"5h-pha_nPoBc","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208310486,"user_tz":-300,"elapsed":885,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"c21dae04-2852-41d3-e0b9-0838ebbe5c7e"},"source":["# Yiddish for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"אויף דער ערשטער טאָג עס סטאַפּט ארבעטן \")"],"execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
אויף דער ערשטער טאָג עס סטאַפּט ארבעטן
\n","
poor
\n","
0
\n","
0.99991
\n","
[-0.03324505686759949, -0.020611954852938652, ...
\n","
[אויף דער ערשטער טאָג עס סטאַפּט ארבעטן]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 אויף דער ערשטער טאָג עס סטאַפּט ארבעטן ... [אויף דער ערשטער טאָג עס סטאַפּט ארבעטן]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"markdown","metadata":{"id":"XSz4WzScaAHj"},"source":["# The Model understands Kygrgyz\n",""]},{"cell_type":"code","metadata":{"id":"DXz6fhJSaAHu","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208310848,"user_tz":-300,"elapsed":369,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"e6a9ef09-b37d-4da8-e0ba-afb4cc8a8b17"},"source":["\t\t\n","# Kygrgyz for: 'It was the best ever !!'\n","fitted_pipe.predict(\"Бул эң мыкты болду !!\")\n","\t\t"],"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Бул эң мыкты болду !!
\n","
average
\n","
0
\n","
0.989246
\n","
[0.03657503426074982, -0.0562313050031662, -0....
\n","
[Бул эң мыкты болду !!]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Бул эң мыкты болду !! ... [Бул эң мыкты болду !!]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"id":"lh_ZSHlPaAHv","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208311821,"user_tz":-300,"elapsed":990,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"1ffda076-5f2c-4d2a-afa5-af7579807139"},"source":["\n","# Kygrgyz for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"Биринчи күнү эле иштебей калды \")"],"execution_count":42,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
Биринчи күнү эле иштебей калды
\n","
poor
\n","
0
\n","
0.999909
\n","
[0.012193242087960243, 0.014580711722373962, -...
\n","
[Биринчи күнү эле иштебей калды]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 Биринчи күнү эле иштебей калды ... [Биринчи күнү эле иштебей калды]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"DGMVMKaTdJFj"},"source":["# The Model understands Tamil\n",""]},{"cell_type":"code","metadata":{"id":"JWDr_LoCdJFn","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208312526,"user_tz":-300,"elapsed":716,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"645d21ae-34e6-4ff3-83f4-4d7a2c731370"},"source":["# Tamil for: 'It was the best ever !!'\n","fitted_pipe.predict(\"இது எப்போதும் சிறந்தது !! \")"],"execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
இது எப்போதும் சிறந்தது !!
\n","
good
\n","
0
\n","
0.829614
\n","
[-0.03039463423192501, -0.058778341859579086, ...
\n","
[இது எப்போதும் சிறந்தது !!]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 இது எப்போதும் சிறந்தது !! ... [இது எப்போதும் சிறந்தது !!]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"id":"Q6C0BmTtdJFp","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620208312877,"user_tz":-300,"elapsed":366,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"0c8b2a5b-63e2-4088-ebe4-90550f71f9c8"},"source":["\t\t\n","# Tamil for: 'It stopped working on the first day'\n","fitted_pipe.predict(\"இது முதல் நாளில் வேலை செய்வதை நிறுத்தியது \")"],"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
trained_classifier
\n","
origin_index
\n","
trained_classifier_confidence_confidence
\n","
sentence_embedding_labse
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
இது முதல் நாளில் வேலை செய்வதை நிறுத்தியது
\n","
poor
\n","
0
\n","
0.999642
\n","
[0.022033903747797012, -0.00905965268611908, 0...
\n","
[இது முதல் நாளில் வேலை செய்வதை நிறுத்தியது]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence\n","0 இது முதல் நாளில் வேலை செய்வதை நிறுத்தியது ... [இது முதல் நாளில் வேலை செய்வதை நிறுத்தியது]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620209227950,"user_tz":-300,"elapsed":47226,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"68751c51-9c5a-403e-87f6-84fee139f186"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":46,"outputs":[{"output_type":"stream","text":["Stored model in ./model/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620209417898,"user_tz":-300,"elapsed":133849,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"ad55bee4-6794-4df2-bc97-a951f09688cc"},"source":["stored_model_path = './models/classifier_dl_trained'\n","hdd_pipe = nlu.load(path=stored_model_path)\n","preds = hdd_pipe.predict('It worked perfectly.')\n","preds"],"execution_count":1,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_from_disk
\n","
origin_index
\n","
text
\n","
from_disk_confidence_confidence
\n","
sentence
\n","
document
\n","
from_disk
\n","
\n"," \n"," \n","
\n","
0
\n","
[[0.006914880592375994, -0.04569805786013603, ...
\n","
8589934592
\n","
It worked perfectly.
\n","
[0.7951465]
\n","
[It worked perfectly.]
\n","
It worked perfectly.
\n","
[average]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_from_disk ... from_disk\n","0 [[0.006914880592375994, -0.04569805786013603, ... ... [average]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620209419447,"user_tz":-300,"elapsed":1537,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"5ede73d9-5999-4b86-ebda-9f0464cedb00"},"source":["hdd_pipe.print_info()"],"execution_count":2,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@5bb59739) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@5bb59739\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@labse'] has settable params:\n","pipe['bert_sentence@labse'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@labse'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@labse'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@labse'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@labse'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n",">>> pipe['classifier_dl@labse'] has settable params:\n","pipe['classifier_dl@labse'].setClasses(['average', 'poor', 'good']) | Info: get the tags used to trained this ClassifierDLModel | Currently set to : ['average', 'poor', 'good']\n","pipe['classifier_dl@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"EY3jdCj41bJC","executionInfo":{"status":"aborted","timestamp":1620208313836,"user_tz":-300,"elapsed":16,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}}},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo_hotel_reviews.ipynb b/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo_hotel_reviews.ipynb
deleted file mode 100644
index 82e389a2..00000000
--- a/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo_hotel_reviews.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_multi_lingual_multi_class_text_classifier_demo_hotel_reviews.ipynb","provenance":[],"collapsed_sections":["zkufh760uvF3"]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/multi_lingual/multi_class_text_classification/NLU_training_multi_lingual_multi_class_text_classifier_demo_hotel_reviews.ipynb)\n","\n","\n","\n","# Training a Deep Learning Classifier with NLU \n","## ClassifierDL (Multi-class Text Classification)\n","## 3 class Tripadvisor Hotel review classifier training\n","With the [ClassifierDL model](https://nlp.johnsnowlabs.com/docs/en/annotators#classifierdl-multi-class-text-classification) from Spark NLP you can achieve State Of the Art results on any multi class text classification problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n","You can achieve these results or even better on this dataset with training data :\n","\n"," \n","\n","\n","\n","\n","You can achieve these results or even better on this dataset with test data :\n","\n"," \n","\n","\n",""]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dTyEf8lNWMi7","executionInfo":{"status":"ok","timestamp":1620195568586,"user_tz":-300,"elapsed":116506,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"9c394263-780b-4e46-9b97-406bd3a67225"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 06:17:34-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 06:17:34 (33.3 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 68kB/s \n","\u001b[K |████████████████████████████████| 153kB 40.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 18.6MB/s \n","\u001b[K |████████████████████████████████| 204kB 37.1MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download hotel reviews dataset \n","https://www.kaggle.com/andrewmvd/trip-advisor-hotel-reviews\n","\n","Hotels play a crucial role in traveling and with the increased access to information new pathways of selecting the best ones emerged.\n","With this dataset, consisting of 20k reviews crawled from Tripadvisor, you can explore what makes a great hotel and maybe even use this model in your travels!\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620195570511,"user_tz":-300,"elapsed":118409,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"a9ccf638-b1d8-40e6-d4d5-59bc7bc923c3"},"source":["! wget http://ckl-it.de/wp-content/uploads/2021/02/tripadvisor_hotel_reviews_multi_lingual.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 06:19:28-- http://ckl-it.de/wp-content/uploads/2021/02/tripadvisor_hotel_reviews_multi_lingual.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 5332717 (5.1M) [text/csv]\n","Saving to: ‘tripadvisor_hotel_reviews_multi_lingual.csv’\n","\n","tripadvisor_hotel_r 100%[===================>] 5.08M 3.76MB/s in 1.4s \n","\n","2021-05-05 06:19:30 (3.76 MB/s) - ‘tripadvisor_hotel_reviews_multi_lingual.csv’ saved [5332717/5332717]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":419},"id":"y4xSRWIhwT28","executionInfo":{"status":"ok","timestamp":1620195571230,"user_tz":-300,"elapsed":119050,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"ed564426-69ae-4fed-b76f-13e3db7bc103"},"source":["import pandas as pd\n","test_path = '/content/tripadvisor_hotel_reviews_multi_lingual.csv'\n","train_df = pd.read_csv(test_path,sep=\",\")\n","cols = [\"y\",\"text\"]\n","train_df = train_df[cols]\n","from sklearn.model_selection import train_test_split\n","train_df = train_df.iloc[:1500]\n","train_df, test_df = train_test_split(train_df, test_size=0.2)\n","train_df\n","\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
y
\n","
text
\n","
\n"," \n"," \n","
\n","
819
\n","
great
\n","
wonderful weekend fairmont copley plaza enjoye...
\n","
\n","
\n","
338
\n","
great
\n","
nice stayed silver cloud pre-cruise, impressed...
\n","
\n","
\n","
556
\n","
great
\n","
best deal town n't mind shabby lobby moore hot...
\n","
\n","
\n","
1057
\n","
great
\n","
great experience just got rio mar yesterday mi...
\n","
\n","
\n","
400
\n","
poor
\n","
hospital stayed 2 weeks got sick stomach upset...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1410
\n","
great
\n","
great time westin rio mar, know like reading r...
\n","
\n","
\n","
1283
\n","
poor
\n","
different used stayed el san juan total 5 time...
\n","
\n","
\n","
1133
\n","
average
\n","
modern hilton good location stay area pretty r...
\n","
\n","
\n","
1207
\n","
average
\n","
nothing spectacular chosen weekend getaway bui...
\n","
\n","
\n","
486
\n","
average
\n","
avoid summer, stayed intercity times wrote fav...
\n","
\n"," \n","
\n","
1200 rows × 2 columns
\n","
"],"text/plain":[" y text\n","819 great wonderful weekend fairmont copley plaza enjoye...\n","338 great nice stayed silver cloud pre-cruise, impressed...\n","556 great best deal town n't mind shabby lobby moore hot...\n","1057 great great experience just got rio mar yesterday mi...\n","400 poor hospital stayed 2 weeks got sick stomach upset...\n","... ... ...\n","1410 great great time westin rio mar, know like reading r...\n","1283 poor different used stayed el san juan total 5 time...\n","1133 average modern hilton good location stay area pretty r...\n","1207 average nothing spectacular chosen weekend getaway bui...\n","486 average avoid summer, stayed intercity times wrote fav...\n","\n","[1200 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.classifier')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620198160598,"user_tz":-300,"elapsed":2708392,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"6352b01c-81ce-4d7c-851e-231b12fecd36"},"source":["trainable_pipe = nlu.load('xx.embed_sentence.labse train.classifier')\n","# We need to train longer and user smaller LR for NON-USE based sentence embeddings usually\n","# We could tune the hyperparameters further with hyperparameter tuning methods like gridsearch\n","# Also longer training gives more accuracy\n","trainable_pipe['classifier_dl'].setMaxEpochs(60) \n","trainable_pipe['classifier_dl'].setLr(0.005) \n","fitted_pipe = trainable_pipe.fit(train_df)\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict(train_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","from sklearn.metrics import classification_report\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["labse download started this may take some time.\n","Approximate size to download 1.7 GB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"," precision recall f1-score support\n","\n"," average 0.75 0.78 0.77 396\n"," great 0.86 0.91 0.88 391\n"," poor 0.88 0.79 0.83 413\n","\n"," accuracy 0.83 1200\n"," macro avg 0.83 0.83 0.83 1200\n","weighted avg 0.83 0.83 0.83 1200\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
y
\n","
origin_index
\n","
text
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.042161960154771805, 0.0009784834692254663, ...
\n","
great
\n","
wonderful weekend fairmont copley plaza enjoye...
\n","
[wonderful weekend fairmont copley plaza enjoy...
\n","
0.999989
\n","
great
\n","
819
\n","
wonderful weekend fairmont copley plaza enjoye...
\n","
\n","
\n","
1
\n","
[0.028560785576701164, 0.076837457716465, -0.0...
\n","
great
\n","
nice stayed silver cloud pre-cruise, impressed...
\n","
[nice stayed silver cloud pre-cruise, impresse...
\n","
0.999971
\n","
great
\n","
338
\n","
nice stayed silver cloud pre-cruise, impressed...
\n","
\n","
\n","
2
\n","
[-0.04284599423408508, 0.031210748478770256, -...
\n","
poor
\n","
best deal town n't mind shabby lobby moore hot...
\n","
[best deal town n't mind shabby lobby moore ho...
\n","
0.651085
\n","
great
\n","
556
\n","
best deal town n't mind shabby lobby moore hot...
\n","
\n","
\n","
3
\n","
[-0.0364091582596302, 0.006750240921974182, 0....
\n","
great
\n","
great experience just got rio mar yesterday mi...
\n","
[great experience just got rio mar yesterday m...
\n","
0.998592
\n","
great
\n","
1057
\n","
great experience just got rio mar yesterday mi...
\n","
\n","
\n","
4
\n","
[-0.01341036893427372, 0.01906048320233822, 0....
\n","
poor
\n","
hospital stayed 2 weeks got sick stomach upset...
\n","
[hospital stayed 2 weeks got sick stomach upse...
\n","
0.999999
\n","
poor
\n","
400
\n","
hospital stayed 2 weeks got sick stomach upset...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1195
\n","
[-0.07303472608327866, -0.0014495996292680502,...
\n","
great
\n","
great time westin rio mar, know like reading r...
\n","
[great time westin rio mar, know like reading ...
\n","
0.999355
\n","
great
\n","
1410
\n","
great time westin rio mar, know like reading r...
\n","
\n","
\n","
1196
\n","
[-0.04543023928999901, 0.040597811341285706, -...
\n","
poor
\n","
different used stayed el san juan total 5 time...
\n","
[different used stayed el san juan total 5 tim...
\n","
0.999191
\n","
poor
\n","
1283
\n","
different used stayed el san juan total 5 time...
\n","
\n","
\n","
1197
\n","
[-0.020267890766263008, 0.05014576390385628, -...
\n","
average
\n","
modern hilton good location stay area pretty r...
\n","
[modern hilton good location stay area pretty ...
\n","
0.999984
\n","
average
\n","
1133
\n","
modern hilton good location stay area pretty r...
\n","
\n","
\n","
1198
\n","
[-0.019527727738022804, 0.05594026297330856, -...
\n","
average
\n","
nothing spectacular chosen weekend getaway bui...
\n","
[nothing spectacular chosen weekend getaway bu...
\n","
0.998562
\n","
average
\n","
1207
\n","
nothing spectacular chosen weekend getaway bui...
\n","
\n","
\n","
1199
\n","
[-0.013655068352818489, -0.007150351535528898,...
\n","
average
\n","
avoid summer, stayed intercity times wrote fav...
\n","
[avoid summer, stayed intercity times wrote fa...
\n","
0.952325
\n","
average
\n","
486
\n","
avoid summer, stayed intercity times wrote fav...
\n","
\n"," \n","
\n","
1200 rows × 8 columns
\n","
"],"text/plain":[" sentence_embedding_labse ... text\n","0 [0.042161960154771805, 0.0009784834692254663, ... ... wonderful weekend fairmont copley plaza enjoye...\n","1 [0.028560785576701164, 0.076837457716465, -0.0... ... nice stayed silver cloud pre-cruise, impressed...\n","2 [-0.04284599423408508, 0.031210748478770256, -... ... best deal town n't mind shabby lobby moore hot...\n","3 [-0.0364091582596302, 0.006750240921974182, 0.... ... great experience just got rio mar yesterday mi...\n","4 [-0.01341036893427372, 0.01906048320233822, 0.... ... hospital stayed 2 weeks got sick stomach upset...\n","... ... ... ...\n","1195 [-0.07303472608327866, -0.0014495996292680502,... ... great time westin rio mar, know like reading r...\n","1196 [-0.04543023928999901, 0.040597811341285706, -... ... different used stayed el san juan total 5 time...\n","1197 [-0.020267890766263008, 0.05014576390385628, -... ... modern hilton good location stay area pretty r...\n","1198 [-0.019527727738022804, 0.05594026297330856, -... ... nothing spectacular chosen weekend getaway bui...\n","1199 [-0.013655068352818489, -0.007150351535528898,... ... avoid summer, stayed intercity times wrote fav...\n","\n","[1200 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"_1jxw3GnVGlI"},"source":["# 3.1 evaluate on Test Data"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fxx4yNkNVGFl","executionInfo":{"status":"ok","timestamp":1620198282249,"user_tz":-300,"elapsed":2830029,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"5ae17df4-2e52-413a-b381-1192861af2d2"},"source":["preds = fitted_pipe.predict(test_df,output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," average 0.53 0.56 0.54 98\n"," great 0.75 0.77 0.76 103\n"," poor 0.72 0.66 0.69 99\n","\n"," accuracy 0.66 300\n"," macro avg 0.67 0.66 0.66 300\n","weighted avg 0.67 0.66 0.66 300\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yUkk_L8MGcRg"},"source":["#4. Test Model with 20 languages!"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":980},"id":"q2s6nsZZGcRm","executionInfo":{"status":"ok","timestamp":1620198329116,"user_tz":-300,"elapsed":2876883,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"389e674f-fe0f-4740-f47a-5c27bc22e02a"},"source":["train_df = pd.read_csv(\"tripadvisor_hotel_reviews_multi_lingual.csv\")\n","preds = fitted_pipe.predict(train_df[[\"test_sentences\",\"y\"]].iloc[:100],output_level='document')\n","\n","#sentence detector that is part of the pipe generates sone NaNs. lets drop them first\n","preds.dropna(inplace=True)\n","print(classification_report(preds['y'], preds['trained_classifier']))\n","\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":[" precision recall f1-score support\n","\n"," average 0.41 0.55 0.47 33\n"," great 0.66 0.66 0.66 35\n"," poor 0.81 0.53 0.64 32\n","\n"," accuracy 0.58 100\n"," macro avg 0.63 0.58 0.59 100\n","weighted avg 0.62 0.58 0.59 100\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
y
\n","
origin_index
\n","
text
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.013124360702931881, -0.010088308714330196,...
\n","
average
\n","
Tolles Hotel 5 Nächte Ende August 2005. Reserv...
\n","
[Tolles Hotel 5 Nächte Ende August 2005., Rese...
\n","
0.993889
\n","
great
\n","
0
\n","
Tolles Hotel 5 Nächte Ende August 2005. Reserv...
\n","
\n","
\n","
1
\n","
[0.021132875233888626, 0.06491417437791824, -0...
\n","
average
\n","
தூண்டில் மற்றும் சுவிட்ச் அறை விகிதங்கள், ஏற்ற...
\n","
[தூண்டில் மற்றும் சுவிட்ச் அறை விகிதங்கள், ஏற்...
\n","
0.884120
\n","
poor
\n","
1
\n","
தூண்டில் மற்றும் சுவிட்ச் அறை விகிதங்கள், ஏற்ற...
\n","
\n","
\n","
2
\n","
[-0.014877039939165115, 0.08078614622354507, -...
\n","
average
\n","
បន្ទប់ឆែកល្អចូលចិត្តសណ្ឋាគារទីតាំងល្អមិត្តភាព។...
\n","
[បន្ទប់ឆែកល្អចូលចិត្តសណ្ឋាគារទីតាំងល្អមិត្តភាព...
\n","
0.999971
\n","
average
\n","
2
\n","
បន្ទប់ឆែកល្អចូលចិត្តសណ្ឋាគារទីតាំងល្អមិត្តភាព។...
\n","
\n","
\n","
3
\n","
[-0.051047880202531815, 0.04952175170183182, -...
\n","
great
\n","
beste Lage Wert Eigenschaften Waikiki Kopf Hot...
\n","
[beste Lage Wert Eigenschaften Waikiki Kopf Ho...
\n","
0.995766
\n","
great
\n","
3
\n","
beste Lage Wert Eigenschaften Waikiki Kopf Hot...
\n","
\n","
\n","
4
\n","
[-0.04287628456950188, 0.011480937711894512, -...
\n","
poor
\n","
botel not recommended little disappointed hone...
\n","
[botel not recommended little disappointed hon...
\n","
0.999344
\n","
poor
\n","
4
\n","
botel not recommended little disappointed hone...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
95
\n","
[-0.0008096122182905674, 0.03655293583869934, ...
\n","
average
\n","
موقع رائع قضى 7 أيام في نزل القلعة في بداية ال...
\n","
[موقع رائع قضى 7 أيام في نزل القلعة في بداية ا...
\n","
0.998497
\n","
great
\n","
95
\n","
موقع رائع قضى 7 أيام في نزل القلعة في بداية ال...
\n","
\n","
\n","
96
\n","
[-0.005492118652909994, 0.05503822863101959, -...
\n","
great
\n","
super emplacement les lits durs ont vraiment a...
\n","
[super emplacement les lits durs ont vraiment ...
\n","
0.999948
\n","
average
\n","
96
\n","
super emplacement les lits durs ont vraiment a...
\n","
\n","
\n","
97
\n","
[0.009459477849304676, 0.006773392669856548, -...
\n","
average
\n","
ทำเลที่ตั้งดีเยี่ยมโรงแรมที่สมบูรณ์แบบตรงกลางน...
\n","
[ทำเลที่ตั้งดีเยี่ยมโรงแรมที่สมบูรณ์แบบตรงกลาง...
\n","
0.986078
\n","
great
\n","
97
\n","
ทำเลที่ตั้งดีเยี่ยมโรงแรมที่สมบูรณ์แบบตรงกลางน...
\n","
\n","
\n","
98
\n","
[-0.06341180950403214, 0.03327464684844017, -0...
\n","
average
\n","
gerade anfangen, Glanz zu verlieren, blieb Kan...
\n","
[gerade anfangen, Glanz zu verlieren, blieb Ka...
\n","
0.793590
\n","
great
\n","
98
\n","
gerade anfangen, Glanz zu verlieren, blieb Kan...
\n","
\n","
\n","
99
\n","
[0.02874639444053173, -0.009642799384891987, -...
\n","
poor
\n","
bittersweet यादें शानदार अतीत में हाल ही में र...
\n","
[bittersweet यादें शानदार अतीत में हाल ही में ...
\n","
0.999651
\n","
poor
\n","
99
\n","
bittersweet यादें शानदार अतीत में हाल ही में र...
\n","
\n"," \n","
\n","
100 rows × 8 columns
\n","
"],"text/plain":[" sentence_embedding_labse ... text\n","0 [-0.013124360702931881, -0.010088308714330196,... ... Tolles Hotel 5 Nächte Ende August 2005. Reserv...\n","1 [0.021132875233888626, 0.06491417437791824, -0... ... தூண்டில் மற்றும் சுவிட்ச் அறை விகிதங்கள், ஏற்ற...\n","2 [-0.014877039939165115, 0.08078614622354507, -... ... បន្ទប់ឆែកល្អចូលចិត្តសណ្ឋាគារទីតាំងល្អមិត្តភាព។...\n","3 [-0.051047880202531815, 0.04952175170183182, -... ... beste Lage Wert Eigenschaften Waikiki Kopf Hot...\n","4 [-0.04287628456950188, 0.011480937711894512, -... ... botel not recommended little disappointed hone...\n",".. ... ... ...\n","95 [-0.0008096122182905674, 0.03655293583869934, ... ... موقع رائع قضى 7 أيام في نزل القلعة في بداية ال...\n","96 [-0.005492118652909994, 0.05503822863101959, -... ... super emplacement les lits durs ont vraiment a...\n","97 [0.009459477849304676, 0.006773392669856548, -... ... ทำเลที่ตั้งดีเยี่ยมโรงแรมที่สมบูรณ์แบบตรงกลางน...\n","98 [-0.06341180950403214, 0.03327464684844017, -0... ... gerade anfangen, Glanz zu verlieren, blieb Kan...\n","99 [0.02874639444053173, -0.009642799384891987, -... ... bittersweet यादें शानदार अतीत में हाल ही में र...\n","\n","[100 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"RjtuNUcvuJTT"},"source":["# The Model understands Englsih\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"o0vu7PaWkcI7","executionInfo":{"status":"ok","timestamp":1620198331127,"user_tz":-300,"elapsed":2878867,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"b9a0a2fb-0f1b-402f-e45c-8532767633d7"},"source":["fitted_pipe.predict(\"It was the best stay of my life, I loved it!! \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.025105198845267296, -0.0444340780377388, -0...
\n","
great
\n","
It was the best stay of my life, I loved it!!
\n","
[It was the best stay of my life, I loved it!, !]
\n","
0.999519
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.025105198845267296, -0.0444340780377388, -0... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"1ykjRQhCtQ4w","executionInfo":{"status":"ok","timestamp":1620198332354,"user_tz":-300,"elapsed":2880072,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"b9b37106-be3b-4bc2-ad8d-984ec72be447"},"source":["fitted_pipe.predict(\"It was the worst stay of my life, I hated it!! \")\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.02957884594798088, -0.042081158608198166, -...
\n","
poor
\n","
It was the worst stay of my life, I hated it!!
\n","
[It was the worst stay of my life, I hated it!...
\n","
0.997514
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.02957884594798088, -0.042081158608198166, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"markdown","metadata":{"id":"vohym-XbuNHn"},"source":["# The Model understands German\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"dzaaZrI4tVWc","executionInfo":{"status":"ok","timestamp":1620198332759,"user_tz":-300,"elapsed":2880425,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"bd46e5c1-d196-42f5-f714-8d6abe96d7cc"},"source":["# German for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"Es war der beste Aufenthalt meines Lebens, ich habe es geliebt !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.025617901235818863, -0.050006747245788574, ...
\n","
great
\n","
Es war der beste Aufenthalt meines Lebens, ich...
\n","
[Es war der beste Aufenthalt meines Lebens, ic...
\n","
0.999732
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.025617901235818863, -0.050006747245788574, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"BbhgTSBGtTtJ","executionInfo":{"status":"ok","timestamp":1620198333653,"user_tz":-300,"elapsed":2881288,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"7e47b6b4-ae79-4638-bff1-f3ce7c6cafdb"},"source":["\t\t\n","# German for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"Es war der schlimmste Aufenthalt meines Lebens, ich hasste es !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.022492462769150734, -0.05308641120791435, -...
\n","
poor
\n","
Es war der schlimmste Aufenthalt meines Lebens...
\n","
[Es war der schlimmste Aufenthalt meines Leben...
\n","
0.973955
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.022492462769150734, -0.05308641120791435, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"a1JbtmWquQwj"},"source":["# The Model understands Chinese\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"kYSYqtoRtc-P","executionInfo":{"status":"ok","timestamp":1620198334808,"user_tz":-300,"elapsed":2882416,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"03cb7d9c-7e27-4a88-9fa9-c9166b0b2d75"},"source":["# Chinese for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"这是我一生中最美好的时光,我喜欢它!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.011552233248949051, -0.06316248327493668, -...
\n","
great
\n","
这是我一生中最美好的时光,我喜欢它!!
\n","
[这是我一生中最美好的时光,我喜欢它!!]
\n","
0.999965
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.011552233248949051, -0.06316248327493668, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"06v9SD-QtlBU","executionInfo":{"status":"ok","timestamp":1620198335872,"user_tz":-300,"elapsed":2883457,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"f0933f0f-0efe-446e-ffb3-45189853d5e9"},"source":["# Chinese for: 'It was awful!! '\n","fitted_pipe.predict(\"太糟糕了!!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.025874070823192596, -0.0665956661105156, -...
\n","
poor
\n","
太糟糕了!!
\n","
[太糟糕了!!]
\n","
0.999621
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [-0.025874070823192596, -0.0665956661105156, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"markdown","metadata":{"id":"9h7CvN4uu9Pb"},"source":["# Model understands Afrikaans\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"VMPhbgw9twtf","executionInfo":{"status":"ok","timestamp":1620198337259,"user_tz":-300,"elapsed":2884820,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"16a873e7-bf3f-4d4e-f28f-a488b7df7e00"},"source":["\t\t\n","# Afrikaans for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"Dit was die beste verblyf in my lewe, ek was mal daaroor !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.027523020282387733, -0.034727904945611954, ...
\n","
great
\n","
Dit was die beste verblyf in my lewe, ek was m...
\n","
[Dit was die beste verblyf in my lewe, ek was ...
\n","
0.999458
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.027523020282387733, -0.034727904945611954, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"zWgNTIdkumhX","executionInfo":{"status":"ok","timestamp":1620198338526,"user_tz":-300,"elapsed":2886068,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"ea6ff455-cb57-45db-dc5a-7dd349c4e9b7"},"source":["# Afrikaans for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"Dit was die slegste verblyf in my lewe, ek het dit gehaat !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.020533405244350433, -0.044794000685214996, ...
\n","
poor
\n","
Dit was die slegste verblyf in my lewe, ek het...
\n","
[Dit was die slegste verblyf in my lewe, ek he...
\n","
0.994397
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.020533405244350433, -0.044794000685214996, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"rSEPkC-Bwnpg"},"source":["# The model understands Vietnamese\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"7ksJosuTOYpE","executionInfo":{"status":"ok","timestamp":1620198339068,"user_tz":-300,"elapsed":2886589,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"c866d07f-87ab-4d52-f1b1-34e30a478dfe"},"source":["# Vietnamese for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"Đó là kỳ nghỉ tuyệt vời nhất trong đời tôi, tôi yêu nó !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [-0.011885211803019047, -0.06412354856729507, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"IlkmAaMoxTuy"},"source":["# The model understands Japanese\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"1IfJu3q8wwUt","executionInfo":{"status":"ok","timestamp":1620198340855,"user_tz":-300,"elapsed":2888342,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"b651a4da-8344-44bb-a076-9c287a65aa89"},"source":["# Japanese for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"それは私の人生で最高の滞在でした、私はそれを愛していました!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.03218996152281761, -0.045211367309093475, -...
\n","
great
\n","
それは私の人生で最高の滞在でした、私はそれを愛していました!!
\n","
[それは私の人生で最高の滞在でした、私はそれを愛していました!!]
\n","
0.999946
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.03218996152281761, -0.045211367309093475, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"-RjXWbFIPvIs","executionInfo":{"status":"ok","timestamp":1620198341788,"user_tz":-300,"elapsed":2889260,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"176a9c53-165c-46e8-bf12-4347064b298e"},"source":["# Japanese for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"人生最悪の滞在でした、嫌いでした!! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.0015585105866193771, -0.04200108349323273, ...
\n","
poor
\n","
人生最悪の滞在でした、嫌いでした!!
\n","
[人生最悪の滞在でした、嫌いでした!!]
\n","
0.999895
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.0015585105866193771, -0.04200108349323273, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"GITfT7FK0CGv"},"source":["# The model understands Zulu\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"ifRhs6e7OcR3","executionInfo":{"status":"ok","timestamp":1620198342364,"user_tz":-300,"elapsed":2889821,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"e00114d7-4b8d-4ef1-d2ba-93fa0a2cbcf5"},"source":["# Zulu for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"Kwakungukuhlala okuhle kakhulu empilweni yami, ngangikuthanda !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.020903216674923897, -0.05406734347343445, -...
\n","
great
\n","
Kwakungukuhlala okuhle kakhulu empilweni yami,...
\n","
[Kwakungukuhlala okuhle kakhulu empilweni yami...
\n","
0.999816
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.020903216674923897, -0.05406734347343445, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"6uelDwq4xdWv","executionInfo":{"status":"ok","timestamp":1620198343127,"user_tz":-300,"elapsed":2890569,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"d503e624-8998-446e-d50d-96250c5de876"},"source":["# Zulu for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"Kwakuwukuhlala okubi kakhulu empilweni yami, ngangikuzonda !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.012615696527063847, -0.05433850735425949, -...
\n","
poor
\n","
Kwakuwukuhlala okubi kakhulu empilweni yami, n...
\n","
[Kwakuwukuhlala okubi kakhulu empilweni yami, ...
\n","
0.998103
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.012615696527063847, -0.05433850735425949, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"VGVvzl_30a0T"},"source":["# The Model understands Turkish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"DRNnuEeQz2pd","executionInfo":{"status":"ok","timestamp":1620198344168,"user_tz":-300,"elapsed":2891594,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"d6472c75-d880-4400-f766-6f16df786e56"},"source":["\n","# Turkish for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"Hayatımın en iyi kalışıydı, onu sevdim! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.01813078671693802, -0.01860048621892929, -0...
\n","
great
\n","
Hayatımın en iyi kalışıydı, onu sevdim!
\n","
[Hayatımın en iyi kalışıydı, onu sevdim!]
\n","
0.999777
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.01813078671693802, -0.01860048621892929, -0... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"aOSsiK6J0jWs","executionInfo":{"status":"ok","timestamp":1620198345052,"user_tz":-300,"elapsed":2892459,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"dba6c1dc-5efe-4756-c067-55b0e9c45c0f"},"source":["# Turkish for: 'It was awful!! '\n","fitted_pipe.predict(\"Berbattı!!\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.00401829881593585, -0.05757148563861847, -0...
\n","
poor
\n","
Berbattı!!
\n","
[Berbattı!!]
\n","
0.999184
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.00401829881593585, -0.05757148563861847, -0... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"markdown","metadata":{"id":"803qL2gt0vlb"},"source":["# The Model understands Hebrew\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"XQ5VCtxw0pc0","executionInfo":{"status":"ok","timestamp":1620198345808,"user_tz":-300,"elapsed":2893201,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"f113bd48-aff7-471d-d792-d98f207442b6"},"source":["\t\t\n","# Hebrew for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"זה היה השהייה הכי טובה בחיי, אהבתי את זה !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.021775638684630394, -0.041435129940509796, ...
\n","
great
\n","
זה היה השהייה הכי טובה בחיי, אהבתי את זה !!
\n","
[זה היה השהייה הכי טובה בחיי, אהבתי את זה !, !]
\n","
0.999886
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.021775638684630394, -0.041435129940509796, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"9w2ZHfns05A4","executionInfo":{"status":"ok","timestamp":1620198346343,"user_tz":-300,"elapsed":2893722,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"5c48f33a-3123-4f1b-9558-d4f775f41795"},"source":["# Hebrew for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"זה היה השהייה הגרועה בחיי, שנאתי את זה !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.02067272923886776, -0.04731830209493637, -0...
\n","
poor
\n","
זה היה השהייה הגרועה בחיי, שנאתי את זה !!
\n","
[זה היה השהייה הגרועה בחיי, שנאתי את זה !, !]
\n","
0.994378
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.02067272923886776, -0.04731830209493637, -0... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"SDlpd33H1HIX"},"source":["# The Model understands Telugu\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"Kc5n1bzv1BJT","executionInfo":{"status":"ok","timestamp":1620198347793,"user_tz":-300,"elapsed":2895149,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"f220ad9c-7148-466e-86f9-ada2c63c432d"},"source":["# Telugu for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"ఇది నా జీవితంలో ఉత్తమమైన కాలం, నేను దానిని ఇష్టపడ్డాను !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.018417486920952797, -0.05317000299692154, -...
\n","
great
\n","
ఇది నా జీవితంలో ఉత్తమమైన కాలం, నేను దానిని ఇష్...
\n","
[ఇది నా జీవితంలో ఉత్తమమైన కాలం, నేను దానిని ఇష...
\n","
0.999768
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.018417486920952797, -0.05317000299692154, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":25}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"-l-u6vrz1Obe","executionInfo":{"status":"ok","timestamp":1620198348624,"user_tz":-300,"elapsed":2895964,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"ac93039e-bc03-44c8-c68a-33232a908cad"},"source":["\t\t\n","# Telugu for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"ఇది నా జీవితంలో చెత్తగా ఉంది, నేను అసహ్యించుకున్నాను !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.0009551368420943618, -0.055933333933353424...
\n","
poor
\n","
ఇది నా జీవితంలో చెత్తగా ఉంది, నేను అసహ్యించుకు...
\n","
[ఇది నా జీవితంలో చెత్తగా ఉంది, నేను అసహ్యించుక...
\n","
0.997909
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [-0.0009551368420943618, -0.055933333933353424... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"nziBUe8t1Zwn"},"source":["# Model understands Russian\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"Ckyjl3YQ1VFn","executionInfo":{"status":"ok","timestamp":1620198349365,"user_tz":-300,"elapsed":2896690,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"dc8a0ec9-45b1-4b57-e99d-d93dc973c9fd"},"source":["# Russian for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"Это был лучший отдых в моей жизни, мне очень понравилось !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.024515425786376, -0.038578882813453674, -0....
\n","
great
\n","
Это был лучший отдых в моей жизни, мне очень п...
\n","
[Это был лучший отдых в моей жизни, мне очень ...
\n","
0.999933
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.024515425786376, -0.038578882813453674, -0.... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"GIdWkfGv1gFz","executionInfo":{"status":"ok","timestamp":1620198350023,"user_tz":-300,"elapsed":2897334,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"20d306a4-a129-4254-f790-783bcb5f096d"},"source":["# Russian for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"Это было худшее пребывание в моей жизни, я ненавидел его !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.006281793583184481, -0.03218899294734001, ...
\n","
poor
\n","
Это было худшее пребывание в моей жизни, я нен...
\n","
[Это было худшее пребывание в моей жизни, я не...
\n","
0.99909
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [-0.006281793583184481, -0.03218899294734001, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"markdown","metadata":{"id":"8R1j9mwz2Cm4"},"source":["# Model understands Urdu\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"j4zwvRV11pcG","executionInfo":{"status":"ok","timestamp":1620198350894,"user_tz":-300,"elapsed":2898189,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"abd208d6-a177-43be-8cf5-10a9c519054a"},"source":["\t\t\n","# Urdu for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"یہ میری زندگی کا بہترین قیام تھا ، مجھے اس سے پیار تھا !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.016523491591215134, -0.043619364500045776, ...
\n","
great
\n","
یہ میری زندگی کا بہترین قیام تھا ، مجھے اس سے ...
\n","
[یہ میری زندگی کا بہترین قیام تھا ، مجھے اس سے...
\n","
0.99906
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.016523491591215134, -0.043619364500045776, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SxzTuK4b2UKV","executionInfo":{"status":"ok","timestamp":1620198352126,"user_tz":-300,"elapsed":2899401,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"d86aa2c7-5b93-4c26-d88c-c0f78c6d20b3"},"source":["\n","# Urdu for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"یہ میری زندگی کا بدترین قیام تھا ، مجھے اس سے نفرت تھی !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.010332365520298481, -0.032978493720293045, ...
\n","
poor
\n","
یہ میری زندگی کا بدترین قیام تھا ، مجھے اس سے ...
\n","
[یہ میری زندگی کا بدترین قیام تھا ، مجھے اس سے...
\n","
0.999414
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.010332365520298481, -0.032978493720293045, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"markdown","metadata":{"id":"RoNg-C3k1qcX"},"source":["# Model understands Hindi\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"QZ9RT5Wv1r1n","executionInfo":{"status":"ok","timestamp":1620198352904,"user_tz":-300,"elapsed":2900164,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"da03d122-ea78-49be-f32a-d13881ade6c6"},"source":["\n","\t\t\n","# hindi for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"यह मेरे जीवन का सबसे अच्छा प्रवास था, मुझे यह पसंद था !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.02085619606077671, -0.03468615561723709, -0...
\n","
great
\n","
यह मेरे जीवन का सबसे अच्छा प्रवास था, मुझे यह ...
\n","
[यह मेरे जीवन का सबसे अच्छा प्रवास था, मुझे यह...
\n","
0.999576
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.02085619606077671, -0.03468615561723709, -0... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"quM-IL2i12-B","executionInfo":{"status":"ok","timestamp":1620198353928,"user_tz":-300,"elapsed":2901160,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"e9441983-11d1-40ef-a3fb-9226090f1286"},"source":["\t\t\n","# hindi for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"यह मेरे जीवन का सबसे बुरा पड़ाव था, मुझे इससे नफरत थी !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.016908325254917145, -0.031606487929821014, ...
\n","
poor
\n","
यह मेरे जीवन का सबसे बुरा पड़ाव था, मुझे इससे ...
\n","
[यह मेरे जीवन का सबसे बुरा पड़ाव था, मुझे इससे...
\n","
0.997818
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.016908325254917145, -0.031606487929821014, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"markdown","metadata":{"id":"R4ByHOZn35Lc"},"source":["# The model understands Tartar\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"2JrzusSQ18F5","executionInfo":{"status":"ok","timestamp":1620198354440,"user_tz":-300,"elapsed":2901648,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"b6dbc736-7f45-4f3f-a6e9-f8670e104cee"},"source":["# Tartar for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"Бу минем тормышымның иң яхшы торышы иде, мин аны яраттым !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.03618491441011429, -0.039926089346408844, -...
\n","
great
\n","
Бу минем тормышымның иң яхшы торышы иде, мин а...
\n","
[Бу минем тормышымның иң яхшы торышы иде, мин ...
\n","
0.998828
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.03618491441011429, -0.039926089346408844, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"J06Xm_Ln4AYu","executionInfo":{"status":"ok","timestamp":1620198355266,"user_tz":-300,"elapsed":2902464,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"41025457-6042-43d2-a59d-7f5bf8473b0d"},"source":["# Tartar for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"Бу минем тормышымның иң начар торышы иде, мин аны нәфрәт иттем !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.02321663685142994, -0.029130201786756516, -...
\n","
poor
\n","
Бу минем тормышымның иң начар торышы иде, мин ...
\n","
[Бу минем тормышымның иң начар торышы иде, мин...
\n","
0.997359
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.02321663685142994, -0.029130201786756516, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"markdown","metadata":{"id":"HKj5yWwwMplH"},"source":["# The Model understands French\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"CUHcJZfJMplL","executionInfo":{"status":"ok","timestamp":1620198356607,"user_tz":-300,"elapsed":2903761,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"fb255bf5-95c1-4294-d356-d29b9fa01a6f"},"source":["# French for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"C'était le meilleur séjour de ma vie, j'ai adoré !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.01606627181172371, -0.04228140786290169, -0...
\n","
great
\n","
C'était le meilleur séjour de ma vie, j'ai ado...
\n","
[C'était le meilleur séjour de ma vie, j'ai ad...
\n","
0.999963
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.01606627181172371, -0.04228140786290169, -0... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"57NY2XoTMplM","executionInfo":{"status":"ok","timestamp":1620198357772,"user_tz":-300,"elapsed":2904893,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"a3438d73-be2b-4062-a530-9c24e50552c1"},"source":["# French for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"C'était le pire séjour de ma vie, je l'ai détesté !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.01693568006157875, -0.04099532589316368, -0...
\n","
poor
\n","
C'était le pire séjour de ma vie, je l'ai déte...
\n","
[C'était le pire séjour de ma vie, je l'ai dét...
\n","
0.993391
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.01693568006157875, -0.04099532589316368, -0... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"markdown","metadata":{"id":"jD2TBgT0Nq6F"},"source":["# The Model understands Thai\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"gBp11S5GNq6S","executionInfo":{"status":"ok","timestamp":1620198358335,"user_tz":-300,"elapsed":2905437,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"9923f5b0-b87b-4a8d-f724-40686380863f"},"source":["\t\t\n","# Thai for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"เป็นการพักที่ดีที่สุดในชีวิตฉันชอบมาก !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.009586114436388016, -0.03715920448303223, ...
\n","
great
\n","
เป็นการพักที่ดีที่สุดในชีวิตฉันชอบมาก !!
\n","
[เป็นการพักที่ดีที่สุดในชีวิตฉันชอบมาก !!]
\n","
0.99977
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [-0.009586114436388016, -0.03715920448303223, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"R6nKI7C3QKa3","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620198359199,"user_tz":-300,"elapsed":2906273,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"7182a070-f20d-4fcf-8f41-e81e8bddeefa"},"source":["\t\t\n","# Thai for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"เป็นการพักที่แย่ที่สุดในชีวิตฉันเกลียดมัน !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.017850901931524277, -0.04203420504927635, ...
\n","
poor
\n","
เป็นการพักที่แย่ที่สุดในชีวิตฉันเกลียดมัน !!
\n","
[เป็นการพักที่แย่ที่สุดในชีวิตฉันเกลียดมัน !!]
\n","
0.995932
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [-0.017850901931524277, -0.04203420504927635, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"mLItI4KZOElB"},"source":["# The Model understands Khmer\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"SWbqMgAwOElC","executionInfo":{"status":"ok","timestamp":1620198359905,"user_tz":-300,"elapsed":2906957,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"4237cadb-40f2-447c-c4a5-2d4deb1d2e26"},"source":["# Khmer for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"វាជាការស្នាក់នៅដ៏ល្អបំផុតក្នុងជីវិតខ្ញុំស្រឡាញ់វាណាស់ !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.005058396607637405, -0.05372818931937218, -...
\n","
great
\n","
វាជាការស្នាក់នៅដ៏ល្អបំផុតក្នុងជីវិតខ្ញុំស្រឡាញ...
\n","
[វាជាការស្នាក់នៅដ៏ល្អបំផុតក្នុងជីវិតខ្ញុំស្រឡា...
\n","
0.999139
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.005058396607637405, -0.05372818931937218, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"beoCtm4xQf2P","executionInfo":{"status":"ok","timestamp":1620198360466,"user_tz":-300,"elapsed":2907497,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"283ec20e-a215-4472-9365-c7c48736114c"},"source":["# Khmer for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"វាជាការស្នាក់នៅដ៏អាក្រក់បំផុតក្នុងជីវិតខ្ញុំស្អប់វាណាស់ !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.01400111336261034, -0.046681344509124756, ...
\n","
poor
\n","
វាជាការស្នាក់នៅដ៏អាក្រក់បំផុតក្នុងជីវិតខ្ញុំស្...
\n","
[វាជាការស្នាក់នៅដ៏អាក្រក់បំផុតក្នុងជីវិតខ្ញុំស...
\n","
0.999332
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [-0.01400111336261034, -0.046681344509124756, ... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"markdown","metadata":{"id":"lvE-LbNiPoBT"},"source":["# The Model understands Yiddish\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"sZlmLhajPoBb","executionInfo":{"status":"ok","timestamp":1620198361579,"user_tz":-300,"elapsed":2908595,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"27cfc11e-7e79-42b3-c39d-1992ca0855f9"},"source":["\t\t\n","# Yiddish for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"דאָס איז געווען דער בעסטער בלייַבן פון מיין לעבן, איך ליב געהאט עס !! \")\n","\t\t"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.00014696447760798037, -0.05793645977973938,... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"markdown","metadata":{"id":"XSz4WzScaAHj"},"source":["# The Model understands Kygrgyz\n",""]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"DXz6fhJSaAHu","executionInfo":{"status":"ok","timestamp":1620198362814,"user_tz":-300,"elapsed":2909788,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"862885ed-dc41-4120-acad-b98752927b28"},"source":["\n","# Kygrgyz for: 'It was the best stay of my life, I loved it!!'\n","fitted_pipe.predict(\"Бул менин жашоомдогу эң жакшы жашоо болду, мен аны жакшы көрчүмүн !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.03286881372332573, -0.046127691864967346, -...
\n","
great
\n","
Бул менин жашоомдогу эң жакшы жашоо болду, мен...
\n","
[Бул менин жашоомдогу эң жакшы жашоо болду, ме...
\n","
0.99892
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.03286881372332573, -0.046127691864967346, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":97},"id":"lh_ZSHlPaAHv","executionInfo":{"status":"ok","timestamp":1620198363774,"user_tz":-300,"elapsed":2910734,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"25d6c89e-4e7f-4c80-c109-5fd10b2ec646"},"source":["# Kygrgyz for: 'It was the worst stay of my life, I hated it!!'\n","fitted_pipe.predict(\"Бул менин жашоомдогу эң жаман калуу болду, мен аны жек көрдүм !! \")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_labse
\n","
trained_classifier
\n","
document
\n","
sentence
\n","
trained_classifier_confidence_confidence
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.023127034306526184, -0.04409907013177872, -...
\n","
poor
\n","
Бул менин жашоомдогу эң жаман калуу болду, мен...
\n","
[Бул менин жашоомдогу эң жаман калуу болду, ме...
\n","
0.998581
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_labse ... origin_index\n","0 [0.023127034306526184, -0.04409907013177872, -... ... 0\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620199174424,"user_tz":-300,"elapsed":3721344,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"51d06cab-b03f-4958-ace7-a18747ab13a4"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1620199461885,"user_tz":-300,"elapsed":128907,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"56d69686-c2e0-4c0c-8577-c5811ad855ab"},"source":["stored_model_path = './models/classifier_dl_trained' \n","hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('It was a good experince!')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_from_disk
\n","
from_disk_confidence_confidence
\n","
sentence
\n","
text
\n","
origin_index
\n","
from_disk
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[[0.059547893702983856, -0.039290569722652435,...
\n","
[0.99978]
\n","
[It was a good experince!]
\n","
It was a good experince!
\n","
8589934592
\n","
[great]
\n","
It was a good experince!
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_from_disk ... document\n","0 [[0.059547893702983856, -0.039290569722652435,... ... It was a good experince!\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620199462950,"user_tz":-300,"elapsed":1055,"user":{"displayName":"Gammer Otaku","photoUrl":"","userId":"18042713576744284398"}},"outputId":"6dd1bf2a-b862-413c-a4cb-d3870b6613b5"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@2fc02b14) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@2fc02b14\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['bert_sentence@labse'] has settable params:\n","pipe['bert_sentence@labse'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert_sentence@labse'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert_sentence@labse'].setDimension(768) | Info: Number of embedding dimensions | Currently set to : 768\n","pipe['bert_sentence@labse'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert_sentence@labse'].setIsLong(False) | Info: Use Long type instead of Int type for inputs buffer - Some Bert models require Long instead of Int. | Currently set to : False\n","pipe['bert_sentence@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n",">>> pipe['classifier_dl@labse'] has settable params:\n","pipe['classifier_dl@labse'].setClasses(['average', 'great', 'poor']) | Info: get the tags used to trained this ClassifierDLModel | Currently set to : ['average', 'great', 'poor']\n","pipe['classifier_dl@labse'].setStorageRef('labse') | Info: unique reference name for identification | Currently set to : labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"c4Z3pq6jZ-m-"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/named_entity_recognition/NLU_training_NER_demo.ipynb b/examples/colab/Training/named_entity_recognition/NLU_training_NER_demo.ipynb
deleted file mode 100644
index 568a5a74..00000000
--- a/examples/colab/Training/named_entity_recognition/NLU_training_NER_demo.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_NER_demo.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/named_entity_recognition/NLU_training_NER_demo.ipynb)\n","\n","\n","\n","# Training a Named Entity Recognition (NER) model with NLU \n","With the [NER_DL model](https://nlp.johnsnowlabs.com/docs/en/annotators#ner-dl-named-entity-recognition-deep-learning-annotator) from Spark NLP you can achieve State Of the Art results on any NER problem \n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191530267,"user_tz":-300,"elapsed":115129,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"23d94588-aeb0-4b83-fc5c-9345a0274e99"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:10:15-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 05:10:16 (1.54 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 72kB/s \n","\u001b[K |████████████████████████████████| 153kB 52.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 46.2MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download conll2003 dataset"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1620191531629,"user_tz":-300,"elapsed":116460,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"ba3d6f05-3c79-4939-f31e-437137762cd3"},"source":["! wget https://github.com/patverga/torch-ner-nlp-from-scratch/raw/master/data/conll2003/eng.train"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 05:12:10-- https://github.com/patverga/torch-ner-nlp-from-scratch/raw/master/data/conll2003/eng.train\n","Resolving github.com (github.com)... 140.82.114.3\n","Connecting to github.com (github.com)|140.82.114.3|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://raw.githubusercontent.com/patverga/torch-ner-nlp-from-scratch/master/data/conll2003/eng.train [following]\n","--2021-05-05 05:12:10-- https://raw.githubusercontent.com/patverga/torch-ner-nlp-from-scratch/master/data/conll2003/eng.train\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 3283420 (3.1M) [text/plain]\n","Saving to: ‘eng.train’\n","\n","eng.train 100%[===================>] 3.13M --.-KB/s in 0.09s \n","\n","2021-05-05 05:12:11 (36.4 MB/s) - ‘eng.train’ saved [3283420/3283420]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.ner')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":199},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1620192283869,"user_tz":-300,"elapsed":868676,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a62cbe12-b235-482a-87e9-98d5f5de5444"},"source":["import nlu\n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# Since there are no\n","train_path = '/content/eng.train'\n","trainable_pipe = nlu.load('train.ner')\n","fitted_pipe = trainable_pipe.fit(dataset_path=train_path)\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict('Donald Trump and Angela Merkel dont share many oppinions')\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n","glove_100d download started this may take some time.\n","Approximate size to download 145.3 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
word_embedding_glove
\n","
document
\n","
entities_class
\n","
entities
\n","
token
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[Donald Trump and Angela Merkel dont share man...
\n","
[[-0.5496799945831299, -0.488319993019104, 0.5...
\n","
Donald Trump and Angela Merkel dont share many...
\n","
[PER, PER]
\n","
[Donald Trump, Angela Merkel]
\n","
[Donald, Trump, and, Angela, Merkel, dont, sha...
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... origin_index\n","0 [Donald Trump and Angela Merkel dont share man... ... 0\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"owFhjKqzQiv5","executionInfo":{"status":"ok","timestamp":1620192283871,"user_tz":-300,"elapsed":868665,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"327dc82a-49c2-4362-c982-31e8208ca9f4"},"source":["# Check out the Parameters of the NER model we can configure\n","trainable_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['named_entity_recognizer_dl'] has settable params:\n","pipe['named_entity_recognizer_dl'].setMinEpochs(0) | Info: Minimum number of epochs to train | Currently set to : 0\n","pipe['named_entity_recognizer_dl'].setMaxEpochs(2) | Info: Maximum number of epochs to train | Currently set to : 2\n","pipe['named_entity_recognizer_dl'].setLr(0.001) | Info: Learning Rate | Currently set to : 0.001\n","pipe['named_entity_recognizer_dl'].setPo(0.005) | Info: Learning rate decay coefficient. Real Learning Rage = lr / (1 + po * epoch) | Currently set to : 0.005\n","pipe['named_entity_recognizer_dl'].setBatchSize(8) | Info: Batch size | Currently set to : 8\n","pipe['named_entity_recognizer_dl'].setDropout(0.5) | Info: Dropout coefficient | Currently set to : 0.5\n","pipe['named_entity_recognizer_dl'].setVerbose(0) | Info: Level of verbosity during training | Currently set to : 0\n","pipe['named_entity_recognizer_dl'].setUseContrib(True) | Info: whether to use contrib LSTM Cells. Not compatible with Windows. Might slightly improve accuracy. | Currently set to : True\n","pipe['named_entity_recognizer_dl'].setValidationSplit(0.0) | Info: Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off. | Currently set to : 0.0\n","pipe['named_entity_recognizer_dl'].setEvaluationLogExtended(False) | Info: Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off. | Currently set to : False\n","pipe['named_entity_recognizer_dl'].setIncludeConfidence(True) | Info: whether to include confidence scores in annotation metadata | Currently set to : True\n","pipe['named_entity_recognizer_dl'].setEnableOutputLogs(False) | Info: Whether to use stdout in addition to Spark logs. | Currently set to : False\n","pipe['named_entity_recognizer_dl'].setEnableMemoryOptimizer(False) | Info: Whether to optimize for large datasets or not. Enabling this option can slow down training. | Currently set to : False\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@4b329158) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@4b329158\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['glove@glove_100d'] has settable params:\n","pipe['glove@glove_100d'].setIncludeStorage(True) | Info: whether to include indexed storage in trained model | Currently set to : True\n","pipe['glove@glove_100d'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['glove@glove_100d'].setDimension(100) | Info: Number of embedding dimensions | Currently set to : 100\n","pipe['glove@glove_100d'].setStorageRef('glove_100d') | Info: unique reference name for identification | Currently set to : glove_100d\n",">>> pipe['default_tokenizer'] has settable params:\n","pipe['default_tokenizer'].setTargetPattern('\\S+') | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n","pipe['default_tokenizer'].setContextChars(['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]) | Info: character list used to separate from token boundaries | Currently set to : ['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]\n","pipe['default_tokenizer'].setCaseSensitiveExceptions(True) | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n","pipe['default_tokenizer'].setMinLength(0) | Info: Set the minimum allowed legth for each token | Currently set to : 0\n","pipe['default_tokenizer'].setMaxLength(99999) | Info: Set the maximum allowed legth for each token | Currently set to : 99999\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['chunk_converter@entities'] has settable params:\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"25RTuUXMFyEA"},"source":["# 4. Lets use BERT embeddings instead of the default Glove_100d ones!"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QMxPpeiDGNVi","executionInfo":{"status":"ok","timestamp":1620192283872,"user_tz":-300,"elapsed":868623,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3e37419d-f473-4282-9a72-5fd03a5153c2"},"source":["# We can use nlu.print_components(action='embed') to see every possibler sentence embedding we could use. Lets use bert!\n","nlu.print_components(action='embed')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed') returns Spark NLP model glove_100d\n","nlu.load('en.embed.glove') returns Spark NLP model glove_100d\n","nlu.load('en.embed.glove.100d') returns Spark NLP model glove_100d\n","nlu.load('en.embed.bert') returns Spark NLP model bert_base_uncased\n","nlu.load('en.embed.bert.base_uncased') returns Spark NLP model bert_base_uncased\n","nlu.load('en.embed.bert.base_cased') returns Spark NLP model bert_base_cased\n","nlu.load('en.embed.bert.large_uncased') returns Spark NLP model bert_large_uncased\n","nlu.load('en.embed.bert.large_cased') returns Spark NLP model bert_large_cased\n","nlu.load('en.embed.biobert') returns Spark NLP model biobert_pubmed_base_cased\n","nlu.load('en.embed.biobert.pubmed_base_cased') returns Spark NLP model biobert_pubmed_base_cased\n","nlu.load('en.embed.biobert.pubmed_large_cased') returns Spark NLP model biobert_pubmed_large_cased\n","nlu.load('en.embed.biobert.pmc_base_cased') returns Spark NLP model biobert_pmc_base_cased\n","nlu.load('en.embed.biobert.pubmed_pmc_base_cased') returns Spark NLP model biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed.biobert.clinical_base_cased') returns Spark NLP model biobert_clinical_base_cased\n","nlu.load('en.embed.biobert.discharge_base_cased') returns Spark NLP model biobert_discharge_base_cased\n","nlu.load('en.embed.elmo') returns Spark NLP model elmo\n","nlu.load('en.embed.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed.albert.base_uncased') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed.albert.large_uncased') returns Spark NLP model albert_large_uncased\n","nlu.load('en.embed.albert.xlarge_uncased') returns Spark NLP model albert_xlarge_uncased\n","nlu.load('en.embed.albert.xxlarge_uncased') returns Spark NLP model albert_xxlarge_uncased\n","nlu.load('en.embed.xlnet') returns Spark NLP model xlnet_base_cased\n","nlu.load('en.embed.xlnet_base_cased') returns Spark NLP model xlnet_base_cased\n","nlu.load('en.embed.xlnet_large_cased') returns Spark NLP model xlnet_large_cased\n","nlu.load('en.embed.electra') returns Spark NLP model electra_small_uncased\n","nlu.load('en.embed.electra.small_uncased') returns Spark NLP model electra_small_uncased\n","nlu.load('en.embed.electra.base_uncased') returns Spark NLP model electra_base_uncased\n","nlu.load('en.embed.electra.large_uncased') returns Spark NLP model electra_large_uncased\n","nlu.load('en.embed.covidbert') returns Spark NLP model covidbert_large_uncased\n","nlu.load('en.embed.covidbert.large_uncased') returns Spark NLP model covidbert_large_uncased\n","nlu.load('en.embed.bert.small_L2_128') returns Spark NLP model small_bert_L2_128\n","nlu.load('en.embed.bert.small_L4_128') returns Spark NLP model small_bert_L4_128\n","nlu.load('en.embed.bert.small_L6_128') returns Spark NLP model small_bert_L6_128\n","nlu.load('en.embed.bert.small_L8_128') returns Spark NLP model small_bert_L8_128\n","nlu.load('en.embed.bert.small_L10_128') returns Spark NLP model small_bert_L10_128\n","nlu.load('en.embed.bert.small_L12_128') returns Spark NLP model small_bert_L12_128\n","nlu.load('en.embed.bert.small_L2_256') returns Spark NLP model small_bert_L2_256\n","nlu.load('en.embed.bert.small_L4_256') returns Spark NLP model small_bert_L4_256\n","nlu.load('en.embed.bert.small_L6_256') returns Spark NLP model small_bert_L6_256\n","nlu.load('en.embed.bert.small_L8_256') returns Spark NLP model small_bert_L8_256\n","nlu.load('en.embed.bert.small_L10_256') returns Spark NLP model small_bert_L10_256\n","nlu.load('en.embed.bert.small_L12_256') returns Spark NLP model small_bert_L12_256\n","nlu.load('en.embed.bert.small_L2_512') returns Spark NLP model small_bert_L2_512\n","nlu.load('en.embed.bert.small_L4_512') returns Spark NLP model small_bert_L4_512\n","nlu.load('en.embed.bert.small_L6_512') returns Spark NLP model small_bert_L6_512\n","nlu.load('en.embed.bert.small_L8_512') returns Spark NLP model small_bert_L8_512\n","nlu.load('en.embed.bert.small_L10_512') returns Spark NLP model small_bert_L10_512\n","nlu.load('en.embed.bert.small_L12_512') returns Spark NLP model small_bert_L12_512\n","nlu.load('en.embed.bert.small_L2_768') returns Spark NLP model small_bert_L2_768\n","nlu.load('en.embed.bert.small_L4_768') returns Spark NLP model small_bert_L4_768\n","nlu.load('en.embed.bert.small_L6_768') returns Spark NLP model small_bert_L6_768\n","nlu.load('en.embed.bert.small_L8_768') returns Spark NLP model small_bert_L8_768\n","nlu.load('en.embed.bert.small_L10_768') returns Spark NLP model small_bert_L10_768\n","nlu.load('en.embed.bert.small_L12_768') returns Spark NLP model small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('ar.embed') returns Spark NLP model arabic_w2v_cc_300d\n","nlu.load('ar.embed.cbow') returns Spark NLP model arabic_w2v_cc_300d\n","nlu.load('ar.embed.cbow.300d') returns Spark NLP model arabic_w2v_cc_300d\n","nlu.load('ar.embed.aner') returns Spark NLP model arabic_w2v_cc_300d\n","nlu.load('ar.embed.aner.300d') returns Spark NLP model arabic_w2v_cc_300d\n","nlu.load('ar.embed.glove') returns Spark NLP model arabic_w2v_cc_300d\n","For language NLU provides the following Models : \n","nlu.load('bn.embed.glove') returns Spark NLP model bengaliner_cc_300d\n","nlu.load('bn.embed') returns Spark NLP model bengaliner_cc_300d\n","For language NLU provides the following Models : \n","nlu.load('fi.embed.bert.') returns Spark NLP model bert_finnish_cased\n","nlu.load('fi.embed.bert.cased.') returns Spark NLP model bert_finnish_cased\n","nlu.load('fi.embed.bert.uncased.') returns Spark NLP model bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('he.embed') returns Spark NLP model hebrew_cc_300d\n","nlu.load('he.embed.glove') returns Spark NLP model hebrew_cc_300d\n","nlu.load('he.embed.cbow_300d') returns Spark NLP model hebrew_cc_300d\n","For language NLU provides the following Models : \n","nlu.load('hi.embed') returns Spark NLP model hindi_cc_300d\n","For language NLU provides the following Models : \n","nlu.load('fa.embed') returns Spark NLP model persian_w2v_cc_300d\n","nlu.load('fa.embed.word2vec') returns Spark NLP model persian_w2v_cc_300d\n","nlu.load('fa.embed.word2vec.300d') returns Spark NLP model persian_w2v_cc_300d\n","For language NLU provides the following Models : \n","nlu.load('zh.embed') returns Spark NLP model bert_base_chinese\n","nlu.load('zh.embed.bert') returns Spark NLP model bert_base_chinese\n","For language NLU provides the following Models : \n","nlu.load('ur.embed') returns Spark NLP model urduvec_140M_300d\n","nlu.load('ur.embed.glove.300d') returns Spark NLP model urduvec_140M_300d\n","nlu.load('ur.embed.urdu_vec_140M_300d') returns Spark NLP model urduvec_140M_300d\n","For language NLU provides the following Models : \n","nlu.load('xx.embed') returns Spark NLP model glove_840B_300\n","nlu.load('xx.embed.glove.840B_300') returns Spark NLP model glove_840B_300\n","nlu.load('xx.embed.glove.6B_300') returns Spark NLP model glove_6B_300\n","nlu.load('xx.embed.bert_multi_cased') returns Spark NLP model bert_multi_cased\n","nlu.load('xx.embed.bert') returns Spark NLP model bert_multi_cased\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":199},"id":"Xz7xnvbCFxE3","executionInfo":{"status":"ok","timestamp":1620193033467,"user_tz":-300,"elapsed":1617963,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"9b215cae-fab0-4333-dc4b-224fb80f29e4"},"source":["# Add bert word embeddings to pipe \n","fitted_pipe = nlu.load('bert train.ner').fit(dataset_path=train_path)\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict('Donald Trump and Angela Merkel dont share many oppinions')\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["small_bert_L2_128 download started this may take some time.\n","Approximate size to download 16.1 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
document
\n","
word_embedding_bert
\n","
entities_class
\n","
entities
\n","
token
\n","
origin_index
\n","
\n"," \n"," \n","
\n","
0
\n","
[Donald Trump and Angela Merkel dont share man...
\n","
Donald Trump and Angela Merkel dont share many...
\n","
[[-0.447601318359375, 1.0348621606826782, 0.51...
\n","
[PER, PER]
\n","
[Donald Trump, Angela Merkel dont]
\n","
[Donald, Trump, and, Angela, Merkel, dont, sha...
\n","
0
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... origin_index\n","0 [Donald Trump and Angela Merkel dont share man... ... 0\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 5. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620193052249,"user_tz":-300,"elapsed":1636720,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b30131b2-f8ff-443f-d8ff-98b5ff26d4a2"},"source":["stored_model_path = './models/classifier_dl_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/classifier_dl_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 6. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":97},"executionInfo":{"status":"ok","timestamp":1620193057841,"user_tz":-300,"elapsed":1642287,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"2e60866d-9d3b-4785-ce39-cefea4b2ea17"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Donald Trump and Angela Merkel dont share many oppinions on laws about cheeseburgers')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
text
\n","
sentence
\n","
origin_index
\n","
document
\n","
entities_class
\n","
entities
\n","
token
\n","
word_embedding_from_disk
\n","
\n"," \n"," \n","
\n","
0
\n","
Donald Trump and Angela Merkel dont share many...
\n","
[Donald Trump and Angela Merkel dont share man...
\n","
8589934592
\n","
Donald Trump and Angela Merkel dont share many...
\n","
[PER, PER]
\n","
[Donald Trump, Angela Merkel dont]
\n","
[Donald, Trump, and, Angela, Merkel, dont, sha...
\n","
[[-0.6870571374893188, 1.1118954420089722, 0.5...
\n","
\n"," \n","
\n","
"],"text/plain":[" text ... word_embedding_from_disk\n","0 Donald Trump and Angela Merkel dont share many... ... [[-0.6870571374893188, 1.1118954420089722, 0.5...\n","\n","[1 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620193057843,"user_tz":-300,"elapsed":1642279,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"95443872-f5fc-4431-b803-20e2020949c1"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@75127717) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@75127717\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['default_tokenizer'] has settable params:\n","pipe['default_tokenizer'].setCaseSensitiveExceptions(True) | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n","pipe['default_tokenizer'].setTargetPattern('\\S+') | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n","pipe['default_tokenizer'].setMaxLength(99999) | Info: Set the maximum allowed length for each token | Currently set to : 99999\n","pipe['default_tokenizer'].setMinLength(0) | Info: Set the minimum allowed length for each token | Currently set to : 0\n",">>> pipe['bert@small_bert_L2_128'] has settable params:\n","pipe['bert@small_bert_L2_128'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['bert@small_bert_L2_128'].setCaseSensitive(False) | Info: whether to ignore case in tokens for embeddings matching | Currently set to : False\n","pipe['bert@small_bert_L2_128'].setDimension(128) | Info: Number of embedding dimensions | Currently set to : 128\n","pipe['bert@small_bert_L2_128'].setMaxSentenceLength(128) | Info: Max sentence length to process | Currently set to : 128\n","pipe['bert@small_bert_L2_128'].setStorageRef('small_bert_L2_128') | Info: unique reference name for identification | Currently set to : small_bert_L2_128\n",">>> pipe['named_entity_recognizer_dl@small_bert_L2_128'] has settable params:\n","pipe['named_entity_recognizer_dl@small_bert_L2_128'].setBatchSize(8) | Info: Size of every batch | Currently set to : 8\n","pipe['named_entity_recognizer_dl@small_bert_L2_128'].setIncludeConfidence(True) | Info: whether to include confidence scores in annotation metadata | Currently set to : True\n","pipe['named_entity_recognizer_dl@small_bert_L2_128'].setClasses(['O', 'B-ORG', 'I-ORG', 'I-MISC', 'I-PER', 'B-LOC', 'B-MISC', 'I-LOC']) | Info: get the tags used to trained this NerDLModel | Currently set to : ['O', 'B-ORG', 'I-ORG', 'I-MISC', 'I-PER', 'B-LOC', 'B-MISC', 'I-LOC']\n","pipe['named_entity_recognizer_dl@small_bert_L2_128'].setStorageRef('small_bert_L2_128') | Info: unique reference name for identification | Currently set to : small_bert_L2_128\n",">>> pipe['ner_to_chunk_converter'] has settable params:\n","pipe['ner_to_chunk_converter'].setPreservePosition(True) | Info: Whether to preserve the original position of the tokens in the original document or use the modified tokens | Currently set to : True\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"USD6d66Sw6_P"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/Training/part_of_speech/NLU_training_POS_demo.ipynb b/examples/colab/Training/part_of_speech/NLU_training_POS_demo.ipynb
deleted file mode 100644
index a6a23e1a..00000000
--- a/examples/colab/Training/part_of_speech/NLU_training_POS_demo.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_training_POS_demo.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zkufh760uvF3"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/Training/part_of_speech/NLU_training_POS_demo.ipynb)\n","\n","\n","\n","# Training a Named Entity Recognition (POS) model with NLU \n","With the [POS tagger](https://nlp.johnsnowlabs.com/docs/en/annotators#postagger-part-of-speech-tagger) from Spark NLP you can achieve State Of the Art results on any POS problem.\n","It uses an Averaged Percetron Model approach under the hood.\n","\n","This notebook showcases the following features : \n","\n","- How to train the deep learning POS classifier\n","- How to store a pipeline to disk\n","- How to load the pipeline from disk (Enables NLU offline mode)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"dur2drhW5Rvi"},"source":["# 1. Install Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"hFGnBCHavltY"},"source":["import os\n","! apt-get update -qq > /dev/null \n","# Install java\n","! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"PATH\"] = os.environ[\"JAVA_HOME\"] + \"/bin:\" + os.environ[\"PATH\"]\n","! pip install nlu pyspark==2.4.7 > /dev/null \n","\n","import nlu"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"IWp5LbydCkqC"},"source":[""]},{"cell_type":"markdown","metadata":{"id":"f4KkTfnR5Ugg"},"source":["# 2. Download French POS dataset"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OrVb5ZMvvrQD","executionInfo":{"status":"ok","timestamp":1607932039873,"user_tz":-60,"elapsed":80981,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"76f3b769-a646-444b-fdfc-d764d4b74e45"},"source":["! wget https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/fr/pos/UD_French/UD_French-GSD_2.3.txt"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2020-12-14 07:47:19-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/fr/pos/UD_French/UD_French-GSD_2.3.txt\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.143.238\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.143.238|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 3565213 (3.4M) [text/plain]\n","Saving to: ‘UD_French-GSD_2.3.txt’\n","\n","UD_French-GSD_2.3.t 100%[===================>] 3.40M 15.8MB/s in 0.2s \n","\n","2020-12-14 07:47:19 (15.8 MB/s) - ‘UD_French-GSD_2.3.txt’ saved [3565213/3565213]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0296Om2C5anY"},"source":["# 3. Train Deep Learning Classifier using nlu.load('train.pos')\n","\n","You dataset label column should be named 'y' and the feature column with text data should be named 'text'"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3ZIPkRkWftBG","executionInfo":{"status":"ok","timestamp":1607932112061,"user_tz":-60,"elapsed":153158,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c6032381-0446-484a-8c4e-0ad9fc500c48"},"source":["import nlu\n","# load a trainable pipeline by specifying the train. prefix and fit it on a datset with label and text columns\n","# Since there are no\n","train_path = '/content/UD_French-GSD_2.3.txt'\n","trainable_pipe = nlu.load('train.pos')\n","fitted_pipe = trainable_pipe.fit(dataset_path=train_path)\n","\n","# predict with the trainable pipeline on dataset and get predictions\n","preds = fitted_pipe.predict('Donald Trump and Angela Merkel dont share many oppinions')\n","preds"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
token
\n","
pos
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n"," \n"," \n","
\n","
0
\n","
Donald
\n","
PROPN
\n","
\n","
\n","
0
\n","
Trump
\n","
PROPN
\n","
\n","
\n","
0
\n","
and
\n","
CCONJ
\n","
\n","
\n","
0
\n","
Angela
\n","
PROPN
\n","
\n","
\n","
0
\n","
Merkel
\n","
PROPN
\n","
\n","
\n","
0
\n","
dont
\n","
PRON
\n","
\n","
\n","
0
\n","
share
\n","
VERB
\n","
\n","
\n","
0
\n","
many
\n","
ADJ
\n","
\n","
\n","
0
\n","
oppinions
\n","
NOUN
\n","
\n"," \n","
\n","
"],"text/plain":[" token pos\n","origin_index \n","0 Donald PROPN\n","0 Trump PROPN\n","0 and CCONJ\n","0 Angela PROPN\n","0 Merkel PROPN\n","0 dont PRON\n","0 share VERB\n","0 many ADJ\n","0 oppinions NOUN"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"2BB-NwZUoHSe"},"source":["# 4. Lets save the model"]},{"cell_type":"code","metadata":{"id":"eLex095goHwm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1607932114637,"user_tz":-60,"elapsed":155726,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"24d34ea2-dcc1-42b2-a5c6-10d345b76a3c"},"source":["stored_model_path = './models/pos_trained' \n","fitted_pipe.save(stored_model_path)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Stored model in ./models/pos_trained\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"e_b2DPd4rCiU"},"source":["# 5. Lets load the model from HDD.\n","This makes Offlien NLU usage possible! \n","You need to call nlu.load(path=path_to_the_pipe) to load a model/pipeline from disk."]},{"cell_type":"code","metadata":{"id":"SO4uz45MoRgp","colab":{"base_uri":"https://localhost:8080/","height":485},"executionInfo":{"status":"ok","timestamp":1607932120301,"user_tz":-60,"elapsed":161383,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"db790b35-a51d-4226-8a0b-bb3e9e39e368"},"source":["hdd_pipe = nlu.load(path=stored_model_path)\n","\n","preds = hdd_pipe.predict('Donald Trump and Angela Merkel dont share many oppinions on laws about cheeseburgers')\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Fitting on empty Dataframe, could not infer correct training method!\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
token
\n","
pos
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n"," \n"," \n","
\n","
0
\n","
Donald
\n","
PROPN
\n","
\n","
\n","
0
\n","
Trump
\n","
PROPN
\n","
\n","
\n","
0
\n","
and
\n","
CCONJ
\n","
\n","
\n","
0
\n","
Angela
\n","
PROPN
\n","
\n","
\n","
0
\n","
Merkel
\n","
PROPN
\n","
\n","
\n","
0
\n","
dont
\n","
PRON
\n","
\n","
\n","
0
\n","
share
\n","
VERB
\n","
\n","
\n","
0
\n","
many
\n","
ADJ
\n","
\n","
\n","
0
\n","
oppinions
\n","
NOUN
\n","
\n","
\n","
0
\n","
on
\n","
PRON
\n","
\n","
\n","
0
\n","
laws
\n","
VERB
\n","
\n","
\n","
0
\n","
about
\n","
ADV
\n","
\n","
\n","
0
\n","
cheeseburgers
\n","
NOUN
\n","
\n"," \n","
\n","
"],"text/plain":[" token pos\n","origin_index \n","0 Donald PROPN\n","0 Trump PROPN\n","0 and CCONJ\n","0 Angela PROPN\n","0 Merkel PROPN\n","0 dont PRON\n","0 share VERB\n","0 many ADJ\n","0 oppinions NOUN\n","0 on PRON\n","0 laws VERB\n","0 about ADV\n","0 cheeseburgers NOUN"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"e0CVlkk9v6Qi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1607932120301,"user_tz":-60,"elapsed":161374,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"6bb7769e-f545-40b8-f0ef-90fd9f32c149"},"source":["hdd_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n",">>> pipe['sentence_detector'] has settable params:\n","pipe['sentence_detector'].setCustomBounds([]) | Info: characters used to explicitly mark sentence bounds | Currently set to : []\n","pipe['sentence_detector'].setDetectLists(True) | Info: whether detect lists during sentence detection | Currently set to : True\n","pipe['sentence_detector'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector'].setMaxLength(99999) | Info: Set the maximum allowed length for each sentence | Currently set to : 99999\n","pipe['sentence_detector'].setMinLength(0) | Info: Set the minimum allowed length for each sentence. | Currently set to : 0\n","pipe['sentence_detector'].setUseAbbreviations(True) | Info: whether to apply abbreviations at sentence detection | Currently set to : True\n","pipe['sentence_detector'].setUseCustomBoundsOnly(False) | Info: Only utilize custom bounds in sentence detection | Currently set to : False\n",">>> pipe['regex_tokenizer'] has settable params:\n","pipe['regex_tokenizer'].setCaseSensitiveExceptions(True) | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n","pipe['regex_tokenizer'].setTargetPattern('\\S+') | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n","pipe['regex_tokenizer'].setMaxLength(99999) | Info: Set the maximum allowed length for each token | Currently set to : 99999\n","pipe['regex_tokenizer'].setMinLength(0) | Info: Set the minimum allowed length for each token | Currently set to : 0\n",">>> pipe['sentiment_dl'] has settable params:\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"o3jCHbIsMZrn"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/chunkers/NLU_chunking_example.ipynb b/examples/colab/component_examples/chunkers/NLU_chunking_example.ipynb
deleted file mode 100644
index c3ddbc8d..00000000
--- a/examples/colab/component_examples/chunkers/NLU_chunking_example.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_chunking_example.ipynb","provenance":[{"file_id":"1tW833T3HS8F5Lvn6LgeDd5LW5226syKN","timestamp":1599398724652},{"file_id":"1CYzHfQyFCdvIOVO2Z5aggVI9c0hDEOrw","timestamp":1599354735581}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"FgtBtiBmV1fD"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/chunkers/NLU_chunking_example.ipynb)\n","\n","# Grammatical Chunk Matching with NLU\n","With the chunker you can filter a data set based on Part of Speech Tags with Regex patterns. \n"," \n","I.e. You could get all nouns or adjectives in your datset with the following parameterization.\n","```\n","pipe['default_chunker'].setRegexParsers(['+', '+'])\n","```\n","\n","See [here](https://www.rexegg.com/regex-quickstart.html) for a great reference of Regex operators\n","\n","## Overview of all Part of Speech Tags : \n","\n","\n","|Tag |Description | Example|\n","|------|------------|------|\n","|CC| Coordinating conjunction | This batch of mushroom stew is savory **and** delicious |\n","|CD| Cardinal number | Here are **five** coins |\n","|DT| Determiner | **The** bunny went home |\n","|EX| Existential there | **There** is a storm coming |\n","|FW| Foreign word | I'm having a **déjà vu** |\n","|IN| Preposition or subordinating conjunction | He is cleverer **than** I am |\n","|JJ| Adjective | She wore a **beautiful** dress |\n","|JJR| Adjective, comparative | My house is **bigger** than yours |\n","|JJS| Adjective, superlative | I am the **shortest** person in my family |\n","|LS| List item marker | A number of things need to be considered before starting a business **,** such as premises **,** finance **,** product demand **,** staffing and access to customers |\n","|MD| Modal | You **must** stop when the traffic lights turn red |\n","|NN| Noun, singular or mass | The **dog** likes to run |\n","|NNS| Noun, plural | The **cars** are fast |\n","|NNP| Proper noun, singular | I ordered the chair from **Amazon** |\n","|NNPS| Proper noun, plural | We visted the **Kennedys** |\n","|PDT| Predeterminer | **Both** the children had a toy |\n","|POS| Possessive ending | I built the dog'**s** house |\n","|PRP| Personal pronoun | **You** need to stop |\n","|PRP$| Possessive pronoun | Remember not to judge a book by **its** cover |\n","|RB| Adverb | The dog barks **loudly** |\n","|RBR| Adverb, comparative | Could you sing more **quietly** please? |\n","|RBS| Adverb, superlative | Everyone in the race ran fast, but John ran **the fastest** of all |\n","|RP| Particle | He ate **up** all his dinner |\n","|SYM| Symbol | What are you doing **?** |\n","|TO| to | Please send it back **to** me |\n","|UH| Interjection | **Wow!** You look gorgeous |\n","|VB| Verb, base form | We **play** soccer |\n","|VBD| Verb, past tense | I **worked** at a restaurant |\n","|VBG| Verb, gerund or present participle | **Smoking** kills people |\n","|VBN| Verb, past participle | She has **done** her homework |\n","|VBP| Verb, non-3rd person singular present | You **flit** from place to place |\n","|VBZ| Verb, 3rd person singular present | He never **calls** me |\n","|WDT| Wh-determiner | The store honored the complaints, **which** were less than 25 days old |\n","|WP| Wh-pronoun | **Who** can help me? |\n","|WP\\$| Possessive wh-pronoun | **Whose** fault is it? |\n","|WRB| Wh-adverb | **Where** are you going? |\n","\n","\n","\n","\n","\n","\n","\n","\n","Chunks are Named \n"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620179685939,"user_tz":-300,"elapsed":125170,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"eb2572e7-b80f-46a7-be9f-b4889ee9a952"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-05 01:52:41-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-05 01:52:41 (1.19 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 69kB/s \n","\u001b[K |████████████████████████████████| 153kB 44.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.0MB/s \n","\u001b[K |████████████████████████████████| 204kB 33.7MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"NYQRU3pRO146"},"source":["# 2. Load the Chunker and print parameters"]},{"cell_type":"code","metadata":{"id":"pmpZSNvGlyZQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604911109685,"user_tz":-60,"elapsed":116018,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"577793a4-9888-41f7-fd49-431b957b2166"},"source":["import nlu \n","\n","pipe = nlu.load('match.chunks')\n","# Now we print the info to see at which index which com,ponent is and what parameters we can configure on them \n","pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["match_chunks download started this may take some time.\n","Approx size to download 4.3 MB\n","[OK!]\n","The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('disabled') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : disabled\n",">>> pipe['sentence_detector'] has settable params:\n","pipe['sentence_detector'].setCustomBounds([]) | Info: characters used to explicitly mark sentence bounds | Currently set to : []\n","pipe['sentence_detector'].setDetectLists(True) | Info: whether detect lists during sentence detection | Currently set to : True\n","pipe['sentence_detector'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector'].setMaxLength(99999) | Info: Set the maximum allowed length for each sentence | Currently set to : 99999\n","pipe['sentence_detector'].setMinLength(0) | Info: Set the minimum allowed length for each sentence. | Currently set to : 0\n","pipe['sentence_detector'].setUseAbbreviations(True) | Info: whether to apply abbreviations at sentence detection | Currently set to : True\n","pipe['sentence_detector'].setUseCustomBoundsOnly(False) | Info: Only utilize custom bounds in sentence detection | Currently set to : False\n",">>> pipe['regex_tokenizer'] has settable params:\n","pipe['regex_tokenizer'].setCaseSensitiveExceptions(True) | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n","pipe['regex_tokenizer'].setTargetPattern('\\S+') | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n","pipe['regex_tokenizer'].setMaxLength(99999) | Info: Set the maximum allowed length for each token | Currently set to : 99999\n","pipe['regex_tokenizer'].setMinLength(0) | Info: Set the minimum allowed length for each token | Currently set to : 0\n",">>> pipe['sentiment_dl'] has settable params:\n",">>> pipe['default_chunker'] has settable params:\n","pipe['default_chunker'].setRegexParsers(['
?*+']) | Info: an array of grammar based chunk parsers | Currently set to : ['
?*+']\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"9RRmIv9ZbaX3"},"source":["# 3. Configure pipe to only match nounds and adjvectives and predict on data"]},{"cell_type":"code","metadata":{"id":"j2ZZZvr1uGpx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604911117028,"user_tz":-60,"elapsed":123353,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f773883e-31fa-4029-f9c6-86e9fa1387ff"},"source":["# Lets set our Chunker to only match NN\n","pipe['default_chunker'].setRegexParsers(['+', '+'])\n","# Now we can predict with the configured pipeline\n","pipe.predict(\"Jim and Joe went to the big blue market next to the town hall\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
pos
\n","
chunk
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n"," \n"," \n","
\n","
0
\n","
[NNP, CC, NNP, VBD, TO, DT, JJ, JJ, NN, JJ, TO...
\n","
market
\n","
\n","
\n","
0
\n","
[NNP, CC, NNP, VBD, TO, DT, JJ, JJ, NN, JJ, TO...
\n","
town hall
\n","
\n","
\n","
0
\n","
[NNP, CC, NNP, VBD, TO, DT, JJ, JJ, NN, JJ, TO...
\n","
big blue
\n","
\n","
\n","
0
\n","
[NNP, CC, NNP, VBD, TO, DT, JJ, JJ, NN, JJ, TO...
\n","
next
\n","
\n"," \n","
\n","
"],"text/plain":[" pos chunk\n","origin_index \n","0 [NNP, CC, NNP, VBD, TO, DT, JJ, JJ, NN, JJ, TO... market\n","0 [NNP, CC, NNP, VBD, TO, DT, JJ, JJ, NN, JJ, TO... town hall\n","0 [NNP, CC, NNP, VBD, TO, DT, JJ, JJ, NN, JJ, TO... big blue\n","0 [NNP, CC, NNP, VBD, TO, DT, JJ, JJ, NN, JJ, TO... next"]},"metadata":{"tags":[]},"execution_count":3}]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/chunkers/NLU_n-gram.ipynb b/examples/colab/component_examples/chunkers/NLU_n-gram.ipynb
deleted file mode 100644
index 5acf7f73..00000000
--- a/examples/colab/component_examples/chunkers/NLU_n-gram.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_n-gram.ipynb","provenance":[{"file_id":"1JrlfuV2jNGTdOXvaWIoHTSf6BscDMkN7","timestamp":1599401257319},{"file_id":"1svpqtC3cY6JnRGeJngIPl2raqxdowpyi","timestamp":1599400881246},{"file_id":"1tW833T3HS8F5Lvn6LgeDd5LW5226syKN","timestamp":1599398724652},{"file_id":"1CYzHfQyFCdvIOVO2Z5aggVI9c0hDEOrw","timestamp":1599354735581}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"el9TLbo3dgYs"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/chunkers/NLU_n-gram.ipynb)\n","\n","# Getting n-Grams with NLU\n","N-Grams are subsequences of text with N tokens. \n","Some of their applications are used for auto completion of sentences, auto spell check and grammar check. \n","In general they are als overy useful for gaining insight about a text dataset. \n","\n","Examples of n-grams : \n","1. Hello world (is a 2 gram)\n","2. I like peanutbutter (is a 3 gram)\n","3. I like peanutbutter and jelly ( is a 5 gram) \n","\n","\n","\n","\n","\n","\n","# 1. Install Java and NLU"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619905099619,"user_tz":-120,"elapsed":150651,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"7320fa31-3708-47ae-eba3-8bc689e5e729"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:35:49-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-01 21:35:49 (31.0 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 76kB/s \n","\u001b[K |████████████████████████████████| 153kB 15.4MB/s \n","\u001b[K |████████████████████████████████| 204kB 17.0MB/s \n","\u001b[K |████████████████████████████████| 204kB 47.4MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Gph8XOL1Pzpl"},"source":["# 2. Load pipeline and predict on sample data\n","\n","By default NLU is configured to get 2 grams "]},{"cell_type":"code","metadata":{"id":"pmpZSNvGlyZQ","colab":{"base_uri":"https://localhost:8080/","height":348},"executionInfo":{"status":"ok","timestamp":1619905141406,"user_tz":-120,"elapsed":192431,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0b9a728c-db24-48ff-a5b3-1b87322ab670"},"source":["import nlu\n","example_text = [\"A person like Jim or Joe\", \n"," \"An organisation like Microsoft or PETA\",\n"," \"A location like Germany\",\n"," \"Anything else like Playstation\", \n"," \"Person consisting of multiple tokens like Angela Merkel or Donald Trump\",\n"," \"Organisations consisting of multiple tokens like JP Morgan\",\n"," \"Locations consiting of multiple tokens like Los Angeles\", \n"," \"Anything else made up of multiple tokens like Super Nintendo\",]\n","\n","pipe = nlu.load('ngram')\n","pipe.predict(example_text)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
token
\n","
ngram
\n","
\n"," \n"," \n","
\n","
0
\n","
A person like Jim or Joe
\n","
[A person like Jim or Joe]
\n","
[A, person, like, Jim, or, Joe]
\n","
[A person, person like, like Jim, Jim or, or Joe]
\n","
\n","
\n","
1
\n","
An organisation like Microsoft or PETA
\n","
[An organisation like Microsoft or PETA]
\n","
[An, organisation, like, Microsoft, or, PETA]
\n","
[An organisation, organisation like, like Micr...
\n","
\n","
\n","
2
\n","
A location like Germany
\n","
[A location like Germany]
\n","
[A, location, like, Germany]
\n","
[A location, location like, like Germany]
\n","
\n","
\n","
3
\n","
Anything else like Playstation
\n","
[Anything else like Playstation]
\n","
[Anything, else, like, Playstation]
\n","
[Anything else, else like, like Playstation]
\n","
\n","
\n","
4
\n","
Person consisting of multiple tokens like Ange...
\n","
[Person consisting of multiple tokens like Ang...
\n","
[Person, consisting, of, multiple, tokens, lik...
\n","
[Person consisting, consisting of, of multiple...
\n","
\n","
\n","
5
\n","
Organisations consisting of multiple tokens li...
\n","
[Organisations consisting of multiple tokens l...
\n","
[Organisations, consisting, of, multiple, toke...
\n","
[Organisations consisting, consisting of, of m...
\n","
\n","
\n","
6
\n","
Locations consiting of multiple tokens like Lo...
\n","
[Locations consiting of multiple tokens like L...
\n","
[Locations, consiting, of, multiple, tokens, l...
\n","
[Locations consiting, consiting of, of multipl...
\n","
\n","
\n","
7
\n","
Anything else made up of multiple tokens like ...
\n","
[Anything else made up of multiple tokens like...
\n","
[Anything, else, made, up, of, multiple, token...
\n","
[Anything else, else made, made up, up of, of ...
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... ngram\n","0 A person like Jim or Joe ... [A person, person like, like Jim, Jim or, or Joe]\n","1 An organisation like Microsoft or PETA ... [An organisation, organisation like, like Micr...\n","2 A location like Germany ... [A location, location like, like Germany]\n","3 Anything else like Playstation ... [Anything else, else like, like Playstation]\n","4 Person consisting of multiple tokens like Ange... ... [Person consisting, consisting of, of multiple...\n","5 Organisations consisting of multiple tokens li... ... [Organisations consisting, consisting of, of m...\n","6 Locations consiting of multiple tokens like Lo... ... [Locations consiting, consiting of, of multipl...\n","7 Anything else made up of multiple tokens like ... ... [Anything else, else made, made up, up of, of ...\n","\n","[8 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"fvWCtpHCwOYz"},"source":["## Configure the Ngram with custom parameters\n","Use the pipe.print_info() to see all configurable parameters and infos about them for every NLU component in the pipeline pipeline. \n","Even tough only 'ngram' is loaded, many NLU component dependencies are automatically loaded into the pipeline and also configurable. \n","\n","\n","By default the n-gram algorithm is configured with n=2"]},{"cell_type":"code","metadata":{"id":"j2ZZZvr1uGpx","colab":{"base_uri":"https://localhost:8080/","height":406},"executionInfo":{"status":"ok","timestamp":1619905142330,"user_tz":-120,"elapsed":193349,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"ba9aeeeb-f75a-478e-8182-21be37e0c284"},"source":["pipe.print_info()\n","# Lets configure the NGRAM to get get us 5grams\n","pipe['ngram'].setN(5)\n","\n","# Now we can predict with the configured pipeline\n","pipe.predict(\"Jim and Joe went to the market next to the town hall\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['ngram'] has settable params:\n","pipe['ngram'].setN(2) | Info: number elements per n-gram (>=1) | Currently set to : 2\n","pipe['ngram'].setEnableCumulative(False) | Info: whether to calculate just the actual n-grams or all n-grams from 1 through n | Currently set to : False\n",">>> pipe['default_tokenizer'] has settable params:\n","pipe['default_tokenizer'].setTargetPattern('\\S+') | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n","pipe['default_tokenizer'].setContextChars(['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]) | Info: character list used to separate from token boundaries | Currently set to : ['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]\n","pipe['default_tokenizer'].setCaseSensitiveExceptions(True) | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n","pipe['default_tokenizer'].setMinLength(0) | Info: Set the minimum allowed legth for each token | Currently set to : 0\n","pipe['default_tokenizer'].setMaxLength(99999) | Info: Set the maximum allowed legth for each token | Currently set to : 99999\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@4bb2f221) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@4bb2f221\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
token
\n","
ngram
\n","
\n"," \n"," \n","
\n","
0
\n","
Jim and Joe went to the market next to the tow...
\n","
[Jim and Joe went to the market next to the to...
\n","
[Jim, and, Joe, went, to, the, market, next, t...
\n","
[Jim and Joe went to, and Joe went to the, Joe...
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... ngram\n","0 Jim and Joe went to the market next to the tow... ... [Jim and Joe went to, and Joe went to the, Joe...\n","\n","[1 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"JJaMftSyhtYj"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/E2E_classification.ipynb b/examples/colab/component_examples/classifiers/E2E_classification.ipynb
deleted file mode 100644
index 1693ecda..00000000
--- a/examples/colab/component_examples/classifiers/E2E_classification.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"E2E_classification.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"KlgAA9yVHw1n"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/E2E_classification.ipynb)\n","\n","\n","# E2E Classification with NLU \n","\n","# 1. Install Java and NLU"]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620090982581,"user_tz":-120,"elapsed":127433,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"483e0603-0e6e-42ba-f8e4-0c4068b1fa27"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 01:14:15-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0.002s \n","\n","2021-05-04 01:14:16 (993 KB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 74kB/s \n","\u001b[K |████████████████████████████████| 153kB 46.7MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.8MB/s \n","\u001b[K |████████████████████████████████| 204kB 55.8MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yR5KzFUmH9vY"},"source":["# 2. Download E2E model and predict classes for sample string"]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":237},"executionInfo":{"status":"ok","timestamp":1620091135570,"user_tz":-120,"elapsed":280407,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"9b5b1834-2aa8-4ccb-c80a-9d863592f10d"},"source":["import nlu\n","e2e_pipe = nlu.load('e2e')\n","e2e_pipe.predict('E2E is a dataset for training generative models')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["multiclassifierdl_use_e2e download started this may take some time.\n","Approximate size to download 11.7 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
text
\n","
e2e_confidence_confidence
\n","
origin_index
\n","
sentence
\n","
sentence_embedding_tfhub_use
\n","
e2e
\n","
\n"," \n"," \n","
\n","
0
\n","
E2E is a dataset for training generative models
\n","
E2E is a dataset for training generative models
\n","
[0.7497887, 0.7497887, 0.7497887]
\n","
8589934592
\n","
[E2E is a dataset for training generative models]
\n","
[[0.021445205435156822, -0.039284929633140564,...
\n","
[priceRange[£20-25], priceRange[moderate], fam...
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... e2e\n","0 E2E is a dataset for training generative models ... [priceRange[£20-25], priceRange[moderate], fam...\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"d_0V8ZW6Irwf"},"source":["# 3. Download Dataset"]},{"cell_type":"code","metadata":{"id":"gpeS8DWBlrun","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620091147264,"user_tz":-120,"elapsed":292094,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"30415235-c330-49fd-b9ba-a25c8332c3db"},"source":["! wget -N https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv -P /tmp\n","import pandas as pd\n","df = pd.read_csv('/tmp/train-balanced-sarcasm.csv')\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 01:18:55-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.88.253\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.88.253|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 255268960 (243M) [text/csv]\n","Saving to: ‘/tmp/train-balanced-sarcasm.csv’\n","\n","train-balanced-sarc 100%[===================>] 243.44M 47.4MB/s in 5.8s \n","\n","2021-05-04 01:19:01 (41.8 MB/s) - ‘/tmp/train-balanced-sarcasm.csv’ saved [255268960/255268960]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"nx-EnSDPIp7n"},"source":["# 4. Predict on pandas dataset and visualize predictions\n"]},{"cell_type":"code","metadata":{"id":"3V5l-B6nl43U","colab":{"base_uri":"https://localhost:8080/","height":733},"executionInfo":{"status":"ok","timestamp":1620091172935,"user_tz":-120,"elapsed":317759,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"1207d875-6684-4550-9d5b-3f98fbc29b91"},"source":["e2e_pipe = nlu.load('e2e')\n","df['text'] = df['comment']\n","e2e_predictions = e2e_pipe.predict(df['text'].iloc[0:1000], output_level='sentence')\n","e2e_predictions"],"execution_count":null,"outputs":[{"output_type":"stream","text":["multiclassifierdl_use_e2e download started this may take some time.\n","Approximate size to download 11.7 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
text
\n","
e2e_confidence_confidence
\n","
origin_index
\n","
sentence
\n","
sentence_embedding_tfhub_use
\n","
e2e
\n","
\n"," \n"," \n","
\n","
0
\n","
NC and NH.
\n","
NC and NH.
\n","
[0.8991002, 0.8991002, 0.8991002, 0.8991002, 0...
\n","
0
\n","
NC and NH.
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
[eatType[restaurant], name[The Phoenix], price...
\n","
\n","
\n","
1
\n","
You do know west teams play against west teams...
\n","
You do know west teams play against west teams...
\n","
[0.913401, 0.913401]
\n","
1
\n","
You do know west teams play against west teams...
\n","
[-0.0254225991666317, 0.05448468029499054, -0....
\n","
[customer rating[average], near[The Portland A...
\n","
\n","
\n","
2
\n","
They were underdogs earlier today, but since G...
\n","
They were underdogs earlier today, but since G...
\n","
[0.9653829, 0.9653829, 0.9653829, 0.9653829]
\n","
2
\n","
They were underdogs earlier today, but since G...
\n","
[-0.0035701016895473003, -0.030124755576252937...
\n","
[food[French], customer rating[1 out of 5], pr...
\n","
\n","
\n","
3
\n","
This meme isn't funny none of the \"new york ni...
\n","
This meme isn't funny none of the \"new york ni...
\n","
[0.9862992, 0.9862992, 0.9862992, 0.9862992, 0...
\n","
3
\n","
This meme isn't funny none of the \"new york ni...
\n","
[0.06464719027280807, -0.023972544819116592, -...
\n","
[customer rating[low], eatType[coffee shop], p...
\n","
\n","
\n","
4
\n","
I could use one of those tools.
\n","
I could use one of those tools.
\n","
[0.9699397, 0.9699397, 0.9699397, 0.9699397]
\n","
4
\n","
I could use one of those tools.
\n","
[0.028676817193627357, 0.0199710875749588, 0.0...
\n","
[near[Café Sicilia], priceRange[cheap], priceR...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
995
\n","
Have you bound your pistol on mouse wheel?
\n","
Have you bound your pistol on mouse wheel?
\n","
[0.97372156, 0.97372156, 0.97372156, 0.9737215...
\n","
8589935087
\n","
Have you bound your pistol on mouse wheel?
\n","
[-0.04123315587639809, 0.049579471349716187, -...
\n","
[eatType[pub], customer rating[5 out of 5], fo...
\n","
\n","
\n","
996
\n","
Imagine showing that to someone a little over ...
\n","
Imagine showing that to someone a little over ...
\n","
[0.8345744, 0.8345744, 0.8345744]
\n","
8589935088
\n","
Imagine showing that to someone a little over ...
\n","
[0.0263528935611248, -0.06056991219520569, -0....
\n","
[priceRange[moderate], food[Fast food], family...
\n","
\n","
\n","
997
\n","
I wish Schumer and Reid had not endorsed Keith...
\n","
I wish Schumer and Reid had not endorsed Keith...
\n","
[0.9901957, 0.9901957]
\n","
8589935089
\n","
I wish Schumer and Reid had not endorsed Keith...
\n","
[0.07649341225624084, 0.05448545515537262, -0....
\n","
[priceRange[moderate], familyFriendly[no]]
\n","
\n","
\n","
998
\n","
yeah, god forbid jesse look out for his fans b...
\n","
yeah, god forbid jesse look out for his fans b...
\n","
[0.94846725, 0.94846725, 0.94846725, 0.9484672...
\n","
8589935090
\n","
yeah, god forbid jesse look out for his fans b...
\n","
[0.049849480390548706, -0.054164644330739975, ...
\n","
[name[The Wrestlers], customer rating[low], fo...
\n","
\n","
\n","
999
\n","
Beer city USA
\n","
Beer city USA
\n","
[0.8884889, 0.8884889, 0.8884889, 0.8884889, 0...
\n","
8589935091
\n","
Beer city USA
\n","
[-0.05082784965634346, -0.045025862753391266, ...
\n","
[eatType[pub], priceRange[moderate], food[Fast...
\n","
\n"," \n","
\n","
1113 rows × 7 columns
\n","
"],"text/plain":[" document ... e2e\n","0 NC and NH. ... [eatType[restaurant], name[The Phoenix], price...\n","1 You do know west teams play against west teams... ... [customer rating[average], near[The Portland A...\n","2 They were underdogs earlier today, but since G... ... [food[French], customer rating[1 out of 5], pr...\n","3 This meme isn't funny none of the \"new york ni... ... [customer rating[low], eatType[coffee shop], p...\n","4 I could use one of those tools. ... [near[Café Sicilia], priceRange[cheap], priceR...\n",".. ... ... ...\n","995 Have you bound your pistol on mouse wheel? ... [eatType[pub], customer rating[5 out of 5], fo...\n","996 Imagine showing that to someone a little over ... ... [priceRange[moderate], food[Fast food], family...\n","997 I wish Schumer and Reid had not endorsed Keith... ... [priceRange[moderate], familyFriendly[no]]\n","998 yeah, god forbid jesse look out for his fans b... ... [name[The Wrestlers], customer rating[low], fo...\n","999 Beer city USA ... [eatType[pub], priceRange[moderate], food[Fast...\n","\n","[1113 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"WdnY9n1LTmed","colab":{"base_uri":"https://localhost:8080/","height":426},"executionInfo":{"status":"ok","timestamp":1620091256110,"user_tz":-120,"elapsed":1112,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"9e3b3aea-ffac-4eeb-c307-c1e023f164d4"},"source":["e2e_predictions.explode('e2e').e2e.iloc[0:100].value_counts().plot.bar(title='Top 100 E2E classes')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":7},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"iLoh-9CpAWHs"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/NLU_language_classification.ipynb b/examples/colab/component_examples/classifiers/NLU_language_classification.ipynb
deleted file mode 100644
index 6b1c34cf..00000000
--- a/examples/colab/component_examples/classifiers/NLU_language_classification.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_language_classification.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"EUR9cb-iQvNG"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/NLU_language_classification.ipynb)\n","\n","# Language Classification with NLU\n","\n","NLU can differentiate between 300 different languages by leveraging multi lingual embeddings. \n","Some of the supported languages are the following : \n","\n"," - Bulgarian \n"," - Czech\n"," - German\n"," - Greek \n"," - English \n"," - Spanish \n"," - Finnish \n"," - French \n"," - Croatian \n"," - Hungarian \n"," - Italy \n"," - Norwegian \n"," - Polish,\n"," - Portuguese \n"," - Romanian\n"," - Russian \n"," - Slovak\n"," - Swedish\n"," - Turkish \n"," - Ukrainian\n","\n","\n","# 1. Install Java and NLU"]},{"cell_type":"code","metadata":{"id":"nEIvUhjfzW7u"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZE4c3HMSkGGu"},"source":["# 2. Load NLU pipeline and predict language for data"]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1609330480751,"user_tz":-60,"elapsed":88606,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f14217a7-125c-4801-886c-b3929507bee5"},"source":["import nlu\n","lang_pipe = nlu.load('lang')\n","lang_pipe.predict(['NLU is an open-source text processing library for advanced natural language processing for the Python language.',\n"," 'NLU est une bibliothèque de traitement de texte open source pour le traitement avancé du langage naturel pour les langages de programmation Python.',\n"," 'NLU ist eine Open-Source Text verarbeitungs Software fuer fortgeschrittene natuerlich sprachliche Textverarbeitung in der Python Sprache '\n"," ])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["detect_language_20 download started this may take some time.\n","Approx size to download 3 MB\n","[OK!]\n","Fitting on empty Dataframe, could not infer correct training method!\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
language
\n","
language_confidence
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n","
\n"," \n"," \n","
\n","
0
\n","
NLU is an open-source text processing library ...
\n","
en
\n","
0.986721
\n","
\n","
\n","
1
\n","
NLU est une bibliothèque de traitement de text...
\n","
fr
\n","
0.999822
\n","
\n","
\n","
2
\n","
NLU ist eine Open-Source Text verarbeitungs So...
\n","
de
\n","
0.678322
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... language_confidence\n","origin_index ... \n","0 NLU is an open-source text processing library ... ... 0.986721\n","1 NLU est une bibliothèque de traitement de text... ... 0.999822\n","2 NLU ist eine Open-Source Text verarbeitungs So... ... 0.678322\n","\n","[3 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"t__qJjT9L94X"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/cyberbullying_cassification_for_racism_and_sexism.ipynb b/examples/colab/component_examples/classifiers/cyberbullying_cassification_for_racism_and_sexism.ipynb
deleted file mode 100644
index dd36ecb3..00000000
--- a/examples/colab/component_examples/classifiers/cyberbullying_cassification_for_racism_and_sexism.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"cyberbullying_cassification_for_racism_and_sexism.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"ZE4c3HMSkGGu"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/cyberbullying_cassification_for_racism_and_sexism.ipynb)\n","\n","# Cyberbullying Classification with NLU\n","\n","Racism and Sexism is a problem of increasing size and occurence. \n","Fortunately we can leverage the structure of natural language with the latest deep learning algorithms with NLU in just one line.\n","\n","\n","The Cyblerbullyinh classifier model uses universal sentence embeddings and is trained with the classifierdl algorithm provided by Spark NLP."]},{"cell_type":"code","metadata":{"id":"ttF6zriPzm3C","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620090952624,"user_tz":-120,"elapsed":113592,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b41c571e-4836-4c49-810e-993962c15154"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 01:14:00-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-04 01:14:00 (1.72 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 76kB/s \n","\u001b[K |████████████████████████████████| 153kB 50.8MB/s \n","\u001b[K |████████████████████████████████| 204kB 20.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 48.6MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":267},"executionInfo":{"status":"ok","timestamp":1620091076191,"user_tz":-120,"elapsed":237151,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b87a8185-8302-4f03-f33b-85d0f259d905"},"source":["import nlu\n","news_pipe = nlu.load('classify.cyberbullying')\n","news_pipe.predict(['All women have to wear pretty clothes', 'All black people are good at math'])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classifierdl_use_cyberbullying download started this may take some time.\n","Approximate size to download 21.3 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_tfhub_use
\n","
document
\n","
origin_index
\n","
text
\n","
cyberbullying_confidence_confidence
\n","
cyberbullying
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[[-0.08050180226564407, -0.013558609411120415,...
\n","
All women have to wear pretty clothes
\n","
0
\n","
All women have to wear pretty clothes
\n","
[0.9999635]
\n","
[sexism]
\n","
[All women have to wear pretty clothes]
\n","
\n","
\n","
1
\n","
[[-0.03739425539970398, -0.06323890388011932, ...
\n","
All black people are good at math
\n","
8589934592
\n","
All black people are good at math
\n","
[0.5287629]
\n","
[racism]
\n","
[All black people are good at math]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_tfhub_use ... sentence\n","0 [[-0.08050180226564407, -0.013558609411120415,... ... [All women have to wear pretty clothes]\n","1 [[-0.03739425539970398, -0.06323890388011932, ... ... [All black people are good at math]\n","\n","[2 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"YitayXd-Fomz"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/emotion_classification.ipynb b/examples/colab/component_examples/classifiers/emotion_classification.ipynb
deleted file mode 100644
index 433b5251..00000000
--- a/examples/colab/component_examples/classifiers/emotion_classification.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"emotion_classification.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"tOHVDa9DQQR5"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/emotion_classification.ipynb)\n","\n","\n","# Sentiment Classification with NLU for Twitter\n","\n","# 1. Setup Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620089184638,"user_tz":-120,"elapsed":381148,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"6aeca6da-a44b-4be0-d110-c813387a20a0"},"source":["!wget https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh -O - | bash\n","\n","import nlu"],"execution_count":1,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:40:04-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-04 00:40:04 (1.31 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 65kB/s \n","\u001b[K |████████████████████████████████| 153kB 814kB/s \n","\u001b[K |████████████████████████████████| 204kB 719kB/s \n","\u001b[K |████████████████████████████████| 204kB 1.1MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ZE4c3HMSkGGu"},"source":["# 2. Load pipeline and get sample predictions"]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":237},"executionInfo":{"status":"ok","timestamp":1620090341315,"user_tz":-120,"elapsed":1537817,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c9e25128-ff85-4578-9f45-288cb7a1d6a9"},"source":["import nlu\n","sentiment_pipe = nlu.load('emotion')\n","sentiment_pipe.predict('@elonmusk Tesla stock price is too high imo')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["classifierdl_use_emotion download started this may take some time.\n","Approximate size to download 21.3 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
emotion_confidence_confidence
\n","
text
\n","
origin_index
\n","
emotion
\n","
document
\n","
sentence_embedding_tfhub_use
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.9999976]
\n","
@elonmusk Tesla stock price is too high imo
\n","
8589934592
\n","
[sadness]
\n","
@elonmusk Tesla stock price is too high imo
\n","
[[0.08604438602924347, 0.04703635722398758, -0...
\n","
[@elonmusk Tesla stock price is too high imo]
\n","
\n"," \n","
\n","
"],"text/plain":[" emotion_confidence_confidence ... sentence\n","0 [0.9999976] ... [@elonmusk Tesla stock price is too high imo]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"F4tv_Y23P_--"},"source":["# 3. Define a list of String for predictions"]},{"cell_type":"code","metadata":{"id":"yzmZCOypnpeX","executionInfo":{"status":"ok","timestamp":1620090341317,"user_tz":-120,"elapsed":1537816,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}}},"source":["example_tweets = [\n","\"@VirginAmerica Hi, Virgin! I'm on hold for 40-50 minutes -- are there any earlier flights from LA to NYC tonight; earlier than 11:50pm?\",\n","\"@VirginAmerica is there special assistance if I travel alone w/2 kids and 1 infant? Priority boarding?\",\n","\"@VirginAmerica thank you for checking in. tickets are purchased and customer is happy\", \n","\"@VirginAmerica is your website ever coming back online?\",\n","\"@VirginAmerica - Is Flight 713 from Love Field to SFO definitely Cancelled Flightled for Monday, February 23?\",\n","\"@VirginAmerica Is flight 0769 out of LGA to DFW on time?\",\n","\"@VirginAmerica my drivers license is expired by a little over a month. Can I fly Friday morning using my expired license?\",\n","\"@VirginAmerica having problems Flight Booking Problems on the web site. keeps giving me an error and to contact by phone. phone is 30 minute wait.\",\n","\"@VirginAmerica How do I reschedule my Cancelled Flightled flights online? The change button is greyed out!\",\n","\"@VirginAmerica I rang, but there is a wait for 35 minutes!! I can book the same ticket through a vendor, fix your site\",\n","\"@VirginAmerica got a flight (we were told) for 4:50 today..,checked my email and its for 4;50 TOMORROW. This is unacceptable.\",\n","\"@VirginAmerica our flight into lga was Cancelled Flighted. We're stuck in Dallas. I called to reschedule, told I could get a flight for today...(1/2)\",\n","\"@virginamerica why don't any of the pairings include red wine?! Only white is offered :( #redwineisbetter\"\n","]\n"],"execution_count":3,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KTq6yhq0QA6p"},"source":["# 4. Get predictions for list of strings"]},{"cell_type":"code","metadata":{"id":"1GZ3BQlBQD5j","colab":{"base_uri":"https://localhost:8080/","height":437},"executionInfo":{"status":"ok","timestamp":1620090342919,"user_tz":-120,"elapsed":1539409,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f9b8afbb-eaa5-444b-84e0-6662799f7804"},"source":["sentiment_pipe.predict(example_tweets)"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
emotion_confidence_confidence
\n","
origin_index
\n","
emotion
\n","
document
\n","
sentence_embedding_tfhub_use
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.92608595, 0.92608595]
\n","
0
\n","
[sadness, sadness]
\n","
@VirginAmerica Hi, Virgin! I'm on hold for 40-...
\n","
[[0.034392621368169785, 0.04743622615933418, -...
\n","
[@VirginAmerica Hi, Virgin!, I'm on hold for ...
\n","
\n","
\n","
1
\n","
[0.9875567, 0.9875567]
\n","
1
\n","
[surprise, surprise]
\n","
@VirginAmerica is there special assistance if ...
\n","
[[-0.07632657885551453, -0.00686881598085165, ...
\n","
[@VirginAmerica is there special assistance if...
\n","
\n","
\n","
2
\n","
[0.99999917]
\n","
2
\n","
[joy]
\n","
@VirginAmerica thank you for checking in. tick...
\n","
[[-0.0030431197956204414, -0.02072464115917682...
\n","
[@VirginAmerica thank you for checking in. tic...
\n","
\n","
\n","
3
\n","
[0.9928368]
\n","
3
\n","
[sadness]
\n","
@VirginAmerica is your website ever coming bac...
\n","
[[-0.002968168817460537, -0.000566655478905886...
\n","
[@VirginAmerica is your website ever coming ba...
\n","
\n","
\n","
4
\n","
[0.9349201]
\n","
4
\n","
[joy]
\n","
@VirginAmerica - Is Flight 713 from Love Field...
\n","
[[-0.001066288212314248, 0.07673103362321854, ...
\n","
[@VirginAmerica - Is Flight 713 from Love Fiel...
\n","
\n","
\n","
5
\n","
[0.9999956]
\n","
5
\n","
[joy]
\n","
@VirginAmerica Is flight 0769 out of LGA to DF...
\n","
[[0.027866005897521973, 0.07844573259353638, -...
\n","
[@VirginAmerica Is flight 0769 out of LGA to D...
\n","
\n","
\n","
6
\n","
[0.99150836, 0.99150836]
\n","
6
\n","
[joy, joy]
\n","
@VirginAmerica my drivers license is expired b...
\n","
[[-0.0589623749256134, -0.06640151143074036, -...
\n","
[@VirginAmerica my drivers license is expired ...
\n","
\n","
\n","
7
\n","
[0.9313905, 0.9313905]
\n","
7
\n","
[joy, joy]
\n","
@VirginAmerica having problems Flight Booking ...
\n","
[[-0.007869902998209, 0.06951839476823807, -0....
\n","
[@VirginAmerica having problems Flight Booking...
\n","
\n","
\n","
8
\n","
[0.94544274, 0.94544274]
\n","
8
\n","
[sadness, sadness]
\n","
@VirginAmerica How do I reschedule my Cancelle...
\n","
[[-0.047210294753313065, 0.0797676295042038, -...
\n","
[@VirginAmerica How do I reschedule my Cancell...
\n","
\n","
\n","
9
\n","
[0.99922204, 0.99922204]
\n","
9
\n","
[joy, joy]
\n","
@VirginAmerica I rang, but there is a wait for...
\n","
[[-0.004450463689863682, 0.06487753242254257, ...
\n","
[@VirginAmerica I rang, but there is a wait fo...
\n","
\n","
\n","
10
\n","
[0.97494715, 0.97494715, 0.97494715]
\n","
10
\n","
[surprise, surprise, surprise]
\n","
@VirginAmerica got a flight (we were told) for...
\n","
[[-0.01818418689072132, 0.04061070457100868, -...
\n","
[@VirginAmerica got a flight (we were told) fo...
\n","
\n","
\n","
11
\n","
[0.9951147, 0.9951147, 0.9951147, 0.9951147]
\n","
11
\n","
[sadness, sadness, sadness, sadness]
\n","
@VirginAmerica our flight into lga was Cancell...
\n","
[[0.017033610492944717, 0.08045562356710434, -...
\n","
[@VirginAmerica our flight into lga was Cancel...
\n","
\n","
\n","
12
\n","
[0.99992037, 0.99992037, 0.99992037]
\n","
12
\n","
[sadness, sadness, sadness]
\n","
@virginamerica why don't any of the pairings i...
\n","
[[0.005574456881731749, -0.06231074407696724, ...
\n","
[@virginamerica why don't any of the pairings ...
\n","
\n"," \n","
\n","
"],"text/plain":[" emotion_confidence_confidence ... sentence\n","0 [0.92608595, 0.92608595] ... [@VirginAmerica Hi, Virgin!, I'm on hold for ...\n","1 [0.9875567, 0.9875567] ... [@VirginAmerica is there special assistance if...\n","2 [0.99999917] ... [@VirginAmerica thank you for checking in. tic...\n","3 [0.9928368] ... [@VirginAmerica is your website ever coming ba...\n","4 [0.9349201] ... [@VirginAmerica - Is Flight 713 from Love Fiel...\n","5 [0.9999956] ... [@VirginAmerica Is flight 0769 out of LGA to D...\n","6 [0.99150836, 0.99150836] ... [@VirginAmerica my drivers license is expired ...\n","7 [0.9313905, 0.9313905] ... [@VirginAmerica having problems Flight Booking...\n","8 [0.94544274, 0.94544274] ... [@VirginAmerica How do I reschedule my Cancell...\n","9 [0.99922204, 0.99922204] ... [@VirginAmerica I rang, but there is a wait fo...\n","10 [0.97494715, 0.97494715, 0.97494715] ... [@VirginAmerica got a flight (we were told) fo...\n","11 [0.9951147, 0.9951147, 0.9951147, 0.9951147] ... [@VirginAmerica our flight into lga was Cancel...\n","12 [0.99992037, 0.99992037, 0.99992037] ... [@virginamerica why don't any of the pairings ...\n","\n","[13 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"jJ9JM3NNBid5"},"source":["# Plot emotion distribution"]},{"cell_type":"code","metadata":{"id":"bULisBD3BfHz","colab":{"base_uri":"https://localhost:8080/","height":462},"executionInfo":{"status":"ok","timestamp":1620090344431,"user_tz":-120,"elapsed":1540916,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"798185c6-b89f-46ce-9c87-d32ed69608b3"},"source":["predictions = sentiment_pipe.predict(example_tweets)\n","predictions.emotion.value_counts().plot.bar()"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":5},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"AauFqgThBpoC","executionInfo":{"status":"ok","timestamp":1620090344434,"user_tz":-120,"elapsed":1540917,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}}},"source":[""],"execution_count":5,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/fake_news_classification.ipynb b/examples/colab/component_examples/classifiers/fake_news_classification.ipynb
deleted file mode 100644
index 0d082cc1..00000000
--- a/examples/colab/component_examples/classifiers/fake_news_classification.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"fake_news_classification.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"ZE4c3HMSkGGu"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/fake_news_classification.ipynb)\n","\n","# Fake News Classification with NLU\n","\n","Fake news is a problem of increasing size and occurence. \n","Fortunately we can leverage the structure of natural language with the latest deep learning algorithms with NLU in just one line.\n","\n","\n","The fake news classifiers model uses universal sentence embeddings and is trained with the classifierdl algorithm provided by Spark NLP."]},{"cell_type":"code","metadata":{"id":"5f5nvmE7zi34","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620089003172,"user_tz":-120,"elapsed":309605,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"05211e36-c96f-4911-cdad-012b67e51a2b"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:38:14-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-04 00:38:14 (45.3 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 62kB/s \n","\u001b[K |████████████████████████████████| 153kB 409kB/s \n","\u001b[K |████████████████████████████████| 204kB 605kB/s \n","\u001b[K |████████████████████████████████| 204kB 586kB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"status":"ok","timestamp":1620090071777,"user_tz":-120,"elapsed":1378194,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"dec89365-a012-4277-da2c-c45d4244f497"},"source":["import nlu\n","news_pipe = nlu.load('en.classify.fakenews')\n","news_pipe.predict(['Unicorns have been sighted on Mars!', '5G and Bill Gates cause COVID', 'Trump to Visit California After Criticism Over Silence on Wildfires' ])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classifierdl_use_fakenews download started this may take some time.\n","Approximate size to download 21.4 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
fakenews_confidence_confidence
\n","
origin_index
\n","
document
\n","
fakenews
\n","
text
\n","
sentence_embedding_tfhub_use
\n","
\n"," \n"," \n","
\n","
0
\n","
[Unicorns have been sighted on Mars!]
\n","
[1.0]
\n","
0
\n","
Unicorns have been sighted on Mars!
\n","
[FAKE]
\n","
Unicorns have been sighted on Mars!
\n","
[[-0.01756167598068714, 0.015006818808615208, ...
\n","
\n","
\n","
1
\n","
[5G and Bill Gates cause COVID]
\n","
[0.9999248]
\n","
8589934592
\n","
5G and Bill Gates cause COVID
\n","
[FAKE]
\n","
5G and Bill Gates cause COVID
\n","
[[0.08615710586309433, 0.034103237092494965, -...
\n","
\n","
\n","
2
\n","
[Trump to Visit California After Criticism Ove...
\n","
[0.76037985]
\n","
8589934593
\n","
Trump to Visit California After Criticism Over...
\n","
[FAKE]
\n","
Trump to Visit California After Criticism Over...
\n","
[[0.03416172042489052, 0.006567075382918119, -...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... sentence_embedding_tfhub_use\n","0 [Unicorns have been sighted on Mars!] ... [[-0.01756167598068714, 0.015006818808615208, ...\n","1 [5G and Bill Gates cause COVID] ... [[0.08615710586309433, 0.034103237092494965, -...\n","2 [Trump to Visit California After Criticism Ove... ... [[0.03416172042489052, 0.006567075382918119, -...\n","\n","[3 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"id":"m_bOI6Hp7RFT","executionInfo":{"status":"ok","timestamp":1620090699008,"user_tz":-120,"elapsed":10061,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"fe81c1aa-c8eb-4b35-dd32-74d290d36bd4"},"source":["import nlu\n","news_pipe = nlu.load('en.classify.fakenews')\n","news_pipe.predict(['Unicorns have been sighted on Mars!', '5G and Bill Gates cause COVID', 'Trump to Visit California After Criticism Over Silence on Wildfires' ], output_level='document')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classifierdl_use_fakenews download started this may take some time.\n","Approximate size to download 21.4 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
fakenews_confidence_confidence
\n","
origin_index
\n","
document
\n","
fakenews
\n","
text
\n","
sentence_embedding_tfhub_use
\n","
\n"," \n"," \n","
\n","
0
\n","
[Unicorns have been sighted on Mars!]
\n","
1
\n","
0
\n","
Unicorns have been sighted on Mars!
\n","
FAKE
\n","
Unicorns have been sighted on Mars!
\n","
[-0.01756167598068714, 0.015006818808615208, -...
\n","
\n","
\n","
1
\n","
[5G and Bill Gates cause COVID]
\n","
0.999925
\n","
8589934592
\n","
5G and Bill Gates cause COVID
\n","
FAKE
\n","
5G and Bill Gates cause COVID
\n","
[0.08615710586309433, 0.034103237092494965, -0...
\n","
\n","
\n","
2
\n","
[Trump to Visit California After Criticism Ove...
\n","
0.76038
\n","
8589934593
\n","
Trump to Visit California After Criticism Over...
\n","
FAKE
\n","
Trump to Visit California After Criticism Over...
\n","
[0.03416172042489052, 0.006567075382918119, -0...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... sentence_embedding_tfhub_use\n","0 [Unicorns have been sighted on Mars!] ... [-0.01756167598068714, 0.015006818808615208, -...\n","1 [5G and Bill Gates cause COVID] ... [0.08615710586309433, 0.034103237092494965, -0...\n","2 [Trump to Visit California After Criticism Ove... ... [0.03416172042489052, 0.006567075382918119, -0...\n","\n","[3 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"id":"AdlYjZJpkO_x","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620090071778,"user_tz":-120,"elapsed":1378190,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"5c85b1f9-6cfb-405c-f272-d57afcea3b2a"},"source":["news_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['classifier_dl@tfhub_use'] has settable params:\n","pipe['classifier_dl@tfhub_use'].setClasses(['FAKE', 'REAL']) | Info: get the tags used to trained this ClassifierDLModel | Currently set to : ['FAKE', 'REAL']\n","pipe['classifier_dl@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['use@tfhub_use'] has settable params:\n","pipe['use@tfhub_use'].setDimension(512) | Info: Number of embedding dimensions | Currently set to : 512\n","pipe['use@tfhub_use'].setLoadSP(False) | Info: Whether to load SentencePiece ops file which is required only by multi-lingual models. This is not changeable after it's set with a pretrained model nor it is compatible with Windows. | Currently set to : False\n","pipe['use@tfhub_use'].setStorageRef('tfhub_use') | Info: unique reference name for identification | Currently set to : tfhub_use\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@5ec97954) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@5ec97954\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yzmZCOypnpeX"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/intent_classification_airlines_ATIS.ipynb b/examples/colab/component_examples/classifiers/intent_classification_airlines_ATIS.ipynb
deleted file mode 100644
index 6968ca2a..00000000
--- a/examples/colab/component_examples/classifiers/intent_classification_airlines_ATIS.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"intent_classification_airlines_ATIS.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"ZE4c3HMSkGGu"},"source":["\n","\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/intent_classification_airlines_ATIS.ipynb)\n","\n","# Intent Classification with NLU\n","\n","|Type | \tDescription |\n","|------|--------------|\n"," | atis_airfare|air fares, like **500 $**\n"," | atis_ground_service|gorund services like, **Transporation**\n"," | atis_flight|aits flights like, **6B12**\n"," | atis_airline|atis airline like, **Emirates**\n"," | atis_abbreviation|atis abbreviations like, **air fare q**\n"," \n","# What is the ATIS Dataset?\n","ATIS dataset provides large number of messages and their associated intents that can be used in training a classifier. Within a chatbot, intent refers to the goal the customer has in mind when typing in a question or comment. While entity refers to the modifier the customer uses to describe their issue, the intent is what they really mean. For example, a user says, ‘I need new shoes.’ The intent behind the message is to browse the footwear on offer. Understanding the intent of the customer is key to implementing a successful chatbot experience for end-user.\n","https://www.kaggle.com/hassanamin/atis-airlinetravelinformationsystem\n","\n"]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620088796429,"user_tz":-120,"elapsed":140369,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"cbee71b0-9328-48f9-d070-d30fcf0f52f7"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:37:36-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-04 00:37:37 (47.4 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 65kB/s \n","\u001b[K |████████████████████████████████| 153kB 45.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 23.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 55.4MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"51Zr-JvU4xEg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620088799035,"user_tz":-120,"elapsed":142965,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"3b03550f-fb74-4dd2-e362-067cb524e073"},"source":["# Download the dataset \n","! wget http://ckl-it.de/wp-content/uploads/2021/01/atis_intents.csv\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:39:56-- http://ckl-it.de/wp-content/uploads/2021/01/atis_intents.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 391936 (383K) [text/csv]\n","Saving to: ‘atis_intents.csv’\n","\n","atis_intents.csv 100%[===================>] 382.75K 288KB/s in 1.3s \n","\n","2021-05-04 00:39:58 (288 KB/s) - ‘atis_intents.csv’ saved [391936/391936]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yZ7R8bho2kXI"},"source":["# Predict Intent of Airline messages"]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":566},"executionInfo":{"status":"ok","timestamp":1620089047943,"user_tz":-120,"elapsed":391868,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2cd5e268-d41c-4ac8-bab3-4450109ee6a1"},"source":["import nlu \n","import pandas as pd\n","\n","df = pd.read_csv(\"atis_intents.csv\")\n","df.columns = [\"flight\",\"text\"]\n","\n","preds = nlu.load('en.classify.intent.airline').predict(df[\"text\"],output_level='sentence')\n","preds"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classifierdl_use_atis download started this may take some time.\n","Approximate size to download 21.1 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
origin_index
\n","
intent
\n","
text
\n","
sentence_embedding_tfhub_use
\n","
sentence
\n","
intent_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
what flights are available from pittsburgh to ...
\n","
0
\n","
atis_flight
\n","
what flights are available from pittsburgh to...
\n","
[0.037106938660144806, 0.0727505013346672, -0....
\n","
what flights are available from pittsburgh to ...
\n","
0.999994
\n","
\n","
\n","
1
\n","
what is the arrival time in san francisco for ...
\n","
1
\n","
atis_flight
\n","
what is the arrival time in san francisco for...
\n","
[0.020266082137823105, 0.044293809682130814, -...
\n","
what is the arrival time in san francisco for ...
\n","
0.999997
\n","
\n","
\n","
2
\n","
cheapest airfare from tacoma to orlando
\n","
2
\n","
atis_airfare
\n","
cheapest airfare from tacoma to orlando
\n","
[0.05529679358005524, 0.0694049745798111, -0.0...
\n","
cheapest airfare from tacoma to orlando
\n","
0.997928
\n","
\n","
\n","
3
\n","
round trip fares from pittsburgh to philadelph...
\n","
3
\n","
atis_airfare
\n","
round trip fares from pittsburgh to philadelp...
\n","
[0.044724948704242706, 0.07032939791679382, -0...
\n","
round trip fares from pittsburgh to philadelph...
\n","
1
\n","
\n","
\n","
4
\n","
i need a flight tomorrow from columbus to minn...
\n","
4
\n","
atis_flight
\n","
i need a flight tomorrow from columbus to min...
\n","
[-0.0009330636239610612, 0.0720256119966507, -...
\n","
i need a flight tomorrow from columbus to minn...
\n","
0.999996
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
4972
\n","
what is the airfare for flights from denver to...
\n","
8589937516
\n","
atis_airfare
\n","
what is the airfare for flights from denver t...
\n","
[0.015531656332314014, 0.06927467882633209, -0...
\n","
what is the airfare for flights from denver to...
\n","
0.999503
\n","
\n","
\n","
4973
\n","
do you have any flights from denver to baltimo...
\n","
8589937517
\n","
atis_flight
\n","
do you have any flights from denver to baltim...
\n","
[0.03598876670002937, 0.06490834802389145, -0....
\n","
do you have any flights from denver to baltimo...
\n","
0.999994
\n","
\n","
\n","
4974
\n","
which airlines fly into and out of denver
\n","
8589937518
\n","
atis_airline
\n","
which airlines fly into and out of denver
\n","
[0.0314473956823349, 0.0699605792760849, -0.06...
\n","
which airlines fly into and out of denver
\n","
1
\n","
\n","
\n","
4975
\n","
does continental fly from boston to san franci...
\n","
8589937519
\n","
atis_flight
\n","
does continental fly from boston to san franc...
\n","
[0.01851840876042843, 0.07567648589611053, -0....
\n","
does continental fly from boston to san franci...
\n","
0.994565
\n","
\n","
\n","
4976
\n","
is there a delta flight from denver to san fra...
\n","
8589937520
\n","
atis_flight
\n","
is there a delta flight from denver to san fr...
\n","
[0.026785779744386673, 0.06964033842086792, -0...
\n","
is there a delta flight from denver to san fra...
\n","
0.999779
\n","
\n"," \n","
\n","
4977 rows × 7 columns
\n","
"],"text/plain":[" document ... intent_confidence_confidence\n","0 what flights are available from pittsburgh to ... ... 0.999994\n","1 what is the arrival time in san francisco for ... ... 0.999997\n","2 cheapest airfare from tacoma to orlando ... 0.997928\n","3 round trip fares from pittsburgh to philadelph... ... 1\n","4 i need a flight tomorrow from columbus to minn... ... 0.999996\n","... ... ... ...\n","4972 what is the airfare for flights from denver to... ... 0.999503\n","4973 do you have any flights from denver to baltimo... ... 0.999994\n","4974 which airlines fly into and out of denver ... 1\n","4975 does continental fly from boston to san franci... ... 0.994565\n","4976 is there a delta flight from denver to san fra... ... 0.999779\n","\n","[4977 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"HH6KBffB2pY_"},"source":["# Plot Distribution of Intent in Messages"]},{"cell_type":"code","metadata":{"id":"WdnY9n1LTmed","colab":{"base_uri":"https://localhost:8080/","height":388},"executionInfo":{"status":"ok","timestamp":1620089047945,"user_tz":-120,"elapsed":391865,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"451ef7a3-67b3-4710-b3dd-69154ad27ad7"},"source":["preds.intent.value_counts().plot.bar(title='Distribution of message intents')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":4},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAX0AAAFiCAYAAAAa+QgfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZwdVZ338c+XsCk70iImQBCjDCgEjIDCoyAjAi64IOIIMgwaHcFl1GcERwVRXEdURkVBkEURUXFARZnIIMijQAKGQEAkBpCELWwhbJHA9/mjTts3zb3pJZ1bN6nv+/W6r646tf1udffvnnvq1CnZJiIimmG1ugOIiIjuSdKPiGiQJP2IiAZJ0o+IaJAk/YiIBknSj4hokCT9hpD0bUmfHKN9bSHpYUnjyvxvJb1rLPZd9vcrSYeO1f5GcNzPSrpX0l3dPnYvKb/b59UdR6wYSj/9lZ+kW4FNgSXAk8ANwJnAybafGsW+3mX7NyPY5rfA921/dyTHKtseCzzf9sEj3XYsSdoCuAnY0vY9dcayMpFkYJLtOWOwrz2o/o4mLHdg0VFq+quO19teD9gS+ALwMeDUsT6IpNXHep89YgvgviT8WOXZzmslfwG3Av84qGxn4CngRWX+dOCzZXoT4BfAg8D9wO+oKgBnlW0eAx4G/h2YCBg4HPgrcFlL2eplf78FPg9cBTwEnA9sXJbtAcxrFy+wD/A34IlyvGtb9veuMr0a8AngNuAeqm8wG5Rl/XEcWmK7F/iPZZynDcr2C8r+PlH2/4/lPT9V4ji9zbZ7APPKObkHuBN4I7Af8OdyHj/esv5qwFHAX4D7gHNbzsnawPdL+YPAdGDTsuyfgbnAIuAW4B2lfGvgf8s29wI/ADZsOd5OwB/Ldj8GftT/+y7LXwfMLMf7PbD9Ms6Tqb59QfV3803gl2XfVwJbl2WXlXUfKeftbUMdq/zuPwrMAhaWONcG1hn0O3gYeC7V3/EMqr+ru4ET6v5/W9lftQeQ1xj8Etsk/VL+V+Bfy/TpDCT9zwPfBtYor//DQFPfUvtiILGeWf4xn0H7pD8feFFZ56dUX9NhGUm/TB/bv27L8t8ykPT/BZgDPA9YFzgPOGtQbKeUuHYAFgP/0OE8nUn1gbRe2fbPwOGd4hy07R5UzWefKufs3VQfHmeX/W1XktZWZf0PAlcAE4C1gO8APyzL3gP8HHgmMA54CbB+OXcPAS8s620GbFemnw+8uuyrjyrhfq0sW5PqQ+yDJbY3U32Y9v++d6T6oNqlHO/Q8jtYq8N7HZz076NKvqtTfdic027d4RyrTF9FldA3Bm4E3ruMv5U/AIeU6XWBXev+f1vZX2neWbXdQfWPNdgTVAllS9tP2P6dy3/VMhxr+xHbj3VYfpbt620/AnwSOLD/Qu9yegdV7W6u7YeBo4GDBjUzfdr2Y7avBa6lSv5LKbEcBBxte5HtW4GvAIeMIJYngONtPwGcQ/WN6etlf7OprqX0H/u9VN865tleTPXhdkCJ+wngWVTJ8knbV9t+qGz3FPAiSc+wfWfZL7bn2J5me7HtBcAJwCvLNrtSJeQTy+/zPKrE2m8q8B3bV5bjnUH14bjrMN/3z2xfZXsJVdKfvIx1h3OsE23fYft+qg+/Ze3vCeD5kjax/bDtK4YZc3SQpL9qG0/V7DDYl6lqz/8jaa6ko4axr9tHsPw2qhrnJsOKctmeW/bXuu/VqS5c92vtbfMoVY1wsE1KTIP3NX4Esdxn+8ky3f/hd3fL8sdajr0l8DNJD0p6kKpG+2SJ+yzgIuAcSXdI+pKkNcoH5tuoPjDulPRLSdsASNpU0jmS5kt6iKp5qP/8PheYP+iDu/X3sSXwkf5YSjybl+2GYzjndyTHGsn+DgdeAPxJ0nRJrxtmzNFBkv4qStJLqRLa5YOXlZrpR2w/D3gD8GFJe/Uv7rDLob4JbN4yvQVVDe1eqvbeZ7bENY6qeWK4+72DKpG07nsJSyfb4bi3xDR4X/NHuJ/huh3Y1/aGLa+1bc8vtfFP294WeDlVG/g7AWxfZPvVVN/E/kTVdAXwOapz9WLb6wMHAyrL7gTGS9LA4Zf6fdxO9Q2lNZZn2v7hCnrfoz3W0/4WbN9s++3As4EvAj+RtM4Yx9woSfqrGEnrl9rQOVRt5de1Wed1kp5fksRCqhpof9fOu6naz0fqYEnbSnomcBzwk1Ir/jOwtqTXSlqD6uLpWi3b3Q1MlNTpb/GHwL9J2krSulTJ70elqWHYSiznAsdLWk/SlsCHqWrMK8K3y7G2BJDUJ2n/Mr2npBeXD8CHqD6Mniq1+f1LUltMdTGz//eyXplfKGk88H9bjvUHqt/hkZJWL8fZuWX5KcB7Je2iyjrl97HeGLzPwX8vy3Osu4FnSdqgv0DSwZL6XHU9frAUj6gbciwtSX/V8XNJi6hqWv9B1eZ7WId1JwG/oUoifwC+ZfuSsuzzwCfKV/OPjuD4Z1Fd9LuLqjfGBwBsLwTeB3yXqlb9CFUvmH4/Lj/vk3RNm/2eVvZ9GVVvlseB948grlbvL8efS/UN6Oyy/xXh68AFVE1oi6gu6u5Slj0H+AlVwr8RuJTqPa5G9UF0B1Wz3CuBfy3bfJqqh85Cqp405/UfyPbfqC7eHk6VGA+m6p21uCyfQXXh+RvAA1RNe/88Ru/zWOCM8vdy4PIcy/afqD7k55b9PZeqh9dsSQ9TndODlnFdKYYhN2dFrIIkXQl82/b36o4lektq+hGrAEmvlPSc0rxzKLA98Ou644res6reXRnRNC+kumaxDlXz1QG276w3pOhFad6JiGiQNO9ERDRIkn5ERIP0dJv+Jpts4okTJ9YdRkTESuXqq6++13Zfu2U9nfQnTpzIjBkz6g4jImKlIum2TsuGbN6RtLakqyRdK2m2pE+X8tMl3SJpZnlNLuWSdKKkOZJmSdqpZV+HSrq5vLr+ZKSIiKYbTk1/MfAq2w+X2+gvl/Srsuz/2v7JoPX3pbrjcxLVHYgnAbtI2hg4BphCNcbG1ZIusP3AWLyRiIgY2pA1fVceLrP9468vq5/n/sCZZbsrgA0lbQa8Bphm+/6S6KdR3WIdERFdMqzeO5LGSZpJ9XCEabavLIuOL004X5XUP4jWeJYe1nVeKetUHhERXTKspF8ehjCZ6ilAO0t6EdXDLLYBXkr1oI6PjUVAkqZKmiFpxoIFC8ZilxERUYyon77tB4FLgH3KU31cngr0PQaGcp3P0mN5TyhlncoHH+Nk21NsT+nra9vjKCIiRmk4vXf6JG1Ypp9B9ZzOP5V2esqY7G8Eri+bXAC8s/Ti2RVYWMYAuQjYW9JGkjYC9i5lERHRJcPpvbMZ1XjZ46g+JM61/QtJ/yupj+rpPTOpHvEGcCGwH9U42o9SxnS3fb+kzwDTy3rHlWdkRkREl/T0gGtTpkzx8t6cNfGoX45RNMvn1i+8tu4QIqIhJF1te0q7ZRl7JyKiQZL0IyIaJEk/IqJBkvQjIhokST8iokGS9CMiGiRJPyKiQZL0IyIaJEk/IqJBkvQjIhokST8iokGS9CMiGiRJPyKiQZL0IyIaJEk/IqJBkvQjIhokST8iokGS9CMiGiRJPyKiQZL0IyIaJEk/IqJBhkz6ktaWdJWkayXNlvTpUr6VpCslzZH0I0lrlvK1yvycsnxiy76OLuU3SXrNinpTERHR3nBq+ouBV9neAZgM7CNpV+CLwFdtPx94ADi8rH848EAp/2pZD0nbAgcB2wH7AN+SNG4s30xERCzbkEnflYfL7BrlZeBVwE9K+RnAG8v0/mWesnwvSSrl59hebPsWYA6w85i8i4iIGJZhtelLGidpJnAPMA34C/Cg7SVllXnA+DI9HrgdoCxfCDyrtbzNNhER0QXDSvq2n7Q9GZhAVTvfZkUFJGmqpBmSZixYsGBFHSYiopFG1HvH9oPAJcDLgA0lrV4WTQDml+n5wOYAZfkGwH2t5W22aT3Gyban2J7S19c3kvAiImIIw+m90ydpwzL9DODVwI1Uyf+AstqhwPll+oIyT1n+v7Zdyg8qvXu2AiYBV43VG4mIiKGtPvQqbAacUXrarAaca/sXkm4AzpH0WeCPwKll/VOBsyTNAe6n6rGD7dmSzgVuAJYAR9h+cmzfTkRELMuQSd/2LGDHNuVzadP7xvbjwFs77Ot44PiRhxkREWMhd+RGRDRIkn5ERIMk6UdENEiSfkREgyTpR0Q0SJJ+RESDJOlHRDRIkn5ERIMk6UdENEiSfkREgyTpR0Q0SJJ+RESDJOlHRDRIkn5ERIMk6UdENEiSfkREgyTpR0Q0SJJ+RESDJOlHRDRIkn5ERIMk6UdENEiSfkREgwyZ9CVtLukSSTdImi3pg6X8WEnzJc0sr/1atjla0hxJN0l6TUv5PqVsjqSjVsxbioiITlYfxjpLgI/YvkbSesDVkqaVZV+1/Z+tK0vaFjgI2A54LvAbSS8oi78JvBqYB0yXdIHtG8bijURExNCGTPq27wTuLNOLJN0IjF/GJvsD59heDNwiaQ6wc1k2x/ZcAEnnlHWT9CMiumREbfqSJgI7AleWoiMlzZJ0mqSNStl44PaWzeaVsk7lg48xVdIMSTMWLFgwkvAiImIIw076ktYFfgp8yPZDwEnA1sBkqm8CXxmLgGyfbHuK7Sl9fX1jscuIiCiG06aPpDWoEv4PbJ8HYPvuluWnAL8os/OBzVs2n1DKWEZ5RER0wXB67wg4FbjR9gkt5Zu1rPYm4PoyfQFwkKS1JG0FTAKuAqYDkyRtJWlNqou9F4zN24iIiOEYTk1/N+AQ4DpJM0vZx4G3S5oMGLgVeA+A7dmSzqW6QLsEOML2kwCSjgQuAsYBp9mePYbvJSIihjCc3juXA2qz6MJlbHM8cHyb8guXtV1ERKxYuSM3IqJBkvQjIhokST8iokGS9CMiGiRJPyKiQZL0IyIaJEk/IqJBkvQjIhokST8iokGS9CMiGiRJPyKiQZL0IyIaJEk/IqJBkvQjIhokST8iokGS9CMiGiRJPyKiQZL0IyIaJEk/IqJBkvQjIhokST8iokGGTPqSNpd0iaQbJM2W9MFSvrGkaZJuLj83KuWSdKKkOZJmSdqpZV+HlvVvlnTointbERHRznBq+kuAj9jeFtgVOELStsBRwMW2JwEXl3mAfYFJ5TUVOAmqDwngGGAXYGfgmP4PioiI6I4hk77tO21fU6YXATcC44H9gTPKamcAbyzT+wNnunIFsKGkzYDXANNs32/7AWAasM+YvpuIiFimEbXpS5oI7AhcCWxq+86y6C5g0zI9Hri9ZbN5paxTeUREdMmwk76kdYGfAh+y/VDrMtsGPBYBSZoqaYakGQsWLBiLXUZERDGspC9pDaqE/wPb55Xiu0uzDeXnPaV8PrB5y+YTSlmn8qXYPtn2FNtT+vr6RvJeIiJiCMPpvSPgVOBG2ye0LLoA6O+Bcyhwfkv5O0svnl2BhaUZ6CJgb0kblQu4e5eyiIjoktWHsc5uwCHAdZJmlrKPA18AzpV0OHAbcGBZdiGwHzAHeBQ4DMD2/ZI+A0wv6x1n+/4xeRcRETEsQyZ925cD6rB4rzbrGziiw75OA04bSYARETF2ckduRESDJOlHRDRIkn5ERIMk6UdENEiSfkREgyTpR0Q0SJJ+RESDJOlHRDRIkn5ERIMk6UdENEiSfkREgyTpR0Q0SJJ+RESDJOlHRDRIkn5ERIMk6UdENEiSfkREgyTpR0Q0SJJ+RESDJOlHRDRIkn5ERIMMmfQlnSbpHknXt5QdK2m+pJnltV/LsqMlzZF0k6TXtJTvU8rmSDpq7N9KREQMZTg1/dOBfdqUf9X25PK6EEDStsBBwHZlm29JGidpHPBNYF9gW+DtZd2IiOii1YdawfZlkiYOc3/7A+fYXgzcImkOsHNZNsf2XABJ55R1bxhxxBERMWrL06Z/pKRZpflno1I2Hri9ZZ15paxTeUREdNFok/5JwNbAZOBO4CtjFZCkqZJmSJqxYMGCsdptREQwyqRv+27bT9p+CjiFgSac+cDmLatOKGWdytvt+2TbU2xP6evrG014ERHRwaiSvqTNWmbfBPT37LkAOEjSWpK2AiYBVwHTgUmStpK0JtXF3gtGH3ZERIzGkBdyJf0Q2APYRNI84BhgD0mTAQO3Au8BsD1b0rlUF2iXAEfYfrLs50jgImAccJrt2WP+biIiYpmG03vn7W2KT13G+scDx7cpvxC4cETRRUTEmModuRERDZKkHxHRIEn6ERENkqQfEdEgSfoREQ2SpB8R0SBJ+hERDZKkHxHRIEn6ERENkqQfEdEgSfoREQ2SpB8R0SBJ+hERDZKkHxHRIEn6ERENkqQfEdEgSfoREQ2SpB8R0SBJ+hERDZKkHxHRIEn6ERENMmTSl3SapHskXd9StrGkaZJuLj83KuWSdKKkOZJmSdqpZZtDy/o3Szp0xbydiIhYluHU9E8H9hlUdhRwse1JwMVlHmBfYFJ5TQVOgupDAjgG2AXYGTim/4MiIiK6Z8ikb/sy4P5BxfsDZ5TpM4A3tpSf6coVwIaSNgNeA0yzfb/tB4BpPP2DJCIiVrDRtulvavvOMn0XsGmZHg/c3rLevFLWqTwiIrpouS/k2jbgMYgFAElTJc2QNGPBggVjtduIiGD0Sf/u0mxD+XlPKZ8PbN6y3oRS1qn8aWyfbHuK7Sl9fX2jDC8iItoZbdK/AOjvgXMocH5L+TtLL55dgYWlGegiYG9JG5ULuHuXsoiI6KLVh1pB0g+BPYBNJM2j6oXzBeBcSYcDtwEHltUvBPYD5gCPAocB2L5f0meA6WW942wPvjgcEREr2JBJ3/bbOyzaq826Bo7osJ/TgNNGFF1ERIyp3JEbEdEgSfoREQ2SpB8R0SBJ+hERDZKkHxHRIEn6ERENkqQfEdEgSfoREQ2SpB8R0SBJ+hERDZKkHxHRIEn6ERENkqQfEdEgSfoREQ2SpB8R0SBJ+hERDZKkHxHRIEn6ERENMuTjEmPVMfGoX9YdAgC3fuG1dYcQ0Vip6UdENEiSfkREgyxX0pd0q6TrJM2UNKOUbSxpmqSby8+NSrkknShpjqRZknYaizcQERHDNxY1/T1tT7Y9pcwfBVxsexJwcZkH2BeYVF5TgZPG4NgRETECK6J5Z3/gjDJ9BvDGlvIzXbkC2FDSZivg+BER0cHyJn0D/yPpaklTS9mmtu8s03cBm5bp8cDtLdvOK2UREdEly9tlc3fb8yU9G5gm6U+tC21bkkeyw/LhMRVgiy22WM7wIiKi1XLV9G3PLz/vAX4G7Azc3d9sU37eU1afD2zesvmEUjZ4nyfbnmJ7Sl9f3/KEFxERg4y6pi9pHWA124vK9N7AccAFwKHAF8rP88smFwBHSjoH2AVY2NIMFNFVuVEtmmp5mnc2BX4mqX8/Z9v+taTpwLmSDgduAw4s618I7AfMAR4FDluOY0dExCiMOunbngvs0Kb8PmCvNuUGjhjt8SIiYvnljtyIiAZJ0o+IaJAk/YiIBknSj4hokCT9iIgGSdKPiGiQJP2IiAbJ4xIjGi53JzdLavoREQ2SpB8R0SBJ+hERDZKkHxHRIEn6ERENkqQfEdEgSfoREQ2SpB8R0SC5OSsiomjCjWqp6UdENEiSfkREgyTpR0Q0SJJ+RESDJOlHRDRI15O+pH0k3SRpjqSjun38iIgm62rSlzQO+CawL7At8HZJ23YzhoiIJut2TX9nYI7tubb/BpwD7N/lGCIiGku2u3cw6QBgH9vvKvOHALvYPrJlnanA1DL7QuCmrgXY2SbAvXUH0SNyLgbkXAzIuRjQC+diS9t97Rb03B25tk8GTq47jlaSZtieUnccvSDnYkDOxYCciwG9fi663bwzH9i8ZX5CKYuIiC7odtKfDkyStJWkNYGDgAu6HENERGN1tXnH9hJJRwIXAeOA02zP7mYMo9RTzU01y7kYkHMxIOdiQE+fi65eyI2IiHrljtyIiAZJ0o+IaJAk/YiIBknSj4hokCT9DiRdPJyyppC0u6TDynSfpK3qjqkOkl4g6WJJ15f57SV9ou646iBpU0mnSvpVmd9W0uF1x1UHSbtJmibpz5LmSrpF0ty642onSX8QSWtL2hjYRNJGkjYur4nA+Hqjq4ekY4CPAUeXojWA79cXUa1OoToPTwDYnkV1v0kTnU7V/fq5Zf7PwIdqi6ZepwInALsDLwWmlJ89p+eGYegB76H6w30ucDWgUv4Q8I26gqrZm4AdgWsAbN8hab16Q6rNM21fJam1bEldwdRsE9vnSjoa/n4fzpN1B1WThbZ/VXcQw5GkP4jtrwNfl/R+2/9Vdzw94m+2LckAktapO6Aa3Stpa6D/XBwA3FlvSLV5RNKzGDgXuwIL6w2pNpdI+jJwHrC4v9D2NfWF1F5uzloGSS8HJtLy4Wj7zNoCqomkjwKTgFcDnwf+BTi7iR+Kkp5Hdcfly4EHgFuAg23fWmdcdZC0E/BfwIuA64E+4IDS5NUoki5pU2zbr+p6MENI0u9A0lnA1sBMoP8rq21/oL6ouk9VO8YEYBtgb6rmrotsT6s1sJqVbzur2V5Udyx1krQ61RDoAm6y/UTNIcUQkvQ7kHQjsK1zgpB0ne0X1x1HL5C0FvAWnv4N8Li6YqqLpCOAH9h+sMxvBLzd9rfqjaz7JG0AHAO8ohRdChxnu+eau9J7p7PrgefUHUSPuEZST/ZEqMH5VE97WwI80vJqonf3J3wA2w8A764xnjqdBiwCDiyvh4Dv1RpRB7mQO4ikn1NdmFoPuEHSVSx9YeYNdcVWo12Ad0i6jSrBiaqpa/t6w6rFBNv71B1EjxgnSf3fhsszsNesOaa6bG37LS3zn5Y0s7ZoliFJ/+n+s+4AetBr6g6gh/xe0ottX1d3ID3g18CPJH2nzL+nlDXRY5J2t305VDdrAY/VHFNbadOPYZP0bGDt/nnbf60xnFpIugF4PlWvncU0+FuPpNWoEv1epWga8F3bjeurL2kycAawAdXfxP3AP9u+ttbA2kjS70DSIkr/4xYLgRnAR2z35C3WK4KkNwBfobph7R5gS+BG29vVGlgNJG3Zrtz2bd2OJXqPpPUBbD9UdyydpHmns68B84CzqT65D6LqwnkN1UWbPWqLrPs+A+wK/Mb2jpL2BA6uOaaukrR++UdudBdNAEnn2j5Q0nU8vWJEk771SDrY9vclfXhQOQC2T6glsGVI0u/sDbZ3aJk/WdJM2x+T9PHaoqrHE7bvk7SapNVsXyLpa3UH1WVnA6+jGprDDAzPQZl/Xh1B1eSD5efrao2iN/Tfnd5uWJKebEZJ0u/sUUkHAj8p8wcAj5fpnvxlrkAPSloXuAz4gaR7aFg3RduvKz8bObpoK9v9w068BTjH9h11xlMn2/0XsX9j+/+1LisXc3tO2vQ7KLfbfx14GVWSvwL4N2A+8JL+q/SrMklr2V5c7j59nKp2+w6qi1U/sH1frQF2URlyoKNeHGNlRSujrx5IddHyR8CPbd9db1T1kHSN7Z2GKusFSfrRUf8fraSzbB9Sdzx16jC2Sr+eHGOlWyRtD7yNquY/z/Y/1hxS10h6GdU4TB8CvtqyaH3gTYOaiHtCmncGkfTvtr8k6b9of5GqSWPvrCnpn4CXS3rz4IW2z6shplrY3rN0UXzZ4K/xwT3AXcB9wLNrjqXb1gTWpcqlre36D1E1CfecJP2nu7H8nFFrFL3hvVTNORsCrx+0zFTDyDaG7ackfYPq2QKNJ+l9VM07fcCPqYZluKHeqLrL9qXApZJOX1m67aZ5J5ap1G6Ptn183bH0Akn/CfwBOK/pg/FJ+hxwru2eHG6gmyT1Af8ObMfSNzD2XLNfkv4gLWPvtNXEsXck/dF2arf8/aa9dagGXOu/uG3b69caWJeVcXZm296m7lh6gaT/obqY/VGqb8iHAgtsf6zWwNpI887TZeydp7tY0ltI7RbbTX1M5FJsPynpJklbNHE4jjaeZftUSR9safKZXndQ7STpP92nbO8l6Yu9+Cldk/cAHwaWSGpk7VbSNrb/1KnrZhO7bAIbAbPLSLR/v2+jid+Ggf6Hx9wp6bXAHcDGNcbTUZL+021WHpP4BknnsPSdl438507tFqg+9KZSjUE0mIGea7vtgk/WHUAP+Wx5kMpHqB4huT7VfT09J236g5QHXR8O7A5MZ9Dt9r14YWZFSe02hlIGoJtk+zeSngmMa/ojJHtdkn4Hkj5p+zPLWL6d7dndjKnbJJ1se+rK9NDnbpD0ImBblu6lcWZ9EdVD0rupvv1sbHtrSZOAb9vea4hNVxkr4309Sfqj1Ku3WMeKVYYe2IMq6V8I7Atcbrsnb8RZkcqToXYGruzv3dW05ylLer3tn0s6tN1y22d0O6ahpE1/9DT0KquO1G7/7gBgB+CPtg+TtCnw/Zpjqsti23/rH0ZY0uo0bDBC2z8vk9etLM2dSfqj15g/7k61W6CJSf+xcmfukvLAjHuAzesOqiaXlmHGnyHp1cD7gJ8Psc2q6iuSnkM1Ku+PbF9fd0CdrFZ3ALFSOIDqkXh32T6Mqqa7Qb0h1WaGpA2BU6jG1r+G6g7dJjoKWABcR9Wt90LgE7VGVBPbewJ7Up2P70i6TlJPnou06Y+SpCts71p3HN0g6SrbO0u6muoPexHV4xIbfTempInA+rZntZSt8hf425G0MTCh9Vw0laQXUw3J8Dbba9Ydz2Cp6XcgabcyjjySDpZ0QuvzUZuS8IvUbtuwfWubJHdWLcHUQNJvJa1fEv7VwCmSvjrUdqsiSf8g6VhJ11P10/89MKHmsNpKTb8DSbOomjG2B04HvgscaPuVdcZVt9Rul61J4xT1v1dJ7wI2t32MpFlNekZuP0l/AM6hepBMTz9JLDX9zpaUcWb2B75h+5u0fw5mozS9djsMTapFrS5pM6rhlX9RdzB1sv0y4GRWghyRpN/ZIklHAwcDvyxDDK9Rc0y9qlHdV+PvjgMuAubYnl4eMXpzzTHVQtLrgZnAr8v8ZEkX1BtVe0n6nb0NWAwcbvsuqva5L9cbUs9qUu12KH+rO4Busf1j29vbfl+Zn2v7Lf3LS6WpKY6lulHtQYDyjIGt6gyok5eN6UEAAAfpSURBVCT9DmzfZfsE278r839t6M1I0SIX+EfkrXUH0EVP2F44qKwnK0NJ+oNIurz8XCTpoZbXIkkP1R1fj2pM7RY4CXhU0g5UIyr+hWbepDYcTWr2m12eJz1O0qQyFs/v6w6qnST9QWzvXn6uZ3v9ltd6TRo/vlVqt0vJBf7h68ma7gryfqpHJS4GzgYWAh+qNaIOkvQ7kPS0HintyhoitdsBucA/fI2o6ZdHR/7S9n/Yfml5fcL243XH1k6Sfmfbtc6UwaReUlMsdUvtdkAu8A/fj+sOoBtsPwk8VR6i0vNyc9YgpRb3ceAZwKMti54ATrbdpB4JAEi6lKor2mHAK6gGGbu2SUPoxoBOY8f368Ux5Fc0SecDOwLTWPrRkT13LpL0O5D0eeBLwAsYGE7Yti+rL6p6lNED/wmYbvt3krYA9mhSbyZJl9veXdIilk54TXxecP/Y8btRjbz6ozL/VuAG2++tJbAarUzj6Sfpd1CeCvQBqq/vM4FdgT809WlREYNJugLY3faSMr8G8LuGXdj/O0lrAttQVQpust2TvdrSpt/ZB4CXAreVYVN3pNx40RTpvvp0ucC/lI2oHgDeb91S1jiS9qPq4HAi8A1gjqR9642qvTxEpbPHbT8uCUlrlQeEv7DuoLqptftq3bH0kFzgH/AF4I/lGcqiut5zbK0R1ecEYE/bcwAkbQ38EvhVrVG1kZp+Z/PKcML/DUwrF2puqzmmWqR2W13gL+3527d+6wHuBs6vObxa2P4esAvwM+A84GW92IbdJYv6E34xl+q5Ez0nbfrDIOmVVE+K+nWvttOtSIMfAl9qt7Nsb1tjWLXIBf6lSRoPbElLq0GTzoWkN5fJV1Odh3Op2vTfCvy1f1yiXpLmnWGwfWndMdShtfvqoDb8J6iGkW2iucBlDLrADzTuAr+kL1LdtzAbeKoUm+r8NMXrW6bvBvqft7GAgUpBT0lNP4aU2u0ASddRXeC/wvZkSdsAn7P95iE2XeVIugnY3vbiumOJ4UtNP4YjtdsBjb/A32Iu1RAUjU/65VkCX6f63zDV/8e/2Z5ba2BtJOnHcPR3X73C9p79tduaY6rL4Av8D9DQC/xUd6zPlHQxLYm/F+9C7YKzgW8CbyrzBwE/pLrQ3VPSvBNDkjTd9kslzQR2sb1Y0mzb2w258SosF/hXnrtQV7R2zwaWdK3tHeqKqZPU9GM4Urtto6kX+Ps1MbkPJmnjMvkrSUdRPRzdVBe4L6wtsGVITT9GpOm12xgg6RbaDLxm+3k1hFOLlnPQbhhp9+K5SNKPiFGR9KyW2bWp+qZvbPtTNYUUw5CkHxFjRtLVths5LIWkF1GNOvr3/vm9OBJt2vQjYlQk7dQyuxowhYbmFEnHAHtQJf0LgX2By+nBJ8w18hcUEWPiKy3TS4BbgQPrCaV2BwA7AH+0fZikTYHv1xxTW0n6ETEqZcjxqDxm+ylJSyStT/V0uc3rDqqdJP2IGJXyTNhjqIZUBrgUOM72wvqiqs2M0q35FOBq4GGqu3J7Ti7kRsSoSPopcD3Q31//EGCHJo5D1ErSRGB927NayrazPbu2oFok6UfEqEiaaXvyUGXx9OHJ65SHqETEaD0maff+GUm7AY/VGE8va3fzVi3Sph8Ro/Ve4MzStg/wANB2PJ54+p3LdUnSj4gRkzQOOMT2DqW3CrYfGmKz6AFJ+hExYraf7G/aSbIflp4ZpyoXciNiVCSdBIwHfgw80l9u+7zagqpJuZ4x0/Yjkg4GdgK+brvnRqNN0o+IUZH0vTbFtv0vXQ+mZpJmUd2Ruz1wOvBd4EDbr1zWdnVI0o+IWE79XTIlfQqYb/vUXuqm2Spt+hExKpJObFO8EJhh+/xux1OzRZKOBg4GXiFpNarnB/ec9NOPiNFaG5gM3Fxe2wMTgMMlfa3OwGrwNqrnBB9u+y6q8/DlekNqL807ETEqkq4AdrP9ZJlfHfgdsDtwne1t64wv2ktNPyJGayNg3Zb5daienPUkVa13lSfp8vJzkaSHWl6LJPVkV9a06UfEaH0JmCnpt1TDDLwC+JykdYDf1BlYt9juv1dhvbpjGa4070TEqEnaDNi5zE63fUfLsp4ZWXJFk3SW7UOGKusFSfoRsUL0apfFFWHwey3XN2b14nWNtOlHxIrSMyNLriiSjpa0CNi+tU0fuBvoyW6rqelHxArRsJr+56mucbyAqisrVHcnX1ZfVO3lQm5ExPKbC1xG1T9/JrAr1eMSX1VnUO2keSciVpSeGVmyCz4AvBS4rTwwfkfgwXpDai9JPyJGRdJupXsmkg6WdIKkLfuX2961vui67nHbjwNIWsv2n4AX1hxTW0n6ETFaJwGPStoB+AjwF+DMekOqzTxJGwL/DUyTdD7Qc8MqQy7kRsQorUwjS3aTpFcCGwC/tt1zTVy5kBsRo7XSjCzZTbYvrTuGZUnzTkSM1kozsmQMSPNORESDpHknIkZE0uW2dy93orbWGkV1Q9L6NYUWw5CafkREg6RNPyJGRdJZwymL3pKkHxGjtV3rTBlZ8iU1xRLDlKQfESOyMo4sGQPSph8Ro7IyjSwZA9J7JyJGa6UZWTIGpHknIkZrpRlZMgYk6UfEaK00I0vGgDTvRMRoDR5Z8gF6dGTJGJALuRGx3Hp9ZMkYkKQfEdEgadOPiGiQJP2IiAZJ0o+IaJAk/YiIBknSj4hokP8PKUGmta6by8MAAAAASUVORK5CYII=\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/question_classification.ipynb b/examples/colab/component_examples/classifiers/question_classification.ipynb
deleted file mode 100644
index b287c554..00000000
--- a/examples/colab/component_examples/classifiers/question_classification.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"question_classification.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"TJENwMRq1ZCO"},"source":["\n","\n","\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/question_classification.ipynb)\n","\n","# Question classification based on the TREC dataset\n","The [TREC dataset](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.11.2766&rep=rep1&type=pdf) is dataset for question classification consisting of open-domain, fact-based questions divided into broad semantic categories. It has both a six-class (TREC-6) and a fifty-class (TREC-50) version. Both have 5,452 training examples and 500 test examples, but TREC-50 has finer-grained labels. Models are evaluated based on accuracy.\n","\n"]},{"cell_type":"code","metadata":{"id":"L5mXUh6x0hOn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620087734818,"user_tz":-120,"elapsed":110495,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"6038423c-b29b-4f5f-80d5-bd94e696077c"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:20:24-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-04 00:20:24 (1.15 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 63kB/s \n","\u001b[K |████████████████████████████████| 153kB 38.5MB/s \n","\u001b[K |████████████████████████████████| 204kB 24.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 27.1MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"uNVO-xvd0qeq","colab":{"base_uri":"https://localhost:8080/","height":237},"executionInfo":{"status":"ok","timestamp":1620090602459,"user_tz":-120,"elapsed":9756,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2faf0e23-c546-4bc2-d17b-d1c1db707a63"},"source":["# Detect sentences automatically and generates a 1 to S mapping for every row, where S is the amount of Sentences per row\n","import nlu\n","nlu.load('en.classify.questions').predict('How expensive is the Watch? How many Dollars?')\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classifierdl_use_trec50 download started this may take some time.\n","Approximate size to download 21.2 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_tfhub_use
\n","
origin_index
\n","
questions_confidence_confidence
\n","
document
\n","
text
\n","
questions
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[[0.05180951952934265, 0.03128404542803764, -0...
\n","
8589934592
\n","
[0.9999299, 0.9999299]
\n","
How expensive is the Watch? How many Dollars?
\n","
How expensive is the Watch? How many Dollars?
\n","
[ NUM_count, NUM_count]
\n","
[How expensive is the Watch?, How many Dollars?]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_tfhub_use ... sentence\n","0 [[0.05180951952934265, 0.03128404542803764, -0... ... [How expensive is the Watch?, How many Dollars?]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"ES6Zv6UR00pT","colab":{"base_uri":"https://localhost:8080/","height":237},"executionInfo":{"status":"ok","timestamp":1620088411908,"user_tz":-120,"elapsed":9408,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"26920fda-6bcf-4036-82e4-ade56e471d66"},"source":["# 1 to 1 mapping of row to label via outputlevel == document\n","import nlu\n","nlu.load('en.classify.questions').predict('Whats the fastest way to Berlin?', output_level='document')\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classifierdl_use_trec50 download started this may take some time.\n","Approximate size to download 21.2 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_tfhub_use
\n","
origin_index
\n","
questions_confidence_confidence
\n","
document
\n","
text
\n","
questions
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[-0.05183497443795204, 0.023114148527383804, -...
\n","
8589934592
\n","
0.999947
\n","
Whats the fastest way to Berlin?
\n","
Whats the fastest way to Berlin?
\n","
DESC_manner
\n","
[Whats the fastest way to Berlin?]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_tfhub_use ... sentence\n","0 [-0.05183497443795204, 0.023114148527383804, -... ... [Whats the fastest way to Berlin?]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"65FCkQsYyAoR"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/sarcasm_classification.ipynb b/examples/colab/component_examples/classifiers/sarcasm_classification.ipynb
deleted file mode 100644
index c9887de1..00000000
--- a/examples/colab/component_examples/classifiers/sarcasm_classification.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"sarcasm_classification.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"ZE4c3HMSkGGu"},"source":["\n","\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/sarcasm_classification.ipynb)\n","\n","\n","# Sarcasm Classification with NLU\n"," \n","Knowing the difference between when somebody is serious or sarcastic can yield inisghts about users of social media plattforms like Twitter, Reddit, Facebook, etc.. \n","\n","\n","NLU provides a classifier pretrained on [sarcasm dataset](https://arxiv.org/abs/1704.05579) consisting of 1.3 million sarcastic comments from the internet forum Reddit. \n","\n","The Sarcasm classifier model uses universal sentence embeddings and is trained with the classifierdl algorithm provided by Spark NLP. \n","https://www.kaggle.com/danofer/sarcasm\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620087716539,"user_tz":-120,"elapsed":98736,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2b53662c-0f80-4450-bbd5-20435a473322"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:20:18-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \rInstalling NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-04 00:20:18 (45.7 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 70kB/s \n","\u001b[K |████████████████████████████████| 153kB 52.0MB/s \n","\u001b[K |████████████████████████████████| 204kB 23.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 46.4MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"51Zr-JvU4xEg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620087723742,"user_tz":-120,"elapsed":105927,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b5ae12e0-bf13-4c66-f646-ba4803469f4d"},"source":["# Download the dataset \n","! wget -N https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv -P /tmp"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:21:56-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.17.22\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.17.22|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 255268960 (243M) [text/csv]\n","Saving to: ‘/tmp/train-balanced-sarcasm.csv’\n","\n","train-balanced-sarc 100%[===================>] 243.44M 41.1MB/s in 5.8s \n","\n","2021-05-04 00:22:02 (41.8 MB/s) - ‘/tmp/train-balanced-sarcasm.csv’ saved [255268960/255268960]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":237},"executionInfo":{"status":"ok","timestamp":1620087845697,"user_tz":-120,"elapsed":227876,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"54a0a8c1-6630-4aa9-95ef-0c286d65434a"},"source":["import nlu\n","sarcasm_pipe = nlu.load('en.classify.sarcasm')\n","sarcasm_pipe.predict('gotta love the teachers who give exams on the day after halloween')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classifierdl_use_sarcasm download started this may take some time.\n","Approximate size to download 21.3 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sarcasm_confidence_confidence
\n","
document
\n","
origin_index
\n","
sarcasm
\n","
text
\n","
sentence_embedding_tfhub_use
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.99999106]
\n","
gotta love the teachers who give exams on the ...
\n","
8589934592
\n","
[sarcasm]
\n","
gotta love the teachers who give exams on the ...
\n","
[[-0.05071105435490608, 0.038035523146390915, ...
\n","
[gotta love the teachers who give exams on the...
\n","
\n"," \n","
\n","
"],"text/plain":[" sarcasm_confidence_confidence ... sentence\n","0 [0.99999106] ... [gotta love the teachers who give exams on the...\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"gpeS8DWBlrun"},"source":["import pandas as pd\n","df = pd.read_csv('/tmp/train-balanced-sarcasm.csv')\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3V5l-B6nl43U","colab":{"base_uri":"https://localhost:8080/","height":566},"executionInfo":{"status":"ok","timestamp":1620087869866,"user_tz":-120,"elapsed":252039,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"70b60c32-0996-4f17-bfdf-bf5bd9edbf57"},"source":["# Detect sentences automatically and generates a 1 to S mapping for every row, where S is the amount of Sentences per row\n","sarcasm_pipe = nlu.load('en.classify.sarcasm')\n","df['text'] = df['comment']\n","sarcasm_predictions = sarcasm_pipe.predict(df['text'].iloc[0:1000])\n","sarcasm_predictions"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classifierdl_use_sarcasm download started this may take some time.\n","Approximate size to download 21.3 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sarcasm_confidence_confidence
\n","
document
\n","
origin_index
\n","
sarcasm
\n","
text
\n","
sentence_embedding_tfhub_use
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.999997]
\n","
NC and NH.
\n","
0
\n","
[normal]
\n","
NC and NH.
\n","
[[-0.06570463627576828, -0.03522052243351936, ...
\n","
[NC and NH.]
\n","
\n","
\n","
1
\n","
[0.99998283]
\n","
You do know west teams play against west teams...
\n","
1
\n","
[normal]
\n","
You do know west teams play against west teams...
\n","
[[-0.0254225991666317, 0.05448468029499054, -0...
\n","
[You do know west teams play against west team...
\n","
\n","
\n","
2
\n","
[0.9997936]
\n","
They were underdogs earlier today, but since G...
\n","
2
\n","
[normal]
\n","
They were underdogs earlier today, but since G...
\n","
[[-0.0035701016895473003, -0.03012475557625293...
\n","
[They were underdogs earlier today, but since ...
\n","
\n","
\n","
3
\n","
[0.9942914]
\n","
This meme isn't funny none of the \"new york ni...
\n","
3
\n","
[normal]
\n","
This meme isn't funny none of the \"new york ni...
\n","
[[0.06464719027280807, -0.023972544819116592, ...
\n","
[This meme isn't funny none of the \"new york n...
\n","
\n","
\n","
4
\n","
[0.999838]
\n","
I could use one of those tools.
\n","
4
\n","
[normal]
\n","
I could use one of those tools.
\n","
[[0.028676817193627357, 0.0199710875749588, 0....
\n","
[I could use one of those tools.]
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
995
\n","
[0.99998844]
\n","
Have you bound your pistol on mouse wheel?
\n","
8589935087
\n","
[normal]
\n","
Have you bound your pistol on mouse wheel?
\n","
[[-0.04123315587639809, 0.049579471349716187, ...
\n","
[Have you bound your pistol on mouse wheel?]
\n","
\n","
\n","
996
\n","
[0.99159735]
\n","
Imagine showing that to someone a little over ...
\n","
8589935088
\n","
[normal]
\n","
Imagine showing that to someone a little over ...
\n","
[[0.0263528935611248, -0.06056991219520569, -0...
\n","
[Imagine showing that to someone a little over...
\n","
\n","
\n","
997
\n","
[0.9997123]
\n","
I wish Schumer and Reid had not endorsed Keith...
\n","
8589935089
\n","
[normal]
\n","
I wish Schumer and Reid had not endorsed Keith...
\n","
[[0.07649341225624084, 0.05448545515537262, -0...
\n","
[I wish Schumer and Reid had not endorsed Keit...
\n","
\n","
\n","
998
\n","
[0.9423373]
\n","
yeah, god forbid jesse look out for his fans b...
\n","
8589935090
\n","
[normal]
\n","
yeah, god forbid jesse look out for his fans b...
\n","
[[0.049849480390548706, -0.054164644330739975,...
\n","
[yeah, god forbid jesse look out for his fans ...
\n","
\n","
\n","
999
\n","
[0.99999344]
\n","
Beer city USA
\n","
8589935091
\n","
[normal]
\n","
Beer city USA
\n","
[[-0.05082784965634346, -0.045025862753391266,...
\n","
[Beer city USA]
\n","
\n"," \n","
\n","
1000 rows × 7 columns
\n","
"],"text/plain":[" sarcasm_confidence_confidence ... sentence\n","0 [0.999997] ... [NC and NH.]\n","1 [0.99998283] ... [You do know west teams play against west team...\n","2 [0.9997936] ... [They were underdogs earlier today, but since ...\n","3 [0.9942914] ... [This meme isn't funny none of the \"new york n...\n","4 [0.999838] ... [I could use one of those tools.]\n",".. ... ... ...\n","995 [0.99998844] ... [Have you bound your pistol on mouse wheel?]\n","996 [0.99159735] ... [Imagine showing that to someone a little over...\n","997 [0.9997123] ... [I wish Schumer and Reid had not endorsed Keit...\n","998 [0.9423373] ... [yeah, god forbid jesse look out for his fans ...\n","999 [0.99999344] ... [Beer city USA]\n","\n","[1000 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"WdnY9n1LTmed","colab":{"base_uri":"https://localhost:8080/","height":580},"executionInfo":{"status":"ok","timestamp":1620087870210,"user_tz":-120,"elapsed":252379,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b5df7e44-388d-4c21-cc2d-c863d41c32a7"},"source":["sarcasm_predictions.sarcasm.value_counts().plot.bar(title='Counts of Normal and Sarcasm predicted sentences')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":6},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"e7vPdtmquQfU","colab":{"base_uri":"https://localhost:8080/","height":491},"executionInfo":{"status":"ok","timestamp":1620088256908,"user_tz":-120,"elapsed":14809,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"e7c30ae7-8009-42ea-ff01-ead4532e6115"},"source":["# 1 to 1 mapping of row to label via outputlevel == document\n","sarcasm_pipe = nlu.load('en.classify.sarcasm')\n","df['text'] = df['comment']\n","sarcasm_predictions = sarcasm_pipe.predict(df['text'].iloc[0:1000], output_level='document')\n","sarcasm_predictions.sarcasm.value_counts().plot.bar(title='Counts of Normal and Sarcasm predicted for each row')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classifierdl_use_sarcasm download started this may take some time.\n","Approximate size to download 21.3 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":7},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"2uX3ByXRyL9s"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/sentiment_classification.ipynb b/examples/colab/component_examples/classifiers/sentiment_classification.ipynb
deleted file mode 100644
index f54f7dfe..00000000
--- a/examples/colab/component_examples/classifiers/sentiment_classification.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"sentiment_classification.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"tOHVDa9DQQR5"},"source":["\n","\n","\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/sentiment_classification.ipynb)\n","\n","# Sentiment Classification with NLU for Twitter\n","\n","# 1. Setup Java 8 and NLU"]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620072925705,"user_tz":-120,"elapsed":117531,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"dbe7d560-bd34-4813-fde4-d28ec042f532"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-03 20:13:28-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.108.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-03 20:13:28 (33.2 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 68kB/s \n","\u001b[K |████████████████████████████████| 153kB 45.4MB/s \n","\u001b[K |████████████████████████████████| 204kB 20.8MB/s \n","\u001b[K |████████████████████████████████| 204kB 30.7MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ZE4c3HMSkGGu"},"source":["# 2. Load pipeline and get sample predictions"]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":205},"executionInfo":{"status":"ok","timestamp":1620073052913,"user_tz":-120,"elapsed":244725,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"6de11c23-c2c7-487f-b646-acf72872d3a0"},"source":["import nlu\n","sentiment_pipe = nlu.load('en.sentiment.twitter')\n","sentiment_pipe.predict('@elonmusk Tesla stock price is too high imo')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["analyze_sentimentdl_use_twitter download started this may take some time.\n","Approx size to download 935.1 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_twitter
\n","
text
\n","
document
\n","
sentiment
\n","
origin_index
\n","
sentiment_confidence
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[[0.08604438602924347, 0.04703635722398758, -0...
\n","
@elonmusk Tesla stock price is too high imo
\n","
@elonmusk Tesla stock price is too high imo
\n","
[negative]
\n","
8589934592
\n","
[1.0]
\n","
[@elonmusk Tesla stock price is too high imo]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_twitter ... sentence\n","0 [[0.08604438602924347, 0.04703635722398758, -0... ... [@elonmusk Tesla stock price is too high imo]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"F4tv_Y23P_--"},"source":["# 3. Define a list of String for predictions"]},{"cell_type":"code","metadata":{"id":"yzmZCOypnpeX"},"source":["example_tweets = [\n","\"@VirginAmerica Hi, Virgin! I'm on hold for 40-50 minutes -- are there any earlier flights from LA to NYC tonight; earlier than 11:50pm?\",\n","\"@VirginAmerica is there special assistance if I travel alone w/2 kids and 1 infant? Priority boarding?\",\n","\"@VirginAmerica thank you for checking in. tickets are purchased and customer is happy\", \n","\"@VirginAmerica is your website ever coming back online?\",\n","\"@VirginAmerica - Is Flight 713 from Love Field to SFO definitely Cancelled Flightled for Monday, February 23?\",\n","\"@VirginAmerica Is flight 0769 out of LGA to DFW on time?\",\n","\"@VirginAmerica my drivers license is expired by a little over a month. Can I fly Friday morning using my expired license?\",\n","\"@VirginAmerica having problems Flight Booking Problems on the web site. keeps giving me an error and to contact by phone. phone is 30 minute wait.\",\n","\"@VirginAmerica How do I reschedule my Cancelled Flightled flights online? The change button is greyed out!\",\n","\"@VirginAmerica I rang, but there is a wait for 35 minutes!! I can book the same ticket through a vendor, fix your site\",\n","\"@VirginAmerica got a flight (we were told) for 4:50 today..,checked my email and its for 4;50 TOMORROW. This is unacceptable.\",\n","\"@VirginAmerica our flight into lga was Cancelled Flighted. We're stuck in Dallas. I called to reschedule, told I could get a flight for today...(1/2)\",\n","\"@virginamerica why don't any of the pairings include red wine?! Only white is offered :( #redwineisbetter\"\n","]\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KTq6yhq0QA6p"},"source":["# 4. Get predictions for list of strings"]},{"cell_type":"code","metadata":{"id":"1GZ3BQlBQD5j","colab":{"base_uri":"https://localhost:8080/","height":663},"executionInfo":{"status":"ok","timestamp":1620073054495,"user_tz":-120,"elapsed":246299,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"76086007-51fa-4aae-b57b-e5aa60646d84"},"source":["# By default NLU will predict the senitment of every SENTENCE in each row.\n","# This is why you have multiple sentiment labels in the sentiment column\n","sentiment_pipe.predict(example_tweets)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_twitter
\n","
document
\n","
sentiment
\n","
origin_index
\n","
sentiment_confidence
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[[0.034392621368169785, 0.04743622615933418, -...
\n","
@VirginAmerica Hi, Virgin! I'm on hold for 40-...
\n","
[positive, positive]
\n","
0
\n","
[1.0, 1.0]
\n","
[@VirginAmerica Hi, Virgin!, I'm on hold for ...
\n","
\n","
\n","
1
\n","
[[-0.07632657885551453, -0.00686881598085165, ...
\n","
@VirginAmerica is there special assistance if ...
\n","
[negative, negative]
\n","
1
\n","
[1.0, 1.0]
\n","
[@VirginAmerica is there special assistance if...
\n","
\n","
\n","
2
\n","
[[-0.0030431197956204414, -0.02072464115917682...
\n","
@VirginAmerica thank you for checking in. tick...
\n","
[positive]
\n","
2
\n","
[1.0]
\n","
[@VirginAmerica thank you for checking in. tic...
\n","
\n","
\n","
3
\n","
[[-0.002968168817460537, -0.000566655478905886...
\n","
@VirginAmerica is your website ever coming bac...
\n","
[negative]
\n","
3
\n","
[1.0]
\n","
[@VirginAmerica is your website ever coming ba...
\n","
\n","
\n","
4
\n","
[[-0.001066288212314248, 0.07673103362321854, ...
\n","
@VirginAmerica - Is Flight 713 from Love Fiel...
\n","
[negative]
\n","
4
\n","
[1.0]
\n","
[@VirginAmerica - Is Flight 713 from Love Fie...
\n","
\n","
\n","
5
\n","
[[0.027866005897521973, 0.07844573259353638, -...
\n","
@VirginAmerica Is flight 0769 out of LGA to DF...
\n","
[negative]
\n","
5
\n","
[1.0]
\n","
[@VirginAmerica Is flight 0769 out of LGA to D...
\n","
\n","
\n","
6
\n","
[[-0.0589623749256134, -0.06640151143074036, -...
\n","
@VirginAmerica my drivers license is expired b...
\n","
[negative, negative]
\n","
6
\n","
[1.0, 1.0]
\n","
[@VirginAmerica my drivers license is expired ...
\n","
\n","
\n","
7
\n","
[[-0.007869902998209, 0.06951839476823807, -0....
\n","
@VirginAmerica having problems Flight Booking ...
\n","
[negative, negative]
\n","
7
\n","
[1.0, 1.0]
\n","
[@VirginAmerica having problems Flight Booking...
\n","
\n","
\n","
8
\n","
[[-0.047210294753313065, 0.0797676295042038, -...
\n","
@VirginAmerica How do I reschedule my Cancelle...
\n","
[negative, negative]
\n","
8
\n","
[1.0, 1.0]
\n","
[@VirginAmerica How do I reschedule my Cancell...
\n","
\n","
\n","
9
\n","
[[-0.004450463689863682, 0.06487753242254257, ...
\n","
@VirginAmerica I rang, but there is a wait for...
\n","
[negative, negative]
\n","
9
\n","
[1.0, 1.0]
\n","
[@VirginAmerica I rang, but there is a wait fo...
\n","
\n","
\n","
10
\n","
[[-0.01818418689072132, 0.04061070457100868, -...
\n","
@VirginAmerica got a flight (we were told) for...
\n","
[negative, negative, negative]
\n","
10
\n","
[1.0, 1.0, 1.0]
\n","
[@VirginAmerica got a flight (we were told) fo...
\n","
\n","
\n","
11
\n","
[[0.017033610492944717, 0.08045562356710434, -...
\n","
@VirginAmerica our flight into lga was Cancell...
\n","
[negative, negative, negative, negative]
\n","
11
\n","
[1.0, 1.0, 1.0, 1.0]
\n","
[@VirginAmerica our flight into lga was Cancel...
\n","
\n","
\n","
12
\n","
[[0.005574456881731749, -0.06231074407696724, ...
\n","
@virginamerica why don't any of the pairings i...
\n","
[negative, negative, negative]
\n","
12
\n","
[0.74997646, 0.74997646, 0.74997646]
\n","
[@virginamerica why don't any of the pairings ...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_twitter ... sentence\n","0 [[0.034392621368169785, 0.04743622615933418, -... ... [@VirginAmerica Hi, Virgin!, I'm on hold for ...\n","1 [[-0.07632657885551453, -0.00686881598085165, ... ... [@VirginAmerica is there special assistance if...\n","2 [[-0.0030431197956204414, -0.02072464115917682... ... [@VirginAmerica thank you for checking in. tic...\n","3 [[-0.002968168817460537, -0.000566655478905886... ... [@VirginAmerica is your website ever coming ba...\n","4 [[-0.001066288212314248, 0.07673103362321854, ... ... [@VirginAmerica - Is Flight 713 from Love Fie...\n","5 [[0.027866005897521973, 0.07844573259353638, -... ... [@VirginAmerica Is flight 0769 out of LGA to D...\n","6 [[-0.0589623749256134, -0.06640151143074036, -... ... [@VirginAmerica my drivers license is expired ...\n","7 [[-0.007869902998209, 0.06951839476823807, -0.... ... [@VirginAmerica having problems Flight Booking...\n","8 [[-0.047210294753313065, 0.0797676295042038, -... ... [@VirginAmerica How do I reschedule my Cancell...\n","9 [[-0.004450463689863682, 0.06487753242254257, ... ... [@VirginAmerica I rang, but there is a wait fo...\n","10 [[-0.01818418689072132, 0.04061070457100868, -... ... [@VirginAmerica got a flight (we were told) fo...\n","11 [[0.017033610492944717, 0.08045562356710434, -... ... [@VirginAmerica our flight into lga was Cancel...\n","12 [[0.005574456881731749, -0.06231074407696724, ... ... [@virginamerica why don't any of the pairings ...\n","\n","[13 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"lLv-Y5_1UT8A","colab":{"base_uri":"https://localhost:8080/","height":438},"executionInfo":{"status":"ok","timestamp":1620073247696,"user_tz":-120,"elapsed":1719,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"63ba26b1-48fd-47a0-ade1-8ef32a024ee3"},"source":["# If you configure the output level to document, you get one sentiment label for the entire row.\n","# 1 to 1 mapping between row and labels.\n","sentiment_pipe.predict(example_tweets, output_level='document')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_twitter
\n","
document
\n","
sentiment
\n","
origin_index
\n","
sentiment_confidence
\n","
sentence
\n","
\n"," \n"," \n","
\n","
0
\n","
[0.03229847177863121, 0.07190719246864319, -0....
\n","
@VirginAmerica Hi, Virgin! I'm on hold for 40-...
\n","
negative
\n","
0
\n","
1.000000
\n","
[@VirginAmerica Hi, Virgin!, I'm on hold for ...
\n","
\n","
\n","
1
\n","
[-0.07405534386634827, 0.03392260521650314, -0...
\n","
@VirginAmerica is there special assistance if ...
\n","
negative
\n","
1
\n","
1.000000
\n","
[@VirginAmerica is there special assistance if...
\n","
\n","
\n","
2
\n","
[-0.0030431197956204414, -0.020724641159176826...
\n","
@VirginAmerica thank you for checking in. tick...
\n","
positive
\n","
2
\n","
1.000000
\n","
[@VirginAmerica thank you for checking in. tic...
\n","
\n","
\n","
3
\n","
[-0.002968168817460537, -0.0005666554789058864...
\n","
@VirginAmerica is your website ever coming bac...
\n","
negative
\n","
3
\n","
1.000000
\n","
[@VirginAmerica is your website ever coming ba...
\n","
\n","
\n","
4
\n","
[-0.001066288212314248, 0.07673103362321854, -...
\n","
@VirginAmerica - Is Flight 713 from Love Fiel...
\n","
negative
\n","
4
\n","
1.000000
\n","
[@VirginAmerica - Is Flight 713 from Love Fie...
\n","
\n","
\n","
5
\n","
[0.027866005897521973, 0.07844573259353638, -0...
\n","
@VirginAmerica Is flight 0769 out of LGA to DF...
\n","
negative
\n","
5
\n","
1.000000
\n","
[@VirginAmerica Is flight 0769 out of LGA to D...
\n","
\n","
\n","
6
\n","
[-0.056550897657871246, -0.043615031987428665,...
\n","
@VirginAmerica my drivers license is expired b...
\n","
negative
\n","
6
\n","
1.000000
\n","
[@VirginAmerica my drivers license is expired ...
\n","
\n","
\n","
7
\n","
[-0.017857229337096214, 0.06905556470155716, -...
\n","
@VirginAmerica having problems Flight Booking ...
\n","
negative
\n","
7
\n","
1.000000
\n","
[@VirginAmerica having problems Flight Booking...
\n","
\n","
\n","
8
\n","
[-0.06491848826408386, 0.06875065714120865, -0...
\n","
@VirginAmerica How do I reschedule my Cancelle...
\n","
negative
\n","
8
\n","
1.000000
\n","
[@VirginAmerica How do I reschedule my Cancell...
\n","
\n","
\n","
9
\n","
[0.028607569634914398, 0.07750366628170013, 0....
\n","
@VirginAmerica I rang, but there is a wait for...
\n","
negative
\n","
9
\n","
1.000000
\n","
[@VirginAmerica I rang, but there is a wait fo...
\n","
\n","
\n","
10
\n","
[-0.008518190123140812, 0.06485836207866669, -...
\n","
@VirginAmerica got a flight (we were told) for...
\n","
negative
\n","
10
\n","
1.000000
\n","
[@VirginAmerica got a flight (we were told) fo...
\n","
\n","
\n","
11
\n","
[-0.023282280191779137, 0.07736584544181824, -...
\n","
@VirginAmerica our flight into lga was Cancell...
\n","
negative
\n","
11
\n","
1.000000
\n","
[@VirginAmerica our flight into lga was Cancel...
\n","
\n","
\n","
12
\n","
[0.01874721422791481, -0.07889366149902344, -0...
\n","
@virginamerica why don't any of the pairings i...
\n","
negative
\n","
12
\n","
0.985577
\n","
[@virginamerica why don't any of the pairings ...
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_twitter ... sentence\n","0 [0.03229847177863121, 0.07190719246864319, -0.... ... [@VirginAmerica Hi, Virgin!, I'm on hold for ...\n","1 [-0.07405534386634827, 0.03392260521650314, -0... ... [@VirginAmerica is there special assistance if...\n","2 [-0.0030431197956204414, -0.020724641159176826... ... [@VirginAmerica thank you for checking in. tic...\n","3 [-0.002968168817460537, -0.0005666554789058864... ... [@VirginAmerica is your website ever coming ba...\n","4 [-0.001066288212314248, 0.07673103362321854, -... ... [@VirginAmerica - Is Flight 713 from Love Fie...\n","5 [0.027866005897521973, 0.07844573259353638, -0... ... [@VirginAmerica Is flight 0769 out of LGA to D...\n","6 [-0.056550897657871246, -0.043615031987428665,... ... [@VirginAmerica my drivers license is expired ...\n","7 [-0.017857229337096214, 0.06905556470155716, -... ... [@VirginAmerica having problems Flight Booking...\n","8 [-0.06491848826408386, 0.06875065714120865, -0... ... [@VirginAmerica How do I reschedule my Cancell...\n","9 [0.028607569634914398, 0.07750366628170013, 0.... ... [@VirginAmerica I rang, but there is a wait fo...\n","10 [-0.008518190123140812, 0.06485836207866669, -... ... [@VirginAmerica got a flight (we were told) fo...\n","11 [-0.023282280191779137, 0.07736584544181824, -... ... [@VirginAmerica our flight into lga was Cancel...\n","12 [0.01874721422791481, -0.07889366149902344, -0... ... [@virginamerica why don't any of the pairings ...\n","\n","[13 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"2JoXWgM24-x4"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/sentiment_classification_movies.ipynb b/examples/colab/component_examples/classifiers/sentiment_classification_movies.ipynb
deleted file mode 100644
index c044f8e6..00000000
--- a/examples/colab/component_examples/classifiers/sentiment_classification_movies.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"sentiment_classification_movies.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"E67EyHcAMT92"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/sentiment_classification_movies.ipynb)\n","\n","# Sentiment Classification with NLU for Movies\n","\n","Based on IMDB dataset\n","The Sentiment classifier model uses universal sentence embeddings and is trained with the classifierdl algorithm provided by Spark NLP.\n","\n","\n","# 1. Install Java and NLU"]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620072919356,"user_tz":-120,"elapsed":117325,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"1dbd7171-de3c-46be-b9bd-90fb9e9a6cfb"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-03 20:13:22-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-03 20:13:22 (34.1 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 74kB/s \n","\u001b[K |████████████████████████████████| 153kB 43.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.4MB/s \n","\u001b[K |████████████████████████████████| 204kB 43.0MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ZE4c3HMSkGGu"},"source":["\n","# 2. Load the NLU sentiment pipeline and predict on a sample string"]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":187},"executionInfo":{"status":"ok","timestamp":1620073060055,"user_tz":-120,"elapsed":258018,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"28bf8b37-8323-4027-a5c3-bc6a3ab65be9"},"source":["import nlu\n","sentiment_pipe = nlu.load('en.sentiment.imdb')\n","sentiment_pipe.predict('The movie matrix was pretty cool ')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["analyze_sentimentdl_use_imdb download started this may take some time.\n","Approx size to download 935.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence_embedding_imdb
\n","
sentence
\n","
sentiment
\n","
document
\n","
origin_index
\n","
sentiment_confidence
\n","
text
\n","
\n"," \n"," \n","
\n","
0
\n","
[[0.026432784274220467, -0.05069664120674133, ...
\n","
[The movie matrix was pretty cool ]
\n","
[pos]
\n","
The movie matrix was pretty cool
\n","
8589934592
\n","
[1.0]
\n","
The movie matrix was pretty cool
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence_embedding_imdb ... text\n","0 [[0.026432784274220467, -0.05069664120674133, ... ... The movie matrix was pretty cool \n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"bv2-9p4hMsfd"},"source":["# 3. Define a list of String for predictions"]},{"cell_type":"code","metadata":{"id":"yzmZCOypnpeX"},"source":["\n","movie_reviews = [\n"," \"I thought this was a wonderful way to spend time on a too hot summer weekend, sitting in the air conditioned theater and watching a light-hearted comedy. The plot is simplistic, but the dialogue is witty and the characters are likable (even the well bread suspected serial killer). While some may be disappointed when they realize this is not Match Point 2: Risk Addiction, I thought it was proof that Woody Allen is still fully in control of the style many of us have grown to love.
This was the most I'd laughed at one of Woody's comedies in years (dare I say a decade?). While I've never been impressed with Scarlet Johanson, in this she managed to tone down her 'sexy' image and jumped right into a average, but spirited young woman.
This may not be the crown jewel of his career, but it was wittier than 'Devil Wears Prada' and more interesting than 'Superman' a great comedy to go see with friends.\",\n","\"Basically there's a family where a little boy (Jake) thinks there's a zombie in his closet & his parents are fighting all the time.
This movie is slower than a soap opera... and suddenly, Jake decides to become Rambo and kill the zombie.
OK, first of all when you're going to make a film you must Decide if its a thriller or a drama! As a drama the movie is watchable. Parents are divorcing & arguing like in real life. And then we have Jake with his closet which totally ruins all the film! I expected to see a BOOGEYMAN similar movie, and instead i watched a drama with some meaningless thriller spots.
3 out of 10 just for the well playing parents & descent dialogs. As for the shots with Jake: just ignore them.\",\n","\"Petter Mattei's 'Love in the Time of Money' is a visually stunning film to watch. Mr. Mattei offers us a vivid portrait about human relations. This is a movie that seems to be telling us what money, power and success do to people in the different situations we encounter.
This being a variation on the Arthur Schnitzler's play about the same theme, the director transfers the action to the present time New York where all these different characters meet and connect. Each one is connected in one way, or another to the next person, but no one seems to know the previous point of contact. Stylishly, the film has a sophisticated luxurious look. We are taken to see how these people live and the world they live in their own habitat.
The only thing one gets out of all these souls in the picture is the different stages of loneliness each one inhabits. A big city is not exactly the best place in which human relations find sincere fulfillment, as one discerns is the case with most of the people we encounter.
The acting is good under Mr. Mattei's direction. Steve Buscemi, Rosario Dawson, Carol Kane, Michael Imperioli, Adrian Grenier, and the rest of the talented cast, make these characters come alive.
We wish Mr. Mattei good luck and await anxiously for his next work.\",\n","\"Probably my all-time favorite movie, a story of selflessness, sacrifice and dedication to a noble cause, but it's not preachy or boring. It just never gets old, despite my having seen it some 15 or more times in the last 25 years. Paul Lukas' performance brings tears to my eyes, and Bette Davis, in one of her very few truly sympathetic roles, is a delight. The kids are, as grandma says, more like 'dressed-up midgets' than children, but that only makes them more fun to watch. And the mother's slow awakening to what's happening in the world and under her own roof is believable and startling. If I had a dozen thumbs, they'd all be 'up' for this movie.\",\n","\"I sure would like to see a resurrection of a up dated Seahunt series with the tech they have today it would bring back the kid excitement in me.I grew up on black and white TV and Seahunt with Gunsmoke were my hero's every week.You have my vote for a comeback of a new sea hunt.We need a change of pace in TV and this would work for a world of under water adventure.Oh by the way thank you for an outlet like this to view many viewpoints about TV and the many movies.So any ole way I believe I've got what I wanna say.Would be nice to read some more plus points about sea hunt.If my rhymes would be 10 lines would you let me submit,or leave me out to be in doubt and have me to quit,If this is so then I must go so lets do it.\",\n","\"This show was an amazing, fresh & innovative idea in the 70's when it first aired. The first 7 or 8 years were brilliant, but things dropped off after that. By 1990, the show was not really funny anymore, and it's continued its decline further to the complete waste of time it is today.
It's truly disgraceful how far this show has fallen. The writing is painfully bad, the performances are almost as bad - if not for the mildly entertaining respite of the guest-hosts, this show probably wouldn't still be on the air. I find it so hard to believe that the same creator that hand-selected the original cast also chose the band of hacks that followed. How can one recognize such brilliance and then see fit to replace it with such mediocrity? I felt I must give 2 stars out of respect for the original cast that made this show such a huge success. As it is now, the show is just awful. I can't believe it's still on the air.\",\n","\"Encouraged by the positive comments about this film on here I was looking forward to watching this film. Bad mistake. I've seen 950+ films and this is truly one of the worst of them - it's awful in almost every way: editing, pacing, storyline, 'acting,' soundtrack (the film's only song - a lame country tune - is played no less than four times). The film looks cheap and nasty and is boring in the extreme. Rarely have I been so happy to see the end credits of a film.
The only thing that prevents me giving this a 1-score is Harvey Keitel - while this is far from his best performance he at least seems to be making a bit of an effort. One for Keitel obsessives only.\",\n","\"If you like original gut wrenching laughter you will like this movie. If you are young or old then you will love this movie, hell even my mom liked it.
Great Camp!!!\",\n","\"Phil the Alien is one of those quirky films where the humour is based around the oddness of everything rather than actual punchlines.
At first it was very odd and pretty funny but as the movie progressed I didn't find the jokes or oddness funny anymore.
Its a low budget film (thats never a problem in itself), there were some pretty interesting characters, but eventually I just lost interest.
I imagine this film would appeal to a stoner who is currently partaking.
For something similar but better try 'Brother from another planet'\",\n","\"I saw this movie when I was about 12 when it came out. I recall the scariest scene was the big bird eating men dangling helplessly from parachutes right out of the air. The horror. The horror.
As a young kid going to these cheesy B films on Saturday afternoons, I still was tired of the formula for these monster type movies that usually included the hero, a beautiful woman who might be the daughter of a professor and a happy resolution when the monster died in the end. I didn't care much for the romantic angle as a 12 year old and the predictable plots. I love them now for the unintentional humor.
But, about a year or so later, I saw Psycho when it came out and I loved that the star, Janet Leigh, was bumped off early in the film. I sat up and took notice at that point. Since screenwriters are making up the story, make it up to be as scary as possible and not from a well-worn formula. There are no rules.\",\n","\"So im not a big fan of Boll's work but then again not many are. I enjoyed his movie Postal (maybe im the only one). Boll apparently bought the rights to use Far Cry long ago even before the game itself was even finsished.
People who have enjoyed killing mercs and infiltrating secret research labs located on a tropical island should be warned, that this is not Far Cry... This is something Mr Boll have schemed together along with his legion of schmucks.. Feeling loneley on the set Mr Boll invites three of his countrymen to play with. These players go by the names of Til Schweiger, Udo Kier and Ralf Moeller.
Three names that actually have made them selfs pretty big in the movie biz. So the tale goes like this, Jack Carver played by Til Schweiger (yes Carver is German all hail the bratwurst eating dudes!!) However I find that Tils acting in this movie is pretty badass.. People have complained about how he's not really staying true to the whole Carver agenda but we only saw carver in a first person perspective so we don't really know what he looked like when he was kicking a**..
However, the storyline in this film is beyond demented. We see the evil mad scientist Dr. Krieger played by Udo Kier, making Genetically-Mutated-soldiers or GMS as they are called. Performing his top-secret research on an island that reminds me of 'SPOILER' Vancouver for some reason. Thats right no palm trees here. Instead we got some nice rich lumberjack-woods. We haven't even gone FAR before I started to CRY (mehehe) I cannot go on any more.. If you wanna stay true to Bolls shenanigans then go and see this movie you will not be disappointed it delivers the true Boll experience, meaning most of it will suck.
There are some things worth mentioning that would imply that Boll did a good work on some areas of the film such as some nice boat and fighting scenes. Until the whole cromed/albino GMS squad enters the scene and everything just makes me laugh.. The movie Far Cry reeks of scheisse (that's poop for you simpletons) from a fa,r if you wanna take a wiff go ahead.. BTW Carver gets a very annoying sidekick who makes you wanna shoot him the first three minutes he's on screen.\",\n"," ]\n","\n"," "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PL7FrJJMMrkl"},"source":["# 4. Predict for each element in the list of strings"]},{"cell_type":"code","metadata":{"id":"FojX0W1LMmvZ","colab":{"base_uri":"https://localhost:8080/","height":568},"executionInfo":{"status":"ok","timestamp":1620073062500,"user_tz":-120,"elapsed":260454,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"063ec3ff-79f6-4785-bd26-0f111fdc5ba2"},"source":["# By default NLU will predict the senitment of every SENTENCE in each row.\n","# This is why you have multiple sentiment labels in the sentiment column\n","sentiment_pipe.predict(movie_reviews)\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
"],"text/plain":[" sentence_embedding_imdb ... sentiment_confidence\n","0 [0.021378064528107643, 0.058234769850969315, -... ... 1.000000\n","1 [-0.005944231990724802, -0.05775683373212814, ... ... 0.999661\n","2 [0.05090215429663658, 0.04202255234122276, -0.... ... 1.000000\n","3 [-0.027248885482549667, 0.0074381157755851746,... ... 1.000000\n","4 [-0.03813941031694412, -0.03322296217083931, 0... ... 1.000000\n","5 [0.0529070682823658, 0.051987115293741226, 0.0... ... 0.986852\n","6 [0.0682281032204628, -0.022019388154149055, -0... ... 1.000000\n","7 [-0.0654454156756401, 0.005620448384433985, -0... ... 1.000000\n","8 [0.029466671869158745, -0.03923017159104347, -... ... 0.849794\n","9 [-0.05255771800875664, -0.05770969018340111, -... ... 0.999759\n","10 [0.03336203098297119, -0.059207797050476074, -... ... 0.925236\n","\n","[11 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":6}]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/spam_classification.ipynb b/examples/colab/component_examples/classifiers/spam_classification.ipynb
deleted file mode 100644
index d5393ad1..00000000
--- a/examples/colab/component_examples/classifiers/spam_classification.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"spam_classification.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"ZE4c3HMSkGGu"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/spam_classification.ipynb)\n","\n","\n","# Spam Classification with NLU\n","\n","Spam is a problem of increasing size and occurence. \n","Fortunately we can leverage the structure of natural language with the latest deep learning algorithms with NLU in just one line.\n","\n","\n","The Spam classifier model uses universal sentence embeddings and is trained with the classifierdl algorithm provided by Spark NLP."]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620072890611,"user_tz":-120,"elapsed":102472,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"4021ff86-45f8-45f0-84eb-a571ece685e8"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-03 20:13:08-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-03 20:13:08 (40.9 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 66kB/s \n","\u001b[K |████████████████████████████████| 153kB 43.7MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 48.9MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":354},"executionInfo":{"status":"ok","timestamp":1620073015152,"user_tz":-120,"elapsed":226996,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0a3189f1-92fa-4bba-d13a-b276f14e2b97"},"source":["news_pipe = nlu.load('en.classify.spam')\n","news_pipe.predict(['Please sign up for this FREE membership it costs $$NO MONEY$$ just your mobile number!', 'Order our AMAZING product now, instant weight loss and hair gain!', 'Bill please be at the meeeting in the lunch room at 2PM.'])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["classifierdl_use_spam download started this may take some time.\n","Approximate size to download 21.3 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
spam
\n","
text
\n","
document
\n","
sentence_embedding_tfhub_use
\n","
sentence
\n","
origin_index
\n","
spam_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
[spam]
\n","
Please sign up for this FREE membership it cos...
\n","
Please sign up for this FREE membership it cos...
\n","
[[0.008322705514729023, 0.009957313537597656, ...
\n","
[Please sign up for this FREE membership it co...
\n","
0
\n","
[1.0]
\n","
\n","
\n","
1
\n","
[spam]
\n","
Order our AMAZING product now, instant weight ...
\n","
Order our AMAZING product now, instant weight ...
\n","
[[0.03330731764435768, -0.047974273562431335, ...
\n","
[Order our AMAZING product now, instant weight...
\n","
8589934592
\n","
[0.99997854]
\n","
\n","
\n","
2
\n","
[ham]
\n","
Bill please be at the meeeting in the lunch ro...
\n","
Bill please be at the meeeting in the lunch ro...
\n","
[[0.03500664234161377, 0.041489869356155396, -...
\n","
[Bill please be at the meeeting in the lunch r...
\n","
8589934593
\n","
[1.0]
\n","
\n"," \n","
\n","
"],"text/plain":[" spam ... spam_confidence_confidence\n","0 [spam] ... [1.0]\n","1 [spam] ... [0.99997854]\n","2 [ham] ... [1.0]\n","\n","[3 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"058LbTT5yTMl"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/toxic_classification.ipynb b/examples/colab/component_examples/classifiers/toxic_classification.ipynb
deleted file mode 100644
index a511665b..00000000
--- a/examples/colab/component_examples/classifiers/toxic_classification.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"toxic_classification.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"pXAJHMWpHbfU"},"source":["\n","\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/toxic_classification.ipynb)\n","\n","# Toxic text classification with NLU\n","\n","\n","\n","# 1. Install Java and\n"]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620088494008,"user_tz":-120,"elapsed":112384,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"762d45b3-0d35-4fa8-c326-ed758eace623"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:33:01-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-04 00:33:02 (38.1 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 63kB/s \n","\u001b[K |████████████████████████████████| 153kB 55.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 25.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 56.8MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bf6HiUL2ITeU"},"source":["# 2. Load toxic model and predict classes for sample string"]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":237},"executionInfo":{"status":"ok","timestamp":1620088641897,"user_tz":-120,"elapsed":260264,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c94352eb-5899-4823-f13e-0c16a01f7f60"},"source":["import nlu\n","toxic_pipe = nlu.load('en.classify.toxic')\n","toxic_pipe.predict('You are to stupid')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["multiclassifierdl_use_toxic download started this may take some time.\n","Approximate size to download 11.6 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
toxic
\n","
origin_index
\n","
text
\n","
sentence_embedding_tfhub_use
\n","
toxic_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
You are to stupid
\n","
[You are to stupid]
\n","
[toxic, insult]
\n","
8589934592
\n","
You are to stupid
\n","
[[-0.03398505970835686, 0.0007853527786210179,...
\n","
[0.968485, 0.968485]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... toxic_confidence_confidence\n","0 You are to stupid ... [0.968485, 0.968485]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"Nwa23gTdIb6Z"},"source":["# 3. Download sample dataset"]},{"cell_type":"code","metadata":{"id":"gpeS8DWBlrun","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620088652780,"user_tz":-120,"elapsed":271142,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"7c23dd36-e775-4b0f-e9dd-3be350d4c272"},"source":["# Download the dataset \n","! wget -N https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv -P /tmp\n","import pandas as pd\n","df = pd.read_csv('/tmp/train-balanced-sarcasm.csv')\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:37:21-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.106.182\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.106.182|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 255268960 (243M) [text/csv]\n","Saving to: ‘/tmp/train-balanced-sarcasm.csv’\n","\n","train-balanced-sarc 100%[===================>] 243.44M 47.4MB/s in 5.6s \n","\n","2021-05-04 00:37:27 (43.7 MB/s) - ‘/tmp/train-balanced-sarcasm.csv’ saved [255268960/255268960]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"TqfyoFhgIhHk"},"source":["# 4. Predict on pandas dataset and visualize predictions\n","\n"]},{"cell_type":"code","metadata":{"id":"3V5l-B6nl43U","colab":{"base_uri":"https://localhost:8080/","height":566},"executionInfo":{"status":"ok","timestamp":1620088672489,"user_tz":-120,"elapsed":290845,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"8890df86-0348-4e2b-d2e6-72ca0f0b47c5"},"source":["toxic_pipe = nlu.load('toxic')\n","df['text'] = df['comment']\n","toxic_predictions = toxic_pipe.predict(df['text'].iloc[0:1000], output_level='document')\n","toxic_predictions"],"execution_count":null,"outputs":[{"output_type":"stream","text":["multiclassifierdl_use_toxic download started this may take some time.\n","Approximate size to download 11.6 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
toxic
\n","
origin_index
\n","
text
\n","
sentence_embedding_tfhub_use
\n","
toxic_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
NC and NH.
\n","
[NC and NH.]
\n","
NaN
\n","
0
\n","
NC and NH.
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
NaN
\n","
\n","
\n","
1
\n","
You do know west teams play against west teams...
\n","
[You do know west teams play against west team...
\n","
NaN
\n","
1
\n","
You do know west teams play against west teams...
\n","
[-0.0254225991666317, 0.05448468029499054, -0....
\n","
NaN
\n","
\n","
\n","
2
\n","
They were underdogs earlier today, but since G...
\n","
[They were underdogs earlier today, but since ...
\n","
NaN
\n","
2
\n","
They were underdogs earlier today, but since G...
\n","
[-0.0035701016895473003, -0.030124755576252937...
\n","
NaN
\n","
\n","
\n","
3
\n","
This meme isn't funny none of the \"new york ni...
\n","
[This meme isn't funny none of the \"new york n...
\n","
NaN
\n","
3
\n","
This meme isn't funny none of the \"new york ni...
\n","
[0.06464719027280807, -0.023972544819116592, -...
\n","
NaN
\n","
\n","
\n","
4
\n","
I could use one of those tools.
\n","
[I could use one of those tools.]
\n","
NaN
\n","
4
\n","
I could use one of those tools.
\n","
[0.028676817193627357, 0.0199710875749588, 0.0...
\n","
NaN
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
995
\n","
Have you bound your pistol on mouse wheel?
\n","
[Have you bound your pistol on mouse wheel?]
\n","
NaN
\n","
8589935087
\n","
Have you bound your pistol on mouse wheel?
\n","
[-0.04123315587639809, 0.049579471349716187, -...
\n","
NaN
\n","
\n","
\n","
996
\n","
Imagine showing that to someone a little over ...
\n","
[Imagine showing that to someone a little over...
\n","
NaN
\n","
8589935088
\n","
Imagine showing that to someone a little over ...
\n","
[0.0263528935611248, -0.06056991219520569, -0....
\n","
NaN
\n","
\n","
\n","
997
\n","
I wish Schumer and Reid had not endorsed Keith...
\n","
[I wish Schumer and Reid had not endorsed Keit...
\n","
NaN
\n","
8589935089
\n","
I wish Schumer and Reid had not endorsed Keith...
\n","
[0.07649341225624084, 0.05448545515537262, -0....
\n","
NaN
\n","
\n","
\n","
998
\n","
yeah, god forbid jesse look out for his fans b...
\n","
[yeah, god forbid jesse look out for his fans ...
\n","
[toxic]
\n","
8589935090
\n","
yeah, god forbid jesse look out for his fans b...
\n","
[0.049849480390548706, -0.054164644330739975, ...
\n","
[0.60551816]
\n","
\n","
\n","
999
\n","
Beer city USA
\n","
[Beer city USA]
\n","
NaN
\n","
8589935091
\n","
Beer city USA
\n","
[-0.05082784965634346, -0.045025862753391266, ...
\n","
NaN
\n","
\n"," \n","
\n","
1000 rows × 7 columns
\n","
"],"text/plain":[" document ... toxic_confidence_confidence\n","0 NC and NH. ... NaN\n","1 You do know west teams play against west teams... ... NaN\n","2 They were underdogs earlier today, but since G... ... NaN\n","3 This meme isn't funny none of the \"new york ni... ... NaN\n","4 I could use one of those tools. ... NaN\n",".. ... ... ...\n","995 Have you bound your pistol on mouse wheel? ... NaN\n","996 Imagine showing that to someone a little over ... ... NaN\n","997 I wish Schumer and Reid had not endorsed Keith... ... NaN\n","998 yeah, god forbid jesse look out for his fans b... ... [0.60551816]\n","999 Beer city USA ... NaN\n","\n","[1000 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"WdnY9n1LTmed","colab":{"base_uri":"https://localhost:8080/","height":354},"executionInfo":{"status":"ok","timestamp":1620088672839,"user_tz":-120,"elapsed":291191,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"8c2fa9c8-125d-44f5-bf28-989303d4fed9"},"source":["toxic_predictions.explode('toxic').toxic.value_counts().plot.bar(title='Counts of Toxic predicted sentences')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":5},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"2j0JyTVWIm95"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/classifiers/unsupervised_keyword_extraction_with_YAKE.ipynb b/examples/colab/component_examples/classifiers/unsupervised_keyword_extraction_with_YAKE.ipynb
deleted file mode 100644
index 38c96410..00000000
--- a/examples/colab/component_examples/classifiers/unsupervised_keyword_extraction_with_YAKE.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"unsupervised_keyword_extraction_with_YAKE.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"s4ljYpQNp50r"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/classifiers/unsupervised_keyword_extraction_with_YAKE.ipynb)\n","\n","\n","# Unsupervised keyword extraction with NLU using the YAKE algorithm\n","\n","We can use the YAKE algorithm to extract keywords from text data.\n","\n","Yake is an Unsupervised, Corpus-Independent, Domain and Language-Independent and Single-Document keyword extraction algorithm.\n","\n"," Yake is a novel feature-based system for multi-lingual keyword extraction, which supports texts of different sizes, domain or languages. Unlike other approaches, It follows an unsupervised approach which builds upon features extracted from the text, making it thus applicable to documents written in different languages without the need for further knowledge. This can be beneficial for a large number of tasks and a plethora of situations where access to training corpora is either limited or restricted.\n","\n"," \n","\n","\n","# 1. Install Java and NLU"]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620088842965,"user_tz":-120,"elapsed":112472,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0116b9bf-9189-4b1f-c1e0-d0257c558d0c"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:38:50-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-04 00:38:50 (1.72 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 77kB/s \n","\u001b[K |████████████████████████████████| 153kB 56.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 24.8MB/s \n","\u001b[K |████████████████████████████████| 204kB 60.1MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"kHtLKNXDtZf5"},"source":["# 2. Load the Yake model and predict some sample keywords"]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":147},"executionInfo":{"status":"ok","timestamp":1620088881507,"user_tz":-120,"elapsed":151007,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"eda904ec-0292-4eb9-e78c-328eb4293fce"},"source":["import nlu\n","keyword_pipe = nlu.load('yake')\n","keyword_pipe.predict('gotta love the teachers who give exams on the day after halloween')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
token
\n","
text
\n","
keywords_confidence
\n","
document
\n","
origin_index
\n","
keywords
\n","
\n"," \n"," \n","
\n","
0
\n","
[gotta love the teachers who give exams on the...
\n","
[gotta, love, the, teachers, who, give, exams,...
\n","
gotta love the teachers who give exams on the ...
\n","
[0.5309364299940568, 0.6388072684290906, 0.389...
\n","
gotta love the teachers who give exams on the ...
\n","
8589934592
\n","
[gotta, give, halloween]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... keywords\n","0 [gotta love the teachers who give exams on the... ... [gotta, give, halloween]\n","\n","[1 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"5lrDNzw3tcqT"},"source":["3.1 Download sample dataset"]},{"cell_type":"code","metadata":{"id":"gpeS8DWBlrun","colab":{"base_uri":"https://localhost:8080/","height":602},"executionInfo":{"status":"ok","timestamp":1620088893732,"user_tz":-120,"elapsed":163227,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"62a0a1c5-4238-448e-9aac-384c635f4a37"},"source":["import pandas as pd\n","# Download the dataset \n","! wget -N https://ckl-it.de/wp-content/uploads/2020/11/60kstackoverflow.csv -P /tmp\n","# Load dataset to Pandas\n","p = '/tmp/60kstackoverflow.csv'\n","df = pd.read_csv(p)\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-04 00:41:21-- https://ckl-it.de/wp-content/uploads/2020/11/60kstackoverflow.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 50356825 (48M) [text/csv]\n","Saving to: ‘/tmp/60kstackoverflow.csv’\n","\n","60kstackoverflow.cs 100%[===================>] 48.02M 4.91MB/s in 11s \n","\n","2021-05-04 00:41:32 (4.48 MB/s) - ‘/tmp/60kstackoverflow.csv’ saved [50356825/50356825]\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
Id
\n","
Title
\n","
Body
\n","
Tags
\n","
CreationDate
\n","
Y
\n","
\n"," \n"," \n","
\n","
0
\n","
34552656
\n","
Java: Repeat Task Every Random Seconds
\n","
<p>I'm already familiar with repeating tasks e...
\n","
<java><repeat>
\n","
2016-01-01 00:21:59
\n","
LQ_CLOSE
\n","
\n","
\n","
1
\n","
34553034
\n","
Why are Java Optionals immutable?
\n","
<p>I'd like to understand why Java 8 Optionals...
\n","
<java><optional>
\n","
2016-01-01 02:03:20
\n","
HQ
\n","
\n","
\n","
2
\n","
34553174
\n","
Text Overlay Image with Darkened Opacity React...
\n","
<p>I am attempting to overlay a title over an ...
\n","
<javascript><image><overlay><react-native><opa...
\n","
2016-01-01 02:48:24
\n","
HQ
\n","
\n","
\n","
3
\n","
34553318
\n","
Why ternary operator in swift is so picky?
\n","
<p>The question is very simple, but I just cou...
\n","
<swift><operators><whitespace><ternary-operato...
\n","
2016-01-01 03:30:17
\n","
HQ
\n","
\n","
\n","
4
\n","
34553755
\n","
hide/show fab with scale animation
\n","
<p>I'm using custom floatingactionmenu. I need...
\n","
<android><material-design><floating-action-but...
\n","
2016-01-01 05:21:48
\n","
HQ
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
44995
\n","
60461435
\n","
Convert List<String> to string C# - asp.net - ...
\n","
<p>I am new to this and I am asking for help t...
\n","
<c#><asp.net><sql-server>
\n","
2020-02-29 02:22:18
\n","
LQ_CLOSE
\n","
\n","
\n","
44996
\n","
60461754
\n","
Does Python execute code from the top or botto...
\n","
<p>I am working on learning Python and was won...
\n","
<python>
\n","
2020-02-29 03:33:59
\n","
LQ_CLOSE
\n","
\n","
\n","
44997
\n","
60462001
\n","
how to change payment date in Azure?
\n","
<p>It looks like it costs 8 days per month in ...
\n","
<azure><billing>
\n","
2020-02-29 04:34:16
\n","
LQ_CLOSE
\n","
\n","
\n","
44998
\n","
60465318
\n","
how to implement fill in the blank in Swift
\n","
<p>\"I _____ any questions.\"</p>\\n\\n<p>I want t...
\n","
<ios><swift>
\n","
2020-02-29 12:50:43
\n","
LQ_CLOSE
\n","
\n","
\n","
44999
\n","
60468018
\n","
How can I make a c# application outside of vis...
\n","
<p>I'm very new to programming and I'm teachin...
\n","
<c#><visual-studio>
\n","
2020-02-29 17:55:56
\n","
LQ_CLOSE
\n","
\n"," \n","
\n","
45000 rows × 6 columns
\n","
"],"text/plain":[" Id ... Y\n","0 34552656 ... LQ_CLOSE\n","1 34553034 ... HQ\n","2 34553174 ... HQ\n","3 34553318 ... HQ\n","4 34553755 ... HQ\n","... ... ... ...\n","44995 60461435 ... LQ_CLOSE\n","44996 60461754 ... LQ_CLOSE\n","44997 60462001 ... LQ_CLOSE\n","44998 60465318 ... LQ_CLOSE\n","44999 60468018 ... LQ_CLOSE\n","\n","[45000 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"uLWu8DG3tfjz"},"source":["## 3.2 Predict on sample dataset\n","NLU expects a text column, thus we must create it from the column that contains our text data"]},{"cell_type":"code","metadata":{"id":"3V5l-B6nl43U","colab":{"base_uri":"https://localhost:8080/","height":626},"executionInfo":{"status":"ok","timestamp":1620089043974,"user_tz":-120,"elapsed":313464,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"46f6af6c-5e3f-47c1-8ca5-b1f96a145b73"},"source":["keyword_pipe = nlu.load('yake')\n","keyword_predictions = keyword_pipe.predict(df['Title'])\n","keyword_predictions"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
token
\n","
text
\n","
keywords_confidence
\n","
document
\n","
origin_index
\n","
keywords
\n","
\n"," \n"," \n","
\n","
0
\n","
[Java: Repeat Task Every Random Seconds]
\n","
[Java, :, Repeat, Task, Every, Random, Seconds]
\n","
Java: Repeat Task Every Random Seconds
\n","
[0.26804494089513314, 0.1840422979793308, 0.16...
\n","
Java: Repeat Task Every Random Seconds
\n","
0
\n","
[seconds, random seconds, every random seconds]
\n","
\n","
\n","
1
\n","
[Why are Java Optionals immutable?]
\n","
[Why, are, Java, Optionals, immutable, ?]
\n","
Why are Java Optionals immutable?
\n","
[0.5798862558280943, 0.5798862558280943, 0.506...
\n","
Why are Java Optionals immutable?
\n","
1
\n","
[java, optionals, java optionals]
\n","
\n","
\n","
2
\n","
[Text Overlay Image with Darkened Opacity Reac...
\n","
[Text, Overlay, Image, with, Darkened, Opacity...
\n","
Text Overlay Image with Darkened Opacity React...
\n","
[0.26804494089513314, 0.1840422979793308, 0.16...
\n","
Text Overlay Image with Darkened Opacity React...
\n","
2
\n","
[native, react native, opacity react native]
\n","
\n","
\n","
3
\n","
[Why ternary operator in swift is so picky?]
\n","
[Why, ternary, operator, in, swift, is, so, pi...
\n","
Why ternary operator in swift is so picky?
\n","
[0.749415309854081, 0.749415309854081, 0.74941...
\n","
Why ternary operator in swift is so picky?
\n","
3
\n","
[operator, swift, picky]
\n","
\n","
\n","
4
\n","
[hide/show fab with scale animation]
\n","
[hide/show, fab, with, scale, animation]
\n","
hide/show fab with scale animation
\n","
[0.749415309854081, 0.35454977464777665, 0.361...
\n","
hide/show fab with scale animation
\n","
4
\n","
[scale, animation, scale animation]
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
44995
\n","
[Convert List<String> to string C# - asp.net -...
\n","
[Convert, List<String>, to, string, C#, -, asp...
\n","
Convert List<String> to string C# - asp.net - ...
\n","
[0.5158447575919991, 0.598088886267735, 0.5569...
\n","
Convert List<String> to string C# - asp.net - ...
\n","
8589957059
\n","
[convert, sql, sql server]
\n","
\n","
\n","
44996
\n","
[Does Python execute code from the top or bott...
\n","
[Does, Python, execute, code, from, the, top, ...
\n","
Does Python execute code from the top or botto...
\n","
[0.5957413617872535, 0.38901365812444827, 0.61...
\n","
Does Python execute code from the top or botto...
\n","
8589957060
\n","
[python, script, python execute]
\n","
\n","
\n","
44997
\n","
[how to change payment date in Azure?]
\n","
[how, to, change, payment, date, in, Azure, ?]
\n","
how to change payment date in Azure?
\n","
[0.749415309854081, 0.749415309854081, 0.57988...
\n","
how to change payment date in Azure?
\n","
8589957061
\n","
[change, date, azure]
\n","
\n","
\n","
44998
\n","
[how to implement fill in the blank in Swift]
\n","
[how, to, implement, fill, in, the, blank, in,...
\n","
how to implement fill in the blank in Swift
\n","
[0.6440026141535695, 0.6440026141535695, 0.290...
\n","
how to implement fill in the blank in Swift
\n","
8589957062
\n","
[implement, fill, swift]
\n","
\n","
\n","
44999
\n","
[How can I make a c# application outside of vi...
\n","
[How, can, I, make, a, c#, application, outsid...
\n","
How can I make a c# application outside of vis...
\n","
[0.749415309854081, 0.749415309854081, 0.74941...
\n","
How can I make a c# application outside of vis...
\n","
8589957063
\n","
[make, application, outside]
\n","
\n"," \n","
\n","
45000 rows × 7 columns
\n","
"],"text/plain":[" sentence ... keywords\n","0 [Java: Repeat Task Every Random Seconds] ... [seconds, random seconds, every random seconds]\n","1 [Why are Java Optionals immutable?] ... [java, optionals, java optionals]\n","2 [Text Overlay Image with Darkened Opacity Reac... ... [native, react native, opacity react native]\n","3 [Why ternary operator in swift is so picky?] ... [operator, swift, picky]\n","4 [hide/show fab with scale animation] ... [scale, animation, scale animation]\n","... ... ... ...\n","44995 [Convert List to string C# - asp.net -... ... [convert, sql, sql server]\n","44996 [Does Python execute code from the top or bott... ... [python, script, python execute]\n","44997 [how to change payment date in Azure?] ... [change, date, azure]\n","44998 [how to implement fill in the blank in Swift] ... [implement, fill, swift]\n","44999 [How can I make a c# application outside of vi... ... [make, application, outside]\n","\n","[45000 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"51gbhpalss1f"},"source":["# 3. Checkout the count of each predicted keyword. \n","To do that, we need to eplode the keywords column first and then we can use the value_counts function to get the count of each keyword. "]},{"cell_type":"code","metadata":{"id":"WdnY9n1LTmed","colab":{"base_uri":"https://localhost:8080/","height":632},"executionInfo":{"status":"ok","timestamp":1620089188423,"user_tz":-120,"elapsed":457908,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"20ec9ee1-1360-40a2-abf7-4ca9f6c64c6f"},"source":["keyword_pipe = nlu.load('yake')\n","keyword_predictions = keyword_pipe.predict(df['Title'])\n","keyword_predictions.explode('keywords').keywords.value_counts()[0:100].plot.bar(title='Top 100 Keywords in Stack Overflow Questions', figsize=(20,8))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":5},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"fWhCd-z3t8fB"},"source":["# 4. Lets configure the model \n","\n","You can configure the following parameters for YAKE : \n","\n","- setMinNGrams(int) Select the minimum length of a extracted keyword\n","- setMaxNGrams(int) Select the maximum length of a extracted keyword\n","- setNKeywords(int) Extract the top N keywords\n","- setStopWords(list) Set the list of stop words\n","- setThreshold(float) Each keyword will be given a keyword score greater than 0. (Lower the score better the keyword) Set an upper bound for the keyword score from this method.\n","- setWindowSize(int) Yake will construct a co-occurence matrix. You can set the - window size for the cooccurence matrix construction from this method. ex: - windowSize=2 will look at two words to both left and right of a candidate word."]},{"cell_type":"code","metadata":{"id":"bjYUPe4Tt9J9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620089188424,"user_tz":-120,"elapsed":457904,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c604b0e2-20cb-459d-cd23-4ceeab4479db"},"source":["keyword_pipe.print_info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['yake'] has settable params:\n","pipe['yake'].setMinNGrams(1) | Info: Minimum N-grams a keyword should have | Currently set to : 1\n","pipe['yake'].setMaxNGrams(3) | Info: Maximum N-grams a keyword should have | Currently set to : 3\n","pipe['yake'].setNKeywords(3) | Info: Number of Keywords to extract | Currently set to : 3\n","pipe['yake'].setWindowSize(3) | Info: Window size for Co-Occurrence | Currently set to : 3\n","pipe['yake'].setThreshold(-1.0) | Info: Keyword Score threshold | Currently set to : -1.0\n","pipe['yake'].setStopWords(['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', 'should', 'now', \"i'll\", \"you'll\", \"he'll\", \"she'll\", \"we'll\", \"they'll\", \"i'd\", \"you'd\", \"he'd\", \"she'd\", \"we'd\", \"they'd\", \"i'm\", \"you're\", \"he's\", \"she's\", \"it's\", \"we're\", \"they're\", \"i've\", \"we've\", \"you've\", \"they've\", \"isn't\", \"aren't\", \"wasn't\", \"weren't\", \"haven't\", \"hasn't\", \"hadn't\", \"don't\", \"doesn't\", \"didn't\", \"won't\", \"wouldn't\", \"shan't\", \"shouldn't\", \"mustn't\", \"can't\", \"couldn't\", 'cannot', 'could', \"here's\", \"how's\", \"let's\", 'ought', \"that's\", \"there's\", \"what's\", \"when's\", \"where's\", \"who's\", \"why's\", 'would']) | Info: the words to be filtered out. by default it's english stop words from Spark ML | Currently set to : ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', 'should', 'now', \"i'll\", \"you'll\", \"he'll\", \"she'll\", \"we'll\", \"they'll\", \"i'd\", \"you'd\", \"he'd\", \"she'd\", \"we'd\", \"they'd\", \"i'm\", \"you're\", \"he's\", \"she's\", \"it's\", \"we're\", \"they're\", \"i've\", \"we've\", \"you've\", \"they've\", \"isn't\", \"aren't\", \"wasn't\", \"weren't\", \"haven't\", \"hasn't\", \"hadn't\", \"don't\", \"doesn't\", \"didn't\", \"won't\", \"wouldn't\", \"shan't\", \"shouldn't\", \"mustn't\", \"can't\", \"couldn't\", 'cannot', 'could', \"here's\", \"how's\", \"let's\", 'ought', \"that's\", \"there's\", \"what's\", \"when's\", \"where's\", \"who's\", \"why's\", 'would']\n",">>> pipe['default_tokenizer'] has settable params:\n","pipe['default_tokenizer'].setTargetPattern('\\S+') | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n","pipe['default_tokenizer'].setContextChars(['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]) | Info: character list used to separate from token boundaries | Currently set to : ['.', ',', ';', ':', '!', '?', '*', '-', '(', ')', '\"', \"'\"]\n","pipe['default_tokenizer'].setCaseSensitiveExceptions(True) | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n","pipe['default_tokenizer'].setMinLength(0) | Info: Set the minimum allowed legth for each token | Currently set to : 0\n","pipe['default_tokenizer'].setMaxLength(99999) | Info: Set the maximum allowed legth for each token | Currently set to : 99999\n",">>> pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'] has settable params:\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setStorageRef('SentenceDetectorDLModel_c83c27f46b97') | Info: storage unique identifier | Currently set to : SentenceDetectorDLModel_c83c27f46b97\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setEncoder(com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@4962d0ab) | Info: Data encoder | Currently set to : com.johnsnowlabs.nlp.annotators.sentence_detector_dl.SentenceDetectorDLEncoder@4962d0ab\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setImpossiblePenultimates(['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']) | Info: Impossible penultimates | Currently set to : ['Bros', 'No', 'al', 'vs', 'etc', 'Fig', 'Dr', 'Prof', 'PhD', 'MD', 'Co', 'Corp', 'Inc', 'bros', 'VS', 'Vs', 'ETC', 'fig', 'dr', 'prof', 'PHD', 'phd', 'md', 'co', 'corp', 'inc', 'Jan', 'Feb', 'Mar', 'Apr', 'Jul', 'Aug', 'Sep', 'Sept', 'Oct', 'Nov', 'Dec', 'St', 'st', 'AM', 'PM', 'am', 'pm', 'e.g', 'f.e', 'i.e']\n","pipe['deep_sentence_detector@SentenceDetectorDLModel_c83c27f46b97'].setModelArchitecture('cnn') | Info: Model architecture (CNN) | Currently set to : cnn\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('shrink') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : shrink\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"j2sKSJnJumGl"},"source":["## 4.1 Lets configure the Yake model to give us 5 Keywords instead of 3"]},{"cell_type":"code","metadata":{"id":"IoqUSGNUulch","colab":{"base_uri":"https://localhost:8080/","height":578},"executionInfo":{"status":"ok","timestamp":1620089467362,"user_tz":-120,"elapsed":736838,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"955a7186-07a8-4dea-c80c-3cdb0ceef6be"},"source":["keyword_pipe['yake'].setNKeywords(3) \n","keyword_predictions = keyword_pipe.predict(df['Title'])\n","keyword_predictions\n","keyword_predictions.explode('keywords').keywords.value_counts()[0:100].plot.bar(title='Count of top 100 predicted keywords with new parameters.', figsize=(20,8))\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":7},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"mpoXJLoPvDBf"},"source":["## 4.2 Lets have some fun and play with YAKES parameters ad configure min,max N gram and window size and see how our plot differs from before!\n","\n","This is definetly fun and yields some interesting results. \n","\n","Have fun playing around!"]},{"cell_type":"code","metadata":{"id":"6sGuv07quMLY","colab":{"base_uri":"https://localhost:8080/","height":766},"executionInfo":{"status":"ok","timestamp":1620090555402,"user_tz":-120,"elapsed":4450,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"ba4bd04f-2492-48eb-d73c-fa062e6737b5"},"source":["keyword_pipe['yake'].setMinNGrams(5) \n","keyword_pipe['yake'].setNKeywords(10) \n","keyword_pipe['yake'].setMaxNGrams(10) \n","keyword_pipe['yake'].setWindowSize(6)\n","keyword_predictions = keyword_pipe.predict(df['Title'])\n","keyword_predictions.explode('keywords').keywords.value_counts()[0:100].plot.bar(title='Count of top 100 predicted keywords with new parameters.', figsize=(20,8))\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":9},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABH4AAALcCAYAAACRsXfGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde7h0dVk38O8t4PmMTwimYh4q08QiPOYxSyVT08wTkVlkHtLq9U1NzUMqlofU1NIXlTzlCRMFE1LBTFMfFBDEI6IoCI+agmYq+Hv/WGvLsJnZe2bv/cDw8/O5rrmePWvuWeueNWvNrPV91lpTrbUAAAAA0J/LXNINAAAAALBzCH4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBgAlVdd+qOr2qvlNVt7yk+7m0qqpWVTca//7HqnrKxTDN36+qD854bO+xp113dh87Q1WdVlW/tpPG/ZCqOmqNx+9UVV/ZGdMGAHY+wQ8AO0VVPbiqto8ByplV9e6quv3FMN0fBw4b9Lwkj26tXbm19omdMP6ZqupmVfWeqvp6VbUpj1+zqt5eVd+tqi9V1YNXPf7gcfh3q+pfq+qaO6PPRbXWHtFae+Z6dVV1TFX94cXRExdorb2+tfbrK/d35jLOhQnVALg4CH4A2HJV9edJ/j7Js5PskeR6SV6W5N6XZF9zun6Sky+haf8wyZuTPHzG4y9N8oMM8/QhSV5eVb+QJOO//5TkgPHx/8kwzzft0nqUzKWReX3JurTN/0tbvwBcMgQ/AGypqrpakmckeVRr7bDW2ndbaz9srb2ztfb4seZyVfX3VXXGePv7qrrc+NhFTtdZddrQa6rqpVV1RFWdW1Ufqaobjo99YHzKCeORRr87pb/LVNWTxyNjzq6qf66qq409fSfJLuPzvzDluVPHX1V/VFWfr6pvVtXhVbXXqt7/tKpOHY/k+buqmvr921r7TGvtkEwJnqrqSknul+QprbXvtNY+mOTwDEFPMgRB72ytfaC19p0kT0ny21V1lWnTWquv8T34z6p6YVV9I8nTxvnzvKr6clWdNZ6+dYWJ8T1+PLLrjKr6g1XTek1V/c3E/XtX1fFVdU5VfaGq7l5Vz0ryq0n+YZy3/zDW/lxVHT3O289U1QMmxrP7OL/PqaqPJrnhtNc64/Xfr4bTp242LhNPGHv5RlW9eeVoqXE5e8yq555YwymBT6+ql4zDdhuPtPq78f4Vqup/J8bzW1V1clV9azyy6ecnxndaVf1lVZ2Y5LtVtWtVHTAuo9+oqr9aNf39ajia7pzxvXjBjNd4bFXdb/z7duN7vv94/65Vdfz494/XubXWoar6i3GdObOqHrbGvD2mqp45LkPnVtVRVXWticdvXVUfGufFCVV1p3H4navqkxN1R1fVxybu/0dV3WfGNNdanm9YVe8b5+XXq+r1VXX1deb/yvJwblV9qqruO1E/uX58a5zmbcfhp4/z6MCJ+qnrTg3r9LuT7DXO6+9U1V7rLI8rpyw+vKq+nOR9VXX5qnrdWPutqvpYVe0x6/0B4CeP4AeArXabJJdP8vY1av4qya2T7JPkFkn2S/LkBabxwCRPT3KNJJ9P8qwkaa3dYXz8FuOpWm+a8tzfH293TvIzSa6c5B9aa99vrV154vkXCRGmjb+q7pLkOUkekGTPJF9K8i+rnnrfJPsm+aUMRz39QRZ3kyTntdY+OzHshCS/MP79C+P9lV6/kOHooJusMc61+rpVklMzHD30rCQHj+PaJ8mNklwnyVOTpKrunuT/JLlbkhsnmXktmqraL8k/J3l8kqsnuUOS01prf5XkP3LBaXaPHneMj07yhiQ/leF9f1lV3XQc3UuT/G+G+f4HmXO+jqHFc5P8WmvtpCSPSXKfJHdMsleS/x7HnSSHJnnoxHNvMb72I5Icm+RO40O/kuRr4+tJhvXgM621b1bVTZK8McnjkmxLcmSSd1bVZSfaelCS/cd5cpMkL88Q6u2VZPckPz1R+6IkL2qtXTVD2PXmGS91sr87Zng/7zBx/9jVT1hjHbp2kquNr/3hSV5aVdeYMd0keXCSh2V43y6bYflIVa3Mu79Jcs1x+NuqaluS/0py46q6VlXtluQXM4QiV6khZNw3wzIyy6zluTKso3sl+fkk103ytFXP/fH8b62dl+QLGYLIq2X4rHldVe05UX+rJCdmeG/ekGGd/5UM68ZDMwSYK58nU9ed1tp3k9wjyRnjvL5ya+2MrL08rrjj+Fp+I8mBY5/XHft5RJLvrTGfAPgJI/gBYKvtnuTr487TLA9J8ozW2tmttR0ZdqwOWKN+tbe31j46TuP1GXao5vWQJC9orZ06HhnzxCQPrI2fMvGQJK9qrX28tfb9cXy3qaq9J2qe21r7ZmvtyxlOgXvQBqZz5STnrBr27SRXmXj822s8Ps1afZ3RWnvJOI//N8lBSf5srD83w2l8DxxrH5Dk1a21k8ad2aetMc2HZ5hfR7fWftRa+2pr7dMzan8zQyj06tbaeeM1l96W5HeqapcMR0A9dTyq7KQMIc16HpchdLpTa+3z47BHJPmr1tpXxvfwaUnuPy4Thye5SVXdeKw9IMmbWms/SPLhDEHF7hkClUOSXGfc4Z8MVn43yRHja/5hhutIXSHJbSf6enFr7fTW2veS3D/Ju8ajt76f4eitH03U/jDJjarqWuPRX/8147UeO/aRsb/nTNyfGvys4YcZ1tkfttaOTPKdJD+7Rv2rW2ufHV/Pm3PBOvrQJEe21o4c3/+jk2xPcs+x9mNjr7+cIcj8zyS3yxAUf6619o01pjl1eW6tfX6c998fP29eMDEfVkzO/7TW3tJaO2Ps8U1JPpchoF7xxXG5PD/JmzKELs8Yp3FUhtD1RlVVWXvdmWat5XHF08bl/nsZ3pvdk9yotXZ+a+241trqzwoAfoIJfgDYat9Icq11gpS9MhwZs+JL47B5fW3i7//JEHrMa9q0d81wZMtGXGh8Y5j0jQz/q7/i9FXTW+S1rvhOkquuGnbVJOfO+fg0a/U1+di2JFdMctx4Ksm3kvzbODzj81aPa5brZjiaYh7XT3KrlWmO031IhqNPtmV43+ad7orHJ3lpa23ygrrXT/L2iWmckuT8JHu01v43w479Q8dThx6U5LVJMu50b88QItwhQ5DyoQxBxWSwsnoZ+dHY96xl5ELzcwzTJgOPh2c4guTT42k9vznjtX44Q2i1R4bg5Z+TXHc87Wq/JB+Y8bxpvrEqzF1vvZu1jl4/Q3A3+Z7ePsNRW8kFRymtzM9jMszLeYKqqctzVe1RVf9SVV+tqnOSvC7JtdZ4bqrq92o4HXGlx5utes5ZE3+vhEWrh105668708xcHmf0+9ok70nyLzWcavm34xFTAJBE8APA1vtwku9nOFVhljMy7NysuN44LEm+m2FHKUlSVdfe4v6mTfu8XHhHbsPjG09P2j3JVydqrrtqemdkcZ9NsuvEkSfJcJrcyvWATh7vr/TxM0kuNz5vlrX6mvxVsa9n2JH9hdba1cfb1SZOjTtzyrhmOT2zr8Wz+pfMTk9y7MQ0rz6eDvMnSXZkeN/mne6KX0/y5BqvfTMxnXusms7lW2sr7+GhGQKnuyb5n9bahyeee2ySuyS5ZYajVY7NcPrNZLCyehmpse/JZWTytV9oflbVFTMsU0Nha59rrT0ow2lUz03y1nG5u5DW2v8kOS7JY5OcNB6l9KEkf57kC621r8+eTTvN6Uleu2peX6m1dvD4+OrgZ+WopXmCn1nL87MzzN+bj6fHPTTD6V+Tfjz/q+r6SV6Z5NFJdm+tXT3JSVOeM4/11p2L/Hpf1l8eL/S88Sisp7fWbprhKLLfTPJ7G+gVgE4JfgDYUq21b2e49stLq+o+VXXFGi58e4+q+tux7I0Zdr63jUcfPDXD/8In43Vrqmqfqrp81j5taJqzMly7Z5Y3JvmzqrrBeErOszOcurPWqWlrjf+NSR429nu5cXwfaa2dNlHz+Kq6RlVdN8NO+LRrD6UGl89wTZSMF229XPLjoz4OS/KMqrpSVd0uw3VMXjs+/fVJ7lVVvzqGAM9Icth4asksc/U1HqHyyiQvrKqfGnu7TlX9xljy5iS/X1U3HUOKv15jmodkmF93HS9ie52q+rnxsdXz9l0Zjlg5YFyGdquqX6mqnx9PsTksw4Wnrzhe9+fArO/kJHfPsHz+1jjsH5M8a9zhz7hc/vgX6Mag50dJnp8L5veKYzPsZH9qDFaOSfKHGU4F2jExf/YfX/NuSf4iQzj6oRk9vjXJb1bV7cfrAD0jE9tsVfXQqto2vi/fGgf/aMp4Vvp7dC4ITY5ZdX+a9dahzXhdhuX0N6pql3EZv1NVrVzD6EMZTiHbL8lHW2snZzzyK+sfoTRreb5KhiPivj1eY+jx64znShmClR3Jj68JdbOFXuVojnXnrCS713BR/BVrLo+r1XBR7JuPpz+ek+HUr1nLAwA/gQQ/AGy51trzMxxV8OQMO0+nZ9jZ/Nex5G8ynCJzYpJPJvn4OCxtuHjxM5L8e4bralzoF77m8LQkh46nSTxgyuOvyrDz/oEkX8xw/ZrHTKmba/yttX/PcA2Wt2U4UuOGuej1O96R4ciL4zNc2PaQGeO+foajA1aO4vleks9MPP7IDNeGOTtD4PQn445xxn8fkSEAOjvDzu4j13kt8/aVJH+Z4ULa/zWeLvPvGa/x0lp7d4ZrqrxvrHnfrJG01j6a4aK/L8xwDaJjc8HRMC/KcC2T/66qF4+h1a9nmJ9nZDh96LkZjmRKhmXqyuPw1yR59Tqvd6WHEzIcFfHKqrrHON3DkxxVVedmuMjwrVY97Z+T3DwXBJQrPpThPVkJJT6VYZn6cUjRWvtMhqNMXpLhCJB7JbnXGBRN6+/kJI/KcNHgMzNc3Hfy1LS7Jzm5hl+he1GSB65cm2aKYzMsCx+YcX+ap2XtdWjDWmunZwgsn5QLPhsen3GbdAw4P57k5In58+EkX2qtnb3O6Gctz0/PcMHnb4/DD1unx09lCPk+nCGYuXmGaw1t1FrrzqczrMunjvN7r8y3PE66doaw8JwMp4UdmzGgrOEXxP5xE70D0IFqbdoRpgDAVqiqluTG7YILCS+FZe1rWVXV7yU5qLV2+0u6Fy7K8gwAszniBwBgDePpa49M8opLuhcAgEUJfgAAZhivxbIjwyk/b7iE2wEAWJhTvQAAAAA65YgfAAAAgE4JfgAAAAA6tevFObFrXetabe+99744JwkAAADQteOOO+7rrbVt0x67WIOfvffeO9u3b784JwkAAADQtar60qzHnOoFAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACd2vWSmvDeTzjiIsNOO3j/S6ATAAAAgD454gcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATq0b/FTV5avqo1V1QlWdXFVPH4e/pqq+WFXHj7d9dn67AAAAAMxr1zlqvp/kLq2171TVbkk+WFXvHh97fGvtrTuvPQAAAAA2at3gp7XWknxnvLvbeGs7sykAAAAANm+ua/xU1S5VdXySs5Mc3Vr7yPjQs6rqxKp6YVVdbqd1CQAAAMDC5gp+Wmvnt9b2SfLTSfarqpsleWKSn0vyK0mumeQvpz23qg6qqu1VtX3Hjh1b1DYAAAAA61noV71aa99K8v4kd2+tndkG30/y6iT7zXjOK1pr+7bW9t22bdvmOwYAAABgLvP8qte2qrr6+PcVktwtyaeras9xWCW5T5KTdmajAAAAACxmnl/12jPJoVW1S4ag6M2ttXdV1fuqaluSSnJ8kkfsxD4BAAAAWNA8v+p1YpJbThl+l53SEQAAAABbYqFr/AAAAABw6SH4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOrVu8FNVl6+qj1bVCVV1clU9fRx+g6r6SFV9vqreVFWX3fntAgAAADCveY74+X6Su7TWbpFknyR3r6pbJ3lukhe21m6U5L+TPHzntQkAAADAotYNftrgO+Pd3cZbS3KXJG8dhx+a5D47pUMAAAAANmSua/xU1S5VdXySs5McneQLSb7VWjtvLPlKkuvsnBYBAAAA2Ii5gp/W2vmttX2S/HSS/ZL83LwTqKqDqmp7VW3fsWPHBtsEAAAAYFEL/apXa+1bSd6f5DZJrl5Vu44P/XSSr854zitaa/u21vbdtm3bppoFAAAAYH7z/KrXtqq6+vj3FZLcLckpGQKg+49lByZ5x85qEgAAAIDF7bp+SfZMcmhV7ZIhKHpza+1dVfWpJP9SVX+T5BNJDtmJfQIAAACwoHWDn9baiUluOWX4qRmu9wMAAADAElroGj8AAAAAXHoIfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU+sGP1V13ap6f1V9qqpOrqrHjsOfVlVfrarjx9s9d367AAAAAMxr1zlqzkvyF621j1fVVZIcV1VHj4+9sLX2vJ3XHgAAAAAbtW7w01o7M8mZ49/nVtUpSa6zsxsDAAAAYHMWusZPVe2d5JZJPjIOenRVnVhVr6qqa2xxbwAAAABswtzBT1VdOcnbkjyutXZOkpcnuWGSfTIcEfT8Gc87qKq2V9X2HTt2bEHLAAAAAMxjruCnqnbLEPq8vrV2WJK01s5qrZ3fWvtRklcm2W/ac1trr2it7dta23fbtm1b1TcAAAAA65jnV70qySFJTmmtvWBi+J4TZfdNctLWtwcAAADARs3zq163S3JAkk9W1fHjsCcleVBV7ZOkJTktyR/vlA4BAAAA2JB5ftXrg0lqykNHbn07AAAAAGyVhX7VCwAAAIBLD8EPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQqXWDn6q6blW9v6o+VVUnV9Vjx+HXrKqjq+pz47/X2PntAgAAADCveY74OS/JX7TWbprk1kkeVVU3TfKEJO9trd04yXvH+wAAAAAsiXWDn9bama21j49/n5vklCTXSXLvJIeOZYcmuc/OahIAAACAxS10jZ+q2jvJLZN8JMkerbUzx4e+lmSPLe0MAAAAgE2ZO/ipqisneVuSx7XWzpl8rLXWkrQZzzuoqrZX1fYdO3ZsqlkAAAAA5jdX8FNVu2UIfV7fWjtsHHxWVe05Pr5nkrOnPbe19orW2r6ttX23bdu2FT0DAAAAMId5ftWrkhyS5JTW2gsmHjo8yYHj3wcmecfWtwcAAADARu06R83tkhyQ5JNVdfw47ElJDk7y5qp6eJIvJXnAzmkRAAAAgI1YN/hprX0wSc14+K5b2w4AAAAAW2WhX/UCAAAA4NJD8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECnBD8AAAAAnRL8AAAAAHRq3eCnql5VVWdX1UkTw55WVV+tquPH2z13bpsAAAAALGqeI35ek+TuU4a/sLW2z3g7cmvbAgAAAGCz1g1+WmsfSPLNi6EXAAAAALbQZq7x8+iqOnE8Fewas4qq6qCq2l5V23fs2LGJyQEAAACwiI0GPy9PcsMk+yQ5M8nzZxW21l7RWtu3tbbvtm3bNjg5AAAAABa1oeCntXZWa+381tqPkrwyyX5b2xYAAAAAm7Wh4Keq9py4e98kJ82qBQAAAOCSset6BVX1xiR3SnKtqvpKkr9Ocqeq2idJS3Jakj/eiT0CAAAAsAHrBj+ttQdNGXzITugFAAAAgC20mV/1AgAAAGCJCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFPrBj9V9aqqOruqTpoYds2qOrqqPjf+e42d2yYAAAAAi5rniJ/XJLn7qmFPSPLe1tqNk7x3vA8AAADAElk3+GmtfSDJN1cNvneSQ8e/D01yny3uCwAAAIBN2ug1fvZorZ05/v21JHtsUT8AAAAAbJFNX9y5tdaStFmPV9VBVbW9qrbv2LFjs5MDAAAAYE4bDX7Oqqo9k2T89+xZha21V7TW9m2t7btt27YNTg4AAACARW00+Dk8yYHj3wcmecfWtAMAAADAVpnn59zfmOTDSX62qr5SVQ9PcnCSu1XV55L82ngfAAAAgCWy63oFrbUHzXjorlvcCwAAAABbaNMXdwYAAABgOQl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOrXuz7kvg72fcMRFhp128P6XQCcAAAAAlx6O+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATu16STew1fZ+whFTh5928P4XW+2s+ou7FgAAAPjJ5ogfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOrXrJd0AO8/eTzhi6vDTDt7/YqudVX9x1wIAAMBPIkf8AAAAAHRK8AMAAADQKcEPAAAAQKcEPwAAAACdEvwAAAAAdErwAwAAANApwQ8AAABApwQ/AAAAAJ0S/AAAAAB0SvADAAAA0CnBDwAAAECndr2kG4CLw95POGLq8NMO3n8pa2fV76zaWfXLUDurfhlqZ9WbFzu/dlb9MtTOql+G2ln15sVy1c6qNy92fu2s+mWonVW/DLWz6s2L5aqdVW9e7PzaWfXLUDurfhlqZ9WbF+vXruaIHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6NSum3lyVZ2W5Nwk5yc5r7W271Y0BQAAAMDmbSr4Gd25tfb1LRgPAAAAAFvIqV4AAAAAndps8NOSHCx9/BkAACAASURBVFVVx1XVQdMKquqgqtpeVdt37NixyckBAAAAMK/NBj+3b639UpJ7JHlUVd1hdUFr7RWttX1ba/tu27Ztk5MDAAAAYF6bCn5aa18d/z07yduT7LcVTQEAAACweRsOfqrqSlV1lZW/k/x6kpO2qjEAAAAANmczv+q1R5K3V9XKeN7QWvu3LekKAAAAgE3bcPDTWjs1yS22sBcAAAAAtpCfcwcAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADol+AEAAADolOAHAAAAoFOCHwAAAIBOCX4AAAAAOiX4AQAAAOiU4AcAAACgU4IfAAAAgE4JfgAAAAA6JfgBAAAA6JTgBwAAAKBTgh8AAACATgl+AAAAADq1qeCnqu5eVZ+pqs9X1RO2qikAAAAANm/DwU9V7ZLkpUnukeSmSR5UVTfdqsYAAAAA2JzNHPGzX5LPt9ZOba39IMm/JLn31rQFAAAAwGZtJvi5TpLTJ+5/ZRwGAAAAwBKo1trGnlh1/yR3b6394Xj/gCS3aq09elXdQUkOGu/+bJLPTBndtZJ8fc5Jq12uPpahdln6WIbaZeljGWqXpY9LW+2y9LEMtcvSxzLULksfy1C7LH1c2mqXpY9lqF2WPpahdln6WIbaZenj0la7LH0sQ+2y9LEMtcvSx8Vde/3W2rapz2itbeiW5DZJ3jNx/4lJnrjBcW1Xu1jtsvSxDLXL0scy1C5LH8tQuyx9XNpql6WPZahdlj6WoXZZ+liG2mXp49JWuyx9LEPtsvSxDLXL0scy1C5LH5e22mXpYxlql6WPZahdlj6WoXbltplTvT6W5MZVdYOqumySByY5fBPjAwAAAGAL7brRJ7bWzquqRyd5T5JdkryqtXbylnUGAAAAwKZsOPhJktbakUmO3II+XqF24dpl6WMZapelj2WoXZY+lqF2Wfq4tNUuSx/LULssfSxD7bL0sQy1y9LHpa12WfpYhtpl6WMZapelj2WoXZY+Lm21y9LHMtQuSx/LULssfSxDbZJNXNwZAAAAgOW2mWv8AAAAALDEBD+wQVV1g3mGXQx9XG6eYcsyXpbPsizLAEAf5t22qKrLVNUDLp6u1mbbl54tffBTVb9TVVcZ/35yVR1WVb80q3a9YVX122vdVtVec63bOn1fcY7X9tx5hi3y+i4OVXWFqvrZLRrX3O/HlOdev6p+baKnq8you1xVPbiqnlRVT125zah9dlVdfeL+Narqb2a08LYpw966Vs/jOC9TVVddp+ayVfWLVXXz8Vfz1vLh9YZtcFled7wbUVU3XPkSrao7VdWfTs7zVbW7b3Z6a/QxbZm7a1X91M6a5mZU1e2q6krj3w+tqhdU1fXXqL99VT1s/HvbOkHOQstyVf1WVT1vvN1rK3quqt2r6iVV9fGqOq6qXjTr/a+qK1XVZca/bzL2s9sar28uVbVLVf3ZAvW3m3PYLlX16Q32dI2q+sV1atZ8r6vqz9e6rTHeRdbVw6pq/5X3ZZ1+L/KZs87yuVKz5ryoqj2q6pCqevd4/6ZV9fA16hdZR+a2ge/2LftOXTXedV/fRpbN8bPyBVX1/Kq67zq1c3//LtjD3PO4ql47z7AN9nGzqnpAVf3eym2N2vfOM2zisduO827NcdcC2y01eOjKe1BV16uq/WbUPmrKeB+5qmajny2LbOOsPGee7eoNbTPM8zk7UTvPNtxOWacXUQts49TO27+Ya9uitfajJP93kREvsl5X1WOr6qrj8n9IDdsZvz5j1HNv+1bVFavqKVX1yvH+javqN9foeZeq2mtc765XVdebVbuoqrrO+Jlxh5XbjLpnVtXdatw226Jp71JV71+gfu59+w30Mtd22cRjc+1LrnrO3J8Xc4xrkeV43c/kdS36++9bcUvy20k+l+TbSc5Jcm6Sc2bUnjj+e/skxyTZP8lHZtR+fL1hSV69xu1Vq2q/mOTU8d/zk3w9yTfGv784o4fbJvlUki+P92+R5GUL9HviGvNt3dc3MfwmSd6b5KTx/i8mefKM2tslOTrJZyde76kzau+V5DMrrz/JPkkO32gPE/P+iCT/neFL4m1JvpnkXWvMiz9K8rEkXxjv3zjJe2fU/luSN2X4UvmLlduM2k/MsQz9XJL7JfnCuCyv3H4/yckzxvuGJFdNcqVx+fhKksfPqN0/yenj8n5ski8nuceUumsn+eUkpyS5ZZJfGm93SvLpNZbl1bdTNzreDa7Tx2e4sPyNxmXu75IcOaP2c0nekuSeGa9JtsYysS3JkzJc7OxVK7c16o8Yl7OVZe4bSY4ap3nAWPPB8d9zJ1/XOq/v8kkeleRls/rY4HhPTFIZPlM+MU7j2Bm1f53knUk+O97fK8l/TqnbyLL8nAzr9R+Mt6OTPHsLej46yVOS3GC8PTnJv8+oPS7JFZNcJ8lp4zLy+hm1f5th3dtt7HtHkoeusVx8dK3lbK3PhlnDxuHvSHK9Ocd7zNjzNTOsox9J8oKNvtdjzczbGn0ssq7+WpLXj8vSwUl+do3x/meSq07cv2nG74lNzot3J3lAkhPG+7sm+eRm1pGJ+kU+4+b+bs/i36mvzPA59b6V22Zf34LL5svG6T9svP1bkpeuUb/I9+/c6+qC83j1d/guST41o3Zbkudl+OGSeebx+5OclWE75mtJ3jql7vLj8ntCkmuMf18zyd6Z/Z362iQfGuf3S8bbi2fUrrvdMjH85UlemuSU8f41knxsRu3x600rF3yOvGFcP54/3j6b5HUzxjvXNs5E/SLb1YtsMxyT+T9bFtmGm3udHh9fZBt8kdp1t3HWWZ9WrzfvTHL4rNuq2o1sWxyc5P8kue7EOnLNNebbIuv1ynfCbyQ5LMkvTHn+RrZ9Vz7bVvZzrpgp68342GMy7EeenOST423WZ9ai2y3PzbAtdOT4Pr1z1jKX4XP7VePr/GiG9fXem+1jfPxqa61zE7Xr7tvngu3i1beZ373zLssTwxfZlzwm839ezL0/suByvO5n8rrzfpHirbol+XySn5+z9hPjv89J8uBpLzLJPTJ8KZ6V5MUTt9dkgY34NXp4ZZJ7rpreP82o/UiGD61PTAw7aVXNn4wr/P9k2DFauX0xU3ZeNvL6MnyZ7rdWHxPDPz1O46eS7L5ym1F7XJKrrRrvrA3rRXo4KsmeE/f3TPKeNd6T45Ncds4+pk5zRu2JSS43cf8KWfUlleTeGTbyvpELB4cvTnLbWf2O/z4kw4fsbpn9gf/pJDeauH/DTPnSSXJgho3Oc8d/V26HJ/ntTSzvs8b7jlnjzWLr9MfHfx+f5DHj31M/uDKEBndL8sZxGs9OcpMZtR/K8OX3gAwbHPdLcr81+nhPkj0m7u8xDrvmIsvMlPG+JckzM2zwHDgu2y/a6PimzLenJnn45LAZ60etWj8usrxtcFk+McllJu7vssayvEjPF5nna6zTK+N9TJL/u/KaZ82L8d/7Jjkkw+fXCWvM5xcm+Yckv5oLNvx+aVXNbTLswJ6e5M8nbk+bNe4kHxjXqfdmxsbyRO3K994fJnn6rPdvkfd6k8vcuuvqxHOuluQR47z5UIaNzN1W1eyf4fvhyhk2tE9Oss8WzIuPre5xreVikfmWOT7jcsF3+3dz0e/2WTvBi3ynnjBOY79xvv1ykl/e7OtbcNn8dCZ2qDMcOX7KGvNkke/fddfVReZxkieOr+u8XHin4RtJnjOjh6OSPDzDTtEdM2ywP3dG7SfH17+yU7lHkqOn1D127O/7ufB/upyQ5NEzxn1K1gkuJt/XrLPdMvHYyjo9uVzM+sz65Kr3epc1xvuBJFeZuH+VJB9YYxladxtn4vF1t6snhi+yzbDQ5+z47zzbcHOv0xPzY95t8EVq193GyQL7F+P6cMckL8oQeNxrvL0hyQtX1W5k2+KLU24XCbWysfV6JWh4UZL7rl4HxvsH5oJt3/dlvm3f7QusT5+f9V6tsbzNu93ymUx8Bsw5jWsn+dMMweu5m+1jnE9fHut+vCyts+7N3Ldf9JaNbZctsi+5yOfFuvsjU5bjc+dYjuf+TJ5129TPuW/CWa21U+as/WpV/VOGD/Ln1nDY+epDyc9Isj3Jb2X4wF1xbpKZh+1X1f4ZUt/LrwxrrT1jSumtW2t/NFHz7qr621njba2dXlWTg85fVfKGDP8z+ZwkT5jst7X2zSmj3Mjru2Jr7aOr+jhvRu23W2vvnvHYaj9srX171XjbFvRw3dbamRP3z0qy1iGQ32+t/WBl3FW16xp9fKiqbt5a++Qa41vx+iTvrapXj/cfluTQVTX3b60dUFVPaq09e45xJsluNZyKcp8k/9Ba+2FVzer33Nba5yfun5rhvb6Q1tqhSQ6tqvu11qYdTnsRNcywhyS5QWvtmeNhptdurX10M+PNYuv0D6vqQRm+ZFdOE5p6mk4bPtmOTnJ0Vd05yeuSPLKqTkjyhNba5OG3V2yt/eWcPSTDMnfWxP2zx2HfrKofri4eD0O9fYbl7IOttU/MGO+NWmu/U1X3bq0dWlVvSPIfs5pYYLznVtUTkzw0yR1qOKVm1ulNP2ittZVlbNYhva21dyR5R1XdZtW8XM/VM/xPYjJsCMyySM9HVdUDk7x5vH//DBup01RV3SbDsrxyKs8uM2pXvuf2T/KWKZ9fq+0z/jv5XdCS3GXi/mUzhBa7ZtjBWXHO2Pc0T1lroqvsWlV7Ztho+Kt1aud6r8fHXrzWiFprf7pq0Nzr6jj+3TO81wdkOMLr9RmW7QMz/K/pynSOGD8Pj8ow/+7bWvvsjNEuMi++O/awMi9uneEInWnmnm+jeT7jFv1uTxb7Tj2vtfbydXpYscjrW2TZ/HyG7+YvjfevOw6bZZHv33nW1bnncWvtOUmeU1XPaa09cY7pJ8OO2SFV9djW2rFJjq2qj82o/V5r7UdVdd546s/ZGebHhbTWXpTkRVX1mNbaS+bs46QMO2ZnrleY+bZbVvywqnbJBevItiQ/mlH7b0neNG6DJ8kfj8Om2SPJDybu/2AcNs1c2ziT5tiuXqlbZJthkc+WRbbhFlmnk8W2wRepnWcb54wM+xXr7l+M60Oq6vmttX0nHnpnVW1fVbvwtkVrba5TbTe4Xh9XVUdlOJr4iePpPBda7je47fuDqrpCLlifbpgh4J3m9Mz+Plpt0e2WUzN8N8+a9o9V1f/LcJTtWRm2Te+f5ONb0Mdh420e6+7b1zqXVJnynbqR7bJF9iUX+bxYd39kg8vxIp/JU11Swc/2qnpTkn/NxELaWpu2wDwgyd2TPK+19q1xpj9+sqC1dkKSE6rq7Um+21o7PxnOOUwy9YJcVfWPGQ7Ju3OS/5dhofjotNokZ1TVkzN8gSTDzsYZM2pPr6rbJmnjl8RjM/zPzWS/307y7ap6UZJvttbOHXu6alXdqrX2kc2+viRfHz+AVj6M7p/ZGxDvr6q/y7DCTr4f0z4ITq6qByfZpapunCEt/tAW9PDeqnpPhv+lSZLfTfLvM2qTYWPsSUmuUFV3S/LIDIc2TnP7JL9fVSv/41bDy2sXOT+ztfbcqjoxyV3HQc9sra3e+fzlqtorye9W1cvH8U2OY9oG/j9lOAzzhCQfqOE6J+fM6Hd7VR2ZYQe4JfmdJB+r8ZpHq9eT1trbFggxX5bhy+4uGY5KOTfDIcC/MqX2P6vqkCR7tdbuUVU3TXKb1tohM3qed51+WIYjAp7VWvtiDdedmHU+6+SO5FkZjvA4PMPO+VsyfImveFdV3bO1duS0cU1xTFW9axxPMiTyx4w7SN9a1cdTM7wPK6/nNVX1ltbatOsorGxQfauqbpbh8P+p1w1acLy/m+TBGY6c+doY2v3djNf25vGL4epV9UcZTsl65YzaJLlvVZ2c5HsZvkR+McmftdZeN6X2OUk+UcP53JXkDhn+52KaRXr+oySPy7AsVIaNgO9W1R9nWF8nr6nwuHGab2+tnVxVP5Phf+ameVcN1zD5XpI/GXd0/ndGbVprd5712ETNyg7ha9r/J+/N43+bqj/+57rXTFdKiuIaipKQWcmQKBmKokSkSVJIc6pLKaXJ0BeZbhGVa8gQMl1zpstFRORSmkRlLtL6/fHa5773+7z3Pmfv973y+z1+6/E4j8/nnPd67/c+5+y99tpreC33+/r4m++Eef8Kd7/IhFeRM1YdgIxeV7r79eH+7srw1rzrBZDC97Nwvj1KW8gp5jVz9QxgpfD51pEh/2fNpsDMDmdYqVoURcZ9zMxShieQAa70WeyL5MMKZnYVCrfOKXyp53ZshhcKZFyztgM72rBB9yoGhtI21aypZ5vy+c9o9SHVdvG4CGPzxQzWgevc/YGYx8zODvfyPOA3ZnZdOF+XvO4EFesvBXN1zGd8jpkt7O6Pm9nOKIrv0MzcbeT3n8O6+icUIZGiG0x4C8egTfNjdOPgHWvCvWn6fAVwlLun5NHiwO3hOcfveps2Y9BbbkbplpDWWxo6DI2fJczsa2h+fDHD+1ngwyjKCmRQyc2RE4DrghwAGUh+mOGt0nEo0KsbqtQZamRLjQ5XM6ehTgev4e3VcaL9xY/dPeeYbdPCZra8u98Ds/HZcoblm8xsT0b10/e3GcOauC9KO/1weHYrufs5mbbPswSOjbtfnuD9ABoD97j7E2Gc7JZpd00zu9jd/xn6tRhKT03NkylIZ1razE5CqXjvy7R7D3r+v2D43X03wVukt0Rr6hPATBNmWNx2ak19IdI9/olk5oMd775Yf3I5OhdE7+/OTHsN9e7tkUx1tGYsg+BADDkef8/wXB5LL6NuL1kjL2r2I18Ism/2uuDuP8/w1sjkJJmM4/9bsoFXIibPCIIVgPvd/d9mtjHakJzQTMgW7zXAm9z9sXC+CHCBu78uwXuLu68a/V0EOM/d35DgfQGa3I2AuRyFeY0oGWa2OAolfBPauPwS2NvdH0rw3oRSCBrDyAQUNpgDr665v+VRbuHr0GSZhfIy703wpjZM7u5vTPAuhCydDSjaL4EDU4pLTR8C/7ZEz9jdz0jxBd4JSJBvjgTBL4FjPTGgLQMmWyEY2u3thSbd8sAfGTb8uLsvX9jOPClhm5kfcftD88QyRkx3HwE1NbMb3X0NM7vJ3V8brt3s7qsleM9Dobn7uftqJkv4Te7+msI+5+b0psDV7v5kx302vL9FG8mp7n5/67PPuvs3o/NHkfLxFAPlvW0wiL9vSBFqQN+uAk7LjKE7gdWacR4Wt5nuPgLeaGYfRMa0VdHzWwT4krv/IMFb3G4thUVs9vxw9ws7eGe6++phDm6FlK/LU+Mi8C/J8CbxL3Pa33EpyIJF3D2nhDcy/GF3fybIsEm5PocN8NcpMHia2YoIk2BZIkdKRnZ+CC3YL3D3FYJSe5S7b9rimwjs5e7f67v36DtF7zqsIRs0cidsoq5w9/U62u5V5MI7+ELGYBnz7dr1ucvjOkcU5NRK6Fnc6e4j0XsRb80cqZFxX0KKbbOBfTvylo48n9aa2qxlX82sqbMyfUiuORXjYgdkkL008L4B4ZecGvFslPpu1InLMm1Xrb+tubowSh8amauVz/gWhAuzKjJGHAvs4O4j92QCZr0CRe4cjjAdDnD3s1L9jb63LJIrt3TwnIKcLY1B/T3A8909BaybfN6551xDZvZK5NwyhGdRGq3b1+4aaOyA1o9k9OoYOk6sVxuKFMzp1cU6w5xShw5XPKcDf40OXsPbq+OY2a10RCN5wkBrZm9Buv096P4mA7t7wthoZtNQetp70MZ5J5QauneC92dos7+Lu68SnuPV7r56mzfwxxv0BVAK7IyOZ7ETsLy7f8US0e4R72z9OLp2Y8fe7IXAeuFZXOPuD2b4pqSuu/sBGf5eWdizprq7n5D70MxehTCPPgFMdPeXFfQjqz+ZCn18G5jP3Zczs9WBr3jLWB10nNvc/ZUdfY/5j0FOvnPD+RbA29199wx/jV5Ws5dcIDeHE7zF+xEzOwLhKMaBD79z9z1LfquWnhPDTw2Z2UxgLfQCz0U5hK9297emeNsCInUtXL/W3dcNivB2KKfuNnd/+bNwG0nK9PeWlKDt4E/eX/T5wgiPozOM9tmk0j6EDdc6aBEa8TjOYR9WY6CQXOHycqT41kPK3qtQ2OBEFGWVmqxHuvse7estnn27Pve0pb+KrM6IeS0yxF0fDEAvQsbD1yZ4r3f3tVtGos7xVtjfH6Fc3L8jBftyZEH/R4tvInCwu39yTn5vblBQtrb1gQfo+cDpmYVkooeovLnc7nYoZ3gJtEA1nvPO6iKF/bjN3V9tCgE+1d3P7zAIXpwwVoxcC9dT8+kxd1804kkqUw15wpNpSp/7CAr3vx5tzg5192Q0kclbvCzDSkBSIbI6g+fNwFFIWZ39zt19RoJ3JpJv10bz6dZMu9e5e7LSzpxQMDSu78FpYfJkXuMZQ2OpIhd4R5TlTJsTkfNmp8I+p9LTHkZOkjNbvNsD57v7o6Yo3TWQYyI1hr7prVDs1LVxyJ5Fg+6zQWEcb9asuWFduCg1/8dsv3T93S5x+WGEudCOQKoxxjcOjy8Df3SlciU3cmb2Inf/W8/9VMus8L3b3X3lvmvRZ51RWOOQpdMnHvUOA2lPe5Pc/ZFMu7lotGeNzMxSm7YMb41sKXYI/H+JrKMyKHQaaOdHAM4gfKZkilGzLkT6adbZYGY3uPtaVuCYzPzW0sAh7v6OxGdHEqLd3f1VYe27wN1Hot1NhuK1m3sKsuUGd3915ndfioxfsX6Rijpq+BcJPI918FRFP5lSUw/tuxaub4Xk8YYoeuYa9E6Oz7RdpD+Z2QyUTXBp9P5+7e6rJHjPRLiBv0/9Zot3RE/K6U7hsxq9bDvgF7nx2+K9m0F63BVo31KautfV7h0IPzAOArnN3V81p22n6DlJ9TKzl6HNQGOFvgJZ7+9PsP/X3f8TXs7h7n64KVImRY+b2RrNomtma6LwtBSdEzZZ30K5jU4mXKrSerg88kysF9r8FUqZuCfR9D2m6JEmZ/+jyIKeo+L7M7Ovo01zb6iimS3KcETTZUi5HxnQZnYhsH2r3Z+6+5sjnqSxw0IOZcrYYaMex8PNbMjj2OJPeSkeRlhIB3rkCTKzvVEaSeMZ/LGZHe3pXPvvA+9GobFrAbugSirxb09yRRfsl1J2WopOb1nANpnZAsgC3RsaG6gZA0+YUtAeQuDYKaoJ8y7Gy6iZ0+6+a/jOUuH3/w9VnJmnxfdMWHCKycy2YTCOL80tkoG3xpDyMArfvhA9j81QWPthoa9xOO0sM2sq2VzSo4TWtHswSqHp9c6avA25+fHJhDw623rCecO4XAhYPMz7JtJtEqqulaLe+YSAMnPUxtZpaOWw2dgJ4X18Di3yI4YfU1nMFRCIX6MEOEpNSNHi7n6KCZuIsP7kDHk1mCs1ueRXmdn30Rh6vLkYbygz75iINzWOv8Fomt7+HX3eHxmrLg1tzgxrXIouNrN3IMNlV7+eMZVPnc/dn8rxRbQA2mDE6QqzgNXMbBN33yfi/ZK7TzOzDVBEw7fR+rpuot3NUNh0TFskrgHVesufQr+bOTQ/ig5NtTudxHvM6BfzomjT2TIOFZp4OuIZZ1xMaBkVHmIUS7Fpv9g5Evhr1t8PIKdAE9WwMZrXy5nZV9w9TjMsfsbUYY1dZWb3orl3urccEoHGkVkAN5rZeu5+DYCZrYtk8gjV6kQVdCOKZorTJv5iZn8FPpTaHPXQyShKNE7LaMhRZPQQVc6lKgMNWp8+w6j+lHonNbLlhwSHQDj/LRojqUjQJi1ypL9ovrbX1hodvIY3lgXzoTE/NFdzhp0uiowSk939Q6YS5jmjRHHqO3V4OSm6H8mlFK0bjL83Abj7P8xsvgxvMWaWmX0TRWjcxgAzyJFDs827CopGe0E4fxBFN92WaHoqmlONDvxHNE5zOu2uaO8Z0/sS10ApVlcgZ1kOtqTpc43+lMK2yuGHLYZ03+sY1nFGnErUwa1AnV62NfA9M7sczefzPZP25u4vN0WKvQFhHv2fmf3T8xFppfuRWty8OaLnCuNnKlosmvDWncO1zRK8DbjkLvSDS+4DTDOzP6HF5yVoQo6Qu381/HuaKQd2gQ7L3TRkPTyWDKBcRCejjey24fzdKHwrpXh+BG3Ev4gm0sUoFSBHxfeHSmN+oTkJQu6tpDf5xyMgwR3C+XvR+0h53xb3KM0utNsW4o2xYyXkrWpCpLcmjwWwH7KwD3kcgZyScx56FyeH83ejTelf0AK9dcT7AST0Hw9tfxMZ5JIgi+5+tw2iNqaGhSLGMIkVnZGvEyk6ngnh7KETUWjsm4lCYzv4i42Y7n5SsMo3Yd5v7zAk1OBlFM9pE8bCG4DXoNKW3ycPfjzTzM5CczBeHEawg8zsG2i8nRQu7W1mr/c8aFqxIQUZy+LUw0s7eF+JxseewPFBCfypu185h+3WAGgfgpSgk9F7fjdavG9E833jmNndP2cCrG/CeZ9AVTli2h3JoKUYBgJ8BL3DJPXNJy/A1ElQDdDmWshQVBre2mvwjAy+NZgrl1l5LnkvwLS7Py/05asIO63BRtqJjOHX3aeaIpqa9eiz3p2mV6PI7Y5kxjNm9iTdhtR70Ab7LIbndSoCclXg9T7AtjsSyYsNUIWLmJr1eUvgGBeIxzbxNQAAIABJREFU9FDqj5ntgZ798ibPbkPPQ6kQOarRW2oMup+K/l8AbT5zeAtHIv3niHD+3nDtgw3DOOMCON9GMfZyALIlxtyYatbfeZDn86+B98Vog7Eu2kjFhp+aZ1yMNebuK5rZOuEe9zOz25H8/nHEM47MAlVhu9rMGi/3MsCdFhxZPhztXasTldKFKKrzl6HdzdGYm4rGVUpXzZK7bxX+FgHzBqqZS1BnoDkJbeC2Qjr2rqgEdYpqZEuNQ+AepC/F8+lRNE+OQfM2phodvJi3kQXh3gyt6cm03hIjUUSNUWL9cN5llDg6OIq+hPTJRVCVzxTV4OVgw5hxE9C6mQMpLgY19zrMrLejSJwSA9XRwL7uPj30YWM0HlIOzhXc/V1h/4sLl2gEVTl8/h5kHI/TUZ9HBvPM3T9mivRaGRlVFgTm8XRWRo3+VINtVVNQYEc0Nhpd+bJwLUfFepm77xZ0yS1Cm/9nZhe6+wfbvMFg/Xq0f1kNGftSen3tfqSNm7cOwkE7K/QxZQwbn3wOSqeNe5CuQ58ruboyMo7sGM6XQ8pqru15gVXCMW9PP16HJswuzZHhm1Fxb6mSydkSfGM8u6L7o668Z837mIFCD5vzyeTLM9eU97y1dT6hfa31+chvMihT2m7rVmTYa84XyLUd+jwfUjYPRrmvc/z+wm/uiRSr45sjw9uUDGzKT86L0jFKfmd+YNHE9Rd0HZm2lkOK+Kub8YYU0TkdQw+i8qy7Acv23M/UxJF7bsVlxsPnV83pey14H4uFsfTMXGirKaG6I1LytiNfYnRkzDIoy5n6bCFkFD46nL8C2CrT9scr+lw1nyiXyXshZfNcmI0xcEWGdxqwZEWf10AGgIfD398Cq7Z4ZiHlflbiGCk/G74zAUU+TEObtw9BWbnmnv6m3meudOnrgYXD/zsD30Ve21zbx4X3cUsYE4cjXKI57fOU1JHhvZNIpiFA6DvD/+1yvOcgENZ7UCTD/O1nEb6/LNqUTY6OpBxsz5++a+H6rl1HwfO5LnO95l0X84bPtgvj4buEcscZvqZ88S3RtWwZXurW39tb59ZcS7zrOXrGheN0cXrkN1obd6BHZgXeyV1H+7m1zrM6UZjXFyJZ1cilnBwaaYOBrjGzdb0xVFyAyltfgqJYc/e3DYqy+zaZ9SP1O7lr0WfXIAyS5nweZDycmBgzMxLj8/pMuzWy5VIEitvomesBl2XaHfm95hoJPbzmedQ+uwRvb8nsMO/eDnwj83lxCfPaIzzjLZHhbvEe3njO74SMeDnenZDh6X7ga+Hdb9/BPxlhqoL0o+dl+M5DGIMl91Yjv69G+7ZmvK1AYl0I/dw4zIeNomMNZMxJtf0hlCL/u3D+CoT1leIt1p/Cc/paaPt64EA6Ssyjqn9bhWOJwt+YiDCGuniK9bLoO/OigIHTEdh1iue/aO/ytoJ+Fu9HWu9t5GjxVsnk1PFcRfw8FLz+jTV8RxRaPELufjtS8pvzWSg9Y4Rqwg8rw9dqvLrnmdnngJ+G9t4FnNt4iOPvmFLIjgRe7AIyWxXYxjMAmZXhlTXlPZ80sw08RCSY2evJp8jtB1xpZpfBbBDIXJRSTXnPlMexCw19opmt4wGYzczWZlAhp+0tnQpca8PVJnJ52e8N7XwMbVKXRt6lEQpj6HK04byjo69QF8VTExpL8GJsSZSKaKqQE3vPq9DxA52KxuNtoc0NUTRbKq+2Zk4vbmavRiGQXwuegTvdve0Fw91zVRdyVFpmHCoqkYU+HoQM0XH4eA5UdSM0ht+Cwrt3yPDVtDsJVW7YPLrmpMtnPmFKFWi8w+9kkBLhCf6p9IQVm9kb3f0SVIYz5V1M9eO9aMMSz6eUF7NKJrv7Ycgh0NB9ptK9KSqujhPoH2jBnQ0QzCACp/lujXe7+c5/0YLdVV0NmB3pUIon8bgp5a1Zc3YkiqJp0ZHIS74aWkuOQ893owz/x5HM/zfy0P8SVQPM9bsotNlDJKQVYB0gg+FMM7uUQXra103Yce3KjyVVQJuqmu2UrkXMbBHPYw7UyLhikGobTheegKJCcrLrGTNbwd1/F767PPko5OJxYQNso9MT19r0hClFYqYpSvDPZNLCAtWsv5daYaXFkmdsZle6+wY2mv6WjUYzlWXflkGU5BnIA5tqfwradK2M9JUtkAf4hBZfkx6exDnM6JI1OtFxSL4OYVpk6M9h7P80avevQY9oR0A00e7H9LWb8XC/zqPI84iK51KgxVCkSBN5uTAy1D5jZu1Ii5qqbDWypSYCehEzW6aRJSHCbJHwWSq9tUYHL+ZtrdMTUPRGLzita4f58zC+P5dgKU7JMkWj78IoVMZItSkbYGc1FSGXMaW23eeJ9BtXFan5GEQcZgsQeEW0u0WFGJAMeCmaByM4htRV07rHBErfRC7uTB7eYwoF0U+uVL37GERfldCeBLzB0MZdNpq50VCN/rSlu+9HVOrchLs3rc1oFamslsB0NLMspmONfmYCin4XkuOXEoD/M+yvRdGA7wn7/LuQ8Te3nhXtR7wOsL9YJmepxko0tw5koTwLhV8+gDZey2R4X4E2L7ejCXIPeU/Gz4DPAL/2gfUxZzX/DYXeVuq8uine5HdQuNo6DFvNf93Rj+L7C59vwcD78uYOvtVRicp7kQC5CYEm5vgXZ2ClzVrk0eS/GeFEHIA2dJ/v4H8HBR7HwLs28iTOCv2+JTzLhVG1jjb/GsiAuBfw2rk0jjdBIasXhnF5GspTT/EWR/GgsP3F0GbsnjBHPtLRj3ORwn4A/d7zY4C3tsbIDzqe8fUopfCt4V0uneGtmdOTwu9+A0VU3An8KMO7IkqBbMb8qsAXM7w7hvH7Q2TknAW8q+O5TU0cuWiiK9Gif0u41/1RXn2K9160WdiREF3R0YfidivH5vIojejB8E7ORlUDFkRVndr8vR48VNmm9rmNzIeOOVIjk1+MNjvnhfOVURpHirfXi9LinwG8NDrfkLynfbvEsSkJ7xV1XvnzkPJxczifp6MPy6KiBw+G4+dkIukYeA+/3DwvMhGb4bMRj2jqWrj+DTRX3x+OC4GDMryroHWmUVpnoKINuX4sidIU3oaMYX3jYwlk4F6GvBy6Ncy7W5EC9x8yUbGBv1fGAae02h46Mu3OisbDXciTNzJHA++myFB/KdIf7gU2mVvjonUt19/JSI5MQmvNd4GX97yPovUXbQDeCXwvHO+kJRPGecY1R3gP30OG1j7eW9GGupmnLwYuTPCdk3jXnbpk4C/SiRBgfOn9LY4i924Kx/eREWO+9nukMtqdcg93PJf+lppLLf4PhGc1Fa3v9yA9aWHgWy3erdAmaxWEFTUDObDmWLbQioDu4HtrmKfTw1y9DznnFgb2SfCvRqEOXskbr9HHIJ08GVnB8Br2TiTPf5Xh3QzJn78hQ9+9wMYZ3qvD+N2Nnog8FNn1FHKWzUCGhhuB3wGbJ/g3Ds/gMuSEnQVs2OIZJ9p9ZpgPsT6UW393TR0Z3sWQw+rGcBwKLNYxjmqin9ZD+vpj4Rk+AzyS4b02/G32JfNQGY2S4c1mYiSu3xyPRSSDctFPTbT6Tghfbd5cfwNfTQT7T5AzIhuZ1OJfBDmXvhbG3n0Zvt79CAKHBjkEHomORzveXbFMzt7DnDbwbB/UbbiKww+pDP9/lu6tCf2M+9tlyHnWwitDW5PoCaELfC9FkQEbNkcH7xrA3sxFg0ur/UVJpDY19xP+1gj8rdBC+ve+CRj4JyKB+/kwye/I8F0X/l6OlIbF6Qk9rHgGxcou6TDvrpS69cPcuw540dzqL0p5ew/wsh7eWuPokijcfBtUqnNujbMmfPzW9rXcuHsW2n0ZMig9EI7T+p5fRT+KworDZyMLZMd8SikCyVDzGplMhWEkfF4cUkydwfMXQVacFo6H0Mb9LuC9Ld47kMFzCaTQvRB4YabdqrWh4j1fFmTVb8P9jZ1Sm7hes/G7mshggRT4qzv6UbTmhHl/F4psmYWU36wxp/XdNVAZ1zl5vkuGv5NTx5y+v9D2/MgAvmpqLla2tQcyXjzBsAFlFnDSHLZdvf7O7Wec++2uPlCRfslgXZ+B9CcjrwMYHcaNOXzW30De8/XDOF4DWGMutLs/wsNasuC53RJ/Fnjn2BAXv3d6DDRB5nyist1O2YKqQEHayJ9MtQ788yMjzWpEqY49fSnSwWt5C9ubSqGRKPAXGSXocCwkeE8ncgAgh86pyJGVSnGbgfB1mvMVaelPZIytdDteig0j4fP5KIQYqXgWG6aODv4bkHPvpjAPdiPveDkY+ALSSTZDeuXXOtru1J+QXnM4qnh1WHT8kLweWZPKehsy9kwjGJ3oTluuCpKoeCc3oPXyByhaa3IP/1zfj1Ahk3PH/zTVy8w+4+4H2zAg12zydGjcgu5+sZmZK6Rt/xCulwIHq0GErwr/Dyk37XSMXDm744CTPQJBztCDoY9Nf9/JIMQxRTXhldvRU7HIzHZ29x9bqwqXdVffKkawD/RM4HPyoKC1FZYwlZJ8ByF8NOpzDIbarjYx++tkqk0gUNztkAAaGaOtPlyMPDi/QoCAs4EYE1QMcFcTGhvoPDPb3N0v6OpvoF50fButSLEQCrE+LqSQbRPxVs9pDwCWTZpHDy3k7tfZMKbdUMivmb3S3e+IwoSbyiBLmdlS3iqtO6Yc+repEsxdZvYxlAqV6/9TZrYnZVXZatqdSjmAdm1luP0ZDSvOpdmdbmZv8xB6bWYvQQaQNaPfrwYcpE4mFwNt1oQUh7auN1VbvACFxb/J8+Wdq8Bo3T0HmNumZ6WiHoUgtyH8+a3AS224os4k8sDDUJ5qubAHgEsAd2/SeUaocs35KjLEX+QqIbwJmie95O43mqostX+/WF64+59DyswPvQcA2NKly+N247SrN7r7JYnvvDzI5FR6asm4OBkZUQ9iOKXjUW+lH1m6kmbc31Vbl6rX3xI9oOYZk640NbupVB+oqwp1Q1ivjwm/9RjSB0Z/zN3N7BekU6Vn0zjpaQwAmdeKf5JEdTETqG3p/e0a/sbpkrnn9nVGKwam0oSqq3oF+hfSjxdA4/7l3iqZ7Ur92hFFbPVSoWzZCOFobM0oOelUa1CUwUqhv6uFeZqsJFmoz47DW1xl2CvS6itTsk4MqVPn0A+VsaJHFa7c/fag391jo7jGICPLnRH/b01AvfF9VadlU1GIwQTQ/CMU9WRIh9q1PTYDb3GFaIbn3ALIAdqUS0+S9xemaehzSD+8FRVlOJd8VesS/elPyCiyDcNFbx5FKagpqkll/QF6vjcDl5uAqR/J8EIBMPaYcnaLDl2wabd2PzIROade2dVuRDUyOUn/a4yfJp8yWb4yQzUboymUI8LvX9oBK8zjDvQutGG6wcxuQBuzCzJGhD0RyvsrzeyPyALdpaTW3F9JxaJG0U6VHM8peMUI9jYo43oamkxdZVxrKiyBQtgfZhAOOkI+XrWJPyArcafRJ9AtaLO7SujLP83sV+4+km/t7o1QvYz+CXouCnm9lQ5jWUTXAGeEefI03YKrjY5/OaPo+N8u+M2Gque0DZe0NDP7GwqN/XWCvcQ4ui/Kx06V2E0pv+PIob2RAWwvtLnchIEAblMNnlNNuy9y96nR+Q/NbJ8Mb1VlOHe/wGS0Xg+Nn73d/cEM+89RdcF3IsyesxiuTASK6PgzMubE7+VRNG9StH+ufwkqNoxQWB2nxuAZ0dKN0SfQA+Ha383s6dBuowBMN7NvoY1CrACnqpCk8CS2T/BBXYWc7YGpHkpUuzAoUuvYOIrcQYxu/HIV9WqwDmqqpjzt7g+Z2QQzm+Du083skBSjDTs8JqAoiVSJ2Cp5ETaf/zWzRT1fKRTSG8nZzTC8oRxn89k7LnyAd3Qo8HcPVV3MbJKZrevu10btbdXR39FOjbf+FukBpc94zE1fcVUod/9o+PcoMzsfRWDk5BuonPva7n59R583CH9TelnuOzVVxmrur+j5Bd3jv2j9WDtc7qoYWCOzMLMPorXyZSgNZz1kxEhtgq8ys++je4wrBqbkbK9scfcp4d8Phg11L1XuGaBAnx2Tt7jKsAmv60CEF3Q+iij8hEfV7CI6AsnLW5CsXwUZzxY1sz182An5FDIc7Mdgfc1tVG8zVVaL8aduD8aupxP8N5jZsQw7MpNy2oRROUIpAw0VhhGk32zeGKCCcecnRI6wiIorRLv7kKw3s6WRYzpHxdhrXoE3SIH+5O43Azeb2UkJw98IBSPMYUhWbBAuH+3uZ6T4vYXpaKqK2CXzeoMkxpGzod3vMsAxvAxlIMVrUNV+JKxld1qECdZFY65pI408pwcamNlwRTQwFkECfypScNbr4O8NP0RhcMlw3Ax/UR534r62QYaq3yP8lVyI7MJkEOPHub/AV1yxiAQSfupauF6DYH8LEcZJuM9c6H9VhSU60n0SvCNo9alr0Xg7H21Y9m2Onvafh0BQ7wP+neGZH3nav4Aifb4MfDnDWxwaG/hnoUW6JkT9eX3vEQE+x9VYFiSPEVGDBVKc5oGUg4tQKsIfkfKU7EPtASyXev9zod2xq7L1tHsxUpAnhmPnjnFc1YeaORI+2xN5wG4FXjc33kdodzJllTTWoKfyVsRbFFJMRWWF6DtHIE/mruE4K1xbGJgeeKZ3HMlqDEFetCvqJdN6qKsIcyBwN3AKylPvlBlUhq1TGNrMKNbBIWSwDqhbcy5C+sL3kfJ9aIdsmRId+6FNQzYlgzoZdyZa948jCn2fC/MjJbNGro0xLm6Kx0KYI7mUvm+WXIs+q1l/a/SWZ+sZ11SF2pbhqlDPR6CxubbvQBFzv2OAL5XTiU7suwbsHP7umzrGvT/GSG8iQBEUPuPaql63oqiHBuvjlcDpGd4aOVsjW36PHLWb0i83q/YM1OmzNbzFVYajZ7ttmFOLdvAWp2Qhg34nPk3EuyDwSeSYPAM5lBYKz3LkPaF1ct/Qn9ORUyK3Tp4dHRci3aGqGlLFM87N6bHxWWBQ4TDz+WR6sNcYD4OuJiVrFhEWL92YvNkU87nwTjanHIOqV85G109De/nlwzGFvBwa0SVS18L1y5FT7WKkQ54FnNXiGSvlNHU8J1W9rAKh2weekcfIpB4kQqvi8MOlkSfrvqjNKgsb8KS7/9fM/mOq+PAA8nTn7m/V0Ne3ooFyErJqXgKsbt0pVo5C5c/y4JWtvb9AxRWLUMjtGgXXoA7B3hi2aj9DK+TaBqHrNf0FuNrMXuPut2Y+b9JdFkKh24tFvz0J5XWn6GtorC2A8nazFCLQ3oAs+/eiEu1XZNhrvDQ1obFQEaVkZq9BXqcXhPMHyUfbTGNQ5Qn0/qYx8OjF9HlGkftT16AizcMVkvym8PkEDx7pFJmqB5zv7o+a0tnWAL7q7jdlvnKqmW3j7n8M398IbRg7Q/ELqKoqWwW9H83L7yE5cTX5dKyiPtTMkZa8aqrDzQTWM7P1PJ0aul7o86vQfJoIPO7pajrFlTRcaTkbEVXecveURxAKQ4o9VFYws+WAP7v7v8L5guSrEe6Jwu6blIUTgNPCXNwktLtJaGd5b4XYm0LxU/Qrd18DeVEb3htJy+SaalNfDJE2m6Ox830zOwU4zkOlqBYta2ZFVefM7GJ33xQpLe1rbXpBZs1IUc2a8zaUErIPMuQsiiLeRsgHlcUm6TQvWwLVyLhmI1JEpupD7dSbVL9PY3QMnErau1xTOcni9SPoOzn9cDOgXe1ri/a1MdffGj2g6hlXUE1VqCkeealdleSmoP6n6M0V/Xh1fBLeR/s9d0Vt56jk/jaiPsLsIjP7FKORNim9pbaq17/c/V9mhpnNH/ThldpMprSJs9y9KNWLOtnySuRw3RNFgZ4D/NRDda0WVe0ZKNBna3htUCkwWWU487Vmvm8JTHP3hy2dXgV1KVl3o+fcS65o+e+QjpZIVX7cHDgkpXsk2u6NoDGzU9x9B0untTZ7s0Pc/czoem/UUfQ+iitE23Bq8QRUhCcVtda00ewBn0TGiRTtHf7WRG/WpGTFqaYLoIi+nOzsjX4cl7wugr1Ezja0grvHVZ4PMLOZGd6rGV2rU9dAECB9NI5MTtJzVc59ZXd/xFRq9DwUVjeDCGvARsPuh8iHw+67QqsAXmhmN/twuejFUFjhdQwvUqlw/uI87jDY/oks5p/zQQjptaayi9C/WC+HQBfXm4P7m0RP6WczWx9t7F/U2tRNYlAavU2NNbKEptJfxjUexKWlqkGGtPeZ2SwkQJv0phhnYHe0AViKYYH5CNrgp2gpd18l81mbFkBW9RneH974Mnd/S2G7NaGxIKv6pWZ2HsOLSWox/AHyBE6H2fnJRzNs4GloHnefXXrU3Z8KoaSzycbDAilO8zCzL7fOm76kNkVfcvdpZrYB8Cb0DI8iEdYc6COoZOnWSBgfFO5lTqkYz6mGwsKexCCbgz7Ec2QGg81Zao605dXpmesxfR+Fl09DSsEuDEqvtqm4xKgJQ+mkRvk0s8XMbEd3P6LN6+6fNrPYOJMNKQ5UbPAMm+VTaaWNZehURhf9aQxjI70EbYoXNLPXMrxZXijTbo1BEHd3M/sLMgb+B62Fp5rZhe7+mRb7VOTV+h4yZO1GK3x8zA3+8Sacj+uRsfzyjo1M8Zrj7o+HZ7gOUtJ/6e7JDaWZrRXu73nh/GGEfdRW2seRcaeizeozoY2JyDud6sdR6PltglIA3onA9GOeVyIFdVEbxvmZRGQsalHNuLjHhGt1ZDj/KC2ZbGZ7hOvLm1mczvQ8FHXXphrZEt9PqR5Q/Iwr6UATXskn0fObRD69MZVK0aVXL4nwHGan1CGj+GzHnQm3rMEXaXAsDOkFR8eNufsPwt/cRi9FvffnIb3JK3Bf0IYQJMdnN0Vab6mSWcD9QQf/OXChmf2D6JlF/a7C+KFOtjyBIiVPCXLuUBRVkNKVi/YMkYFhHmA3M7uHjD5bw8sottXu8a2QTsE9x8zuQIaDPUwpPbnS7zUpWY8j49p0eoxrZvYKpIf1Ohqi3z3EzE5DlUXvyPCl6H4092LqM4wsjhz5seFnDzTmm/u5AkX9xtR+HyX4LPE69B/gJ+6ekrMAmNlWCC5gMhojSXy08O87kNEyldo8RDX6U2KtPcTymLzrAjuZ2X1ojKTG8VgU7AcnIyPw4xmeYjkb0ZNmtkFj7A17+iFojzF1uN/T42wcUyYnyQqCBOY6mdltyHp5MvB9d78sGC5Wi3g26mrD6+reY2YXuPvm0Xmy/b52zWxZOvK4Le3VXc7dZ1X29yvuXrxZbN9f4Xc2Qmk2H0Eb5IYeBc5297tq2sv8xhoMcjiv8Ez0hZm9vi3UUteizyanrvto5BNm9nFP4wql2j0YAYP2AiWb8kbvd/d/BwPKqsAJngD1NrOjgcNLPDphQV+nw0Ld5p+Sup5SBtvzLHctXL8w9PmscP42YK/Yg29mq6G5/BWGhfujKNXlH4l2F0MeiQ3QoncFKhWe4v1kdLoAWpB/4wmQYjO7yQXoehAKIz25udbmjb6zPjKG/QvY0jPAbbXjs5RK2rXxwKhr+7GXK486vja/l+GqdLV7g7uvZWa3+ADUO/lOzOxad183eo/zoHSTEUXAzGa6++qta53vuqLPqbbb61MxMGC0aT+YYYVvEvBpd391xLsrwm1bi2HF7xHgR56PgCy9t72R8e1BZGT4ubs/bQFHz91XaPHPcPc1zexWd39NfK3VZrPB/yPDG/xj3D25yTcZkddGa9DuKJQ/5x0svb8PIjl0SejHRigH//gE7y3Anu5+RTjfADiiPd7GlHHXoJTFx8L5Igjrb8TA3syN6O8iwHnu/oaI523IcbINwxvVR5ECf3Xfs+kik4H1MIQ/4CjkfB+PihUEY8FiFABBt9ouXn8r+9z7jG3gaU9SV78L+3A8cvT9X7j0MZSy+L4M/02o2paH8wkoRWrEC2xmB7l7DiPr/7cU9NZFUXTvU4nPv4dSY0swfsb57XehNNkbgJ+5+2k931mWzJ4hp8c2FOuzNbzjUpgvDwcDWgNBMYLTFDamH2Wg21+FjB3/QgU5Hot4d83090eJdq9k4GjYmuBo6NoLBePpjoHXkTH/J96K4LR0BM297p7EVbWBA8FRKuRfwvU13X1G6jt9ZGYLNJv7rmtjtn035YVppqCqqH9H82SaD2MVjtuHWI5NQHrMHok9h6FsiZTxNrWHmxcZ2GJsnaM8E+UdzdMtkXPpp8A5qedcI2fNbHUE5L0o0i/+DrzPhXHU8OR0uEdRQYJUIYYbEGTCU+F8PpT2nMqumGN6rgw/e6Gw4JvRi1kG+HGs6ES8CxPCJsP5RJTDORI6aPI8NsKo2VAelZtUQZC+wt0vMrOFgIltYRHxrsooEnvqBd7YXsTbinJ0vRhxv/b+rKKqj5lNLl00rMIib0rzGPFu+TBgZMObem4j1xLfW6LVj99Hn+UqoTS8qff3KIrI+jc9QMmmEL+10Lg4F3kBXu3uI1EjZnY7KrXYFaHU8F6AcAJ6w2PDfDjB3Xfq4w38Z6DopzjaZk133zbBuwLybiwV+vsHYBd3vzvBOwml8Ax5X9v3EK5f5HVglPH350de/I0Tn52DNp6bociKJ1Epyfai044mXBmlTzaAtyNRNSXj0zIpnIFGUjgr2t3a3c+uVKCqKsPVzD+TQXB7DwZOkyHvp+4+ksZgZpej6KtjUYTJn9FCmTI0How2UbsgzKyPopz2/RK8tyJMn2YD1ZQOj40o41RtKDJ41tA4m3Yze0fBhiJpCGwo9a7N7ADkGU0pV6/yFqiumV2N1ptTkTHlj8A33D2VZlFjYN8AKX5vQLgoM5Fj4CcRT1fYPRnZeSdSoB4K5y9EGD+p/o4YCrvWnFIZFz5LGQ9HroXrjcHzGqS4P4TWzZcneNd392S0cYL3RyjEPZ6n30npAIXtTXJFaieNKT3Gn86qqDaGcbsL9OzbAAAgAElEQVTkGZsightP+zJIxhsac7/3CCizpw+N/P6xRymRQT/9EpJxIOyQAz3vZU71+ZbMWN4WYZA8HM6fj3AqcmlkRRTpnesjQOZOvbOgvaR+1ZAPV6erllnRdxsnoqNNUdKQY4osSTQ9qJw0pmy5F2FhnUJHJEHEv13U3yu9I8rUzE704Yj95LVa3hoKe6B9gWXc/cNB11/J3c+Zgzar9D0rcDRkvvdC4L3IAfEbpGsfFq9HLf3pP8jok3Ms9zoQcmOnocwYKtH3cu12RsSEcb9ps1cuobCvfReKALrf3d8UfVatP7Xm3n8QBMa3Paq8FvHOfscF/TwWGXMbXfe9wDPu/sGe701EzowPAW/p0PkWQ1X44vUpV6m60QVw90c6eHp1uIi319k4N+k5SfXyFkI3cJ+p7GqKLkaLamNBXhCV2U2lppyAlOlmsr8HbXBHqqFYBZ6EyauzKqMlH+MFbZxQ7GLE/dr7o66qz7FmVrSRoyD0P6IjGU5teKx9zcZLN8PMtkGpb0uh/OnJ4f7ifM3qnEivQ3j/r6uM9LZoo3i4yaOXoi0q2i0OjXV5Ziab2Xye8H4l6P0o2qa59yvCtREKCu56Fsque+TFSdAFFMxTL694k6OFENB7inZAnrhvu7AWlmQ4wqKh4qplleOzOIWzpl13b8qIPuHuQ3giJlyjFBVVhrPxwlJf5FFUm7v/wzIpWWiBnog84Z9AOAfvyPB+FvggZZU0zgd+ZmY/COe7h2uzycer2gCKgDzJzBov/h/CfYyQFUT9ufAAzqzZtANrmvBxYpn8SXf/YsRTU5Wu6csUU1rcqgwbBG9sG30CtavOvZF81bn/mtnzW31Opt+h0rAzkBPh3IzsGgeP4CG0Rjb0KC3sEBt4JS8L4+cnMBv/4tKOtotkXKDHzWyNZnNqZmvSCgmP6Jywqf8WMso7+WorfzUZrkucRasm5mkyIs5UieZI4MXuvkoYH9u4+4ERW7s8ewzmkU1FtrIKR+NUWux9xo1hx8yOAc5w93PD+RbIGBtTXx9eiNbN2cp42Px/LrQ5EeHXdRkEelPqIprihfhBlogqT10LVKt39lGjXy2B5sIl4XwTlMIV61nVMgvAlPK9fdTWVDOb1hqfALMx1XpoHNmyatdGLyYzOwIZHxpD9u5m9iZ33zPzlTbOyETyOCM1vDU0Fc3tRp79EaUijxh+TCku+zNIKwJGHcBj6Hs1VZybfcBu6FmfgCLlHwhGrNsZ7JWSDrIO+jTw2rYDAeF4NlQ8dir1rKrqiRF9BjjXzC6jH/KhoQeQQ+4hWhiQ4+hPpQa+QDUYP2u3jCCXmNnNWW7AFJW2NVrX12BgNGrz9VYMtLQzFxtAT4w8Y3c/zcqx+/5mwhuNnY1FGR9jkT9LqNpdB3rIk9DgPw4pO5tneGsqU4wgnqeuNW0gsNGboms5pPIsknrE8zYkOB8Kf5vjMDJVb6hA3B/j/oqr+sTPoOtauN5UhLi1fa3w3d3SOt8IGZL+zHCVlX1RNFbuWdyMFLHmPjdBAKX/y3F8LQox/TWosgqZigvIwNg+khVzGFQIGjo6+nECCmf8EoWVyCrucUu0oPRVIquZp8XVWBiuPnAbWqg+luFdgVDRAW009gKen+GdSKi61HP/Y43Pjva+Mm67JKrspK51Xc+MtemEtJXoOIt89ZYZyCvYnE8u/b2OftRWWpyADDQNvs7uKGJzjsZF63uL0F/5biZSfF+OKot9CxkyUrwrIkfGr8P5qsAXM7wpmdz5jNGa2lkdEjkB/oBCpZt3PceVTZpnUXIf4frzkWz5JtooXoSA2FPjYnrBbzdy7wTkld8/zKcbUXh1zDu948g+i8z95WTc2qhy0xXIyHE3iqzsu4/5iapEJT6/Bhkh5wnHzsC1Gd6biSqloTUnp+NchtIaYn2ouHpQzz0VVTgK7/rbFe0WP+PUfeeeRc9v7t46PznMu4XRRvN+lL6Z+/4SKO3gAeCv4ftLZHhT+mHu/aXWhpxeVqt3vg45GXdpjgzfBcCS0fmSKDp3boyhOxmtMHpnB3+v3lIqWyL+lyFQ3gfCcRrCbkzx3gEjVfJ+k+D7PFp7/4NSYx9hYKw+aFzeMZ/xDeFvLANyVb3uQAbcJZAe/kLghRneGn2vtorzj4ANM59tGv4mK1jRXcnqamC+6Hw+8tUhe6vfktezzqSyIlPHs7ggPK8DiHTKDO9HkZPjNrRWrtzRbk3Vq0UR7ukN4fgOmfUsjKFnKKtweCMCVm7Ol6dDH0JRefeiYI5NULpgjre3YmD0PE8G7mIAQP5bFAWaavcopI/8IXz3VjJ7VKSjXhPmyR/C+Ht5hnchtNc7Jpy/AtiqZqw8V+DO73f3Q83szShn/L0oQiWFq1LjObvRVF3mmsC7LnkPw79dYLUE3nnIh+39ysxWdvfbczfkFV5dGw9xH+rur6ay0H8tqnBmSoHLPYsai3yvd8uFqXSZmf3Q3e8zs4W8IMUJeNrdHzKzCWY2wd2nm1kboT9ppY1+u7cSQA/thjafX3P3WaZqQCdmeG9E0Q5xqPlfzOyvwIc8yhn2Os8ESHD+DikXndZ5E6DpFxhNAUqFpfaCjkZUM09PpxyBPvZ+/Af4q+eBtE8D1jKzlyNwtjORoB5JvfNCT1R7fAKE8b+IF3r/Wu19ubZdGw9ctqgyXBhrP7KKsFQEOn5l8Cw1udofbvW5Kgza6ystLogWvqPC7zXArinZUTwuQluLooV6w3B+GTLYpcZJTdTfMciT2ACy3mKqcDnitQYmWoSxFLxXOXDgGKTYzOyfaI1N4RC8CylQndGBVldcIe7z7OpQ4Z0kKyO6ohfuQTLxZWhzOW+Cr9Rj3Mi9RhY2dGab0cdMM6VCxrn79aYo4CbFLFt1zoQ19FOEF/I7uqs+LuTu8RrzYzNLRTWCFNNfmVkTJbg9qlqZa/c6G67Ik5QtNozj0NDDwH0Z2VxU4Si869ePfj1NNc8Y+JOp0mNceacX2DTxmz9oXeotVNL6/gMowqaEbjCz7zLAD9oztD2bbLxI86Te2eil8RphZieiTclMBhVaneForYaW9gF4LMiwtUzfTRbSn9D9NLAG8yPdc4RK9ZYK2dLQVLRuNFG2O4drmyV470b33qTULh2utftwEHCQFeCM1PDGZGYvZTQyJ5XG8lRYZxr5vQJ5WfSwu59X2IVifc8Lqji3+Hft+Ozi8G+jQzbRVjHMwdAaF+0Z7kaFac4MPG9DxokU9RaDGEfPsoqKqIFqCtMsjTDcchWpYqqpenU8coTvEM7fi+ZIKh20psLhp4HpQWcwNJ67xsdxKNr4mQ6ehnorBvqgAujlCKOtgS/ZH/hFpt3X+QC77wAz+w5aI0bI67IrpqJ1YP1wno3My9FzZfhptIu3IsvhbWbZuoH7ANPM7E/hey9hUD1AjQ02GfOiUofNxmEZZFVM0WVm1iB6b4aMEmdneE9ACtRf6MFn6TP6BKpC3B/z/pqqPl9kUNUnVzKudyMXUTv0fxPkBUrRR5B1/4uh/xd3tLuUqSrVIqhM/WrIu/bRDP8/wyS5HKVlPEAE5Beo2QyshIRwg6+xNXkDRjEFQ+Be0fks5MFO0YXAqe7+SwAz2xylvExFoHjjhlnHQqlEaJyEhGhnClCgYsFFwTyN+ltj2Gpjbk2KRUXLkNFswrejfxMOUi5uNWG6xCCQKZyBg8zsI2hRvz7041B3Tyr4FVTS7p+QgXcbhhX/R8lXmymqDGcBlwiV7R4xlKaMo+5+ftj8NVUH9/FRIPJiZSuimkqLNSnANcYZqFNenjZVkdmVQcrDiAEjUPHmGs3Ti81sajjfjUyocujvR30YpHgqiihq06+R0fmBxGcxNemQ26G53GyYd0QbuhT1pt81FBS4O1CkxpHAbh3GqN556hVVjawbi6vLIVAs40wpmOe7+6+DwWENMzvQ07gkTTj6KWb2XwS2eUrGAFq8aXf3E0ygkU3I+nYdzqsHw0av2fS9E0UjpugIFDp/S3gOr0HjalEz28NHCyMUV0VFKc5nIUU2ftcpPL6aZ7wjMuaeEe7x8nBtTmleE/Do21GhkqfNLGswNeGYHYgMhuejOfqJIIPb9HGks/0snF/IcMUskG6zFZrTcUr7owjXIkWNXNu9df3djK4RayHjVvaeIrrYRss+X1TwvRJ6GK0NF4Y+bgZcZ8ER0lqza/SWGh3gRe4+NTr/oZntk2n3ecBvwlrmKJruhjC2U2vaSmb2VjSe+/SyYl4z+yah4hbDhruU4WcKGpNLm9lJqIrT+zJNTzezbyGDTuxYGpl77v4jE1htU82zywi+ItJP24aqN2b4e40jkWNtMx/GdPusmd3IMFB9sQMhot7qtxF9K/T5eE+nVsdUUxEVtA5snpC/I+TunzeziWa2FMPPOcZIbVe9ahSXrqpXxeXOXY7+DVCE+1RTFblkEIG7X2wBcypcutO7C49cAXze5Ejsw6sqqhgY6MXo/ht6ilb1rYgap9AT4Tk/hKIgR8hUIGMqktvHBP36c5l3uYK7vyvonbj7Ex32kzT5XAgxqz3CDV6AQqYWQpMtGZYa+OcFVgnHSHoMEhLZI9PmBLQwTkOpAh/q+P270aZrub52n6XnNc79LVdyLfpscaRAbAUs3sG3fcm1Me7xWmSFLgo1R6HVE5DQ2hUZYHKhppcTpUCE8XZ5gq8q3aTy/lKh5k0aXjJdoKLtVVB6w33hmIFAplO8V1a0e134ew3CUpofuLuDv3Oejnlv9yKF5UEkOJ9BANmzgHsSY6go9S58tmvqyPA2YaA7IU/6vGTCUivvr7hdpGCUtntP1zyO+HYPf6ekjo7vbYOMA9+mI8yUipQllP42cnQ9t75rY46LmrZXRsbtHcP5csBnM7znIe/5jeH8nah6U64fW0TP+M1z6RmvhTxEv2RQyvisjrZvKLkWrk9AGFYl6XfZ0OsEb2qe5tJNXkRIt0MpZJfQSt8ad8yH7xTJOAayfQMU0r8lmZSs1vdegRxNz2Q+n9Vx3NPXfsfvLo826U+E8XEled3idKL1JcyBU0MbnWsZijRdtePzqYnj+Ln1jBEGz1jPKNPeXuF5ncvAE31FB38j77dFXulF6UizKuzDROALc/O+oranEaVvFfBvh/Afvwds29HfT1T2I7lWk1izmzFAgd5SKVsuRo6LieHYGbg4w5tcy8isaciJcRIyNnwDbVJzz6KG905CmnPP852ADIIvDPOobx8wPXEk02RRevV9KJ30ciSrculZN6M1ZB0UVbImHSmyyCH2cqT/TkQOkmTaG4pae310/jr65VVJuveFCA+tOX9bx7h4Htp3Xh3G54dRxbfkvYW/t0TXkqnT4bNHkTP3SQZpgI9keD+GdOrbkBO4K82qOI0QGfQ3iM5fD/wqwzsFBVv8NpwvhUDbY543hr/bpY6OfvwMpXo2afUL9b3rwLcR0m3ny3y+Xxij+4djJhnZi4z2z0cO/qawyUg6ezPuw983I+fEq8nrcFcjR2ejR65A2KsVv6ca5rl1ICGzBgFnAQmbrDJQ2fZqYVB/DFitg2/vkmvNYC743b3D39f38UbfmRcpDo2i/DF6NswV91ec8x0+eykShBs2R0W7c4TvEdpoFuuS/OLa3OyhxQ8pAsn8cGTZX6a07Yo+XIDAayeH4zNowZjYMcEXKmz7amCT6Hxj8vnIm6Lw5x3pEaAZwfWVXF9RZNfYeaeZdo8B3hqdbwH8IMNbvAmPvjMfZRu528J8nUZQ3DrG5/wIE+EL9GMj9baLvP8wmqvetVhfUDp+xngn30BK8PvDcSHw9QxvlbKFvCeNATqJfRH4rkIht835muQVjKpxQYXyUvncUpvrZedCu4eg9LGNkeJyBMqzXyN+RtF42wtFamY3IhH/b4Dlo/PlSGBVRJ8vSMdGJOKrwTuqWasvQNUsfxPu7Xjgmwm+6s1nNH5L8E4a7LmDgPfE1zL8zZowA0WjfnJOx0XlfU0Mf5sSzl28I0bT6D2mjKYXE8nwcO3oTNsj+lPqWu0zDu/tdlTJC6RHHZHhfRGS3UeH8XM8GeNT4rtGh4E+ek7HoiozkF9HVgx9uICMETPirVL8K8bFdJSeXmQormj3WelvaLtGb6mRLZPD/f8NRUz+nLmsKyJD4EcY4HzsRh4LspcXORs6DRcRb9KgPxfuaQbRmhDGdQ5/KrtH6eozBcYRpCPcjJyJ9yHdZI0Mb40zNcZn+X14FysU9H0jpAc8jqJ5X976/HKkn54AHIyiu+fISBy1fTcZR3mC19A+4bvIOfn2Dt7Vomd8b3iGyb19eP7G8J6vjQF7QPg7NXFkZTIVeFXhs4nI8LRMc3TwrokyX/ZGAOAlz7APu69xYhxKMJh3jOPNkBH1b8gAfC+q9lj8/p+rcu6GPNzLu/tXzGwZ4CXuPkfpNyFc6kMM8km3RQrGSIlZS5fVGynvGq4fgRaSsxkOa4yres1099VT7Xb0t6pEXcn92SDn+2CGKxpNQqCDQ/ma4TtNOOhQ1TKPwlFtgDOyA4PQ46bdld19nZJ7zpGZnYoEy/dR2tPewFrunsyHN7OLkcGiNzfbzPYL/W4qZLwdbaa/nuC9HHgtUr770k2KycwWRxbuDcKlqxAI28NIyNwd8b4OKYaLuHtv2pslyv6lroXrP0bgZe13/f4W3wQEqnd1OJ8fAdgln7eZ/Qwtjru4qsIshIxPI+WLa8gSJR9T18Zse2M09+5FC9DSyHs4EgZtwqr6LFrUtkSLw4/d/Q0J3vPRe53BILwad//OOO2a2ZLu/mcT9tYIebo0d+M1mE5PZbjA/yIkW5ZlOPR3pOKbCZNkdQ9h5iYsl5s8jRO1Jto4LRouNfgzIyHhZrYDita4lEHK6afd/dQE79oo3WUo7cbTuDbx9xZDWBS5fH3MbHU0LhYNbf8dlaC/OeKpLuUafXdhFPHSTmOMeYrz+y1dvjjqylAZ4+vdfe0O/nbbb0Gbzzi3/sOeCEE2VVj5FvKWLRee41dSstOUVvxpZMR9bbj2a09gFFSu1U054NklsnP3bGbX1axblsE7Sc0pMzsHKfWbIQPck2izm5LJ1yI94BS0LmXLaoe0oj0I+FNorvzA89g2RWRKHz8fre2XeIdiGGT939H8A+kOiyP95cr2szal9f0htNukJSf1pMy7zvHWPuN3IqNF33i7GqUKtOV3KQZalszsG0j/eBJFNTwfOMfdR1K9TVVrjkr0Y0TGmdn30Bj6GcN6S7LkeUV/N0pdd2HUtXm3Q6nuSyBZ0VX2+Vnpb+J3+vSWYtnybJOpctTOaB79CW3qNgBe4+4bj8NrZqehzfjF9OgBYWw+yOg7+XubN/AXVSyKZXHXtXB9f2RUO4MObMKI/3IUAXUsAyPf+1IyIPrOoqHN7N4hyID93H16ON8YObdSqeTNd3qhFoKutCUy0i2LUuBPQrrO1919xYh3Mkqrng8ZfRZFxuoRrKhaCjrDZp7Hy4x52xXq3gX8zlsV6sK9fdPdP2Vl5c6vc/d1mjkY9KJfdelOpRTe36YogmgNUxrzT1LrvZl9HO3N/srwnijZj3CfLyaTIhfxLQR8Eu3vPtSVbmZK6X8pcqythvS9S909iaUU5v96SMZe46NQC530XBl+jkQP+I3u/qqgjF9Qo5Bm2r0FWN9DOc3UQAp5ce9BQvKK6OvPQ1gQqXLuUxM/N7RZNrOfoDD6pRjODc3iAdVs2Cvu721IsdiGAaYNKOzvp81GvtXuncgqm82ZDMaH1VFVmC+32p3u7v9IfGeil4FrNYaRQ5EQN+Tl2ttDOcUE/5nIQFOSm91sQBujy+XunsT4qFR0qvKRS6lGSQ2fnYHAo2MclTXdfdsE753uvlL7eqbdYgXIzG5w97Xi73QYn4qfmwkz4AqGQTk3dPc3RzynuPsOic14du6F781AnuI7o379JCdsE9+fJ7Vwdr2rOWx3YQZAqSsiA955qU2fme2aatsz+Eo1m50ghzZulDETvsilPcaOEmXrZqSMPBDOXwRc1CEP56UMPPdSJA/nCff3AFIIOsHfu5QXyxjhGvK0MS71ew8jL+fMFu8NJPL7vQLUM0UmsNh/o7WhE5sh+s78aKyBUmGT60SYT29EY6GRAUkjbWOMacmLmR4ZijvW6knIQZJaq69x9/WC3DgMbYpOdfcVErxVm08z+w2FeCdB6XsLSvG9y8yWRJuylMFspUYGFbRb7CyqlBcLoSi7dyMjyjlIX7gywbsgwkSMnRhHIODdhdobHxOOxjrofSyN1qfp8YbbzNZHUTn7oDShhiYhD2hqHal5xte6+7qF69PQOJzbFOTlwy5w4SbC6i8JvhkVa1HK+OtzqouEtl/MAKj2ukY+J/juBrb2fuySZ7W/JTSmbFkO4S4ty7DeMkdOwdD2GWgtOxFVIfxz9NkN7r7WmLzFeoCZzUqzDpdoD7xJAG13/0CC93i034t1uImedioV9yHwT0br+bz0GEesVbQBRU0kizbU7s1KyWQEn46qO13d+uyweP9iMqT+omtfNgf9OA6NoV/QU/rdzO4AXtWseyan8G3u/qoE7zXuvl77eqYPn0KZAZuhqM33Ayf7cCDDWAV6TLi9X0TR3hcQ8Krc/dIE793Aurm9Zos3NhI9Q/f+vtgZHp7p6ihd+5/BsPNSzzgozWxVRuVQacGc5wzceV2XFe4mAHf/h+XBsLByVHoj2rQweDExXY2swoujsLWGHiWD2u7uJejyO5rZS1BIbOlC8IyZreBC9MbMlm/1v0299+cV1cUiugcJzqyAcXm7bzazkxvF0Qbe8xGjT6C7TB6Hqd5RES20/yBaEEqppjIUyEv7Z8IYskz1IHe/rFTRQZuyo1A6UvK9mdkh7r6PZark5JQGd/+DDeN1dY2L96PoodPDb1wRrqXoauupUBfRxWb2DlTasG+zU1MRove5RRSDckIalHPv8Hcr6mjeeMPl7r8NxoQRCmPi66hywhZmtjJC1T8uwX61mb3G3W/t60Blu5cDbwjz7gIEBv0uEvMmZ+DpoIXc/bOFvAcBNwXl3ZAi9bmuL3QZfCKa0JprD6G04BythBb2BRCwK+6eqjazqKvyzgeBE9x9SjBeDVFOyWjmYaxkpAw7BbRWOJoiAluhNecjZjbN3Q+Omd39bhsYz6eG9XLE8GMCJtyFUUUgZQRvDLmxcuYMAIBHKCieN+c+j+hpd3+4JbdycqMETLh6rQYODAr+J1HE1CRkTEhRo4DFXuquZ/FrFFmWAz0eNKLKlKeb2RKmiGbIFGIoNfoEWru1+bgkGExTVCMvniBEHAX+Q9HGaGKC90kG5WzblPJ2m8uQ/VEzex9KcVysxTMfwtOYh+HKlI+gTeUI1Txj4A+mSFoPMn5vlA6YonPM7K3u3lVhdSwKyv9HUWTnh5GjcCXSFVnONrOPUhD94BWV6kyV02a6++NmtjMy9B2akmk2GoV5uJklozBRxc1eo09tf0M/lnMVz4ivre2DKlC1NI5s+Tlal8+mvzBGYyBdpnB+H+YhuqRNsSGnltcrgJXdfbmCfjZUA6C9BwIlb9ajK5CheE77EK/DTyIduItqijbcY2ZfYtiZmo3ErKBV24bxhhLr9dbA90xRTT9DYN69ETqF1KSlzUem8mZERRXqAt1kheD87v7tYKB5BMnAL7v7hS22sQr0uPuFweHQRMXs7fmomD8gB1wJ7Y2idnqNRFSAMLucM7OAFc0sV40RmG1IXZVW1gY1+2GfC/mCtQcC25yNbYJyqnP5bN9EqRjnIoF7NpkcY2BfRoGX9snwrpy4tnGGdzmUhnQ6ZYCYpbghm6LJdylSsu4lwmqZw/s7GCm986Iwz78BO2d4T0MT+QfIK3cYWlxSvJeGdl+AQNquBb6X4a0BMqt6xpXj7eMMgMz6sFF2QALuRyi3dhbwzgxvbz4yAZiOOuDaU5H388bw/j6FvK9tvhPD32ROeqbt3yAk+jsLnkUDFvc0/WBxxXmnJc9tDt93Mz5fALygg+945K3aOBzHkAcSPS+MjQaEbR4SgN3hs9srnnFNu428/DjwmfD/zBZPDg/ollwfAv+BtHA4ep7xksjAvQ1K050b7+1byHD+vnCcBxyc4Z2CPGd/RcrbX1BkR4r31tDfC9DGmdSzYADwezIqPNBsbn+L0u9Sba+HNtSPhXf+TMccuZwIbwFtdC9DuDi3J3iL8vuRfP0uCh/flQ6Q8mfzQJui94Sx9gpkeDkqw1uMd0QA8g//rxjGXA73ogYjZvmSa9Fn0ynEOwl9vAspv7PCuLhtLjzjG4nwI8JzzGHE9cqLFv9GaEN2DzICvWMujYvdW+drkpezk6P/J5DRF2qfMdrgn4TkxQMo+iC5NlABkhr4i3CfAm8x6CgVAN4o0uG7COz2BiS3kpgSDKqxrYYwOPYELsvw3kyEtYZ09ZwcOjTcXwl+YHF/o3H/0tZYTa6TlWOzRrb0grNHvFuj9X9WOF+d7j3DAki/Px3p459AKWpzyrsxhcDKlc+tF0CbAHBMAmOtp+1VkE6UnU9k9Bs69JzUPOuYe4uhfdCNKGrjEGCxDO8IeHbqWri+PNrDPojk0Jl0rznzhjF5UniPx3bwnlhyrfV5CXj1ZWidvjQcj4drI+sflVg8FWOiqEDPmG0fh/SPz4d5tS+wb4Z3OoVFVqgAYQY+GMb0P8JvPEkez+32kt/v7NvceHBjPOidwqC5H/gaEpDJylAUotJH/Gsgy/JedAAvIcvvZ9ACuCBSUnPgoDdTCIgZPisWtEhgrhqOEvT90vsrrh5BXXWjBlDxgwyAt3qrG9EPZFb8jMcYbzVAZjWKzv7Ie7ckBYaG6HuL0V3ZJKWkjvQfGRiWCn1eLO5Drh9UVIareL61FSF6nxtwSPh7NtEmKxxnogVlvdZ3dkcGgHspqHQT5l6jQJ2OFKjcgn19PP7jOTYnz7iy3ZtQNNA1BKBBWsovoYq7rykAACAASURBVAJL7XumfrOzKlJIeissVI6lBkTwu2SqwjT3HcZdYzB7MXBhhnd7pBAeEc6XB07raLtYyaCuqsgdRJuKMP7uaL//6P0tgIyYU8LzeHmm3TkG159L724htJ5fH44DyWxIou+UgAnPCG2/NMztacBJpc8i93wyvF0FEDZKHRnem5E8bNbLTVB4/5w+42JnUYm8iHjvRZElOzKXKl8RjDa01iW616eTw5hfGK1v9yOcrzl6xsAWiWsfmQv3eCJS8I9AOuThZJxmgb8KdLSiH6ehiIflwzEFRepmxz1K2f9AfC3B215fJnSMoamJI2fgK+5v4F8byZSXIKzJm1G0ecyzRteRabdGtrwn9HP9wnYXbb3nrKEKGVqPC2N4E+SEmjYXeIuBlSvHW2/FojB/X4ecja8tfB9TKHDoMF6l42eraEPNmnMNijSaJxw701+JcF5kSDwdeLC0H0gfSRoJqAOvTq57zMU9WsEzLi7QM0bbU1JHhrfGSFTjDL8V6XvNvv2V5OX3cSQCV2qO/3mqV8hlm4WMLpsiw8vbPR8i2puGFJMrP78EIG5dFE10NVLsT0KCIEX/cvfDSn4fKeibews3BHm5Uv39N/nQ0hR/6f01KStbokWhHYIft1mTFjJPyKXfAZW2y5KNApl9hwGQ2bkMwk+h7hnXUk0oX026ya7hbwyi7UiRGSJL4IyYWRJnxMvT3o5C0VzLhzbjF5zsh1emqIQ84w1Ce1e4+88Tbf7XzD7j7qegnOE+KnluTYjttzNtLI4idlaOrn0KWMXLgc42AI70TJ5wix4PebcOYALfHRpTZjbJhQeTBe0dp92I9kELzhnufltIDZ0eM3jI9699z+7+vH4uUU2oqdUB3C0HnOshLNjMFjSzZd393kQ3GuyS/5iweB5AIcipe5uGFPrm/B6ksOboxSh6p6GnwrUkeWFKFpJ915rwyQwZSE824XzMTr0McvPr7r4Twk3pC2E/0cw+hNJFegExa6kk1Tr0+ReuFI7OdSHwD6WnRel0qfQ0c4VJfwAZ7w42szYmUoMR86JWyt4kWulKNiiAsGiQbzFvNszaEzhvHfS0uz9kZhPMbIK7TzezQ3LMIQ1pWYaf8Ujaortf3MyhcOlOz2NA7E2PvIhoVe8A4hyTTkZjfAaSD73rE1JoHzGznVDE3+fC97+V4K15xl8ys3+7+yUAZvZplNJ3VMNgZq909zvMLFmcw9PYT2tRiPsUqDgl2sx2yfQjlc66grvHMu2A9hyJ6FEz+zzadG4YdPJkijNwvgkvKwZ2TabAeQEkwpj9xd2vNxVCuADJxDe5+99abKnUw9lNkE7h7JUtEb0GbdrfyPC6l2q3Ju0VpLfEusx0M8ul49fwFqez15C7fzX8e5oJZD0FoP1lZCB6GdoXDTVB+rm9kxCJ5u67hVT4H7eZ3P2+sOZc5OVpg3sAPwqpwLOLNhR+d4RM0B4vBRY0s9cykG+TkDExRQu5+4nR+Y+DLEq1vwWabxsjQ/+xDNLUYr7PoyqEC5pZI8MN6S1HZ/pxNDJYTA9tbIwMiCPg1V4Hf/Fs0QnAdSZ8KxCObS2cQZI8FBwopKIUuSBTF0OOzJJ0s3+5+7/MDDObP6xDOSzWE4Bfmdlf0NrRiWWaov+54Sco6//nAtjL5WPH9AQw01TFqbc6TQU9jbzbCyJlb5aHKjUJOtTMpqBFpw8Q81kRtGPQ2SZQrieBPUxAqf9KMYbcwpFFydOAal9Boe5XhsV4eRRunaK7kKL5LR8GMjvVzDZs8RY9YzM70d3fa2Z7u/uhmd9t0z3ApWbWC2RGnaJTk4/cizNiZofToRy0x3wwlB1mZke6+x4VfSkiG0Xz/4iZbeYtNP9AF5nA2norQpQ8Nw+VS7o2W2b2VOvS75C8KKVdgCPN7O8o7/xyNK5TmFX7omijFczsKhQJ1saeGGejU9KuGtCzuMzMFjGzRYIBY07lIACJ+dj8ZgpLbb2W4tlFU9HzWD+c/xEZYVKYFtMYVjyeCddSoP83BOPBMaH9x5BHbzYFY+TBuXnVsYaklIwfZnifMGEozDSzg5HnM2kodvevmtl5DBwMH3H3G8L/O0V8z5jZZDObz93bYzxFT6GN8X4M7jM33qrIBhUfbyeqZIXmymwKff6vmS2a2ACk6Fzk/byVfrwMC4adnVCpdhjFnqnBiFkJzdPnI09qQ4+i1ORcJx5l8HznQ5vlZKU14J+mKi+XAyeZ2QNEcrHVbrJaGBqHI1ThLHrMIwy5LnlRY/SxQnB+d98q/K1ZJ+cN+tLbge+7+9NmllsTi58xcrqcEzZZb0Ee1be1ePZF6egpA0Juo1qM+xRoCqqetrSZNc7G92V4Y7m3AHKU3kh6XDxpZht4AOM24fg8mWn3XSh65QPu/hcTPlLKsIa7f9qE89fIrKPd/YwUrwmb4gOMVnpK4Q0W9ddGsREXQo6R40yYbvH4rsINGvxEr2xpaHuUllMik28zs/cAE4Ohdi/kZM7RjWa2nrtfEzq1LoomnVPeG0yA8DGwco63Bk91xFhtLYw9Fw7UqWb2pchQ1Ec1Dp2qNcdVQGE1K6g4VUhvRnP3ZUhmNPreI8gQk6LzzOxzqCKiE/YXJsD3tr68C9Knd+8w7uPuB4V1+tjMXEvRwh7hRLn7pSYH1AhZHc5XFVkhDpa7fy3oTk212908U6AnansD4BXuPjXsfxfxFk5YLZUaibzeGX5/0Gd/DlxoZv9ggKnUpuOQAbpEd0rSc1XV69tISe8FjbXK6jQVfbgZpYx8FUUPHAU85e7bJ3gPQg/6dwyXe0tVISpGsH+2yYarRyyEQq9T1SNeGJ0ugBa4F7j7l9u8lb+/iHeUNmzxFj3j4Nl4E/IGbswouPWIoSEYlEYonsTByvrv8H8T5QKKchlSdMzsje5+iQ17i+N2U5EPtwKbIyv1fsFoNlTSMjfWo3bnioW7lKwOzT8lUD1lPLRnryTxa5Gh4VoqjMRmthTaHH4KgSzP0/p8IlLcDkebRqMDJLGiv1XtmtlrkNL/gsD7N5T/ftuc9CO0fXZ0ugCqwjMjI+OOA77jBQDhVlftbaSaTo63xbMskm1tQ+rW7n72OGuIyevfKBldVQAnU1Fy1VQdcUMGEXRJYF4zOwGVcj+LYUNqqurGPcA63hHplpNVUbtJYEArqPgY8RZXWrRMie5MuxuiuXmVu3/T5GzYJ9PuZC+MdjOzDdubGjN7vbtfVfBdQ4aD9dx9BNg8KNFPIiPgTmhcnOQJUEirqBZWQybw9ZcgzLifufuv51K7RWXGLRM5E/GPOM5MUR2fRak8WyJQ0R+7+xsSvMXPOPAvgXClZgDvnxvPOzzj1RHQaLzmZAt82JileMPG4Kfu/pbEZ6sj3WIomiEnX54NMrNpyJn7HuQg3An4jbvvneBdDa1li4ZL/0DwAm0ZvlHXb3rGMWRmqzAA/m94Rwxmof1PUiZbfg582AuiHYK+vR/S+Qw5S7/q7jnn62+QDtAUHFkGpbf8h5Y3v5J3foThNFufRevTiDy3jJE/NZZzxuo+XauPTM7GL6Dqgp9EDp2Znokmq1xzaoog1PT5HZ6ofprh7TI8JPXlin4kK2hmeGsqARdXW7U0CPvItXB9axTRP5+7Lxdk2Fcy422Z9jUATxToCfxTUDTmSu6+YtDxp7l7Lqunk2yMIj1m9g2E5dTrDG99byMkF8/3hJHZzH7l7uuPfrOcnivDz6Moh/sZBlEo7mnP2bPVh7V84G1trr3Xh8Pwmut3I8Ws19JfI2gDf7GFvZbaFvnQdtKLmPhuspRomPQfSrSbKs1YXP6y9BkHxXAP5M3+I8OGn07BafIOkjJGNRsRCxFFPX04wBWxMzXxsWeexfYo7PVKd/9oUDC+5cPhzv8TsvLyrOcAezYbqbDJ/b67b53ir/j94pLEle1eh/JvhyzhuQ2+qaLJG1AI94Phu1d4ohqemV3n7uvMSf9yfS5t11RyfT8fDs/9uru/rsU3EUWV1VTJa//W0ghnaWR8hoXpLJR/3xlqGvq8KVKs1zClNvwkdc9mdiFwuLufFc7fBuzlidK6/2+g2udsZnsj2XkaembbIg/64QneXmN1xHsBSpfORrtFsmoJFFV1STjfBJUYTVbEM3nZtk/JzARvsYHNzD6BlPq5mp4W1qfPMBp1kDJgjhifagxSgX+2QbN1fV9kbPljQRvT0DgvjRopJlM6wg5oQzcp9OnAOWyzqMy4pUt2N+Spd5Jow5DjbKSaTckztuEoLZCB9j/h2hzrnDnDRM4gEb4Tp09f6ZkImsT35kWg0LkUAKwgmsGUTnw4MizPhyJcHnP3RSOeK919g8Tza2T9yHNr5oIFh1bo7xUelXe2EKndGFhL+ltLQXZujAw/5wJboOecjKQN35kU7iubpm1K118VYQ0VGfkq+jy563OPjNk1vJV9qDHyPyvG6tZvLEvCodPiqVlzmiIzvfqhmb0MzZHZMAcoVef+BO/XURGKf4bzxYBPuvsXO25vrpOZ/Qjp572V7kIfD2B4n7q/J6Ld2wYlkwP45pSRKbOm5vaSM1AU5aU+cAomjVcmx3kz1hYElkNO0ldn7m8mMgjeGLU95GiPeEecPe1rZramu8+okfcZI19yj2pmhyGjfldUYMN7BIpWPpthOVRc1es5KefuBXgSZnaKu+/QeuFxG6kXuB3C7VkCLVDZRQqYETZ+y7v7V4JFMRdu9mv0oHst/UFoNuCknWSFYfQRf/H9WUX4uA175iYgS2lubJyJhMRF9Jfiril/WfSMfYz0JpP350QUKcH/Q917h0tWVN3Daw1DGoZBFFQMZEURwRcBUUYFFRUEDIAwggFRQVQGcwJBQXkFUREkZwUJCi+CkmXIOWcD5qwfjhgQGdfvj1XndvXpqnOq+t5h/Pbz3Gemu6ur66SqXXuvvRbJP2EUKbEEDc19CRPZ8fihkrRv+Le4pl0VPCOZqPJ8GKJ7tDJZoxJjAWwz+v1lAdwXAiqCebFubPVXjX5CnSRxjS2uBGdSh30VRpgdBeBypblkGruG5OEYjd6X8G11WU2/RfBc1ZcKpexX8MYgZTVQ05rSht3hko3DozF0BmFLjOQGcPa1HWAvrolO2RjneVcAL5L09zCuL8LI15HATyrA02F/h0vNLkcG6dbMVSFItHYTZKD52k5qd8hBeVxxqXUuwJqxhVWedir8LG0F309vh5FxE8YKPqDW9+I5rlknc3PxsgAupstIz4Czjb9v9RfPs/eGebZ3Q8mKZJGM8P1auDc+BvNujAR+QjLgCzDicQuSawN4saTjE90WyYxrvNKb9vgFB2pS1nuOS3zNSY6vhvepcdrj8undSL5KifLplh8wDQ5knJnpdy6Mdn0YwLHBp/uEpIsTzQ+HERVnwffw2zDMtwhJs8O/NeevQar+Jfhcv4N91dh2gdW/DoMJfosCPpW+fRFPTOh3A/i8LeuX/AuMBrsl0TwZjG/1l0QENJZ7pmuCNSVtx9k/oY5PtbbEsdhIrovhErI1c5vayjVnqQr/8ES4dL+pANk5vLd5ou0WkiZKuyQ9RHJLAI9r4Af2z3ci+XPYJ8gm5EKApxTp1Et/wfF484p5sNrBoDC/7dEx5kcliaFMOOUnR3YYTDiefS+aD6bDibJcGW085poS51sA7E3z+pwDB4FyJZlLw8/oq+OfQ4Wc+yJB/AAjWY8R0liSK0n6bS66nZr8aNTI1soTRcdtj4Q3La+Q9NwQAb1Y0gifxMKK9NdE2EP7muMrjshzODP3GEy+fYgSdZdMlGN09HuDpBcVtp2HynNMw4XjcoxkZoAFSAm6HnQnODv63VYX0uNYqkfyUJjrJZ5o/wo/K7PUQiSR/KKkj/e9F97vhW3motqNxc4ux0M/3QqjCH4SXq8OKzeMZNoTm/YudMkXYOb8diQ8iyIg+Ty49GY2LEH9QPv8hnap7LVUkLXuspp+WQfPLS4VCu1jHpxpcPnCzyTtnGhbBTVlZWkDO5B541iYZz+K0UzfWJnRVt81JVl3wUHPR8LrpWBVt1SGqwa5UpP1vE9RqSYz5Zu5PgddD5CjYyZpesvTxjGG7CKj7B7Jm+J1Pcxvm8KBoaOirz8M4DxJSc661hz3GDzXHKuO0o+widkBDvL/StKrWuPIWiqowLpyjOdGv/1nODjyndR4aWTXifA6uR7J6fDGOXVvFmUyx0wKVFvXOW61mzJ0NcdAxITv9ZZPM5Sdt+6PxwD8XAnEQfjOHeG6vQa+r/eGpZxTa2pTfhs/Iznk2ggCOvVeeP9dMJrx+XAweSaAfSQdHbX5FhxsehqcdJn4CJl1PXyvxve9UdJGNKJgM/i5vk/ScxJt74RRzVeF17NhhP5YSYFxnumFYWPun74DB8yyQf5WsLq3xJFGxd6TOveZcSeFI3L+N82fdCBGy/pSqIpilGlqn5Pb+4R7aEMNqCKWhtX7kmiUhWUl15pjlCyF7/XRX7we5mXbBsP7p4eRQbLQlAGXwQT+28KBqMUl7Z49yOHvZ0vbaL7RZ8GBugMBvBPAaYrQ1RwkgPYC8JXo67NgRdlUKdvJMF9lLy8oyffBpccxEmyOpCM6jumJ8LnYEeY+elb+DIxviwTxwwLSWI2nTvP7koUh2Ivk0oPbwu88RJN0pqw30j+mVSmWoe74eiPyHBAk76NAsldg55PcUlKS8LhlNaTYVeeYLvl6DwZRzlNJJssmUICUCMd/dXCKUlnOx9NeouEA5HnN5oVkis9lc5gXIbYtEu8BBaplNc6JxkA/wZvwy8Pmj7Aznvv+qUhs2jM2J/wbKyplUQQ0vHvl8PurwnW1yd9QZfaahcRylf2+E4bnNvf8VeG9lP0k/E3DMNltzuLswmNwOVaO6+Q2kqehA2rKUX6PZh5ameTKmTmg6aeUF2wxWG0r3silar7/qFA+thCs5jyfCKt6xaTRJ2Ta9iJXGksFeDrsMo5m7y7N9ckEiT6NLoiteZ0sF8vYj1FHxF5qDergtyRfB+A3CEjPxjQgST+pxL+IgugXyGSNNfYHGPnwZ7TQD808G9aihtT02TDx8AWZ/t4A8xaU+AwnwCSir5H0m562K0g6k1aIgaTHSCYRvSrPZL4cLilMlQZXZSh7LHuOG8sFzJBAV5MknASK0eBPlTSBdtV4iBjA9/3KGBB3PjO8F9t1cLb5XakAS8aatPmWcPnpPeE4UlZMSA8Hngc/4oBgTqH2uPDfK5FZcyXNocsPL4I3iaVW4/v2Ev9HtqAJ+oTxXU0yhzDrtccrsNNnGpSN7qFEUhBp3/C7GE16ti2nspobxwKSD4Q1P8nH0rIa4QjAa+q+8MZ9M9iPzN3LNSjTP9MVIc06OQeeX1J2KryuNomBXTAFilOZREqD/D9ALS4zWeksToanOAT71HKTFny77Hwt6VwA55J8sRJUCRn7AHwt/gWf54tgzt0R4zAydxo8P2bXNElfIrk5nDBfC8BnJF3SalYjCNH0+/YwnoYX9OtwEDsVS3m3pK9H332IVl/NBn7guMhz4D1Jcr5jHYl+0hYVx08NaWyqHjmppEEjJZ4Klxh11r6RvAGO9t0UAkArwoifkaxHaF8tZxeOa6YycNaSCPskjq+XdLCJYLOOaLPhZ/oX7GR3lZsVk2LXWoiyv1iDsollAFyXytSwAinx32A0Wus1zUIZHM+LZGRaTJL7XhjuuAaGncdlYTjiCPcIyYPhjEq88btL0sfGHGsndFZ5hMmSKJAkZsisjjO2Pgv30NXh70plsqlj9DulxHKJ/pcD8B918BFEbWeog/tlzN/vRXdxCvg9esbwAdjh+z2G55bU8/9K2HFrz7M5QuOasoLaca+P4cxZjjS6BLkyDpy/Ob4YKZnlGEmtDcygA2oszMnPg1UfO9c+uv69bfPhrOq5rbZbwQHRZ8J+wywAn00F/liIqgrnd12Y7Lx0ndwDRo+uCJfUnKkMGTqNTHgpLP96DYx6fTQzfxdzLtUYjbjdFsAlwR/aGMAXJY0gGFhJzs8Kws/wWRE3YeU5ruEvKUaD1xrJK2A/simf3gjeyM0H7J+RvBsuu9sfTnoMWcbfOxGWll4N9ikXg7kzUtwaq6CHkJ6RRDQGAdoJiWhJn8TjaDW+b+t7qyJN/N88x2+Dj/FbwITK0iOqKxlP/W7Vpj18ZxU4WXQpjRqZXrLG94wjNX8nuU7CZ0tgUPbXJTSxGoDfaoBeXRrAU5Qolyd5Jcy3ciOGUbEplGKxcERo36yTE+gP5jllilGm4VocBqM7BCuyfUDSLzPtXwuLzgCeQy/q6LsIeRiCsgvgkjPAKJAZcJB7tlpcmxxwCDbPRBeHYDKh036v1mhEzFwNo1wO6QtK0Em8ZTr2yjEwoEHbfkeToL2I+l5F5YIQNbygd8FrThPnWAzAnUogwcK1fiO8Vz4DwDnNOUy0LSbRz9kiQfygLOvRWG89cmSz4EWqpPbta3At3ZNJfh6O3iVrMlkhZ0dnwneHH9ibAMwieaiklFxmSYQ9tprj26+gv/tI/gjA0zgsLd5VG1qT4aqRv6w1YphjaAGAXIYrRko0RG1TUrpV4aTGNfjHwQthrgb/wzD66CfwMa0GYI8Q3IozCafBmeEDYbhkYw8rU94ky7PGsM2sPGuhNffDWrBD29zPW6PFB9QaR6kk8b40GXTvpj1EwvfAMCHfUbnFIef8TIG9EYFYLvzOb0hOmmuC5IZwFn/Z8Ho+MnwENIz1eDijsXLIBO0mqasuushUgO7SFPB79NhcOLCWy8LFtgucRVkcUZAI+QzWQSgvK7gc6aBLqiSrKZG4NfFe23qRKxgPadObvQvjmgM7FquRjNeoZWGYc9y2Xe7S/r1UwOz/wl+JLQVfv4YjbVu4HHk9kptJ2iv6rfPDf+fDGeAuK0VVXQirDs0kGTumXQHBZ8LqQLf3jAFwAu4fJHeFN+AHMc95Vsy5VGkfgufuNUheAwdTcmS4R8LPUpO5fGt4L0fO/x2Mcih8GwnkCOuk7WvOcQ26ugYNXmslSqm7w878EzCKlsrNW7vCib4Hw730JGRQtNEm5xHYL0q1ORDAgSQPfLyDPBmr8X1HNtccVfA7pPWVeGM5MpexXjDhAuQ37SehdV1pJMB74Dl+DVgi/ChYGKE9lt7ERJQUXL3l2y8LB5dHjKZAOBneVBPm5Xt7KigBz8WxqMSC8F4qOLpP6vcydgqA60j2CkcE+xedYP8RyffDgi8zM21rUKbPaAemSG4CIBn4gdEZj4Wg3QySy6aCdqzjdX1VK2h3FwciNCMl+KjgEITXunaQ5x2J92pt3ThgEebOHJiiZq98r8yTGn9/e0S8qa3PUj5JE3j9sMyx2thxJLdvBatOl/SaRNc1vKAXAjiDZFPqult4L2U/gYEMJaXva0ranuTrJZ0czuNVvd+KbFEhfnqzHlHb4nrkMcbxHHhiJYDLco4+6+TsGhTNTrCz8wk4U7iwNpmTMnbAbjVcG/ocSfczI9GqtDRrsfxlrdFIk7fDwTvAEPiTJH11qn+rYwzFkpYcrsHfDV4MkzX4of2S8GYHcOYlG9mms7P3NAsNXcL0XEk3JNoW8wGFz5aGa01zxOdNuysBvC4aw7IAvifpZV3f6zOS34TPQ2/NN8kz4cBaQ+T4FgBPkLR9u+3CNA44BppFOotGq+y3mI+ARjRuB+C7GiDE7pa0zmTGMMaYe4NxHENqPARcNldC7SfR9gF1KOEk2l+jQnQWyXjzuhQclHhMCQQdW9nXsJm4SwloOyuQKzVWsmkI7VaBA84jQWU4a5VSWdofLhv5Ruh3JwArSSrZ7HaN+XoAm0haEF5Ph8/NbITzx2GOqhHLzMm9qKpW+3Mlvb5y7E/GMJpopNwhBBj2gMsVdpXLdHLqJsV8TrUWzuta8LXryvbf0fZ9Mu81hJ8HYRi5MgvAR5XOfFarBRWe42J0NSvR4DVGIxW/qQQnRKLtrlr0ZefNRuhZGD7HU6I8uzAst7lub+TH6PdqGAVWou6bVQxMPdu0CtFGAG5Qv8JRL98RjQpeHhVJQRp5+JbG16PLTr+lNHomxYEzMgfUWji2D6GQj49Oht0HB0n3h9FrB0m6PtG2BmVarPgYB+0krUHzDh2lhBop65CHd8DlQjdGx3pc2EeM7IFZwCHIQUJnNoaDBcvCKPLUmIvRQWHMmzbzG81Zc0XmPi7eK9dcj/DZ/rA4yGnwerYjvFe7FcB7JW0atU2dy2yMgeW8oNPg+2ICCQZfv2QJdek8G+0vroT9ht/BVUjFwhiLCvFT4wgW1yOzsvZN0v0wZKrPenlRIluchkK/AZbW+zcDs3hivMXEZKF97/GxknRQVv0omaw/BN/E7UwJwu+kSjeeAOB+klMufynpyzQ8vUGu7KJ82cQlMDy+N6JL8rL25Jd6L9gGKHdS4xr8b6ijBj/cP7shgtKTzELp4WxrPAH+LfFeY8V8QCS3hmuBl4Az/y8A8LnM9XsKDANv7NHw3mRtw4pN+zqtTfTlJIsgw33G0fKGK+DFPXVNzqSj/E8ITsE7Yb6BVL+LAXgdRlFjqRK5Kj4CSb9s3WJ9KnwLw06BgwVN1uktcGAgDsY12c+k1DjSWd0H4efiexieW1Ln7VqSa6sQPg5zRJyBgrICjaKtrqHVmSaMUdkEB4iRibKJ1ABUh1ypsSI0U3C0fw5D3Uttm5bjf2RwBEfW+8q1b3k4izs/vF4GdrIXkGyuT8NRtUno84zwent4A5iyElTVhNUEfcLc+WW4/v8PGNTspwg/58K8ZOeEdWF1eHOSGsPJLAzGj2EbYTAPrU8yiV4FsIDkGhom50/NLWvBaKo2cuVhuCQhZcVqQZXnuAZdnUKDZ1ELdPJsI9gPuin4VDl7CoCbaIGDE+AS7pySzX9D0Odd8P35DDjBtTGMIkghGmfAaOWVJb07PONrRXPZZMZR49sX82CRTO5FJH0u8faDSglFwgAAIABJREFU8PxeIpiwGMmNWpv2RjEwtWb/S9KjzXodgrA5v7KX70jSfHi+nBP6a4KjM0nOTAVHYWLdB6I+fhj8npT9keQ2CokImtw3iVZgBV0HKvn4FKTLwyZ7T3WXxvWiTDme4uP7EIJ2YUw/Cuc7ZTXIw3cBOIEWuyDMPfMuOpF4YKJ9zCFIAK+HUd+xXQvPrStgeC/3MPII/Bp00CEwYuusMIbtAHw+02/vXpnkFvCe6ekcLvmehbzaIzDqixwTAk0fJ/mpVtv/MOKgopNeuT17DS/of2Bk0FGpz1v9Fs+z4ViWh9ek78K+UVVybVEFfv7YdsJJbippXqLtW+EH7v1wPfIzkZHBhjcU9wN4DaLatykYb0rOLke+eDQMlbwDwJXhJspJVtYQkwEFx6fxSQc7TdJ7wr81m5BewmZm+CmAXohngzLKksRGtoJG4YdDE3NwLGYAWCE8VM2OeRZcO5+yGknLW2gp5dUAfJJGxOTIimuh9IydR5kkdOjZ5hjQX7hccCO4xBGSbqdru1N2CoAbOUxcm81Cs1xhpWbTfivJjZtsD8kXYZi0uD2GTdQiME69F6z4mqiMWK6x82DIfQl59RUhoBTzEcxjQOFpGHX3S7oMUWFxnYuOuZDO7n0Uo9dksnxcvcE4VUqNB/tF+Fsi/HXZxnDi4Kcog48XlxXQGa3GpsHlK8vFbTRG2QSNMng3RgOCky1RLSJJrU0gBPs7nb07PXxnDqINUstq1r6D4Os3L/z+ywB8ITjAlwIDxEuY52YrIJJIHoU8DPoAOjP+YQxQVR/MtK21A+D77lJJ/0NyM5hbbsTCvHdl9PpBZKR2a4LxNc8060qsisj5VUH4yfGk7WvOcTEiStKpNPqhQYO/IffMBIf9M3CwuqEB+JykJGm7pL1J7gPPLbsAOJxGqh7fBNIWttFlDSNlE+33gs2F0fnXS9qMRnF9IdP1iTCZchMs/jVcijES+GGFWmewGt++ZnMdz09LwYHKXL81RP61m/YrwoZ06eA77AH7BikrTkxUBkdvpsvqG8T0zsj7T7vDgipfh5/bX8E0HCmroevoFY5oHd8G8H3XW/5eOAdUE/6iLmhXXKobglrPD2tUE8xrbERkoJUMFxLJ8JqEDivKvaP+T+FATQ8A3tThu5fslX8D34PbwHNLYw+je63+B03R0tCxbAf72cDotfk0TK1xBfysvhQGOaTs6ujvcE0RLygq5lkNSPSvQIZEv88WVanX3bBDcTA82R4EYANVSARn+r0tOAB3Slo3bHiukrTxFIy5U86u57vTlYbGFxOThc8W2vEtKmNGgrAxTY3k8i2wPF8c0T1Hw2UXc2FZv6fBDksT+PkrLNl7eKLfXgLtqG0jkf2gpL/QNfhPV0KCnoVQ+uizs+HgzJHhrT0AbCbpDVGbcaC/10vamMOE0l3EgOtjmDQ2h8CqkSS+D96QZDftUfBwcTjQ8ovwehUA9yujEsE6OG/xNWEd8WH2fCbaFhMmk1wBzsi8Cj5nF8MZsdy1vgPOTNyCKHufcqDoMsRtMRqUGMmS0qV6h7eCce+TNOIkslBqvMZob+ylGPDJTdgUzS0/he81whmon8Ib8VKVxFy/18IBi/b1+E6mfSkp51gkqYVjXhW+5zaBz8k1MAfLzxJta9e+leAgNGBURVLRg4bSv7i5z+kg/vWqKPWbCuOgRP0OAP8TgvG5+aKIZDq0vQXOAs5TTwln5TNdVWLFQnL+6Pg6g5gcT9q+9xxzDAJ01kmYPwCrcP45vH4SLKzQeb/RnGu7AHgtjO7aGCaFHUtkIfRZpHJYue41aqK3w/wh/yJ5j9Jles31iP2F3D3/ABJqnbk5ucb3ZaVoSuu7S8IorE072swM/fUSrGc27al202BE06vhteQiuCwkxTfUK64Qtb0Dni+GgqOSdk20XRJGr0zsc+BS8q5nu/dcsIKuo+bYQvvi8vcaYx3h70EA/gIHtD4A+9/3Svp0om1xqW6NnxV9p/HB/wPgGmXUU1mAwuIY5d7Rd3vLbzPfy+2VF298mrCmPzO1d4rarw77Ig059/VwoOjXsLDP1a32K8BzMGB/oYRrZ8qscp5NEc/Ph8vkSvjuFhni50Uwz8C1cPTwVNhZHDGa62B/jGYGUhnHxtn9C8l14Nq3EchdWCAvVSF6JWzkvt84xySXJrlq7NBmLkZsKThoDTEZUHh8NRbOxRclfWQy/YxrU7H5KrDeiK5cr3ooyQ8oLQmfsv0qxtAspusyq7I6YaVQ+sZ2h+Hpe8OT3GUYPb4J6G/LQeyC/t5D8i0wbPlZcBb62o5xzADwVwUJc+aVW2okiV9b0KaK3JbjwXlrrkkN8eEFJF+tNMn3kJXOV8E2VIuIkuTuyMNOH5N0ZOaztp2LsNAgk1VtBeOuJTkUjMv02ys1TvKrkvbiACEwZO3goSSR/LoSNeaJMX9MJtZN8sWkNg4ql7autRnK8G61jXWknLUkqfvDmaXrFEgjcxbWw9JyqNq1bxpMvDwdwJok18wc3//CWePLgQl00H6FY0oaQ6kvO7jQEvaXsCm6Es6M/wF59FMpyTQA/FvS/NYakkMK1jzTNehVqJycH/B8cRX8LOck4q8A8kHzTL8l53gcAvS2hPliyEiYw2X/cXnJw8jLPjcJprfBZTHHwVxH/26eBTgA2LRNlWrPB/Dz9uaIGZVDWI2uaTNO2cSvaGn0/wNwCcmHkAiiB3s0XK9GxWYN5FE3VSU9qPN9a0VTYpsBl1uMWPjdbyCUg5L8E4C3Sbon0XZo0948r7lNu1wWciwyJeGttr3iCpH9W9KfSU4jOU3S5SSTPJjhmf4ygC/TSNZn5Hw0WuX4CwCeJmkLkmvDAfdUeWIxXQcc7BpBYXccX1X5e4UtSfIYjAZdUgjoT8BBu7tgiobvw8/2iKUCPB3W62fFRpctbg+T6RPAiSTPknRAonkvCktjlHuT3AYu9+pFmOUCWzCir22XhL6nw+fjDySvlZRE/cio2TYxfmOppNyCMN6lAKxNlzlPCY8Zy5R1a+bZDcJfgwjcCl6Ldw/X+6DeMSUCygvdwiTweZhvZCaAvSWdnmn7YwBvgkkcOwdLw26/A0utnRT63kfS0Ym2l8EwtM5IfGh7M5zVeTQa/zUaltZtypqS6kaSRiDIHCUmmwXgYCWIyWqPr8YYkB2T6WOyVhKBbrWvJSkujuiyUKkrtI0lOGcAWExpNv8YtrsUnL2+JbWQ0PLTJ8KQ5QkovaQuxEeRhU3WfiiTwZ4BB82aTeJFsBTpCNE0KyTMWSlJTGdHGyTRVZKSije07P2ItYNadHZ5U3iTFQdCHgZwnqQfJfpOXZN3SvpBom0x8SHJN8LQ6mmwc9sV2C42GjGydzM+kh+FiSm3yLTfD174zsFwlnQEIcQCkmiOieQL56PhURqRGif5Qkm3MIMQUBoZcDKMOrqpZ8xbSzqPBVk5jkFIXWMkD4CRA98vaFtMyjnGOHaBn70Xw8/HVfB1Obfzi/39Fq99HCAE2wTvSa44mnPlReHlDermXCkZ671w6cbxMPR9KOqitLDBMjC0vCG5Xg7AqUrLOBeTTNNyx5fBm41t4WD84pJ2T7TdDz3PNIdLrIrQq7WWmg872vb6WlHb4nNc+NttCfPmOmclzEmeAvtj58Ln8fWwE34ngBH+F5KfBXBCav4j+VxFJWU0qfn6oS8CWAd+BpaDCUovjtr+GM4UdwWd1oOv8ecwzAnxMKxQ00k4Hebc5QBcqATBMV2itDfMsXUxnMx9hxIUDmE9nYMCtc7QvvF914XX4ZlwCXUykcFyBGSMBFsMVrP7nNII72sBfLrxw+iA+xckvSTR9kIMNu0x2u6QVrtiRBrHSEyQvBROtB0I87r8AU4KpcY8Dy6pmdhcw2vQyOY6+HAnhvOxHl3edJvSJL6rhL4Wh1EXy8GonBElZxai0aKg6Nvg5zUuf39EUl8ivtNYgZYs7G8c5GGVGAeNoluvFTS/XQn0IetQWEWCEKFtDcKs6BmJxxbmgWdK2pfd1QfFpfLM8OtkgnzFFvaRxwGYKalYWbdgnr0SwJbN/olOfnwPTpLfokyFw1Afiyjwcwe8UO4PT0ZHAXhUCfUdOnP3SjkiPpVjOBeWXL4Ew0RtqcmzZiO3UNSNFqaRPBLmsTkLw+eiSCoztJ1UdDQ4fCMR6JSzFdqnFoPispmOcdQodRWz+Se++0wAX5WU5KtiHZT+RKQXk9Qk1+sghna1qLjbESTM1VMWxjqFlbnwBN7ci2+EnfARVFa0qBIOrq0Gn7tUPTsY4LwsgysvGf47cU3Cd0auC00mfpiGiQ/3TN0XdKnQ61EQ2K4xOtB5Pgynfy2sjDYntZBE42iblCDbpTNhh0m6q2I8ndDfcL/dI+k57e9O1kjeD6sl/Aye33r5wwr6TEHSG1Pm2TsE3vSNZIijNg2nDmES43+hJyCYes46nr0qAYToe08F8GYAHwGwvCbBIcdKlCkrlFAWhpHcDj5nszHKeaEpcBCbktqLYOTmbwB8W9IaibZxML4pCdlf6WB87zOdC6BGjUcCqbVWGcScUrUgpmV9ge7nqYaLq5PHUNJnW+1rysjOhhN794TXa8NBm48BODs+T6xTOZwomyhoW6wYGj5/ErxxIjoSbKxQ66w1JhCQAN6e8lE5nKB4DOZAS57D1H3YsQ8o2rSTXEnSb5lJlGhYWbc4MRF9pyYAXby55qA0JS7rKw7wJvprUNh7wbxvjc2CaRra57247D36Tg3nWbbsOGrTFcwRzIPzVUnn1lznqP8qPyuckzdqIGLzBHieSB3flTANwHEweu63cJA2dS/3qshFbWtKnIsDW+Ecvxp+rj8t6aaewE9xqXzou+HXeQEDv46kzuRewZh7lXVJzpL0Vw5zRU4MF66gGELJBn/2+RqUvi0J4A5Jz8kF79q2qEq9dpXUOFC/BfB6kiMLX7CPAfg+XabTp95SY2cjA29PWDGDPRaeulGVsSJKC28A/oxhBvEcmWmSnwUROWXUdhMYXdIu00sSUkn6McnFwo1+Ii1zO+SAcTyS4hqrUeqqYfNv269gdNOIsV7VKyZPXAoOjiQ5MAD8EgN1nKzJijn/IbmcClBxcOBWDKz8weHIWQ0Ue1c4UPX30O8XYbb7kcCPRiVS14fvlZwtG+6xGLr9dkl3J9peJwcaJ+45WpklBcdviA+brOGvYDLolP0SwN1TGfQBAEl/oqGxl8KL33Zdv6GCkqXIwZkOYBea3LWTLJmF0N9wvz3ASF1hCm1EvW+ypjq4fWP3wYoM0+Fs6bfaz9aYAZU2KedOyJNyVgkghH7XhhGCV8GOTAmhftbCtZ7d33LCashae409Zdlt30LStwF8m+Q+kvbv6TtHig0MNgMHSzoier+YZFqGjH8awKdDAG2ZVNAntO19pjUosUoiaOEyv/Yx1ihfAs6mfopWYOtDNfb6WjXneJznSdInWSit2wR2WM77UlNG9mxFQWJJ95J8jqQHOVouXqNyuBGNBivxy3oVQzlaktaUC64c5vPUfFGk1ln7rAY7BMCr1UJAIn2epwP4lcypsSmAbUmeokgMJLIHaWLub4TXO8PnPWXXknx+36ZdQchATkB1qsNJOi/8W0NU3vhNs5Ani25sOs2l9mZ4jumyv4cgX+PvbYyWX5kJiMRji/2FKlJl1ZW9N3YWDDI4Fv0Kp+eR3APdCOi+MtIV4DLec+PrXDHe2QDewXJRivkwPcMl8HnfHBZc+Vr47TixWiOaVCQIEawpv70K/SXORc9IsM/BSY6rQ9Bndbg0NmfFpfIwQuwRkiC5pKT7SSbnJlaKbqhfWfc0+P65BYOEH6L/zyR5rKRYiexUWL2tQV1vDeC0sOcqUq5dVIGfW0juDGB1SZ+jSzRy0qSfhxebpdCv3lJssixqERwUdRu5KnWjhWhFsr1A9Samhp/leHhSGYq6Zqy0Dvg0WFGtl6SYeY6ZLqvhOihm8+cwPLches5toKpUvdpRbJLfQrqOFahzEP8G4K6wkHSi4lAhYV7juMCTX3zvLAAw4vVmfudWmkw4Z8cA+JCGodvHIOLnCc7Y02G1jf+JfnsWzAcwPFg78u+VM/glm4HmelyAnuvBArlcDiNGBM+ZqwPYjuTIZovkKyT9gJnSJQ2j/qq4lILtj0LlHZh8/B5a0Se+3yZVbhKc6tlwSeaJYfHu4pMptuD87ouBksbVcKnASEZVVmM4LjgVuwC4k+Q1MHn8UAaTLnn7QRMYorN3m0pKydG+Fw5CN8/lVQC+nhnympK2J/n6sAaehrzqFQA8CXYQ/wJvqP+kfEa8hnTwNloppARlWqyEUmjNxiJZlp37kqT9QyBzIiCvllS1elQ1w/1yLQZzO6I+5mOghpK0cL12h+fBmwDMInmopIOjNjXPdGObA2g7ylvE73E85cvaYGavrzXOOa4x1kmYF/G+MCojI9ko1xChjCwzlHtoNHZDg7ADrHi2JAacN43VqBzW+GW9iqEYloVum5CWJC5V6xwnEF4jS/4dABuQXBO+DufCPuaWibbvBPBZOCEqeN7MIZSqNu2sVIcrNZK7hTE/AiOrGr8gFeSr2Vx/CJ431whr2Iowv0xsxf5CCEBfQfKkmuBIzfqLOs6zBlX10XiYiM5bQdDu57TK5biWLMvvsHPCX2Pzcg2jc/xP+P7osmIVORi9/k8YudUgzHJk1MXPiKw4eFb0+kHkA1UAcD7JLVWAMkUdv04vX11kvcq6krYK/yaTNGE/cTe8djTf2T/sFxoajd01ANIU3W+LqtTrSHgSeoWk5wYn4mKl67hL+CS2l3RWzUafFXDQ6DtFWR2SL8SAzHdE3Yghu8a8hGa7v3GO7xol+FUybZ8NBxaeImkdkusC2EYJUjBW8LOQvEFS18Y7bltcBxza9/K5cMCd0JWNbPd7OcqVusZl838MwM+Ulg2vghRnvr8WXF64ZuKzJDRdLUh6Ysxx25Nb7Qg7yc9BVIKgloQ5x6tz/hC8CMeB1JMkjRAUtjaejbT2EyUlER8l5zmcg3fASLCbMNjs/BXAyanFjxWcWZXX4wzYWX9beE5nwCUUY8GrQ5+flSHdNUohNeUKNdDfYt6eGmMd/9RicFneV9qfZfq+BEY7xmibTSW9KtN+Mdgh3gVec86E14q/S9oxapcqecnV4M+Vyek73wvv3yhpIxrqvQcM875RGRRm9L3nwiihD8I8ZiMEqCEokSIdXBU+3wdFbWvut+LShtC+lBOsqiyb5IGwc39qeGsO7OR/KtU+Zwyw/+h1DR/B7TIcfScYdfEJOLAWc4EUP9OMELSwVHVjy8LcOjtHbWPlyxhRmlS+pNEp9zNNUgyluZFWk/TT2Neq8XuifobOceV3i6H/rOB9CZ/XlJEtDV+bxpe8Bg5mPQJntEd8sBIftdIv61UMHcdYoNY5ib5PgPcX8Zy8WOZ5ulXS+iQ/BuCfkg5rz7PN2pabUzNjKC7pCe3HUocrGMePYNLlKVUpooOPC+DgOeHE/TRNshw37EU+gjJS5ar1lxU8hpVjbgftXg4Hn8YK2rG7/GfS4w2/kfLB58NI4QMUBc5q1urQvpT3tOoZqTE6AVpUKt/6Xh+/Tg1fXUpZd67G5KCbSltUgZ9msi2RfTwIzhZnVW+i/pKSlJnvLExCzE5ZzfDQrQs7bL3jHfP4imV76TK6jwI4Wv0Ssb38LJGj92Y4W3x2q+2kSgXCb/TyudBlPGfBGfGRjZzSqIoa0thiCc7Qfgk4OKIw1hzfyq1wcC1WkPp27tpzFPL+OwCfVEb6eWEYI1nmjjZVdc7h/G4MO7oTEqPKy8THQZTH4KDud5QphaBRebdiGLr9QklvTLTdtvR8spAzK8wTp6ilvtXRb41cbg1ipMra81A4jruUIJVjBblkaP8UDNTPbpT0h9bnSTWvxpQO0BbzT4XPbpS0UeqzRNuReTL3LJD8ChwM+QGA4yXdGH32QOzkp8bX0W+K7ywXJKoSCKBVNV8Ko1yeAMuiXpVyallAOsjKpEetsY4TbIg7KGxm7sxttujS4hco8A2G+/62yW5WWcdHcA+cmDgNJiy/IjcHFP72cjDSrhdBG32nSPmS5DGS3sM0H4dSm7nMvdzLtzGVxjpp3UklaabK2EIeweVxOcWp/0WhX0aXrn8NA9TOpQD2as/LoW0KYTYfXhva83htYKSRZ94Ynv+vA/BBOfPfblssS07zcHwVLm3aWg46tnk47oU3bxfAohBtcveYLH2sTXuYAzbVMKn5vNw6WWo0ge6b1KEqxG7S6KZ08puNPxq+U0TCHN6P/dMl4ORuUriFlaTKletvCedZNVpy3KAdM7LkJM+XtFUYb1z+MzLe1veKFbDDvnoBvI4A5ledAe8dZkvKKWJ1Git5T5lAYqu+SmPSVjoOVvDV/Tfboir1+ndwmpr60BWRlyR9L4CPsLs+/M8kLwawGg0fH7LUZgB1cNBi47CsZlOWIkSymgAuBPAQXL/316hN7kEd5/hmoVy2d4akGzlci5gjCSzhZ2lDfzdojSHl8DWT3JDlJrn2xM40n8uO8KazXTecteBIj0SsM23/Q5MUXhnfSykjuSWAo+GsKuFruZukCxLNPwrgcppDZULVK9MvATxPPbworJTBDt95FrwhWBvDXAepa3IryQ3VoZykyjrncH6/HjawvcFCRSiZEDSamQv6BIuh20AHdLsyiFbEmSVznaxCcgllgoAtq5HL3VeRIpakv9CBsbEDPxyvXKGB/n4QA+hvElpM8s0ADoYzzA3c/aMyx0pjXxpj6DX8UwBwDV1qcgaGA3epe/BikjvCyB3AfAQXZfq9E1ZaS9W8twNNN5P8MgYlW++DneEJIzkHVplqrwvLws76iMnlZoCzpJ0on2CvhZ+LQyXleMMaezKG78d/wyjSf4b1GwC2JPkJmLutM/DDPEKwCx1QzAmGdFn2SV1jgoNfzbldrqdtqdXwERwNB7TvAHBlWKv+2vmNDpMDw/Nh9FKnNZsiAL9ObYzamyJJ7wn/9vJx0Kia5wFYrtX3LERrz+NkNdD/Gt6XKuMoRyKA7PqbKls+FlHZcmQN2qfXLwsBmx3b72dsV1j9rwn0bQrPWavRZUvfiNrWZpxPg+fCJimzI8zbM4JcUiRLXtDvLnB54edD0Gc1DK5lY0fBic7V4eMZ2ohjeB7NcXak2oIDlPKPMeDtEAbqcCMWNu0HwOvqhfDe4oOSvplo/km4rO4G5Mtkm/KTHC/ck2DfZT1Wlr6H35rwvYO/+noMFHbbVlOOBVSsvyrgPIPROj9AWgo8t3/6M6yM19jD4b0RY0I5ja7OmECrq6f8p8O+ikIFbACvagXp7uIAYDBUis+KihBU8J4yQmLDnIeLw8itokqVPmMhT1vlOBq+ukcxKLcd2rNnAqgTpvFL1KfMFhXiZye4Xnl9uNxqO9ghHisDGKLj68MT9ggPitJojWI4aOVYilSTQttzJb2+oF318dUYXb71fhiOvz6Dioky0s8V/a7ezsik3gvvPyl6uRRcL/xESZ9pt+34vVykf4tMgCXVR3HEmuZ7OBjAEpJWI/kCGOKZCqLcD2ArhdK1sGn/njIqRqxT9SpB24wjg301HMT8CrwQ7gLDeUeuSTi+NWEHuVM5iWlSzgZm+uH4/iD5JXjjdnbfYsYE/wW8YT2463uL0mg54OfCwdQ4yJBCo9XI5RYjRsYYc025QpI0NrXRpbN9m4fNRpMQuFSTzJ6T/AjsAGwOBzLfCeA0ZVALrEMoNJDiJju5GAbXcSSIX+GMLANgHzjTLFh98vNx0Chs+ldDAq0BI1d6FX6m0sIG+I1wHTzgOeO7cCLgGEk7kTwYRuTMhBMTE1/HqAM1jhLKXTCirJG0XQoux0re9yFh0JSFjZRlt9rOAfC/8MaWMArqE5LOyH2nxDjJLCLJ6Y/HteYYZaEVfb8eDrxtg+HE0sMATpd07bh9h/6L15zW9/qg/8vDgewYXbKfeqTRC8d8PxJcPCnfkgsJeUTyGXDQtNkEXQWXK/wq0fYiGGX0+/D6KXBwdQ78bMUomloFztR61i7Lri4lrzGSR0p672T6SPSZLPVuTOmS76bc841wkOlD8PlNIX9vhHlv7kKUXFcdzyLoJOXRHC59jwNFD8Ml+EWCOcwjUvdDRTlWtP42xzYNrfWX43GelRxDE6x5AYygHQraSXpH4js1ymnfhYOb56oDsRW1vxyFCtjB13q3AuqY5IZwtcJ67WvDuoqQGyS9KDrO6TDSOnV8vUhsjkfuDlZItJeMo8aYKU2PxpwrUe+sFGq1jcEJSwOYrkQ5XXaMiyLwA0xkeF4JT/yXqZw1vKvPFSX9keXyzJ1w0NxE0VhqwmCFrGZoH5c23CDpjx1ta46vWLaXhtE2pLYPwXXXO2Uc62IUCCcJ2+5qy1E+l/UBPEkRnwvJnSV9k+SHkXYEUpvr2xEi1tEkkAso3QJnyeYVtL1JEYcVScKlLCleq2LIdGh/Mgz7z6JtxjEOOJImjil3TSo3Z/vDpJ2nwc//jnC9/60wMfKmUdtmYX8MA1nSkQ11aNvLf9FqX1VPXmqVznIxx09oXyqXewLMPxUjRp6YckZKjeNxdqTmgJyjM/Ts0KitOzLPU04xJBlwDEGzBv14sVr8U4+H1Tgj/3+14EA2KINrNCAdbLcrSnqEtsvA/Bv/Cc/scwBcoIQYA9OcYCcr4mxipgyjsdwmI3x3JQyXIv4u06430MBhIvYiPgK6NGtfDAimr4CTDSXKi6lxLqlJ8nJMpZF8saTrCtsWK5eWrDmTvC+WDb9dwn1Y5OCzjounpmz5KQC+AOBpkragZeJfLOn4RNtL4HMW97uTpM0Tbe9VVO4bfJx75BLPTpnhsKbsIeldrfeba/Jx2Dc9HX5mdgCwvKIEBMcIFC8so9Fat0v6O42gWB+W9s5t5NZQVErV0/c9kp5HKy49XxNMAAAgAElEQVR+W9KF7SBY1LZI3rnWWFf6Hvuz0+Cg0cslvTjRtrccq9a4kALWYwbtimXJ6aDzDgBeBycyTwdwvvLUBRvCpV5XoF8oZEMAJ8AJGMKo0XcBuAfmvTszatuUvsYUA0muG9bxnjZ8gw3SaBnYH4oDP9XnOHyvhqetdxyt9p0CD+MYhyuFmsDdiB8b2laV06VsUZV6QdL9sKzsVNpT6JKoJ8Lrzh+RkWdWGRy0q84xB/srVk0iuT1cujAPyJY2xFZ8fKiQ7ZWzXa8KN/w0dUcOT8QABbIZAgqkdVzVsO3WZrJZHLruz7h06zGYS6K9EDUlHTUKPsVKXQD+LWk+h0vkhtpGx38zye/DkFTBiKZcoKYGMg0Y7rwTyV60TaX9K2y+f0Ty/QB+jcy5rHSqtmk5KceEheTjJD8F2HGSya9XzC10CVucLtd8AxwI+zdDeU/GGnnP49DD0B+e1QslPUxyb9iROyAV7ICfkdMwULnYObw34ixrIAc8Qz1ZnegZieVylwPwc40Gmj8AI0YaNMIlcPCn3WdNcPtD8IKTUnEZKhVgRBpLc6M0tixMUpqyC+ms8bfC6x0A5FAQDYKveRYanqQcTPwuAE2ZXKd8aM3GqNLmYuCMbNY4I5Ps87/NboXniekAQMs5j2x2SoM+wa4E8FIGEQh43twBCQULSV+m4fRNQmcXjaJ4cmUYXYo3Tf+/RX+pM2DYfS7QcALM5zGOYtEJsMrHm8Prt8JzS/I5plVFVsVwkOGUqMl1ANZnhpw90+cTYMe+3e9UQNhvI/k+FCSsUKFcioI1B8P3xcpwoIFwed8vAIyUXpB8PoxqiVW9cj5Z1sHHMBVAY5fTCLkSjsTismW4nPFEDCS7fwivE6n5bUVJ8Yb5JJJ7ZfqdR/J8DEo4tw3vLQNvBrOmvAJn+1ndLf4aXMrU9NGsi3sogTLFqGrdwrQj4dKo9WAlzuPgtSqJuAZwAp0wugm+dlcqL3N9Ho0G+yeA99LI2JyPdAHJ98CE+1NGaCzpOyRfh9HnNKXgFO+jGu7F5Pyv+vKmxodpVL2uUovHUNK+4d8kXcK41g46sEz857MoVE7TQOlsMdi3ejc8/+cIiosVsOUk8fOD/9iU/DZ2Zqv5n+gKhaZUfjvkVY8/DgeQ7oKf1e/D937KepWAc4GdAiuWaC8ZR2M0P9qGGAg8zA17lTgAXU2rAfuGa6mM+Lm4nC5niwzxszCMlQoLC2kMxRl8VpY21BwfB1C7OyWtGzbEVymhNsQKSUQWoEA4Bmybw+UVj8Goo0PUz51TpLRWapUR6+Ph2u9PwE7OnjB31O5Rm1SWobFktoEVkOnw+ULJcIWswH2w47s/DHk/SNL1k+z3Ojhw2AQ4t4P5CTbmALXT3Gc1hOZ7wgvPHXCWZGWYlPClmfY16LPmOZoN19cfDOAzSmRkmVZkymVIXgw73DMlrRwcxd0ktfmqQPJ6OOB0J+wIrwNnaJaDs9YjBPjsyUSPc3+WGCtIYxmhDiInDvB8FcuTxt8ZyWTm7hVWqm7Qpa8nwnPteiH4e5smWSbHCtLYhWU0ErRRCmrm+iNLgqvMkFFGnyf57SYbgOYgE/cBAEvLRKS556lYcW5hGtOlN83cNhky5pq55RtwsOl2DALb0rAQw91w8HF/DEsXN41TqOZrYZLvzvIRZpCBUfsUQvAsOGH1FkQJK0lzE21rlEt715yo7bEAzlEovSO5BYA3SNot0W+tqlcNFUBxyWmNsS6Dfxk8FzbB+DlwMDVV+k7YD2quyTWwuMLIJoMFiO1xLbUOsKd0YyH4kc2c9RkAv5Z0fJ8vQ1M6bAgn+naDfYIRFBpdrbAMgPkyT+Ayoe3vE22nHEET+j0K5vTZDN7YbwcjIHedZL8z4ATTyjIx/LPgDXESVUHyCJhiIE4W/URSKsmVKhuaD6PCb5/EmGuI1ZuEZud70WdLw4GzhhrlfEkfyLQtUcBuqiCSJVRKAxRSFSE7S/pZq91iMMIvSV+RGU+DxE4qAUftiitYQvtzYFDCXnDQ7CF4b7Zlqx1RoEgcte8VeOB4tBrFlUKsKKfLmqT/yj/4Icr+Zb5zR8l7Y47ndQA+Bm8gPgNv+ibb512t19Pa7417fPAkDDhbug6sqPNgpu0lMDpgtfC3NxyASrW9NozzbJgX6I1wbXaq7YsrzsXqifdW62i/DoDbYE6Zn8OZoXWm4JpMgyPrZ8FO4rsRAqSJtjPgKPtN4e8AAEtNwRjubb1m8x48ybTbf6Pkvf+WPzijfh68QP4x/H9NGJExO7S5Hl5s/gCrigz9Ff4O4drX3Of7wZvglQrmltvCvwfCaoDJaxHevwxG+SwW/naGy1lTbW+AZb1vi967O9P2bJjIu3m9drhHV4dh5XHb5y+M5yP0fXW4718LYNkp6O/W2nsW3sxuEr1+SfscRJ89AG8qmtdPys1Z4fOb2tc313flcZ4DB1H3g+flcwF8fyquSfQby8NKVbnPz4QDjZuFv2Nhbrdc+3lwhvGJsMN3A4AvZ9r+OD7PU3hMt8EIyOub+x+ZdbK5l6LXi6E1n0afMTyb+4TXKwPYaIrGfB2MypkW/t4MI70mdS+FfmdHrzeBYemptvchs3ZFbWbD6IQ/wxv8+O+EknPc0fflHX8/yF3r8O+d4d/Fm/OWaHsojFSZAyOe3gQrGKXa9q45UduRe6vjfqvyOcOxZ9ekMe6Hr4Z/z8NAeGPiL/OdefAc2My7GwO4ItN2ldDXH+G1+P/gTflkx71v9PdpOMA3Kd8JFoK5C0Y93xn9/RROAKW+E6+Tv0DHOglg7cR7m2baXgGjkX4Iq+v2+fazQ/vvwz72EQDmZNqOPH+lz2TP+Xs27LvcHV6vC3Ovptre2fp3JpyoSbU9GcATotfLIz+3nAHvs5oxzEDHfAkHiRm9ngYHilNtTwvX45Dw9wDs598E4GOttrfAyIrlC87btQA2i+8JmLNtUtcOXqt/BqPSN4OrMbrGcRCAV/e02S38u2/qr+e7y6DH34N9mqL5IfS3WPj/WjBQYPFM27Pg5MRP4FLui2H+zpLfeXnoe4nM59nnMnXfI9ojwH7RnaXf7+j3eNiv/iQc+PwQnJjIXedPhXt/c9iv/HzN7z3upV4hQnap+pUeqmG3WEgKC7nodqbtivDE1Y5MprI0NaUNQN3xHRMytHvDC/dMOLiTspUk7R+9PoDkDpm2c+FzsSf8IG4GP4gTxiAPCeAtNCHmkCkNCf82HNFuv5dDZKRULJqo9NgmR3KPRQbq11i4j78X7uMRNFDU7ms9v5c6F7WQ6SHEQBhbjhvp2XBmdxX08NqEKLQSYx474xjGtofycpFXh3+3goltX4OWmlGpybNkVwS9uW/jTLeQLvX4NQ0H3RzAF0PWbVqiHWCo6GFwhlmwY5CFGUv6JYfLBXNlZ89WlEWSdC/Nu/Ng6/uA1X+Knw/WlTe9FSbE3RbAwbRi01WSPpg7xh5bguRbALyEBWpBwXaF4fHLwevCQ8iXNhSrbgT7O42CdHSA3BjODCaNhVKgGvBt7BeereVgVZZUn6l5Yz6AmyWd22o7Dz1KIZGto4iHAy4nuTd3bACWk+WJ3wXgFAUyykzbX6LjPE3C5sIO0TmS7gkZyMvjBhxVnGseiC7FuSNgxMor4LXsYbhceIR3LfxGDZpoJzgwcQR8H10PYOeQwX1/9+F22u4ATonu+/8PJltN2d3wpjMHy4ekqwFcTfLmzLOesm/QkPjz0VE+IpczToPnkVx5Z9sa3qa/hEz672AOn5QVK5fK5ex9a05jv6HLeWPhj5yiXa3P2UsFUJmVb363Ru3wQ7BPuAbJawCsCPu0qd/6OTy39BorCLQ1fglHl50GlwD3okwjq1knzwwouoNg3/4gmJJghKsG9uXfAouk/I7kyjBKOGfz4Ln7QDgZkCISL1bU4niExscikPiGNnfSghkp9aZ/hn//QfJp8Hq6UubY1pU04bdKeiiMP2VrSNqh2TdI+gcTzk1kP4b3hw26/ZnhvZQ9A8D6CqguujrjezBfyy3w9WxsB9hnu4nkzXAg/OLgU7Ztmeb+CWNufPUJo5HdLwGwYuu5noWEanCYN2+Hg3+dNASR9SpgSzo6/PcIdfDJhjEk55/mcihNrLw8gHtoUvFYrCQ1h8Ql3BfC80SyhBvAmpK2J/l6SSeH+/KqnvE3XGqNL/ZUOHbQtl5F4sgOhMuRLwcGAg+Z32+rMjbXI7W/+EX4WwI9ZXrh93ZFWTld0h73wI8MTfwPyeXUQUioUOvJDOw287W4zllI1DkzU3cX/W7qBn2JXOZxp6TPkjwEA46Jtp0KR623gp20t8PZktRvfZTDpQ3HKFPaEKz3+KK+a2R7ayQRm4fjb8hvZvvkISeM48u4lky2cyUd2gWlTIxnK3gT0H5Yh+pqS+9jDIIWm8AIjYZzZXsAuQ3X++DsZXNfnIIBZHoiYJrY7CCMt2uz0/DaHIseXhuY+LixpeCN/lAgJePsTVjmvM3OtY/a/QnA6STvk3RHX/txTHX15G+GES5fkqXRV0KrNIIDtaqNMvNIyn5J83CILseciwwXF7ygHgmT/AFeJO8NQag20W3v89Gyk1DI+yDL3j4C32ePwvfkczv67rPd4cX+CRjdnOU2crfAHAqpGvW2JaVyG8cm4cAUb4xYKAXKFgxa/UqMS8Hw4zj4+9NwzJtJink2aoIzt5LcWKFck+bV6Jqnp4d7/c3oCHAHq+G3S5FzN5vEAxRKYcJ52yZ+nsIGcihgLulAAAeyQnEOLrlZn+RtoY+H6HKLnBUH2GsCDSRfJenS1ntvV0L9I8yF65GcFV53SbmvAM8PN2L4eqTmpm/QpbIxafRRShBow8/8wfD90FzDZMBcJuQ+HFZNKbEmYbUPBgmrpLKnKjg7QkD23RjlJUr5T3PgDPg58HFdibzcfQ23DlDm4DfzdC8HVJgHAZcfHBp/RnIufB3b37mVLkNYC/YXHmhfZ44nSdzLaxX1P+XCCmENmA9gDocJtGeSnKk0sXLNOvkimEz8WvjanIqM9LRM+v7l6PUvYD8uZyuEvl4GYE+S/4GRfHGy9jVwkPcZGOYmfRj2A2N7OeplyWdIurEVZ8klzs6nub4Ohq+vkN98TiO5vILSHU3Yndt7PhqC403SZQ1Ec1fClgVwX5jjBHOf3EwrYrXnuie3+vo3LFH+zxAsmTBZfffTIai7FXz/LqBL4w9tBRJLgr9LwHPZdAw/139FwrcI8+b2koo5AFXHGXcNyZ/BPt7ZSqsQNv2tBSdDGtqOrZEBPiAPLkgZQ2BvV7jc/CC6DD5lNQmBdtl5H5daMUeqpG/RibYmOfRxZQQeYN95RJUxZZWB8KVhxNyxwIQfsjSGVVI7bVHJuZ8LOwKXYDgqOLKYMKGSlHqv4rdf3vV5yiHnoKbuenhD/mfYiV8z0bbhJpmoKWZL0em/xTiqKhJLIv6tvWkfo/9epQKOKePKAhULDjgVanhifgxf47sy0f24bc19fD0MKX8svM5yLtVazWaHFbw2me/fKGmjxPv7w5nlb8D3004wkiwl/X4knLk6C8PnbSxZzcfL2IPsCJvZdeGa8dL7bQUYGfAq+LxdDGBPJTKUwSFq+FkAcygcARM7zlDET1DyfLT6ruF9+AlcMtFkXW5Xh4Qoh5ULb1RCmS6021WFqIOwqTkRnieORVBxU5rnaN+uvtqLLsnVYPTKxMYI3liNZIRYIQUa5osPZDYg7bbXw6VsC8Lr6fC5ng3PTbF6To1SyH3huJoxrByO7zEknB2ayPEzMBnlHjTa5mBJ2yb6Tp7nlFNDc6ktgO8hwJvEGbBDN1sRIpDk9TXzJMmnYxTReGWi3Q1wFvamEABaEc7qtrmjJgLsGDhXEwH21NxbE2ggeSXM1fUReHNwHCwyMLIhCEHebRP9jpCq5nydjI9zHBy0bIJNbwWwQC2VpdD2QTi4nVQUTLT/ElyidnbfmlrY38fCJiEZnMisv9fCz09bGr1Imei/3VI+DvOS2YvB1AWrYvgeilFHb29/L7ZUUJIVvFY0v+VRGL0eSXQvHY3YCaYE+ByNoHmqghx1q+374Ux7iUJOjRraEnCJ8+bwc7q3pNPb7cY1ks+FAzYvheelX0gaeYZZoahV+fsXwGjEs8J8uB2MWNoi0Tbm5VsSTlQ8ooRCIMm3wfPnWfC8uR1cmtIWKQHN+bI3nCS9GA6GvUPSvMyYi/dzITjzRrgcCXAA47tw2dcxkoaQJiTXhZPbW8KJ8FPh9fetGuYEWx4O/k4QTAP4bCqYQnIVBe5NGtUzU5ngfem8yTHUVsP3NoLX3TfASejTJX0z0e5KWOnr4fB6Wbja4WXttjVGJ1z2gJHxu8po3pwq8rtgNO66sN83E6ZbOSrTdw2X2iqp9zV5jtReVUaOQQQdfMNXaYBcmwn7LcXVLotK1etspCPOKauB3fZayukpsJrodhOZ/C3Nev8bDEi//qusMkI8jvUqFUg6ly5r+rgqotsoQz/dR/JHAJ7G4Qx4F+noL+H64hIHteY+Xh5GMTUb+pnhvamw80kuo2Hp0EMzE9d5JPeAs5mdKg8clredBme3l8uMoa2acmRw7lLZ2qXg4Gmc3ctloYqtxKGdRN8lyI4L4ZKjmRyUmzSBVWUCqWslHI5NkFC+kvRPDOrT29YmpazNRNeUN30NdnLmwEGPK0hemQryknwzPG/OA7qVC2UCzD4Voonjk9F8r4H5Kt4KO+8jgZ9U4KHHvg3fz/eEY3gZgK/DfBBte1SSGNTj2I2qqoFBLw/PEc01WAauLV/AVnYSJsG9CJZP71QKgVFrNfbbeJ6UywqTz1PleX5Va6N6FweEqDu32t5GZ3B7A8W06saOsCM7QWgMozba9jV4Hnwyyc/DG5K92400HproXPiZuxT9yMqXw8o/TbbzM5K+lWl7LgIhKboz4ZB0RWnQFcCGrfn7B2H+TtmPUZFdhOHoHwLwGI0UzM6HLFMMK0YURzZDLaWnyVjOUW+s/UyP6eD3Bg/pkpi3AFgtPCONLYuBr9G28+BkwRA5d2s8I4GdAvtHmO9jAu2GNL59zI9Jyikwpiwuy/wcussy90K5Qk7Kj8whyW6Cn78NYYTOUSEIs32mfbGFYOr9CGT7MIH2SLlXsPPp0uhV0R/8LQ4Uw0jzYwA8h+SvEUh8M2O4DoGaIQR7/kXyVozSNUDSKXS5VOPvvUlSEu0u6ZLQz8bwPDG3K8Bcs5+TtH8IbjU+2+6Smjmk7YPdAtMqHA8nlJq59obgn8X9PoQWArXDDiS5O7wm3ARgFslDJaXKAJt5cwHJfyI/bxarrbbGfSOAG0l+AUaQnYzBPju2p8BJjsYeDe9N1npLuKOxNvvtK9BfwQJUlJ1PNsDTtigAV6LKOE6p7lKKkryS/kaTopePcQoSMAvVwuZzXwwgyFfC0dRJyRHSbPEHwpHlmIun86ZqotvKlBbQpUJXwbWmh8Gb/c9KKpGAXWQWoturYnhxmDQCg+VKBUkkyRT8/lPhTdGIY5V64Gklq/3hCaavXGEZOMvRZOUXA7CkErLcJHeBs1Bxbeh+YzpY7b7vBLAeHA0/CQ5KvjmTLRrhHgHSdaehbRO4aJTWPifzQrTbXgtvjk8P35kD4H01UejJGsnvI+HQjrHxT/VdhexQoVw105nanDrVWHNW4TheCG+E14G5QVYEsJ0yCk7hOzNhJ/kjAJ4hKVWrXqxcyAIVoqhto7J2KIB5ks5hJsNda2EOOALOCK4Pn/OtJP0y0fYjAJ4FZ4EPhDcSp0k6LNG2BoGxKxyEmIfBfPEFmA9uP0kjCkylxuEyiGYMSRRSyf055qb2DgDvDs5nc86Pk1XUhq4j08pzUho98wDMKdEZFInaPwfAK+FzfJk6pMFDADA1kBSaKImWy/T7RBj9MAsu4/gmgC+mkg8sUG6J2raDri8FkAy6hs3W9k3wNjjh387MQ+fAZW+XY3iNLN38dI25SDFsjH4PgAlXuzgUa/qrQo5zPKWXXpQSna1eDQleG5h0dKRUJ7dutdpUUyKEe+ZQDDhvroNLHX4No2iujtruB5NF9yagQvsmKBwjUpPqeKxTyNle0ll974X3N4gCBc17b1UCuVJrJKepAzXbanshBsHf+L4Y2fjXtI2+swxMJPxw4rOGZ+ibcMAx5hk6SpGiE8lZcglyMvHdca2L9yJ0guowuNR8CZgv5++ZoPLKmXGMrH0kV1fESTVVxgECbicElDKMEC9XZJqaccyC0U9NOeY5AM5UAnFH8tNwqXdDQ/KG0LYmUT8po9XxRqwdwOSAl+h5cJK2t+x8qo1pNcZoCJNWZbwGRo7fGl6/EMDhklJcY+k+FkXgZ2FuYCrGcDUcUPoK7ODvAk92yRuMBZno4FDvKekrFeNYGmZB75QtrzW6lOi9KKjZJ3kCHDS4B8PQ2JRj/Qx4oo1hjXMl/SrRdjbsbL4U5u+4HS5vGslmkvwKjKI4A8NZ3SRMsdZCAOrZ4eVITXvU7mIYPdEbPGAl5C4smg307wbla0OrjGNIh061kVwVdvo2ge+LawDspZbkY2hbLM3I4ZKe4+DgS66kp9ehTXyn4VISXM6Skw+/UdJG0bleBq7BH2vB5oDsby94DmpsFoA3Zhza3jlrHIc9+u50dPA+RO0Ogc/ZTJjv4Gr4uR5xlNiC7tLw5juUhvPeByun9C5KIRjwdHjTsx7s8M3TJMoYW/2/GCa5fASGOY/wtJHlUqAcT+p0JZizAHA5UhLpSvNlHAnzFawTHOdtJI2QcjJT+96+j2vuzzE3tRvCvAkz4fP2VwDvgteg10k6s/2dEqMzuturQ5Z5EhuS86KXS8HX5paUI1cTaCD5QwD/K+mE4A98EcAGqXWE5DEADlMLOZvptybo+kp4nn0Qvh6rwKiDESeWmTKgXHCmMmC2UNYtuqx9GXgTkCQ+/W+zyuDh6gB+I+mR8HppeD74WaLtF+Eg58gaGrWppkSoMVbKjLOwLDO0PR6Fm76SwHb0ftunngfg6IxPXUPsWjuH1wR/a9r2lk+HZ/8dMPr5JgwCPw8DOCkO0JA8X9JWHCQQJz5CPtlYvBcJ7W+GgxdnhTG9DRbBSJXfxrxyS8O+wwOSntduG9q/DqM+agopVWwk7wHwArjE+XAZlZkLYDbljavJaKVnwvQJOX6dmnH8FFboO1PSdQXtX4gBxcCVkm7LtWsHj0huJen8RNtini+SH45eLgXzLt3Xvi9YWdZfauP6DAvDgu90OlxNRJi0eodU0C7bxyIK/BQHXVihklU5eTZcPBMbE2b4T1iXiS5GrpDcGoZ4LSFpNZIvgBEVyc1Z5fHV1Ozfq2Gll64xXwJPWnFN9E6SNk+0fQw9SgVR21SUVKnrXGvBiTkFlkYkjMZ6e8bxrFkoRxyznLPGdP3tfAA/VyszxULi06j9FXCZ0S6wU/IH5DfXNc5LUhGiMY2JCCN5FgxrfgsM294JnsTnJtreIaMAXgMjxvaBZb9TjlmvQ9tqfwQs6Rur6v1E0vsSbYuRHYW//XIYBbc7nO1v7GEA50kaKdUpmbMih/1N8ILQQHfnAPi9MspbNGrsdABnqJ+Xazs40PP7guM8GHbk4nN8pxJlF+G+2FNSVoUoajsNdqAelMm2nwTg6epAKBX02Q6arQ3zVj0EZLPcxXxzrOD4Ce1LuWquQFBj0SAbnpzHWFj7Pub9+Sa49r8IbRO+00vOzbpA8XfgQOBlyKBRxtmQZMb1TFhOO8V3VBxoILly+54g+bL4WkdrwnR4Hnow9J0tW27fm+wIuobPl4Q3zIA3RMXXscsqA2YfhBMvnYphC9Nq7reFPI6a4OHNsAjJo+H1EnDp50gpFMk3wuvCNExhEIyDpGBTCpNNCo7R907w2rE+7NNuB3PspJA5vVxjtEjMljCS4Yyo2Sw4+ZDiMUz51I9Jenei7f1IELvm5t3KObwm+FsVKI58rd1hxGnO11pYPEPFe5HQ/mZJG3CYU7UI+Rt88j0ye6KkkrOkXUvHlvnNPQF8HMAdMC3BygC+KemlibZHIpQ3SnouzSV0ceqZHmMcVMXmn4UoYRo5+jZJd4fXc+AE8AjXDSt5vlrfXRJOsm1aegyTsYTPEDOgJ30GmgvzengevEqRIu8UjGdxDK/VySRtzhYVx8/Ski4LN9/PYXnbW5DmAylWyUKdHOG/giP0I5oM7tdw9jFlG6AwEw2zpR+OMuTKfrATNC+0uZ0mFs1ZzfHV1OxfR3JtZepuW7aipBh6fxLJvTJtS5QKAACyLPqkjOQSmeDSlwG8WgFVFQJo30JakeX7JF9dGDz4O8n1NQy5+2em7RGw03InPGmsA2c1liP53tbvXYA88elJGFVqqJEOPRJ2Xo4Ir98a3htZ/GDn9yWwOgTgRfBa+Pkb4uRhnWpKjTRjM8FuCTsh95BZec/rAZwTnusSh/YVAJ7bPNckT4avyYhJ+hJNPPhXeML9jBLIjlKTM6ZXkDxJ5TXGvXNW6BckD5G0QfTReWFzkLOt4fvozPCcngFng0YWdyVKRXKmOuXCYhUiGRZ/a/T6z8hItLM8YF5TZ91YjRRoMcdPCGLugFbmE2mumho1lqLa9zHvz60BfIUmgzwDwIXKlFuwxT3BgURsKqP6DThQ/BpEgeLMGL6LYZGAEZO0Vfi3a60tsV8ho2anAv48BlJOACvQJO+xtRFLW40xvgtJXoThoGs2iBACPb2BUxYqX0b9Dq1XTcAs032xYliJcTzi05r7bWHaXACfItmoJ3ad5+mx7yPpUeYV6r4Ml2OVCFjUoPNPhH2WhvNm5/BeKik4A+YmWVnSe8LvrKUEMiD83qlhj9CUZb5BmbLMJsBDcoYSZffBfgMn0rbBQHkVcGA7mRxBnU89X1JO+TdlNXP4bADvCJvQzuBvZdvY1zqlx9d6Bl0uVCKucJmkV/a9F6xmLwKYV2oJAJBuawcAACAASURBVLfTggG/hQOavSar2+XId4uVnGt8X0lfg0vqG/s5ydzep1Z1ssZWIFkKqIhRwguACe7K1D20HYBv0xxUL4URWK/OjKGW5yu2GTDaOmk0QGF7SX8Jr5eHyatfk2j7Jhhl+2T42Ebm2RD0IYCXp3zijK0NV3i8FMDBJNeCk55JgZU+I/kKST/gaEL+2SSrEvGLKvBTE3R5kly6MjdyRnNOds3kORe+efaEnZhXwEGllN0NZ897M9FwFhqww9BYjmDr35Lmt8bbtRDXHN8CRqpaNBQ4RzJ5Cjzh/g79i8OfafLNxpmcg8yGS87EPwgjbJ4BBxEWj9uQ3FnSNzmozWz3kazJpCX13qEAZaYZ6o+Fs71tW1xRKZ2kH4aIacreC+AjNIFqX/BgLwBnkRyC3GX6/Q0Cc30Y79rwPfIxOIASL5g1xKdQnXRojfOyOBzw/G0Y80ownDdFflhDZlojzXgLXX63GoBP0ooCuVr4Yoc22I/hjEuzsX1meC9pIdBTFOxhYQlnxaYaqJuzlmFUpx4Cylni4TCOgwAcFJzwfeDFcIS3p8ZorporCxel/SbzWx1WFDCPgmarwaTGQ2UTmb6LpUBRJ3X6BngjVIK6+BMte9sEMLdDa62K5tdiyfVgS9JZ41XRA8eWtEuYV7eA14Wvk7xEiYwqKkiKUREoVgUXDI0OOh4OUPXya3BYRapBnN3aalMTaCgm5dRACWZjuGSwUViZBQefRuaREHTdFgMERlfQtca+ikLly4xlA2YwyfWaKlAMKwzojkN8WpOYGNvYo+pTEjyM7I8kt1HgkqTVUnPnsEbA4kQM0PmbIaDzM21rkoInws9+U874a7hcJxn4CfZ7+DpMB7A0o6RbbHSZ6vHwnmJlkusB2E3SHk0bSXcAuIPkaSrPltf41CXErrH1zuGRjahsdVhN2xpfq1dcgUbOzYCDDMtjEFiaBZdpp6xmL4Lwu9NgNbIPwj7cCAIzjCfeY0yDg1U5oaCGlPwfJJ8G73FWyrQt9n1psv0vAHiapC3CPqC5X9v2bxpp09wTKyJ/PWoDbDWAirkoJEuXxR92hMvIfgEn3HPJ8BqhmbgKYjGYg7Kr7G7FJugT+nyIZG6PcRCArXOB5KgPBb+pVFF8AbzXWQBftz+EvxELa9lHMYrwjtenl8NJ+HbiH2gl4vtsUQV+ajYwNSpZxZOnBhnavyHP4t9YTSa6BrlyT4iMLhY2W3vCiIqc1SwOH4UXn7hmPwdVPh6eQLMqD5G9E4bzfiWM41q45nfEOFAquAp5pYJmM1qrMHYgnNH8GryIbIH8dbyZhunGynBJ9EONsyUr6DwHZZC7ZyuC+km6N2wSHuRoUmUxkhtpmPi02YD3Ehb2WI3z8kwNl938Hg6UpKxGNeWY4AjsA2fnZyKN9gOMOmpKev5Bl/TkrnONQwv4nrsvPNeASchvZlBHkbQNXbKR6q+rdGOihBPAauwp4Sy1yjnrg/AGP37+39P1BZoodIfwtwAOSk7WVgZwNM0BdQuMWLlK0u3thpokb0SH1QTMAW9AYn6VBeG9FMR6JIOUs8rjexAOvJYEflJqLDu12jTz2i/C3xLhr8/OguHYx6E/oAtJ/6Z5dgTzKLwBaTThMySVKoz1BopJninpzUyXySKzcTgSfo4Oo8sMT+wJ1MZrxmMAviWprb5XE8x5T9j8753oJ2dHYlg552+J9wY/6HKMqS7JqJpnSwJmkdUohvUGdCW9J/xb45cVJyYKHfa4/WnwRqtX1Yes4vjYHcCpJL8On+tfwdn2lDXB3wvQH/ytQecXJwUBrCFpB7oUBGFtz6FLQHJ/2M/8CYaRYKnz/FV4Xm7W8TuY55mqKZFI+dS5dbhBksSo26zCEuoUtYqDrVHA+MmIkB1tC+f+M/CGusTXKkEH7QYnR58Gr/3N538FcHim35q9SJw4ewRWZ+uy2Ld/DOaAys2N53FUyfnYTNsa3/ckOOj56fD6h3AAJhX4KVKdHDPAVgOo6EUJJ9bdJ8J7lhtoNEpq/W32/LFQRQ7dGSNeH4NpC7p8uAWMSqiDb5t7bn7fF/SJrAbh/Vf4Pv4ygGN7AmeNn3UsMn6WpH3Dv32+f68tksBP5QbmAJoH4MMYqGTloJjFk2flgr1fzxjjfpMRXUmpB/sD8ATwLxgiexHSZVuN1SwOV8NcABNBiY5+/6hy1bFntDewNJHdiOINnDnrnLwlHR3+e4QSBKod37uIlkW8BM5s/Y/yZMnvhc9dw/NwFQalTiPGcm6N7eFs8d0k9wawPskDMlmde+ia3dPD6x3gYOKSGDiajb0LwAk0WfQE8SlNKHxgbtyFVuO8XMbhUoEd4axGys4nuaUK+AhUJ83YIOhWj/yK+SSnJyb+GocWyAeb4rHWBiSB+hLOIiO5ATxftO/NkUVV0oUhmNyQCd+vDvQITZ65OLwAba8eRQsW1nw3ixWNmnk3fP99FQkkESsUOkL72QCeJenEkA2bKSlFGloTMAfqyiZq6uRrju8fMHw9y1UTj0HSqxipsbTvN7VIDWmkiJRQbmlZMRyb5s3YAeYGmoegLphpfi3J56uAewLpQHEbPdXwgxWXREm6FMClwb+YE/7/S9j5+mZ7UyijP4ZEAhJ9VgUaJP2HLg0vVaMb4mYI3x/y40heLWl2ImjdFaxuggyrS/ocXS781EyQ4WNwSfQVKJtnSwJmjf0dvu8vR/99XxzQjdbqh5u1GsD+SpOUNvfb3sjfb431OuwtW1smCd0JLh35BLwpTpVmxxLm+8P+8teRCECHRM7GwWeAOsjNYb/xpygL/tag84uTggAeDWtCMyevge4g95vhYFGWJzI2Sb9s3Rcl16avz8vCmtrLg1UZaERYb4fm8I7m3wMmeEaWghE6D8BlO0NGchs4CP00GG2wCly2ONRWkkh+XxH/lzrKp1GADpIRQYcD+JSk/TuOJ7aavUiVtdfAnIX7/TIZMfIdkuejQ8kZFb4vgBUknUnyk2FMj5HMbfRLyxvjAFu89+gKsNUAKkpQwtWlyKortZ4O4FeS/kVyUwDbkjxFEaqnZZ8GcHVYowiXW+USnzeTPANGKcXHl0LQ1CC858CllnvA+7drYfT7ZYm2vX4WM1Ux0XiLFcseV3JnTkJxpvJ3eidPFhJLsVKNJWw6TwTwaZkobTqA21RIAFr4GyXHV6NWcASsunUeem78mn5rjFY2+Rkc/T5b0kM97feBnYH3wLWmHwTwYUnfm+Q4Gm6NezFM5J3i4WgkpWfDjtmXYO6XFJHZ0vAE0HCdXAM7do/ADuyIo8Ye4lMGGCfJL6o849DwaxSReNJEkE227ErlVa9qyEyfAGcjV8Vw4CBFln49MtxIAIa4kVhA6pjo/ykYONI3KijgZNquBy8ggM9Fkg+D5PWSNuaw9OyQ4hiHs+CpMafOxQNw4KStOFdTLpY0kmupUFmQhcpQoe3ecKnJTAC3YaAANhJ4YZ1Cx76hzVqSnk3Dsc+StEmi7epwwPwlMFHzT2FC+uR5o+vDD9Nw2cSeSkCmOch0DTnhSiiFVB5fsXJSZk7OCRVsAK9PTUBzPgzbTxIqskJ2meS34Pn7gq45JbS9FyZWL+GeWGhGZ7V3hrPMv4Eh8LMBPF8t4sjgcJ6MMpGA4kADyS/B0tdnx0GdzHjPhoNqjZO4B4DNJL2h7Iiz/RYTibJC+TK0XwbAI5IWhNeLAVhSCf6Vyvv+ArjE4yy5DHo7uJx6pLyltVYfAAdakmt1jeWes472Nao+NRLmNQnHYqPRxvfB/uH+cOL1YEnXJ9pu0g7opd4L728OB9bWhsuDNoFL9+dlxvEdeL3Prs9R22/DWfbD4Y3aXFglb8e+7/b0uxQGPpzgBOJRCiXBrbbLwWtkrKr7uQ5fbojzrHlfBSpS7CYpvgMOHF4q6X9oPpmdlSAppjkOD1cBmoEV4gosJFsObYv3IqU2zr6zcsyN7/soBgGVnO87D77Ol4Rne2MAX5T08kTbNTAc7FgXRlclgx0kP6BCsRGap+0qeA1rABWfTQXdav1qlhNB1wjN3A77TKvCPHXnAniepC07jnEFABuHl9crUz5MK8S2TUqLR6yS6qPLB6erQraAg3NPlrR0os1+6PGzouuwFrxnaa7V1vDeJQcCGR3T4xz4Gbm5Y1Na9rVGPvwLAA7SMKHThyWl4HHFCzYr1FhI3iRpw9ZinVN6KiagKj0+WjL86XBZ01uAIdjfUUoEsEpufI4hP11rNE/PjnB5wP+j7szjdp3K9v899zZs85TelAhFCfUKCYVKpUgyRUWmVyW2Bk0SRRnSIL0p8yxzNMjMNg/brCjRnEpvoULF+fvjWNdzr/u617qute7n2fE7P5/7s/d9Pee17mtc61znOs7j+DG6FqdkfL8GfMpD/Wh4IY/xhLpY5THcB6zWN3EJvreFwfQgxHdwWs2A0dFuURAQJk+7IJhofK8b/1T9e2pCkkQpxQG7iZhsJTSpq2KQT7R7HSJibk8cUsH9ucC+nuFGyrxXJauemNnWaAJwJUysCuztCfJik9TprgzqaDdHnBkjA61JTvYytJq7BUKaze3u7498cqWthGNPXYtr3H29lP9krWbiYIXKUMH3Vgaw6qsQuXvy3bIKhY4QCPw3cKtnkmuR73Lu/qC1EDGeRgc1AdepDGDSvwbe6z1qZ2HfriB8bAWSzG+9FK3cHsowXHph9Bynkk93Aru7+9Xh+3oIbZlMuFi97HJRIrUmgAqTiv3R5LCJAQ4oef66zMzOQ33ayYi77PfR327xYXJ0TKuv23lLJCAVR9QkGqKJw7/RQkBX0vy5qATg9ehaXIZUU0aus5md7O7v7dsWttckGYqVL4P/DYi37m/h+4IoqTQiV19jlk7ovsfTEuZTOlbbQNZ3TwoTo2G/GlWfGgnz4gVHq0CO1phVLgqG93pt9LxnJ2bRMZ+P+DY76RbChO9w4I2h7YvRnGGkv8gkBRoF1W97lNQxszMRmXETk24HLOruW7X2bxJVdzOsAPYKd08qpZrZjxhwnsUL0amS0dT+SXXJaMy5A6Hin+54r+9FyfgSNANWjoyvSWwXT8JLbcx5Z/ExVx7L6mg+uwp6PpYEtvR0wixOdvwATfSzyQ4zS5Z2unuO77P22LvI0hufmkXBGuXpZnz6OPC4ux8xFXOtcc1apZOp3IANFEZ/jigOrgFu9HSiuDjOMolnvM0HPH8LITXVZDlr8vin8JmeI2Z18uEjD0Ju4CnJsEW+s9Ako0SN5UrKM7qp480+zCXnFyaU70Mdxs0wVFd7oo8vwb0+lfK+41oYuL+C7nOWXNYKCXQrf/tClIzrTBoE3+8j6PNGKIHyOJrsTCoJVhoEWFjhREnRNmeRe5qhvwalNBslQxZDndYtwD/dvc0f0vgvhsoL4w4xFQgUI8RSk4xmm7USqiY+hpMZQFYfRtKSSaWuEAxt1EyaQmB9aSYouhMlQv4evi+AkhipAW1+FFg3agYXoYnqpOSRzewNCD7aLv85t+VnqCQzVX6Za7tm4nAFum5FfFOmsqJ10XO6FfBHTySwQj/7RlQi9BAqx3pf5n7c5O5rRQFB1/0oRsS0fIoSiIn9ckF47/lZBVeNCYn0DqRME6/UPYaS5iN8cTVjZK2ZksqH0ZFINbOFXaUuSVh5Zvy9BAVOMUfbBu7+xkke74bufkWF/0hiMbUtbJ8jiwI1logN5kKqIiNyyZVJhkNRP1mifEm7n85tG9esDAE9pWO1pWV9G0sG7B1tpcqWsYGE+asQN0iXhHnNguOUIkftP7MoeA/icmof86R44czscDT5jpXvHkX3dmGPkqSWkBpPbQvbq575VJzTccwpkuIlPK1YdCkaIw5CXKV/RAIfI0lXq0vG1yDjixPbtWb1HFvzoPJ3R8jcZOlg7TGbSuomkCueUacLvnOhBQejgxe0NtlhQpE3NgOViN3q7lsmfIuVyCwiS3f3JFl65FuzKDiSgOxISt6IKAL2QUTMD9a8M5nf/7i7H2oZ9L2nUffJ0klPL7KtgWLoSZeZttodAieYgAJ3uvtK3XsO7Bnh+IkGzSHLDJY1SgHTzWze6ILMB8yb8a0hlqpRY/kICsJXMLNrCRndjO/TVk5ABQXn50IKnGhmW7iIHXvNCtQxfAx5X6tDESyMUBTvAlZACbm1OtquJtC1gow1ddwaWwNvAQ5zwV2XYvh5GteKiE/DhOpsM9vXy2uom07obYhw7AdmluOVMhfJ387AkaGTHCHlBTCzXRCkemngdrSSdz1pMsOTzWxXpODRt0paw410FPCRZjJngscezTBRb2zTfHil/M/kFUuMYZ6ARtYyZW9z930YEPg1k+JUwL4kWgFuy+WmrtuOKGiZm2GJ76HEj7vq9SlXH4CK2nMqlKFMybjXIkWCNRB6JqeQk1LoSK6QItn5bwOLhmdpJ1rkizZAxCxiwxKYC9NNdDkE0zfViSdh+pkgPKcUUqJAUsxV4+7nA+eb2Wvc/fo+/2BXhet2Onp2tkH3cvXQZlulqkZ2+TNoUjGUSAViBN1p4dxmMzpxzo2/S7X6twPNLKeeWGw1SZ9gxSIBwG/Ddd4IOCT0V8m+xerUWHotvMOfRspHjWKUoVKEozK7FRGJBmuUL3tLG4L93SIFJjN7FUq8jGWW4TqwwOmS6oeoGKvjGCu3zev4KeJ2ZqIE+2MoAfzfCBk6kkTzYY4P6JAwR9d4CQacOWuTJ2Sdah6VeVAp71wME+g+Sj72rbV/uKSwe83Eb7YHo5PaVGy4jg+XM34vSqK1F4xuNbO1PZS5maTAc+//42a2nrtfE3zXpfuZr+E8qyEp3iz87odRf7UIGTUkLySCDlasOukVPIkmsZa2PQLcEsa7thVzbJm4bL6FEBiG5g27ufuFLb9pwFu8kHDfzA5GKNdTw6aZphLHVAl3G5Wzuon8OIXK+ZeJ/Hx7BkpOOTVi3H2P1m8tyiBubluNCm8xWToFRNCR1QjN7IhAB18ISZ/lGIBBxrWmL829wyk7AM1thkonU47uXtyuVZS9IeW7m0yIZdC7eELpbwHPDOInDFCNzUCrwIu7+wjZapiAH8+wUsCOqaDIzD6BXpAmUbQjcIG7H9rym4ZQHWdUHPOyiEj00hAMT/fMCpOVZ3TfggKxIQIqd78o4190frUWJjZ7owetWS2aVDY1tFGDIngQkWudWTKJCQHR61F2vfOYzWwdFGSVZKyLOQZKzVTzeoi7f6zQ/yjEMVISBDTP83YUEHNaxcqnmd2Gatq/SpCitzya4S40+N3g7q8Mk+4vegLabGa7A18A/sog0empxK9VcCNZxQpC+NuXUN10vNp3pyf4ksJkYwc0OYLQ2br71xK+NfxaFyNelI8RyWpmjuG+0qy+VdTrB/8rKUcqFtd8h+etgbnenOsLg+9Mdz+8b1v0t40QqsqAi9z9ktbfqxExYb9imH7rWvwb8b+c4wk4b6mF/uJSLyQINcHjUwspqdW7rmSHtxOOJtLD2Qg5t0oY+67zNJJgqG8I/dIdqf6ixszsKwhte2bYtCWwVqo/tTRSqindONAnUR4Wkje7M+iLrkZlciOTn3Cd3oLQPj8LiYZVfZiTrFFjuQKhaeOy7B95Ia9gx/EelJp8dPi/lAGR6GUdSYba41gTTUB+F9p+HrCNZ3ilwj5ZtF2u/2ks0w8l1Sg9DdGv6b93B0714fL7bd09KR7RjEcmGezd0KLiyam2g//qDGgOrm0nZlt+pSUkRcjR4LtE6TtjZsv6FPDNZdr+CjrWC+iRRzcheY+lAB1kZj8B3uyDxddl0FjyMhug9po+ZW4U1/8qfF8WiSakED+vQBO0RcKmvyA+sBwvYDXnWdc7Mo5ZHZqhGBkf/EvR4Eehxa1mkWwLdE2WQHxCe7X8ayg77gU2cff7w/cVUIlMiv6ihuPnTuCVHoRswvh9W+reWR0qZ2UUE17v7qeHZMfW7n5I4XHNjdQXR2JGq0BcmtmN7v5qKysDPha9IyWLgm9A88MhoRmvX5BJHfMbXeIN8bYdJjOPC20Ul05Wtltc9hb8V2eYbzQlUpD/vWci8ZOy3AscEi5HAK+BCaWAPVMDdvDfmMEKySUdSZSRGv6OY9sVkQgv7u4rmFY9v5VJPhUTwAX/IgKqyL/o/GrMKmDCc6pdMxtSKylou5dAN/K9EU0WLuhLEoW/DSm3dE1Wa4+30LcqCLA6Ys7eCUnk+zqUkLjW3Q8xZeT38jQEsrnXtyOo55Nmdk8maHgATdw6n/VaM2XAb2W4LPRV7r55xz7vJJrIeYK8Okxg10bJptj3tpbfxkjedGuUzGlsYaTmMoJga/o9G+Z9uTlz745HxJo/zp1P5Ftbr188cYj26Q08zWwvbyXHcsmczIRrKjizahAxyT6qo996LUqEPBVtm0A3TOKYLwPe6Xklkdg3Rg3NQMjJ36Xe0zGOowl0SoK+mkRqMcrFBrD75hpPZ1By7R4hTUxlSE8hZBEIQTo/Kq1bz92bldM5aiWJBhP6o1Fj+S0MlWUf7e45RZaa4yidcB2LFhtuj7bt7+77Z9otLm0I/s2kGboXw1ZFE+bF0fX4E5ow393Vfp9ZAQm7jcePmOorusoxmlLrw9F1Oy/nb2afRYui54RjeQcisk4idK18wfEUNLm+h2EejlSi+GcIwXs84veb0gmDFXBlBL/UZNA9Xc5+oxeSdpvZW2mhQFD8fiWwq7t/zTIlUNFBdBG7Lhx8Hs35BL+aMqtUSftUvCO9RNA2KIt5AeIv6UXGWwYNnrl3NwDr+oAIfi40h1oPxaztUrv9KafsGIqrzMzQomcq1qrhJboTlR7/X/i+OHq3ezmzLKByPIPwtwo6Cxvmq5qO1EPPdPdPJnwPRHFLrxKZVZClWz0RdKfQjFWUvrf2m4X6t48hNOIxwJOeSLDVmFWUTla2W7VoPVl7phA/cXA/DZUAfGBOnWTmGA5GHeYZDPP2pDqM21HZ0Y1R8JtDPhQTwAX/IoK0WrMCuHK0vUYdo0a54UoKUQS1ZgUEupFvTcZ6AwqVWyqP90g0WJ7F8POWWmWrYo63AmJO6+bWcOBRn0Qtaki67IgmMq9HK1xze4KIzoRyeYf3l91hSrIexGgpVAodtBjwOYaTrp/zDoU4UzniWsG/i4y2NwFhWuV7JYJSx+jFx4ArUsdhgwTmRajc4nfA2e6+QsL3J6gMsjchWPsMhX1KJw7FXEolyRwTnHk7dN/iMrCF0arHGyLftkT1xJ/IE+LOQFxYL2f4GUoSRprZ9YibJobpH+bur0n4/gNxqW3lgxKnqVA5PB+VgVzCcH/Rm8wxJSqvmWwwEtq6Di00XBv6mBUQoXGyDNd6Eqk251EuWbRGM2a34o8R80km7cJv1qi91aixFKv/VE64foNKXb/soewg9xzbaGnDtqgUoxhdlLPwvO3jw+W6X4yfZRuDmyHxOyMk7DbMjxjD9B9D6M7UWH0X4ltoSqymo2TnyH0Ofz8exQHLoYnzdDRJTC163oeQhk+E7/MBt3u0gm/DJawjljnmGuSoIV6yndA9PxNdi5+W7N/RbjG6ZIy2t0PJzovpQQcF/3lRIgz0jo6N1vxPWMk7Mma7vWgG6xalcE+ULFkdGvw+tCj4SPi+CIrLVkrFYFZHinskes7ORP3GVgi9dWnY6dzIt4Zwf1vgYDSmGUqIf9ILKkqsG5UzQWfh7stZD52FDZNY/xv4pSdEkFrnV6LCG5OlT0OclUmy9FozVWO8iOGx7KTo70u5++/HmBMZ8FGEqgTxmJ6e8q083gVQlcQ0BqWTp2TyBhZ8SioxbkUxZFz2dvZk48icPSMcP6jDb6yBx28dO+QG9cZSg3sYBA8Bnose5C5CroYjYPe4WdIcA0+6+z8t1JCHCVLu2Fbx4az0FSYEx4jZgCBtaOUFlUak/GvO73pUytO3DXQNjgJeama/JahjpI4BoQLabaS2QR3fUa3tgThUnkQrzBeh+suU/Tp0MB462pkM6jvb9mXgTd5SbkEEi5OxGSiwjoPuIX6WJjmDAs0a+1cIOJvgc0kiiHOwHLdG8/8Fzexod/905W+rkQGqZn/T6twiwI8y7n9HPEpX0M+jdDziW/kqsCFKLo1wZYTzP9cLy2PCPm1VryPMLKnqBVxmQlZkV4Dc/Q7gDjM71QuJjxFfySJokGpkNT+c8e3lfYqO5Zc2LD9/dTi+IeuYOKxoqj1PkcH3cilFyZzlzCwus1oIaA+S1yGi4+cwPDY8BgwhjryCLyCyk4F7UZ3659Fg3FXG8gHEk7YIei7+D00IU3YfeoauMrOdXeVjOe4nAKwMon8uLe4muvnfYnsJGiOmwvZD7/ELzexUguxyytFUDnlG5plpbDcGKJfZMIRymTTCBfHgrdUEV6Yyo0YgoHknu5RynDQvWZV5a1GoSTRkfI8IydR2cjvF+3A+A+L/Pn6NmQwmXBs2E66M7x9R/3qKibtkJvnn+K0MlzacCNwGTDrxAyzgEdTf3a8MwXZsTTxVw80wZO5+azjPeFs1PyJ6N84w8TmBnu/cuAdKQDcy2P8w0R7smPH9HXoemkTEvAgZFlsXgm2E/y3YdWa2shcgR8NYdwlwiQkBcgrwwZAc+KRXIClbVsyVMYatikolXs9wXJ17r1/FYPL5CstzrjxbrOQdASaSBz9o3tUe+2sYm64GTjWzPxItOoTfOjG0myzLzrT7hLs/YWbN4vO9JpXYlB2KYsMrGSRRvhjO79K2s9dxbc1AalNNguRPwHzoHRp6V2riDFcZ1pUM1Cw/4e4PpXxtGJUzDfX5Z6Z8kZLlWig+xd1vDwmB3HFcZcOqmlnBncrzexjFTFkzs6+5+16WVslL8muZ2cloIfN2IoJwhPhs9muUNrdAyKgcf2LbFkPX7udo4WNZs7qqkox91oVgfppQlhXm8SOoZkRJ8TTqdz6P4tlzGNyf2PZGuYKhsrdJ1czxHQAAIABJREFUHmvWnjWlXm3rySwnOVdMjOKb+hTVpkftHor4SLZHCYcPAj92Ebi2fU9B3BoxAdzu7j4itWcV0uHBv/f8bAy4crRvVh3DxlRusEIUwZw0S8t77pnJ0tYot6TQBw2fxEfd/YHK4/y+u29iacWQ5CpG2K9R/1gddUZZ9Y+O356OVh5eVnPM41ju3c68000p1ATCzvJlocXlMcG/RtWrZgWohry+5DjHUUIqkp+3tHxq1HQS/t8LSw0rNMshtFYMNX4MrYinVGwWQOoVT4eE60tReUEOeRTzX1zjmTpnG3A1NGUWc6NEWGfppRXA9G2AJnkJQo8eB+yUWqmxijKWXGDd3ha2N/2QhX8fAj5VMXntNCuUXTbBvLdGibIzEIL0DxnfYpRL5bGuie7BguF4HwV2QYsrb3P3XJA9x83yKOH9EPppZeCHwMboeU7xPtSo/9SU38Zo0f3RWPn8VJ9lkyhtKDjm3nJdC5L0ufch026xElLwfxujCMEUqmoaSvZMlN8Dx3gHetbKy+++iyYJl6D3eiPEdfWbsM9YpZxWhxxdAt2D96JJ87FoMe+V6P1ezsZbpJ0jXBmh7ftRaXVSsanlm5x8jntt/xNW8o5EvqcgmoxzgOPc/d6OdhdgENs0aIZTPYHssIqybKtAgwf/pRgIu9ycmuyb2evd/XLLLFz5mOrFrd8ofU/XRUi8v5vZe1Dfcriny/RqUDnFdBbhb+2FzBFVzZb/aoyibVIIweXR/Glt9J5fD3w4nt+Y2avcfXbr/CbM8/xaK5ckY2pii+D/U+Bgdz/OhJQ8BJWnpdTsesWNIt/Uc5+bH/ZWYrT8O8veptKeKVWvlCrDI8BsDzXmqUlggf2hNOljdWoln0QrNXehQf6H7n50wg+0enCdmTW1yssA91mAfrcekAcQoVPpDS45vzejFdmlUV1mY48ipY8RM9WZbk/oBGygjhEPftXKDSGZ8FYGncubTKspKaKvopfPMhnlxjwNg1zJWxLkobNOsfbPtnLllq+hIOw01NE2imS3osnHBtHvLY0QHeuGTVcjuOREp+/um4R/qxRDfFj9w+hW/8i18RSqCZ7jVvluPxmC65+Z2YfQiueCGd+/AXeZ5J9LymOKVb28DmkSc4dNkNenHE2r5TN9mBz0y62EyzhKSDujyV4jP38IGrCHJtvuPs6qwgNmti/DgedQkjMEPb9EQWepzQJeG67BxaiEahsSq0024L9oApUTzCzHf9Ekjv5qQlY8RAIRkxmXsG61IAt/+5mJE+s4xHOTsm8zipQ6irTq3A4o2IrtfYltVc9mJlB+BPEnJMsc0ULCdNSHv84ySDBXHf/nQt+9DUJB/cbTsusPmdlC7v6YmX0GBcsH+iTLrFxk5qua0Fr4cBJ4KOlTGQM0Y1SRdHAm0ZBbrdwSlf3c5u47mlZtT8n41qj//CaM7d9FiI2/oHcyZROoPHffP4wnOeThQcBtJsTmRGlD14FYeUn7Tqhc91wG5brtPupVZvZ8YCczO4nh/jCZCKdCCcnMvoXKETdE3BBbooTLiLmQFEeGT69ZnfrleQzEBCCs/GfaLVZQpQI5Go7tZBRTxBPUW8J1gkF8tC5KXjYlLlsxQGe1rUGXzCKDLpmE3Q0silBsfbYGhZNPmFjQaERe5gPm8vRC6Z2I0PwMD+UbPe0uB/zeh8v6/svdf5FwT70jybJld3+PaQFjWzRGOkEsp33cIXHxPJR0+T9Ecj2U9LE6JG/Tbg0aHJR8+j2KnV5sZi9O9BXrA5eTRrzlkG7FVvmeHomQYq9A48mxaIFnJAmSSoB02D2mssXpYWzaE6Gjc7YP/aqahL81cUq72iR13U4D/hctHoLmOacjvh/tGIj6K8/vbkT0//s+x8rYAuCNHvjC3P1xYE/LK5EdTRA3Cv53mtlpwEQsaWYfQICP5cO73dhCpOeRUFCJ0ZHAfHE7zrI8zQHhuHOqmiP2THH8nIY63O+FTZsgSP+LUCZvLJUqE2He81Cg06dWUKNWUrP6WkwEZ1KPKSJIG+P8auTcrwNuYFQFIYXAWNZVRtIrjW6SlH4i0W5KdeMqCpTFoozyO9G1aALkbVFibCRYzWRpc/wFNcotKeTD7a465jYC4hLUgcaT5Xe7+0aRTxX3hGUQIJF/ciAutdBR7croqkAyyJgTZlrB/wkK5A5ACLND3f3GhG8xkij4F5PRTtYsj1JK1a1PBaHxXSgIaALJGWj1bFIKS6GtFJfS/k3yahLtNiskewDzuXg8cqTKvfwXke8uaJK3KpK9XBBBdr/V8muICVdCK+1NYLsp4hkoKkUws2U8rRZUgpQq5jtqtdNw6zhCM3034/cDlIxrSgU2QOPgcog/4OSWfzJA7OoDwuRhKxQgLuTp1bAGfbUeCrC+hO5JlpTVCohgrY4DpzgGCP53ICLY2USys55Qp7IKtTczu8nd1wrJlg0RKu4nniYTrlb/CfutjyZcWQRdjZlW5RvI+k2eKW0Ivk1J+48ZRlWk4P9beQup2t5mZnuicszlGSbFbtodC1kZtd88m82/C6Lr9tqEbzEHXfAv5jsJ/vMg5KOjVeAkisUqFFSDf0kZ8HQ01n401UbC/wZEoP7v8D2LrLQ0V0YSXRL8t0IcYL2JYlPZzWpo4SCOk1PP21kI/d07+bQ6kZdl0TO/Deo3z0BEuzny6luQtPw/w/d5EK9aqiyk2kzIrfci1M1PUB/ydY9Ql2Gc/CxKqBhKXHze3Y9rnVcVkjfsV6pOV8xLNiet5j2N4pbPAr9192M75hfFHIVhPNoHKZeC6CwOTI0hzTF7oaqmmf3YE2p0mXZTVRDtuCVJvtxYJga4AiEHb6LnPY326Y0tgt8iqEx9/XBcV6FneaQawApEiEJ7i5F47nPzLCuoxDCzz7n7fpZG3ifjLDM7ACXLToYJdN5SnlBFz5q7/8c/KMu/YPR9QXRj5kMlVOO2e3zic1zG95bw723Rtjsyvrcmtt3WcRyvQGTJH0KTk5zfDqnPFJ3f81Dm+cLwfWVE2Fx0fh3H8BoUwP0qOtdvZnzvrGj35sT9uL3D/5a+beFYPwr8GmXim8/+qXuNVrXvrTjm6xH8cFr4bI0GipFjT51LwueK8LkeoRRuQZOMf6HBr73/gwhp8SAKqB9GqJWngAdLz6Pj/K5DEMmt0WRqC2CLDv/nIensTRGJ2aR+P7S5Vd82JD0MIg6vbX8LhIz7CipZnIpjXj36rIEkOXN9yx3AYtH3xRH6YrLH8JHQ9v7hcztSZPuP3JMx270tvLM3AC8P25LXIrwni0bfFwUun6Lzm4WCiub7QkgyM+U7AyWKv4nQPseR75PPQ/LNLwqfzwDntXyWRcmY61HQ0nxWR6vLqXa/iRBSO4bPj4D/zfhehFaSm+//FbYtjko92/7F4zEDNZx7wjO3cte9Dv8eBGwXb0v4vh3xFfwd9XVPA/dkfH+EJlkfR33/R1HZbcq3OAYIf5s9Fc9X5v4tGvqJn4X34PiM77KpT8b35L5tqKQMNHF7NPo8hgj/U+1uDiwSfV8UIUJy53cfMG/htUjFWsn4BDhyDt2PG8O/NyAuqnmB+zO+1yCk7Z3hXuyPJhi5tps45/bmmnQ8y29FscuVKD7+FbBxT7u98ROaWN+NeCc+jxbm9sj4jsQdPfd58ej7YihZlfL9CPCCirbvDP+uF67H25r7lPBdP/XJ+F6BSo8uQon+C5D6a8r3doR8j69x71iNyoVOQon7nE8qPszFDJcwPPYthtA5Kd/N0LhzF1pYfW7YPj/wi8T9WyL6vkTu/tV8UDLpLrRY9DkUl3wm43sXGlNvD99fingVp/QdLzjmmvf0KsRv9jMUA0/LPRdo8fKDKKZYGCWwR/oLNBe5ovKYvxSe4/eFz4VkYmI0N8yOzy3fQ1Cy40Woj/s4GrcXb953MuMS3eNTzXtaHFsE/3PCs7Z8+OyXe47CdVqBMM6gBM2FPe0/F1XzLIMQwzm/l6L48EPAyzr8livZFran5q7ZuCXZxlS8JLUfRLQ5d/R9XsKEm+FOdTqqJZwTx3AdSjQ1N3sFtHIV+2yLUEl/IRoU0GBxWabd4kF1Dl/jC9GE/Y7wfS7yndGHEbJjqeZlJhrAW743IqWr+D6NTBjC9kMQUXLp8Ra/fGjVYvno+3JolTT2WT+88L8P/zafjyC4bqrd87te5Jbv8uH5eBjxdXwPraTMh1a+Yt/LEMpnevi8p+MZOhdJrDffV0EM77njOBp4a/R9Y4ScyvmvB+wY/r9kRweTTbwlfHdBgekJDFTRdurZZ/6CdnsnAigRuU54Jv6b4cTL6uO8P5P5MEjgXYGCtKNQCUnKd3vUHx6AkA/3Au+dxG8vF/1/dQQP3hPxKEzV+RVPzsLf5sudf8vvdah//UT4vjxalYx9jkDqZ99Fq/0noAT4b8gP7F9kNFA+sOM4hiaqaHzKTWDOCvfu5yhxfzGq70/5LhaO/dbw+RpR0q/l2/CtAayIkh9zZ3zvJaB3w/dptPrC6G8/bn23ZhuJxAt1AeJBiPS3xPf7CFr9AEoczEt3cnQJBsmiDYFjM77JsSjj2xsDtPz3RwFo7zg57gcF16v1+JQuLLX7yent+z/mMaYmqV0LYRcSLfRlfDYO7/YfwjvSfE7ouidz4oOSs4uiRYGHUPxwQMZ3dvj3rva2jP95oe39UYL5fEQdkPK9F3hx9H0FMgtTaFK0RPQsrw1clfG9ExEEN98XILNIh8pYLkBokXc2n4zvjqiU8AQUAzxIZiETxWH3IFTjh4iS0Rn/4kRx5b2umXze2DqWuXLXLfx9WTRJno1QDckEdPC9BFEbNN83Ix8fpvrpXNL8BOB1mb+9ofX9OqQg1XyfByEgU/vGieIn0GJjLlF8HzAj+j4f+fG0OOEyxr2eF6FpP42SUZ9FKNPJvqfPQ3Oo9cL3F5KJ4aiYtKM5wyJd55TYp2ghMzznj4R7cyeap+b6gAc7Pg+0fD+Cyk0nfb9a7RbHFs3zU7ItbF8elcT9A8WU1wAvyvhuSvki1ArRM7wBisMXzfimYurkOBLe03ej8bxBTCbf09znmVL1OhW40SRZC7qYpwX450RNsLs/FaDvX020MWJWJ9u7P6NqJe1a8mK1mciKuDXC32phwjXn9xx3P9PMPhV8/m1mOcLBf6Js8T4M4HpOmjsEd/+12VBZfa7dG4DzAuSwUzKQtLJYF5P8h4ErbZgFfbfYwVVvepWZneAdMtYtWwzV1t7EME/MCPzQRW6WU9W4pvV9J/QMfBVd2+vIs7av5BGHg7vfbWZd/Dtru/uukf+FJkLyEQslCGugcpbjEcfUKQy4h2L7vpm91d1/2PHbje2Nkgt/Dr+zBDrH49qOJoW1YxDSb5kAO9/N3T8Y+WyMVjxfYGZfj3ZfmIE6T2OfRcF6m9cKGFXzqIHbtvbrhSuHZ/1bXiDliX7spADzbo7xnV6gttJhZyMOjMtcEPQizhQr4H6pvCdNuxOSpMBy1iFJ6qrjnxV9fwANlrE1fBKzKeS/QKvkE/xm7v4XM3srQtyk7CTgJhMpJcA7UACdshe7+1Zmtpm7n2gqY74647u4l5OGFvMdAfejlaemj3th2JayK83s+yhhBQoUrwxjb6pU7yTgejN7iJ7SInf/lJktZqrDj8usUs/g1ohr5DB3/2soHdo7c8z/cvc/m9k0M5vm7leY2dcyvjUcOPtTqFgWbIfwb3yc2XGyxqxFtGnitUiVcLdJ208xsyHS9jDmfxqYz8waYnJD4/xRmd9fAfiNiwB6A1Qqc5KnyzdTPGhdseQ/kFJPV0n779C7/Xb0bjf2GHmuoTli7t6og54T3pUZnhcMqOGgw+v4Th5z9/g9foC84meNgqoxHLM9xXC5XGy9aqQwMe7dh3g/mnLNrLqR13N2/NaknLYRcIippHPoOTSza9x9vcT4nh3XvY6T5Coza96rjVAS+HspRzO7EcVWZyE0bJ/Qx/sR19E3wvH+Gi0Kpexpi8qJQ/nVSDwTSvWW9TSXFu5+WWvT/QzmZY6ST3da4CvziOPOI14502RgM5RsTFmJOl1jNbxktVasiFjynjbPG5F6lolDyQA3s88DX3L3b0a7/T2UAH0HXeNtyXNb1XJW4qL3KKH4OBYlc4doODJt1nCOLoTuWxEJc6m5e61i5ONmtp67XwNg4nR9PNP2A8AbrUPcKLIDKVcjPAdYw8xejBa5LkB0HxOk5qGE8OXAIq0YfGGiOX7LtkNcj4cT5iJhW7E9Y6peZrYGg8nmte6eJNA1s6+iDvQMhh/+1KTrLLRKsh2RbK+7J2UGrVytZET60sw2cPcrE77F3Bpmdg0DqepNCVLVnqnVqzk/U53zFsAlrvrTtRHsb/2E7wPAWrnzb/mejSbX30AD/EzElv6uhO+DaEC4y3seNBuwwk+8fGa2iWeINsM+8yIoHWgl7MnW38eRGBy5PsE3xUrfy4FjZoe4+ycswV3QcV6no2c9Jphe0N23zfhfhCabsf/rPKFYYlJ4+W+UYU4qBdiwStACaJDsTNyZeKI28OEa9Ss9zaJ/IwpKL/AMn1NIBr0SPefx+/AYgsD+JdHuvlHQPqVmo2TC70CDWor5/xZ3X6O9veUzjlJX7+TMzG5DweYHSCTMPU1QjBVwv4x5T2ajScOV0b1OqhvNKTOR8a3Z9A8mPqBbPKFuFO2zOgMOjFmeVwxr+FlmoYnAQwihkFJDugolJ29G7+usXILCCviOon5tEcRHcFP4/upwDBsk2jU0LkyMvYh7Jtk/mxRyPsIoT1tKseTzaAx7gGE+oCQ/g4nf5yXufnzoSxd09wcTfpei9+0gtAjzR3Q/U31LFQdORQwwDU3gihK6NWYVPErhWX6NDxaWFkDlOCkOhYNKA+YwLqyBxrIfoknSyz2hvBOO96+I8BO0aLO4u78v0/YOqe2e5hCc2+eA8qeJPHcPRsfqVAwwHZUStX1TohQpDroveVB1jfyq+fjM7Ei0oHUmeq+3QqjaS8M+7eRLkYJqmMjvwCBx/g7gBHfPJVOLzMbgprNyzo75UaL4LheR/lIIFX3xmMdanSQKfcDOiHPFUHlVUuTFzFZy9/vGOK4F0QH8rcPnLSiBe1U4jtcC/+PuFyV8i5VObZiXbMQ8wc3Z2j+n6jWWOp0NeMl+5C1uK8uoeUXHmkqaFysiToWFseU6j3gHzexFaMK+LoNJ+16eIPGu6TeD/ztRlcVz0XPR9Sxf7+5FwhtWKYIQ9mkSulugmDWX0J0jZlpgPBE9P4aIx9/naS6zXrGpyLdYjTCK4T6O1GqPaL8jZrYZ6n/fTiSwgGLq77h7F5n32PZMJn5Kg74rRvdOB5NWIdtrgxXxzm1h+91o5fNLKAt3KEp2jLw4NYOqVUhVj3F+qyOEySqo9GxJYEt3H0EqmdnFqEa/k6w5+Kak0Wd6WvJxFkoGdGaUg++tiGTz7vD9XajMr4vscx1Gg7OTor9XSwzWWEh2XM0o2ec5kc9dKKif7Qmyt0y7M9DEvWGhn4X4DHKkboujBOLr0GAyC03WU8FkM1FtOqXsxKHGTOoqq6IJw8RqUfgMBc1mdqO7v9oKZA7n1ESg1qyOTPhgVP7XTlb/X+TzfXffxEal35vBOpU46J2cmdlKqM/ZCxHRDlkueDMlD7f3sDpjUok5Ca1IzfLhpNzc6J1bpi+4tUpJ0lKzCrSkmX0CJdaPD5t2REnHsUQEWm2niKP3dfdvZ/znQUHwBgihuKC7j0wKQwLvgyh5t7O739NOmOX6tcYm27+F36gJEO9Dk7ESGeUJ5KG7r2hSaTrL3UeQh1ZBBGsZcYVMoup7aAXugiaR0nPMvQndyLeYGN/qiDarSNutUE0rGg/2Bp5IBamR7wIIYflG1HddAnyh6xqG537F8DWblKgxC4sqfdvC9jvQKnc7gZla0CkWpYj26RS7iPr5tiJjV39/fHtbvK8PLzAVkx8H/wa9Coohc4ntXjXSyPcwhG4/13smFmb2QYT6WxItVJzpPUjXvjnDOMm1GrMCkRcze4+7n5KZTI4kD2v9o/2ewwBh05WwPh8t9BWjRkqslXiZhvry9TNzomQCIzqWE4PfdFQ2M0Jqn2izeTeei8r8Lw/fN0TJlk0S+xwFHOFlaNApMTNbyguIwzv2LyJ3D773A5t6gaKvmTW8ct9jCoWQon2KErpz2kyKdrj7ox0+xWJTVrcIdSMq598H3ZcHLZN8NLPXuPv1hedULD+fs2dKzr243MTdN6xoule2NwRK8wPPMcHom4F4YSRbm7JXo0zqdQjK1sDCR8zdv2JC2zSD6o65QZVKmHDJ+UXHcWuYGPSuAKEB4faQZOtUFwsDTFcJVmwPoDKCC1vtpgazLYGzTfKFr0Uw1zcl/AAws5NRDeXtREohaLLa/M6IxGC45y9sJ8BsjBUgxFHTpwD1I8QRtaAJdm8MB3wp+PETaMJXVOIYApqZZrZAwQTmTBNkelGTSsVOqOxqxKwiOYo4TmLZ0qaMMyU1/euQtPOQRJiJVk1H7NmQ9AlWA1feJvy7e7RtqCSkCUy8Dkb7tKtkc3MUwBwREgSDH1Ei5pCQXLmwou0X+jAk949h2/+ZWfsevIXC8i3qJUlL7XgGaMkNCWjJlKO7H2JCSjTP7QGeWB0dx9y9eXdm0VPyEyYurw2fRRHPTa4sbCYijDwvJH2WZ4DGan67OrFjFauCwW4LgVFvgEidjPLmBORhaO93ZpaTpd8NySL/Fq3ijZgFBB35UpiUHYbe1YPN7GYEv/++ZxLswKVm9jE6ErqRnY/u7aXkS6Ebu94SqOKMHY/KMeKFpZRkd5OAfhctNS2icsrI/mUqq9+BQfny3Kl2wxjTKd/eOo4NGPC+GSqt2yGVgKq0jYD2+LtxYhsomfX1xPaULV06UTGz16Drny1bhup+vtknVwqesn3d/azQx7wBPdtHEkkuR8e8Nppg3xq+L2xmr/aEUiZ63k5DkzhQWcPx6Nq3bTeEDHjKzJp3KNe3vBAhHW5P/G3ECucMsxnEVsuguMtQn/QrYOge1CQagu2AFj1je19r2wLh31xf1rZaf2AiDs8iLiI7l0lKm2cspjhoVAs3Szl6BqGS8HvKzO6zjCpmy3dHmFi0XrlJrpiQYCfEvjZQnJoL2NFU4VCsiDgZayd9aibtpnL0b6O42lCstVtHXPeHkqRPsPnQNYjnWCMlnMFWcPdtwviAu//DbJjrIzrmdkJ319y4ZmklsEdQye+BnlH467JcIrU53Mzcc2nEBfq34Lsf8AO0kD4bAT0a2wwtQn2YwSLUiGJosB1RCecXQtJnOQaqzm37nzAfGzJP07j0ys/32TMl595bbhL5NrJsDfqhS5atd/XVVCO/F1JriKVAHwWOdvdvJNqdB/gCGuwWRIz036k/85F2i6WqS88v8q2Rv6yBYh+KHrDHUVJjNYTMOSXhm4SOeh51sCKq7f0VIiZL1mQG35+gDr/3AQ6JuLejjn82mphc6+7JVZZSM7MDUea7lwPHzM539+TAmPCt5X6a4Mxx92zwGflvxDBc+ZLW32eggORyhEyIk6M/qgiUkmYVqLFni9mYcOWO9jrRX5n3tHgFodbCCtAyDLhftkQ8A3ujyfCGkW9x+ZYNS5IaUp04IDW5ruxbqtCSzwYzs3+j/ucgRBTZi4zpaW+ccoXiVcHgn0IdeCogMZVvn48SQH0yysXIwzCObI3g2kneABtF0A0hK3J9Z9h3OnqedwXekkuChbbblmzbWmV5XRYWaC5ACzkl5WmlaI37EFF0J59F8F0ZBanXu/vpIUjd2t0PKTmHnrZnI0Le+8L3FYHTU++qmc1o9w1m9hyPEA1m9gGEhluBYS6rhdB4PLIwFZLPL0FjTfxspvrZQxCxbm8ZkRWULbf8N0fqg4+E74siVPR3E77FnI42QIMfhMqhTrM8Yus2NNHx8H0aKn1NyU+PPMc1z3afmdlzGT63nNx5zZzhaJQw/2H4vjFCtO+W8D0fia9kEw1hwrsdeufiRP1CaDEmtRD2rDETOrkXnftsMFOVwH+j2KqTYzP4/8TdXxZ9n4aSefG2JAo0anuqOIR6zVTuvTcSX+nsL8zsXmATDzxfplL/H7Tjbxugr9ZHRNPfpX+RpuaYr0PJ5GvDWL0C6r/XSvgehBZpehO6Id57CiWWQYsU86NxcD13z/GndrW5m7t/u2buGa7zqh4WmU0UIne4+0tzfehUm5ltEX2dgRbGfpeaV1iB/Hyv+RSzb5d8CCoNDBQIulQFamTZaiTRipW2kKrI59Eqw1IouD1rCq5DlSxy5fnF8pdX0CF/WXnMjczi5milaxEqpeRa7d3FoCToTvTSN0zzXYoJZwFLFf5Go8KwC/C5+PokfL+OOBRK2n0MQcEfp0cCt/Ka1ErE1iit7YuQHPG2/2l9n8mAI+PB6HMH8KFMuyuiuvOLUcLocjLy2sCSY1yTEgWwxROfnBJSlVwmWu3Lflq+M9DK57mo/9qLSNUi+FwRPtcjJN8tKCnwLzISuigR+HVg2/B9OYIC1hQ8cw33S4M02xIGSlEt3xvCv/Hzln1XK46huG9BqKFp4Rp/KOxzX8unWqp6Tn5Qgv9tCHFzOUKDJNWC5uAxXDsH274HIbo2pF8h52MMVL12De/Bnj3tr4YWYO5F5IpTcczzoaTSOaGPO2KK2j2QSGmxx/d+tDCxHD0SuJXH0Kum9R965kb6hlx/geKBtaPvWwA/bfksgmD4pzMsG5xVWEPJ1t+ghcOm782NT5ujSWfvuE5L6Sn8PxsPUaGIRp1iYI1KXuoYcvejWI00+L8doY0OQ5PWnF+xOk7wr5kzjKjXpraF7bPC/b2MjJx7eLY2CH3U+tFndWCulu/Xuz4d53coWlibOxzLn4D3TOa9i67zfcCD4fsr2+c3ZrszEKL5m0jA4zjguEm016gfrZ/6dOz3DUYlzJPGnoZ5AAAgAElEQVR9OHByybaw/Z3h+XyEqY3tG9WyuL/IqU3d3Ppu7W1h+/Edn+Q9QSiX89Ai+B/R+Ld0xncj1G/+CVW7/AIlq7vOs1funA512Nz7Oic+aE50KwPV51sQh+UCqKS8tr0zm3NgeG7bOadttTGNvKJetfz8SBv/qYvbOvBU0JdMxKReio4XpVgSLfxtHZTJ3775ZPzWSGwbW3K553i7ZJFrJN/mlPzl3eHfY9DqKEwu8bNs16djvysQlPciMgN25HsXSthdjOoxyb2AKMD6IQq2Dkvd+zn9oV4itjj4RJ38PcCGfc9c7p3M+N6BeInWAl7VfDK+Pw33Ymcy8oaR7zqoVOFX4fsrgG9mfH+BVhAeRkokTyFU362pY2EMuczCa3EmSlxsGD5Hk0kUo8TFqtH3VYCzC35jMTKyz2jQWGeM4/4vFChuAjy3w+9Y1G/eiVbRj0BKZinfmoRgcd+C0FcLogDmeBS4vHqq72XHNdgq/JtMvHfs9zKCggua8CQllyvam05G4jnjfzhCzWxLjzxzdP8ui+7NagjxmvIdCUp7jmUjxJt3GLBRgf/zEEHvtXRMVEu2he1nhj7jW+E9ndbz+9unPhnfZlHgifD/ruRBMtE7Bc/oOSip9G0KJp9z6oMmhcegyfMGoT/MTUhWReTnXwrvyI/IT0jemNi2Q8b3fiKp6p7jfTA858nEd8v3bDRG3Yom7R9DpJw5/1QSLJeUuC3eJ7R/Q8Z3/vAuvyR8Xwp4U8b3XJSgnTt8ZgLfzfgui2KrP6HY4bvkJ3IHh75ip/C5BDgo43sHkp9vznFD4NiO61YzZ7gIKTa+KHz2QcjmlO/6qU/HcSzbPHcoabxQ+/kLn6PQAt4e4TOLzBgZ9qtaUEULujuG/y9JfgF4dmirZFFweVTS+3C41+cDy2d8i5OShe9cM4lNJmJ69n0ngwWrLgnzW1vfpwM/zvjeD7xs3PPpOIbiSTsqCfshSmjtgJK73yQzZgPrlmwL2y9BpUhzhc/7kBBQ7riXQItWmyDF6Jxfjdz5HUhUqPm+ZvPMM8m5avQsN/1W9lmOfnsmA7Giyfz2UuHfqjltq42VgPs7zq1Ifj73+Y9z/ITawDMQYdWj4QQ/661yk8h6ZdlsDEk0K+CIiWy2mb0HPTifN7NlUBY9d47/hR4k0ErFH1t/r5JFHuf8KJC/HNO+H6BxjwMfMJHs5TgRes0jmGUoUWqUdK72BAN7ZPtX/MznUTBwrbvfbOLL+FnK0VXedqKJKHALdO2WcfeXRMf5Une/N1eu4xlCxQqr5X4q5swJbW0GnGVmZ7v7lxgui8DMXu/ul6NnaEQ9wdPQ0X+7+5G9Z6b9VzSztRC0cx+TEs93PFHSgwbzNxMY7939DjN7XcIPNJid7YG/xczehO7h8WjAbPMdFMtlVpbfreLDZK1XhHNM2UoekQ26+91m9rKUoyVKFs1spGTRpTTwvwgyXWRmtjWabF2JnocjzGxvdz874b4HCqafRKvuF6EgMGVnoYn1MfRzndT0LS9y95vRPWzq/bdC6LfmnOYk2een0Lmdg1Z+e83ELXAvKhc4EgXukyr38gpehGALo4ChpLYf6urJrw5Q7wsoKKdx8aNdktjW9u3lDbDxuPuORei5vueysTWj/89AqMxbScQMHskdF1gNj1KNNQsiU2pWTy75AYQOaPrVq1F/PGLufpeZfQFxITyG1ClHiISDfTZA5D+Gxsdj0PVLcYrU8E/9Gk2OvcD3/SiZ+gI0tl7MMLdb224xs68wrIg2O+Nbw+n4D6J32MUtkiOVfT9KAn4GvfuXAf+TafeXaMwpsbcCr/Qg5mFmJwK3ob6ybf9y9z+b2TQzm+buV5hZVlXM3Q8zlaiXzBm2RSv3DQ/WrLAtecztPieU+qVIv3dF12lxNHdYGo1tE6VePiAq/gAqV/l3+P4t8nxuMOBbfRtaJHrE0jQqTelrEUcqus7ttnJiK6eh57KRMn8XGt9TAisvdvetzGwzdz8x9F/J8wv9xd6MkszHAj3zhHLMdSpizvhv2b+b2aeATwPzmXg2QePDP1GCLmU1fDk1tnv4zZea2W9RciTHmToD+ANKRoKSGPOh5EpqzD6C0VgktQ2Euj8++n6Cme0VOyTmN01/skyIN1LznBq5812A40xKdobe7V1MZd8HZfYptZpnmTAv/CUhtq+Ip1Jt/T78W1xCaKPl+g+R5qrD6+Tn079XNrZNrVmFnG9IBpyEstYglMcOHpHz2hiSaFbHEXMk6ixf7+4vC4Hlxe6+ZsK3PYF6LTA0gbJKWeQxz29K5S9bbS8OPBImHQugVY+HJtnmTLSS03RmmwNHufsRkzvasY9nLUT8uRnwE4/qTc3saHff1SoU5yp/u839tAjifroh4x9z5kxDE/Gc0lrDBTADBe8LoufipZHP59x9Pyvg94gm13uioPo8hicvnZPrcOxfAd7t7tMTf69RABvpV2yggJfiKtghdUye5ra6hgGZ8KYEMmF3/2zC9xTgG839MrNXA7u7+/YJ39NR0qlJer0blWeMBKrRvdsFlevtZ3meg2KFleB/B0Jd/DF8XxIN3iPXucasknentG+xwA3Ttc3SfC+NeSZpV3qcDdfTmiQCXk/z2rzOW4S2Zrauu1+b8K1RhariRagxq6gnr+kPM/cv9yz38gbYGNx9Yb9VGE3mphZ/Uvsuisbft2T+/nYG3IRXekb+tqSfDX7T0TtZLHhhhfweVsCtE20v5qmoNTM7Fk2qd0Ros8NR6cb/JnwN+CgiFQYlA07PtHslQvHcTD//1AloVbVTlCLcj5M8wSnUcX7Fimg24HRcDU3wFwznOKLWOKfMxPW0B6P9UOq63YlKQP4vfF8cPfepd7pRxzkYoQmy6jjB/yMMCN6nzCr7odsRovlG7+e2uw9RBjTXYjGE1hpRAQ1/Pxhdj8fDbyyKuPVS5Nw1fEfHosTeJ9Ei2J6o/P39Cd+RNjpirYajbRbi23oILXKn+M7uQAmytvrt7MhnPRT7bM1osjrVF7YnyhN/Is9vd5C7p5KQo42YHc4U8+WE/uIQd//YZCbtiXZfg1CHezEsCLMwQkCl7t9lqE9p+stt0ULUGyKfZjyfgRKNd6DruxriBEspuBXLnUf7LALgCd7eca3yWX478GUUO/wRlafd6+4vT/huguZkTRJz5Hkb59msMTP7IpoP/jV8Xwz4qLt/prSNZ0TVC7jVzNZ0rdh2mgv18QrrkGVz9/OB861CEg2tAD2P/KpIbK92kVrdFn7vLybC55TtgwawoQkUggTH53SHmZ3mA0KpRm3qL+0Gxzk/r1gBCsf4CUYD4GQCI57Mh4AlqSRlQtYcDrwGJc6uR2StDyTcd0bX+e9h30OC/1Dix8YjNK2RJD0UJZ1+jpBpBzQvWHTOu4Z/awLw3g4jav/msA8Izvy3rra9TmntlrDPE0jhYHdUlhW3t1/4t0RVJFbSAE0IJpoioXQU3uXNURZ+BZQsGiGKC1aDZvq9Sbq7IV7fBvhDGHBHVrlcK1WlMsPzuftlZmauTP7+JtLSicSPDVQK5gauM7Nfhe/LIqRHynZEK+Izw/dZKCGXsrlMCdytUT/TZbHCyuP0DzrTfBiZ+GdaCEGT/HU2iZRJNHzPhNjoTQiGicN30Hv381TfYhVoSR9DSafC3oZW0k5GQUOJfY3yFbkaVah9+37YzD7u7oea2REk7qHnyckfNpE5emhnSzLjSEl/aANi3uXD/W5sIVTClWr3U2HfLBGsux9uZt8APu3uOfRZ+1j2Q6VHKyNI/cYINl2U+EHPZvIZCxO5NVG5Ekh1cd3UxKOwn23QXU+b2SIlQbKZbUq5+t7NZrarD5LVW6BV1xUTvvO7+002jCIYQSqPaXcBu7i7Aw+akuYpJRZQuetaaKxeGlg29M+pPmq/imN4MHzmCZ+khfuxrJnN44XIPa9QRPOBYuBV9CgGzkH7LkLGfY88UqSxgxB67Qo03ryO/Lm+HaE5ZyJEwMKIyzNnCwEXm1kXwfvX3H2v3DgVP/dRP7RCaT8EPOnu/2yeezObK/U7wQ5m9Frsnzs5d/9kiD2bRY+/k1HJAv7p7m5mTZ+8QMYP6tC5F5rZJ9EY7Ch++mFI4LXH7KPCnGVflKhZkOFF7Nh60eCuio5rQuIgqVLY8i9GVFpA6COU+8hY62nkSi0yttfCfV0v/L9PfbfG5kHXfy6G1eEeRaVkKdsJxR5fRed1HSr3io93QwAzOxeRwd8Vvq9C/ln+qwnBMws41cz+SH5+OC9KRr4IxbbN7+ZUsnrNBgvRyWc5s9sBlKOUvoZK7e7KjDO1aN/muItJ/4GN3f3T0e/9xaQAV5z4eaYQP/cCLwZ+iR6KKZHVszpVmCsQ6uYm+leAbkQZ1ZtDAmhJhPhJKSYMrQCYSnbu8PSqwJWMqk1d5+4fnuz51ZhJEvEMBJl+P6on/ZP3S5X3tXsDgts1WeV3oURGahXjLpQweyJ8n4GudxEyrOc4LkHQv0ZK7z0IYTIiSWpmuwHneGKlM/IZgaLG5olVAZOaTmeHEfmuiiYfTSf2MEK53Z3xbxJsa6NOrivBVmxm9nPgBjQBvdrd75lMe1G7D6KA8sy+RKZVKIAF3/0YKN5ci4LJR9Cq9/0t/w1oyQyj6zwiM2xSNlgPJXAvR6iCgz1awbMx1SOsfFV+KxRoXePuHwz3/UvuvkXXfiVmZl9C/Unzrm6DuCU+Efmsn9q3MU/Ii1udEtKy4Xe3QZOMM9Az8qvIpwotGe33TnT/HD3LqQG12sxsSXf/Uwh2SCVpbbwVuRpVqJESqfY2M9vU3b9nFSi3sN/yCJq+DkLbPoj6zpFn2VTi/EXg+e6+sUkp6jVxIG9a3VsMTRLjSeFjqWRgc+xo8t+syC2LUJipFbliFY4w5rwC8Qm8Ihz/KalxIfjHE8ppKGF0VmqcDJPJuOxlevidycY456PV/pLy1JT6Xk5BZlXExXMlus5LoARManHkQkSoflaIh7YEdnb3jSdzblH7yyKumktD3ziXJ1bGzeynqA8+LvgdgvgZkqiRwt+uQvGY2UmIt+sChu9HLllVcyxDE6Oo7bEnRmMcw42peK3DfymGaQ4eav09tXDXZBCfRsp9X3L3ZCmgqaxwG3RdfuPub4z+9ip3n50bp+Lxacx+6FDgr4jbaw+UOPqxuycXYczseQxKS25sX4uEfxH60Mw+hrj1NgrHvxNwmk8SHZ8Zq6NDGQ8ha2b7MwYavKfNhd39UcuUc8dtm9lR7v4/NocQ+jVmqh55ASpZjvuLyZb1YmbL5mLMhO8I0ji1LWy/pz3WpraF7QughK4xkDs/NROv/wjF5m0kWOlC2ojZGChvq0AphWfoDc24XnhMvcqFqXgvF8uE2GJND2qdYey7JXU/ssf0DCV+khOk0oe2o93b3f2VIXu2CVrxnpW5gb2DQ+T7bjTYrI4milsigsuzEr6pCdRd7v7xhG9x6Ubt+dWYDaSRJ37bAsR/ku3WwO0+ghJOTV32O4AT3D1b911xHFWSpGb2AkbrkWdFf2+g+c9FE6LLw/cNUeJuk0SbxR1GSDLs4+5XhO8bAF/MBbQlCTYzO9Pdt7YBKmXIUs9cCDxfjcoV10U15Xe6++YJ3xkoEJqYXCMyw5Rsd25V9j9qViczXFV+V3EMb0elofO4+3LWvSpf024z8C7n7geY2QsR6dxNHftsQYSKc/fzcr5z2kycSvuSLwGc2/PorLbvN9FCQ9wn/9zdu7g4So9zFZRQXhwFG3+ilaQNY80GKKkel2k8BnzP3Uf4xszsQNSX5FapYt/icoWWzzRUVjiCoo18lnP3By2CpjfbEr4XIvj4PiGRMhdKdkwqeR8CstfTWpFz950TvsUljjYoV5iN+u7HUELppRn/OGb4N/DLVGIk+BaXvdRYTeLOzG5w97VtuEyvK754B8PcOvdn/GqSgRPlDYXnN8Gj4u4rhH7gW56QzLYED4MlyinD9rXRCvfL0Ar5dODvnkYIX4PK+ntRPFYhG1xrc2JiNMYxbIeSDBfTz9t1CkInXe1CWIzze0ugfi9XEvU8YCsU4yyUiVtmuvvhfdvC9hVQAunJEGethhJ/f034TkPI9Dehvv4i4JipiGUsgz509yRiw8R3NHEcnuE7CnFnKt6bLB1BcVIyk1AaO5EU2vy+u2+SmehPqu3QfnGVQGW7RWW9lW0WI92ifXpL5aPtxXQElcc9JeXBmbantedalihnDtubstODgOfQUXYa5gEHoH4uWwYcfNslZF0LVqm5cq6M9BOIbqJ5lnZEwkaHtn1z9oyUek02wdNhc4d/ewnSUgmenLn7qSE4fAPqYN7hGeIvd9/bBqvLIJ6a3ASqpnQDKs7PClaBI2smUL83s7cBv2OANmm3e1k7CEttC1YMHXX3r5gQUM1129Hdb0ueXL392UTOHdezjmSgAUwQ/XchJamY9HsimPQAzTchpVb2QOYV7uUJmWP4ODr33g4DWKBJ+gSfK60bzju/u58cfT/FzPZu+TSlRCNJqQ57Cj0bT6HVuEb6MWUnoQlDs+q0HZpEbNU4NAMUcIEFmHJsmQGqhmNgRYRaa/vmgpy5PULZuPtPTeVkI+aDstQJMuEpsv1QucKV4XduD+c8YiayzJk+XNv75UzQ8E0CLxkaqP6GkoPZZK67n4M4JTotkzx8BJURHugi7Xy9u19uGXScZ1a4bBj18xR6b1L7FyV9gr0eKXQ00PgTkbLdVNhRwEd8OEnbTIqbY70KuMrMTqgY+2YCnzazJ9E7mKol7ypXGOF9C/uchhJQTyG+k4XN7HAXyXvKzkEw7xiufTat8tBgz3H3M01kmrj7v82slDi5y2qIYGtKHG8xQaqPRpPrv6GkUc6SRLCZMbWm7KXYXOWpRQhB4J4wcZ8eEih7kn8uGm6d1VB51/fNLMmto8PwIXLJXJ/lUXlDoe1O4FEJ+//MtGKasofNbF90LXYN55grZf0GGtfPQnwV25MuYwMpR11rZr0onibBYx2Iv0nY0p7hj5qMheTK/mhS66i88fOeWJVHKmvvRX1oM5Hy8L1tx6JFoiNCQuU2tDA5knDJWXjPN0gccy/Be2Q7IJRwbO9LbAP1b2uY2YtRv30+Qoe/NXFsT6O+4uiSc6m0LRmgD3e0gD7MOYdET47cOrY44ToDJWuGyjJzY3T0W6mx+nwGScknE3+P95/ykmsPi6s1bYeE7lUoiXOtd/PrHI+egyZ+fU/YlkSDlpoXlvUCWAGCNlgT+x9W0GaDPl7StNje2MIoGZ6yYjqC8CwdghbFje7x9zozW9UjcZMptGMQEq45rgUQKjM1T90MVdF8mAFKKYeq/AKKE2bQUQYcrKaErJj0390PCfFecy4HeBC0KbVniuOn18bsjL5nPaowNgZHTPi9e8nzdMTtN4HguYltbWvUpq7xHrWp0vOLbCNGWcE3TmwDONAEff0omrgvjF6C+LzGUU3ZOvy7W2v7u2CY/8XE1/Edd/86U29xPSuoBCjXAW+OlJY6B7NgL2ySPsH+gIjBUlbTYTwQAtq4NK2rbKs3weZjMM2jGuG7UJnF0ZnAsLESJaviASqyGo6BGgUpUGd7DMMrGbekHM1sDZScbSPBJrWCT1p1I7eCuJpHK5Gu2t5cWUsRL1miH5z4E/n+8EJ0fU8L39+F+oaHUOJzU6REcXn4f9uStfKmktq5kcz2Vj7JUsXI7kfvZfPsvzBsmworTtLWvHteVid+GroXxeUKKFH9qAnFemHYbzZCnU2Yjack+fcwqWwSbGujycFkreENuJoe3oDC69Yg4g4K79O3TOiKhT0SjUhY8Zjq7qeHhYwm0foJz5R6VAT4WB1vTw2/Rw23Tk0yEJQAu4Cy8oYaHpXj0bPbkIz+NvxGkkTb3e83s+kuFbfjQ9+YInv9efhMY5gzY8RsGPGHmT0MbO+ZsujK5P2cmhh9B03emhLhd6Oy2jcmfLdCara96KeQkJ2FnvkNUYL55aQTLl3tpDjEXgjs5d0E79uiBaflwvPW2EKohCxlT4cE9TsRifgRzZgZtZtESUfHO9kYAOBxV4nJv00ciH9E5xwfR/VY7RGBcrBrzayN+k2N0RNNkOa16U1KjrP4Y2Y7+3Bp8HRUXZFF0FkPQj+y96LE5BbAl8KiytWeptboVb36D9gJBARt+P5T9J4OjQvRPZ4LoeWGVK9bVs0H5ELJfJXhMvWcHQps6mWKaOsB7zOhtp6EqaF8CfZbM/umixJhMeAHJBK24fn6vovP6GnSipCxPd/LUUo1C1Z7IIT7GTBB+p9FpLv7hSh+G8uetYkfBp1RspyGRGfkBQRp7t6Qa1UTMBVaTXB4FgpUmu8PMBiMR6zk/Gw88swmUHoEXd+U7cZANSWG+T6KVtNS7dZk+mcDnzGzlVC513fcPTkJhzpEk9dJkj6AJp8liZ/LzOwihktILs341nQYOyFumuYZv5ooe52w3gTbmBP8bVHn/EEks3gdWsG7LOF7q5mt7cNKVkP3LxqgXtleBTSp8qRQeE9UJAOLJeWDFcsMI5LWvdEEqbi+t8CKV+WBaWa2mAcem5DYy/Xh/wqDWjMJXzJ13GP2g2/0YUjwXRZgwiZkHV5HEN7Y9t6PYhgyM5vfRWTfZQsBPwkBryNEwS3N5CAzaS612iRtp1kgorQECSUMl1i4iAAfMbN/t5NKZnayu7830cTcJlTbO5Dy3L8sgb5DZZ2boNLGeGLwGFIbS9lH0KraCmZ2LVqdz5FLNuiuXi4XKohgQ0Knt8TR3d3MfogQDbj7LzqOs3hMTdy/pjTg+Wb2fE8TiZ5AQYAfbH9GEYLJsobwXuxDAZrYWyXV4dkaKqUbMxlI+NufGUaJ5CaUV5lZI728Ebru38u0u4K7bxMm/Lj7P8wyEGj4R0h83x5iqN/TIq+fOLA6FE8K8Xc0EeKvZb3J+yjRMBcSYHiAqZ0YLeXDBOgHmtk2Gd+7UR+QQ/pOmEkpaAGEmruaSORkXLPA5UJITFuL06WV4L4O3dfnMEy4/xiQS+j+Kzw/2zPo59qo3xqUdHzs66H+7fgw/i7oiRLZYL3ow3HG6tb1moaSs4vEPpVjdGMlScnqxR/gDaaS851RMvUE0nEhoJgfxd1ZhP7ED6pk+Qkk4/5PNNd5Wabp4iqBOWi1CNrtgSNNBOhXo2twjUe8hz5AHz/urdIgE4dkF/CgxP5QmPQBzYvniLn7vmZ2qJl9Cz3zB7sQ7W2/KsEEtKD+Ji9Txy4mug6LKJ80swW8h/jb6lBV6Tb8mafa6DRTOc0O3iqncfc3Z/zXYbTUo1SlYzLHOREcotWixhZCsMIRiJdVSPZG+3Sen41BWldjZraH95DI5TL8jaUy/dG+i6Pk17sQhPslGb8aGc5i8mMzOwdBbi9juCQrqXpj4ltqJHtneaasLwSblxZ2GM8qCwH/xijx91x3ny/h8xM0WWw4F5YB7kOw4qFgNXPvckRmNRwD+1NAImihNDGXKEyZBaRgiW+Nmdn8aGIW8wYc4Ola5O2BTzNIFm+FpIBPTvimeMn2dfczp+CY70BQ+5vC9zUR18Er2vfRhABpCLf7ygpqjmEdhOxa0N2XMZE+7+buH0z4VpNSVxzHYigJEXNbfc4TJNNWQKhoYxBRtt8nE0riTh9G4DV/2xMtQtyBSoaXQYTGr82cX5GSZEgy7omQlSuhZzmrkmcFXC42BhGsiTzzacTR8rJwfy72BF+dCX3xDe9RF60ZU8e8fze7+5o2zMWT5KCzAt4eG0N9L1z/gxgll41RuZuhhOHbGZZcfgwt1OQS1sVmFTwqpoWIN6D4anVTedHp7j6iEBmSjH9AK94fRpPfb3qCx8haKB4krpBE8ViCszC1Lf4b4n6Kk/dX+bAgyFgiAaVmKim4CSErQWPDWp7gYTKh1lZDZaF9IihfRZOsJ1FCdBZwvXcjEPqOdU5zuayMkEnXu1B6ywFbu/shGf/nocSrI/GRHIpvP1RSuJK7r2hmz0f0DOum/Fv7vogE+tAqCI2jfeLr9m/Ex/V5l4pW27cGefhjxJs35WiNkIT8XzRJ3q49ZrZ870PJ1N6FWpNYycMIKXs1cLtnODfDO3gEAzThtcCeniDmnVMW3r0tgEtC/7Y24kvrjGfCs7YlKvN7vruPLA5mYvAkx0/lMR+O1LK/y3B/EVfAVD/HFb8fzz0NoWhuQmJIOZRZjWDCYyi5nS3Bj3xriK5r4tn7KUdVJe3/h8TPT9z9ZdH3acA98bbobyejOvXbibK/uUl74e9PRxP2HBKm8RtHJeA61AG1yfuSPBu15xceniagv9olIz8pM62avZ9BsuNK4NtxgG9pArPGvCextRaasG6GiLA2bf19nARbjbpYEXlmeC7u8QwRaKLd3g5jnIA97DcbrQ6f5glSwsw+JUzzTRLs54TVA6ROkUpK9AarNoBir4ee+8YWQpDrFIHnQQii+3MijoHMBKqIRDAELbuga7Ydw8FkLqn0BrTq004ITlqNocZCMNac++We5zloEnYNL9llkxkoWu2uiRSAFgxtP4qu5z3A2+LkkklRbxbD5XQbeKTGMuYx3IiCmwu8R7Ho2WJTHWyZVgI/DcyHpGdB9+OfiFsuVcaSamcud0/KcZtKfHdGSI+4vxjpwy2QJRf+5u0ELpfo/iXJDDvaGCGCtQHyLE6M5AQFqtVFbXgV/zmIYDZFdD1CJJnaFrZfSWGAb+LiuQzFGFugZNvc7v7+yKfZ750oCG/evW3RiuxIaYOJ/2I/BOffFJVCT3P3EYnm0mRg5D+niFLfhJLmK6OFgXURN+AVLb9apa5icQUzOw8hoGPE36s8IYAQ/OPkvaE+LJm8n2qLEqiGYpEmhpwO/C0zeSkWQYn2WQhx6nwMeJ67zzu5Iy8zGw/VXNP+LkhJ8vLQ5vooiXJcwvd2NJm81QuI1cPfY+XJa7y1iPgfSD9XeyEAACAASURBVIIVk/Pn4r0pSEq+BC1U3YXQOD9GiLokqjcc81ZewK1lQpWvh0ro7kVIolnu/vPOHafQwjEcj5Llx6Bn5JOeWBA2oUaPAFZByLslgS3bCcHI/z1ovrcqSnBdg+Z910c+GyP+qq0RqrSxhVEZeOf4bT2CEJm539Ccb04+x+PMPUvnfHPSauJZM7vWCxLInb/3/0Hi5xtoxT8up7nf3fdI+P4EPbxTelImCOs7vQwKVtNusWRv8C8+P9PK7v8wgFNujiYDk5V8PAZBYZuX4r3AU+6+yyTbPTQc489Rh3ReKokxZoKtWF0s/K1UXvt8lECaklWAXJDVWC7YMhET7ojejVvQwHJx6jmxOqb5NdDAPxUErU2wsByJe4cQCiOTz5DdXtkLOAYqjmNLNJldj1FOn1xS6RTgpSi5ESegxlZjCO2ugSYCL2IKuYMsUeqT2jbJ31gEJspCcj4jg1ftBD/T7o3u/urCCX48IZgH9V9JRZ85YTaGnHvYr1Te96CKJM/IRD60myQzNLOzUJC8HeKkezfqL2YmfL+Kru0ZDK+cpRKpQ/cvTDJurX3uzWwpjzhBQgC1DlqNX92Eqr3Y02jCqsmLVazi1yT5MgH+Vp5YqLE6hOAt7r5G37awvVH2nHg3m20J3xURued/ufsqJontt7v7gW3f4H8JWmWPkyPvdvcRolQrII5v+S+BkLwG3ODuD2eOoUapqxjFYxWIv2if4uT9VJqZrefu1+QSkB37xSWZ8wPTPVGSaWYfQhPPVwG/QNfiane/vO07xrGnkuOPIGW9ZNJ6qs2ELlmneQZTiefIt1EMbBLRCyBUUU5Rb44oT1oF+t4qkIfhb3NiYfleYHd3v8zMDJUP79SOT83sCPS+vYAKhH7Yd0EUL38McRWlVEMPBQ5EfKo/Qqi3D7t7lnC78PzuCEm1NyNqhn2Bk1PjQvCfiwIEbfB9GM2fvgVc4Yny5XDPXonG8jgWeCzsk0IqjwhCAF2CEP/fmQnQ0JD9dyGVz0ELxj/yPFqsmke4Mp7tRVX12bOZ4wcAd/9Q6LyaDqZLJetudEFS5HCTsb8hHoteKFilfd/M3uoFkr3Bas5vF0Tw+ncAUy3s9QxUlyYsTOD2Z3CNr0IrGakJ3Zqth/FyE3x5xKwCOoo6rNfkArfGfMBr8RngIY9kOM0sKcNJhbqY1ZFnLoY4Wm5i+LnIIXMWQ0nMeCIXq4VdFfmWKrfggqvvY+Ia2QShMZ4K2e/DWwmxYqZ57+BYGsfChOqXDOCzJdbLMWCVJILufjZwtpnt68NcB122ZirAmwKbU9xB7UBpOnny1caniHPFWlKuFmg1MsmDi83sXQyXFSQVCEzokg8yXBZ2ZGaS8msTPNZNnDUzgZzS4gQ3QggmN0PvwH/KqgkVLSPvi9TzhszdP9XXt0QW14/PQP1FFxLsxe6+lZlt5lKUamDyKWsmCPFzkFMAusrKuVyy5qNEsF9HpZ7PNbMvoOv7mcy+vwSwFvqxwzYnrOKH/X9nQjdMmKkU5AXovP6bwWrmwogAPWX3IPTARIBPnn+mmLcHWMDMlvdQ0mwqY8mpQz5pWs39WZjA/xY9syk7GvVZ3w7HdGd4LpKJH+qIUkuI4wnn06iJ/iCxrW3FSl0U8HbZIIm+fUkcaMPlDQ9F54eZLd6KQeb1MnGJWjscjQHXoRLgXrOoJBOhzV+AJpapazwDEYLPngPJmG+iY74TvSOrothgETP7gEeICTNLCmz45Bfo/owmyI09Rp735Uwz+zawaLiGO9GtBlasPJl6xjue+51Jc6T+iVGenWJyfhNyZddo/1NMZa6TWlhGJYePgmbIwJdNSPi2NbHpbIbLTrNmZl9GscWC6Bp8lvxY9iZ3/7iJyuEXCD0ZI5fHtWY8eCtK+NxjNsxL1pGsW9HMshN8d3+Omb0cVWN8wYSeus+jxb6QnLsj9NdGQbKDQkGIWgt98enA+d7P01jbdjGJfpg/nojuswEvNLMdMvHTkShpeIRpUez49hzNx+MRLo5nUSzxD7T4M/GzpDmzkvasT/zAxMSt5KSeA/w4TMT76pFrCJJKf7/WeiV7W1Z8fqGtGKnxFAyXtER2HBpEG5Lg9yLUSKoDesrMVvAAjzTx5+QQISdQSFrp7t82sxeEh7+PnR8qZDipUBejgjwTZeuLzAQTngksjUr11kaJuBS6pCb51OyzGuqQ3oquzalokLucwYQM6pjm54iFgOIIBOWdB0HNcwiMRYF7zayLY2AcEkEqkj4gMsOVfepXZ//k7kWBS4lZVP5jZg0cd6L8p2O/doC/NPkAv1fK1YbLCvZiEDBNQ4n0ET4JlNR4jEFyejs0+doq4ft+NJF5AZqkXoySB50WgsnvhsTKpOW1S8zHk3Mvlvet6Vvc/cutfQ8jk4gL1gSDfzUhkB5CY+aIeU85dMs+iSYld6F++YfuPmmZZHc/1VT62pQ4vsMzJY6WQT/SSppG9k93dwtk2JZWb3szKnNZmmFVrMfQe5my68OK78REz8xuJZqc23hlwB8GrjSRAxs6v/YY2NhMlGDZEy0OvB7JYqdsfne/qTVf6Zro1xCl9hLH23gKo8VKXQyLKzQonvaE4VUmxNdOZnYSrdjKR9HHp6Ek62wSq8AMxyDXA6vbFCM0EZHxUcDSJhXVIcsksHYnlGQGn5+FRGnKzgN+4yKh3QChJHKLcbX2O2BnDzxLpkXEzwMfR/cpLpX5QfT/GQhlfB/597rU7gduNCG9HS0g3GlBFjtOIrr7YSGh/ShK6H7WJcHe1Xan8uSYz/3caOLe5khNETrXkPPvTPnCcu+CTpzssDQ/+0/jLx7KcEIf/IQHVLppgStXWng9cKi7/yHz99iaOcjbEKqzrbw6rs028dYuB3wqLBy0F/zGUVnDpAa3DOrnX4Q4ZXKLieugeOsX9Cc7SgUhau3LaBH+4BDffwcpbBWjETusRgH3yyjRdx9MIFpPJ7FQ6u6XApeawBLbhv//GiV1T/FhypMa1H0qnk2i/TLvbpU96xM/lQma/SuaLpadCyudRVCwGqvMCELd+R2PBqkGHfUO0iohIHWMWE3sc6Ya5ZTtjaS642Ay9yAWs9Kb2cEoEdPLzh+sV4azMa9TF0vJayc7T68jhZ2JZE5vcPcNTfwrX8z47s9o8il7DmGi81d0fz8ZrRbeaGbtMoRipvk5aN9A9/osVDqxPYN3q2379TXmQUEKJceG+Da6rlulrY0UYaaazHA/U/nklHAHuftBwEFWUf4TrCbA75VyHaNvA1jFhwmJrzDxMaVsJW9xdoRnfYQIsrWCNg09c1MRXNRCwk9IBUyeKC2kQN43spq+pW3zoyRFzo4Kk4zPoEnBglQkvDtsD5ey30Syx8xmekvtr9ZMBL8Puvv/hsnnRmb2+8zksxj9GKx3FT9MSE40s//H3neGTVKUXZ+zS1gyKMGAoKCAiKBIFFRU1FdBFBCQJCAiIOKiiKKirIIiIEhQgkhOEhXJknOOS1RcRRSVVySDIHC+H6d6p6enaqZqwrPh/c51zfXs9NbUVPd0V911h3M2VIKrrzbWkuygn3TrKwZJF9FR34qH7gElMknUIrh+Fun1vMK/wnWuHGCfQfcs5M/DG8Kfhs9c3+U7xpNcRe3E8VUpRuVcqiuM3obWdeumMPr9HudUmsVzBDxnL9EYA9DpyIGkdcPfnPVoNlrU4L2MRP/7XRtgx9PasHOyKfOdwouSXqrsIbr0JLXpKwnGlWIp1ci1Jd1HK+hNaW7I1SgjpsvEokEBuhT0m+gsqY3NyZXzsMI54W90rQuOnm7OnrpDt6k8uSpMSltH8X0P4E1qz4r8J+wciI33dppuIKe0qCSwnBPQKVZxDrgMvqcrjp854E1zBx+XnOmdi/PosrMXAOwY7pNh2AzbwoHYKbIK4WvRmAsH2NhfW3v9TN051A5EprMDzuz8MywIcTWdGR7l+ClBLSA2Hg40bAcnIQyjBL9EAXdW1bJ2JP0+OLqiCL/ZFnCCxB1oBdm3grO0KzSz7mdBOut+XMSeXRqRAAkLeBdTmO4dPyhz0JRsxLNl55iRCsY0sVw1tjqJb7Zkb+N49vlJOpDkVWgRKm4jKeoYAfACQ/13GN8a8IQX6/eyYExWZS8PpoxJFKSOwmn0S3fpq4kcGU6E721GHK6BFWRiE3m2vDbLMlf+I+k/JKtU7gfCgx09t4jzqZuHfSM1FMpIvkXSnyQ1DcdPwQvYV9Fimk/xe4ysrl7SQyTHh0jNscFp1+GokHQVne1QqfLcrLRE7FnoTGE/Ez1KnDLR1dExALaBN2azosYdhMEzDM9jkIakI+UrwmV/qYyTEgM/R8p1KuhstDejPZMvdn63k1xN0o3hc6uik4OpwqHo/K1jx4D2CNrL8Dz+qZyxZ6AkJbye5TQBLpdLPUc95X1ryJ5b2M6hMh6O6kaf/4DLgvF0NcJmdkjO1K3gCFcdW0eOlaK++TwSdlalNp/Z2Y/0g3Ea/Kz2jOJLOovkOug0zurXOjs7qNC2qeM9aD17K9ClAjGeqJXgzNzF0ZtrbCd4Y78Myb/Byj5Rh1kw6n+kLtmqDXwBwDEhOAH4WnyBjuzvE8Z0MICDmaEwWhtHLGOq4g46MtgC2Vk8kg4BcAjJwyXtmHluuWU6O8Br8/zojP73vTbIZfS/ogVTcvlYrmJ+SWYVjFsfPYJxfeBeWrHvV+H9JnD2++xoZSVGERwaHUIeASfDz/U68HXfCi6DivWT4zzM3gcEZDt0+7nvAVxG8mK0C5tc2qX9KmjNFyum5guUBZZ7BnQqZwedDdORodRlvBNUI3aW9CzNQzUQJO1OB3WekiW/n8NwbIZKGXb5psOyAsktJJ3EkEkWGVusPLWUFzLb2VHNdbVDD4cgSQcSY34KLv/sSCagKQU+iXYF2mHgAAA30OVYQFDATbS9lQ6+1gVIonZnuN+Xhh2Xn6w5VU8jeWto00/W/TU09cTpoY9dYedOhzJr+O4HYPthKu9iot8oZgTHT08HDfsgU4J/7NOQR5DUMxVMIcJNci84+nVi+P7NAby+0d/X4LKKA9CJDk6EPs8PcNr/3xF+Z5KLKV7nvAOAExjIWgE8gXSaN9BuTL6ry+JQkjo6Bd785jp+tgnj/qGkP4XNSEoZo6SEZGfYAH4R3jBcjDR3QUnmyl/DRu43AC4h+QRaab1NZDufAs5EpsNDITU3oNck26yrXw4uR+ioqy/E83QG3Z1hcf07EpwWJDeGa4mvDGM4lORu9egNneHwjjCuuqNrXvTg7gjOrcoheF0Xp2tuiU4pRsUddDi80VsBwK6wgsQJcFlcDCUG/poAtmZG9hPJY+AsmDZSbMQ3L++BnUrVHLUYgAcrZ4Wk5dkiSl6oYWTMi1ZmQBsGiKDloDKWeqaES2pG2a+jI7yxtlWE+giSFyEi71tDydyybu3fL8Prazcn7lCdqWwp+72FrvGvMA8szz4o6pmgP+ux+czOfpQkkhfI2QRdo/gAQPIIOGvng/Cz9xk0Ivgqyw6KkR7X+4o9e1EVUER4olDANRaCDGsHZ8w4RXjAam1fIbk4ydmUQawsZx69k3Hi+NMbbUv4RKbA9kedPPcZeL0+Co7eFmXxhDFkOX1YUKYTAnDX0kTcqc103yhw+gCRkkz4fo6hCsZthR7BuD6wNbwmVdxQ18GO9P/Cz9hUNNaFcfD89Wii39dKOprONqwyEG6JNcxxjhbuA/py6IY57b3oDKbE+N++HBxxFX/nEZJ+E+u3ZL6QA8tXouXI6BZYLgnoZGcoBTxHcsXKbiP5HiSC1jlghC+ytp6L5L9hxbV+BU92q/17Auxouw3te76qhLifrOlclDg72jgda/8VCxitFF6V7bguvH/YgeQZkvar9Xs6fP4XwXupq5QgTC6FpBOCI6a6rhsoTdOwIxzMqLI8r4FVoGM4RA3VyNp3rhT+9pN1vxacXb0RgEVgR05KYa2EdzGKGUHVa2AG60S/PWXnam1jqlBRaUYWKEKMEiR3hstk/olWGmZqc/aW4DyZF270dHUs0rZUUj6LlZ4t6fBsdv5ckLyvEXGIHuuj31slrVS/F1hjZe/yuQ/AmTYXxYxhtiu3AMH5pE554MrhsR/aF5R5AeymmhJCP85DkmcD+K4SdfUqUKRr9Ls4XLoyK5x5NB+Aw2SS6mbbuwB8RCHLh065vbT+PJH8FBxxWg/tRH/PAPiVpFTG1vdg5181l3wa3rynHH1DR5iH9u+yKPXbb8WL8T0AfwvGbVI6nCZ23RbtakG/VGSBYIEaUslzluq33n94dtaCnb5H1P77GQDnSvpDrb9K+SPV3zDmlh/D980L8EI9P1yn3hFhZiCTDxgHO08OiTn+WEbgWW/TdW7JRcnc0vhcVyUy9qHsVzjumwAcBM+fnwzrWkoWdS44+7HamM0H4GQ1lKNq7Y+HnUnRjWGj7d3BUVn9nRvAhZLel2jfNTuo9mxUdf914mFJ6uCrYpkK6LUKpJRd2kQj0LXxRiPRdPbM2+G5uSuxMssEIbLBoFgUO0by3sZaWZTFk/n9E9Eq0/kb0Famc5SkjlIdOjiyA0zWClh044iUDTWtEX6rHWDOqlPpYNzGkvYd43HUy8OrDM+zmvZTaHujpNXorJhDYAfRmZKWjLR9EBHnaGLdG9k+IMcGj9h7dSfmq7CTfX9Jh9U+MypV5PvhPUBbQAf+bdr2JCxQcQ7tV4azwB6Fz/F1ADZRZ5Claj8e3ljXHWZ/qf3/9yXtmdgfAsBrAcyhiCJhPyD5JgAHqZ1qY+QIzpyd0HLcXQPb4B2BdzrwVHE6TnV4qcEXGNpeDeATCllYYd07H86Yv61uC9LKZpcO4ETrCpJrwmIlx4Z9w9yJPW1HiXnsWDge4719CsDk2h6lr4oekjvBVQ+vAvhsl31LpRh4NewM/wdcCZHiou3sYwZw/GQ7aEY4hmPgH6PuHR2fcBJdD3sLKwWpTWF5wo6a01GClsFeNWXENtrGpGdTUq5FiwMzIxMkoxlGCiRutXanS9qYiQhowrF1Emyw1yMOO0n6XM45pBAevLXhCNg/4AjP1pEFfzyAeyUt09lLR5/j4cmwJ1Fqvw6PXMQ2TNUxdpH5HCbYkP4ODoq7FJEDJ7m6pFQ5TKzvBwGsUBmEdNrpnbGN+KgQnqcl4XKJoXEH0WWeF8GZce+HHW3R61bQZ12ZpgPqJDQFyaNhNYWkYyvVX49+F48Z3I021ZyyBuyMOC283wjAfZJ26Pb5HAQDai60UsLngg2MDgJJOkOqMsJfhn/zHyiU2IY2VWbAFbCDq54ZcFHOHDIM9DO3MKFEJimV5Tl0jHLzSXM+vBXOpnoO3YMplTzrjXD53+PwGvDWSNtodpCkbSNtOwILKYcuneb+FXUqn8XO7cOwrZLkGmtsqDugRClM6nOx9iQvRBCEkGWPZ4EJzvuet0K/9wP4WLXJo5WfLpb09tg1HRVYVp72Szg4UtlAWwJ4RdIXBhxDR1AvdiwcXxfmwqqyXHplmVefWwDO3khlKU4XCOd3Dcyfdig8z35fEcGFHOdore3I9gHDcNAwIkVfMl8UflfPgE6jfV3F+WqlVZyr9rOinXoiFVyuB8OnZh+X2lokj47Nzf2AJOF1oSM4FpwV26Fz/zSUvW9wLL8dvhYPKhEoiu0DuvT5AIB3Vr9BsI/ukrRMNc+yUIW3H4Q1ZyWYPmQpuoT3DElNztPU/je6JpA8H1YlrrJ+1oIdYm+BbbkTaXW7L5KMZQZJEf4wkpfCzsuvwHPR0fC93yGCQot5nAVn0h+LwLso6cjE5ejAdF/qpRGl6bOMICmWCnZYpB3gNPaDw0twSupmwxl1ER5Bmk8HAOpR3ZISmWxJ+VRkAvHU0dzazonh77pdW7WjZwlJQV91bAlH7r8MZ668CU6JbEPYED7IdKlds+2rJOdTe5p7rO05AM7JdXiUOKAC+q6r7zGOmNOu4lvYu+GsvIjtNeqbwJvKGB6iy5XejLyF8lH4Pq8igbPD0dixxKi4gzaB551tJf0jbHSS8puZv0lTmaZnKQT8rN9A8h9IO7Zi/fXq93mS+6Nz/v5Q7d+V8seOANZUyCYJG+2i1NguuKFuNMicStcgwjWkPGLXfgg8h47SuSWgpxIZ+y9bzh33fWit0wib2TanT+S7c8fwsYKhnEeX3+0Py78L6RKZ96qVHfR9Wnb4wkRbklxD0nXhzXuRKJNFmQpoT66xlGOnG8Kas5QaxJVdUCIIUcJBtytcPvVH+Hd+C4AvBUftsHglekIu0+maFVfDymoPIl1OZ8AOipLyzYNgx+XkXo4GuvRnPXjtvQ3AYySvk9Q1U2zYYAFflaTzwj+fQqNkLIISIYaifQCt5Hq+8kpdsm3wFGRus7XCd9cJpnPni5LverhyBKL990iV1RepKAcnwz0ZTSfCjoCewfAe39e304ftWcjjYKLn6HWAycOvgTmZembFlOxp6QzTI2CycgJ4C8ntJcXWnRJOx5PRUr4DXPJ5Sphnq+DfB9CHCm8h1gfwboRrK+lRWkFtKpguO58X6bLzWQG8vQrsBRvnBJiM/WoAJ0r6YvjOnsH7Gn6mVgnmkzSlQUoF9Fg5S+oqxG3jnphuHT8kvyFpPybS9TV4mn42QZKc/nYg2gkYo5D0ZwyPOHQQTIGlXM9H+yReP4el4Y1ck0TwGdjTHEOJMbkSekQmWJjBU0UjmlGCHhjJ5josaLPBjqSz0F0FYQHYkXIz2tPdY9ftWVjG9pJG2+g9n7sxK3FABWyNzLr6QlwIL2SnhPefhSPe/4CJ/Kbei5J2C07JKtL2iy4RoKKFEjb27g3XWQA+AuBmBqnbIcwxPVF4H5f0+w/U5qvwe8c2FxV6/iYqU6apcDTsIE1yhxT2V6Ei5VwXPUg54WevvpjPHY71DZYpMlWf6UkyL+lgOt3925L2GmSMQ8L6JO9FnmpZTyUyhYi5+lN96wlamGASOjMUphpI/X53ybNa++3OInkeTEKacuRXnBTPh8jk44jwgQRsC5Mfzwef2xPolBqvMCl3vCjgGqM5Dg8HsIic/bk8gPUUKZFVIccPygQhsjnoJF3AdoWzB2vPXZTQexRgIisO8bn5FZJLSvpj+OwSyFvXUt/dT6DvEQD39HL6BMwnZ4R+AZZx35Pk0DN+6KzfuSWllIWy+aoKkS3E0Mc+YBMAB9GUB8dIeqBL2xIbPAm1MnuKFQNLQPMdbQ07Gar7qI3LdNQBgYCewfAxQJ1H52UAp1ZO/AjmlPTNgr5LSH8PAPBBBXoFWqXxfMQDDtmcjpL2okvDqsy2HSRV57x5aLNn+DtK7sWXJIlBRTU4npq4HnaeLoh2vt1n4DUlhkXVns39GJzZ+G+SHXs/5nNx/Ya10jTYPo3ZWADwp3CNTwNweebc3Ibp1vGDlncwRQI2KAYmSIqhxCgK7d+IzshESsK8BH8Jr9nCqwPqL6o7qWAMOZGJogyefqK1wUGzAlrpo9coQXAYjKuD4XS+V2Elna+qoZoV2q6FHmpvNZRIIBdFPAqR7YCS9AI8IcZIyJ+NHMvF2mpPrZzMFidNmzpMmLDPkXQ2rVS0NMlZEw620oXy1+FV4cqCz07XaDwns8HG6rOS5kt8pOQ3WQMuiasrhh2UcCb+ryJp813GnTsfZpNyAvgxgDvo1FvCpW+TcseUQLYiUw1ZJPNhs7wBXGIxrVGiWlaiRFZlgyT5FvrE0XD2ZRsfwViBifT18H+p7IDs7CCZu2IFxsmPm21LiGOvJ7ms8rjGjoI31keG77k72E8pbrQpMJF5T44flAlCPApnNEY56GBZZ4T/mxXOpqv4cq4keWSXQM2o0DMrrobdYAWkKfC8tTga0s+F6CfQ9w0AF9Clw6kAYoVZaBWmjeGMm6Eh3F87wM/0LQDmJXmwpFgWa9GaU4AS5+ixiAcyo05aSVsEZ/mmAI4LG9ZjYcdAkzx9UtGoe6BwngCQtMOrDOFdG/byxgCW7Ob4HXVAICAnGD5ShL3mbGiJwDzYpfl5JD8hKZXh3kTJnvYZtXNqToHngRg+nvn9FW6HM+e7igqxUAGsEKeTPBLA/CS3g4MjR9UbhEDOwyTXRitotRTs3E1lN10ZAjmVWtiG4dhcAJ6sN2RBxQtrpWnwcz8bvC50lKaF8a0LVyEdQ2fs/Uo1yoBemJ4dP5sAOA/A/IqQLMUQjK59ASwML5TdvMXVgv8knXr7j/C5QZFtFJHcF6F8Bu03RtTxU3J+KkjLLnD6ZC0SLEgdVWEGTz8LA02suB1azpST6DrMWK39KXBt9vrh/WfhMqOYHGhPtbfauLMX19risAx8HZP1t30g2wEViZ5X4+srvbCG8SRXkXRz+J6V0VJkaqboXw3gfXSq8EWwYbEJQvSggaKFUvnlhTMc6s8JScLRx9W6fKTkN4kphp2IuGLYHWH+Oxc90uML58Nq/v47nbb8KIAU99CxNHdI9Qx/U86I6hsqUGSqoaesbQ2XkdwQJlGPRnS6OMGrMQ4jSlqiWpatRMYE3wKcUTQInlI8XX2sEEtfr5DKDsjODmJDYaX6LdROBN1P9Hw1WGUxh2tsTkk3N+6DbqTcfwyvceiiVBMcgR8Ir56CEHAJ2b3VG0n30cSaUyL36OHwvVyV6G8Zjg3El9MHembFVZB0GZ2lVOcvyVU9jfXXT6Dvh7ADdwISAcQafgCLAlwr6ZYQRPtDj8/kYlk5m2hzOCthd9i5G3P8lJRklaDEOXpe7d8TYHsypSwGAAjndyaAOeAs6/UB7EbykLqtKukqmjfnbZIupcVAooqWI8RBAP4K28uE7eQl4U3/MXBWW4V7YGfjYzkdN4K0V8fWECaIcysoXkbWMxhe639O2LZZTNJ21XOoVmlgX8gJFrOdkPvbJF+E7Z1e2U8997S1wMStJC+AlRIFB5+igbPCwHlUVAjxdT1bAawUkn5Cq9M+Dc+f/dDsSAAAIABJREFU35OUUuOs7zF+B1+H1B5jJzj4VVUgnAATxwudVRA9K15q6FmaVju35+Hf7fQw5oPhsq/sOWC6JXcOBvHa8CS/Fhr8D4oTfj4ElyT01LRniyDpnXApQzFBUqLfSiliKjkUE0S4NLns8rmLecn5TUvQyjJJ9BNhGAR0uvHqClLmwTt7Q8yoZVzBLarGkGibUnvLjpCQ/ATsOKzzEaTqb6s602wllJjREIkqVURtHdFzDVgjHZwKx8DPHOBIwxfgNP11JJ1ea1tlnewMqyns1+V5egYm2n0pvKILJfsgCJ8ZwC4kpo3fhPCC2es36akYxjL1xOz5kAWknKMAyS0knURyV8TvoZhiUTbJfO1efgUuBeqmvheVDpb0vQFOseo7W7UstK+MIsEbwGhZJgvEB/oY73jYwVLf9KV4FKYZ2CU7CEg6R7MVVgrHUqLUdyHMa3dGmAc+A2fedI0Mk5wzGK3d2twsKSVj22x7Gly+WeegWxB26lyrmopXbA1PreujBMnD4IzAz8Iby2fh7MlRlj00x7AfHIjsWb7JAmLXUYIuN30X7Gj4WXB+pOyyk+Cg2b1oJ/GNrTnZWQccQIiBLk+7VglyZ5Lrwdlcb4U3k8dLeizYZ/dJenOt7XYAvgjgNZKWDE6JI9RD8XGYSDxPd0p6V/P/aM6lc2AHUNfSNHYGadeHy/sPbbSriHMnwBvsu+DfY3kAt0pavcvY5w7fn8xaD3PLbQA+J1duzAmTYQ8kaELyNgCbqREsVkRIp4++Y6S/35N0RK1NSq0MSD8jWb9JaFsiKpStADZKlOwxCvstEVeolLqqsST3qKH9B+D17n/gPeRpyg9ATtcZP0fAHvsl0E5yCSBJ+PnPXKeIpCqV+upEX/XMlVQfsZraf9H1klVt4WeQLnWaAkehcqM42ec3LTHWjp0MEO1p/5UnOoYLSe6OlhrDJnCq82uADofjrSGyVFd7S5UmlkRIDkR+/S1gx+WxaKVX/x6u/+xw/NSNhvD9b4SftZjRMJLouSyH/E7GyxVObzQnTXS2OcxvASQ828rPBuuHIHyGQmNTOQ42jjrkbCsU/ibP0OSrWwB4fzBqZ0UEhRua7PlQZaSco0DF4zN311btyCaZL7iXAZcS143ww2kS2IEdP5J2DxvFSrXseSS4K8Km9q1oEbFvT3JtSTtFmo+Kb6FySK1UO9bGJzEWCE7RDqiWmYM+soNgjoGhc9bFHDxdsBOAXwBYhuTf4I3wFqnGYf4+Gn5WFgvR4+3VyhCr4zqa4+o0tJeFxRx3WyOfg26ofDn9QgVZcSNESfnmBSQ/qhpfUgosE0wpxZFhrHcBuDo4KlMcP9klWSjLOhjkuXsbulcUbAjgp2qUNUt6nmSTTHgn2Al/U2jzB5LDqFYArWy6WOWY6ILnSW4Mk4IDLmGs7Ivmnul4uFIhh3NpW9hxUAVp94XLhducDArEuSTPBrCiAvEwnekyKXFuy8HBkdeE9/+CHTv3RpovKWkTmgC4+h1Se4YSzFq/tpJ+T5ehxsZ7IvxcXqPunE9VX9WeNkn626eDOes3CShZ1xdGu633X5gi5QU6y6lvsKz6J3uPUYgSLq6epWm1wf4ZwB2wXb5b9buUYLp1/Eg6BMAhJA+XtGPmx24NntrfYDgpnv2QnpUYRc/D6dXNlNQUqexQzy8R7ZiKWNQ6fC53cSgdT1vtq4ZXf38szDRfRZ8/jYhTJGDj8Hf7xvHPotPhWKL21tyc/SJ4lb9JK1HVUVJ/CxQooaDMaLiCVk4aavScZRlKEwF8C8CvJd0bDPaYTGJV0rQ5gLfIJHNvgjMfbq63k/R3uqzgOJUx789IqG8qX4aN5iTZJDNKSGrIVgwjuShsIFS1ytcAmCjpr5Hm2fMhC7nURoAlw9/7JJ3RtWUL2RuH3Hs54Dm6BKIuHVxsDKRQd3YHIyPV94dgxYsq6HE8HHWPYSR8C9PR81y/RhPgDWVb0KZPI7xEYWUkkLNT1w5RyXGKZIs2cBDMifXb8Pm7SL4/0baKstbnnajjTmUcdF/HcPly+gLJy6rMDJkAuO3YGCG7fBO2cb7OvHKTEnLZIlT7gdqhh0mmnvWSkqxFYcdBlXWwJxxkez8ccJ7q+FFZyUu9XEdw2U2Sf1DSVl3+77LGoRclvVT9ZiRnQZcAdS5oZbGfwGVQbyH5LliiOrZR3RwuMTksfPeNALYIe4MvN9o+H36/rGEgP0gLuPxq6lwo6R6Sb0+0/QWAr0m6AkBVdnUUWkTEdbwUzqVay5ZEfoC+G0qCxcfA99qh4fvvgEvf2qhP+t3HFaDkNylZ13MUwPrFfsivjtkFmXuMQkzKbaiy0rTllSa2z8J06/ipUOD0AZzu/zyAj9a7QJ9Euf1krhQaRb8Nr1xkn1/mxqgfvpySxaGk37WQT5RcBEkH0lKjVV3mNpLuSLTNVhiS9GKITl4GRzK6cfGUREii9bfBix1z9JUooZQYDaOKnh+HzAyl8PtfXXs/BTW55gYOg3+HD8HEuM/CfE0rNxuG7IVXSc6nLgSpMyr62FSeg1YJSVcDR2WKYcfCWW4VgfEW4dhHIm1L5sMSLrUTJW3Z61ghPkFnBn4LLaK/rijMqsi+l1EoHTxCPARnMVXn+aZwLIZsvoUcsFV6FzWCh2D8FkGN0iuSP4H5TzrAvOygCtkKK8NG6trWnMTJayzpkYZzIRqYKHHcMZODLjj5V4CzLobCl1MKOhtmTgAL0rwMdRXANyY+cza8Jl6oPJnvXJxLl3G/AGBHkgshkQ2qsszDkQimAJ2Bidp/xZ6REr6q7KwDFnBF5l43Jni4amOOOdiuCsHCOcJm8UtoZSwNgklwUPBK+MvvJBm1h4MdlspYbJLMXkNyH3ht7xVALAnSAsDdEUdKKoNursrpE77/SsbVngDz1FwE70NOhgNXW3cZRy6yg8WSrqDLoVaGMxh3gLPpmpy3oyTEBsp+k+x1PQS0LkQrKNihADYASqp/roKzpKr33fYY2Sj1HwRHT8rZA9YUzmOO+liANIXp3vFTgj4jaD1B19DuA0tw1lNYl6i1iRqe3YwiFZLLFp5fz42RCgiga5iEzMUBKMoOyiZK7gdhkemZqRKMxHXQKcEX4+xYBy6TmsrFQzLFxVOPkABOlUxFSCbA5GgVV9L/woR/n0Tc0VeihJJtNIwwel6SoVSCVeX62DtCv0/QWWQpPAurV12C9rKCkcu4jwr1xSGGLuc2khISAAvJ8pQVjiO5S6xh4XxYQjD7jvqb8IwPOq9cBEtpz03yaeQZ7CXIvpdVLh08VLCdzP9+OrVZsOM4lqHU79rTDZUBP2ojuF/MCWcXxNAzO6iGUoWVYaK6tkvDm5HKSftJJH7ngEdoWVvRZQ0TMZxMkCwFt+Dk31TST5HeFI4a28OR5TegnbrgaQA/S3zmMDgr6RCaL+LYDDuqJ9RZvvkchjN/jEowBSgITKCsJKsk66BnyQsLSYcLHWsVdg9jmQzfVxcgoQJYiP9Gsr9SwgILwU6wN6PdTo6V9VW8gnVxiY4AIl0yfiO8t+gZpK3+H3amVOX7V8MB7ximkPwunJkGOAjVodYLeBNO8vYwZsJZyv/qMo4sBGfzgWhXAo2CznyeC77HroFLGDsIskewljb7LwmcF40lOHqGptzNdvLqrOqYYP9vJOnJ8H4BWCXrY7U2KV7QDqcy+xNXyEF1ndaAfRGnhfcboTBDaqZy/LCgxpjkRgAukvQMyT1gSeK9u3ih9wTwU9jzug3Mm1FHseGZ41BqtC+poe65MSLZNf0ysUksWRxKsoOya19HjHPh6FdOPfIByOTiKYmQlDowJd1Ok33lKKH0NBrGIHpekqFUgv+GTX3V70Lo/huejT6zAadj9LuIjqqE5HFa8r3ifdkUQJT4LxLBrxbK2HzYk0stOBYrJ2eVGkuY+PsXA5wTJO0GK66cI2kUTpee9/IATr5sMFKGEjlWXBJNk3PGSLH7yiZUEGUYtRGci4aBOB52xseyE4qyg6qsMbo8d0KszahQXdsQhV5RIZuZ5CR43UthBzjo8UZY5vd3cNR7UJRw0JVwBw0dcnnGwSR3jmWIJD5zKYBLad61TcO/H4GDeid1WeO7IthVFT8b4Gj3EV0/lIdfhE3Td2Gn4NwoUBHtgezAhAoyK0PWwUVolft0yzrIKXnpRrIec3ZElShr4+sQsYGDgMdIOir0MT4c60qcnoF7SW4GK3y+Dc56uD7R9hzYGXEpenBl5QYQZbW7n8sCFFnPpaT/wPuyn2Y0/zyA76Nl810TjnWg5sCrbIrFwnP4sKRuCoZdEbFxACT3fHfDAarlYPv4SZI3yCWusb6PRXxN/XytTXZZGMl5ZaW518CVGH+u/d9r6vcmyYMk7cIEL25izzcK1PdYudU/C1VOH2BqkG2RRptsXlBJa4a/Qw1CVYFRkjsCWLO6D0kegcLMypnK8YOyGuPvSjqD5Jqwetj+sKc4plYyhyytybCoTKLZ2aemaPdpeOY4lPo9vxyS6dsKxlqhZHGYhPzsoJLa11FiUeWnzWdz8bCM66QI7FSHWYrkUwAmNyMEcsr4UQCOChP6olKHtN+oo+clGUolOATArwEsTPKHoc89urQ/E8B/JL0CTDWgZh/COKYZCrNm6hhVCcnn4fv+p/BcdD3S3BpZEfyAGJdam6EuaR8A+5DcR9K3+j6DLpD0Kbar5M0BYBb15j3phZx7eWgRoCZYUJqikNIcjMrTJP0t4yu+Xvv3BLiMo2+DejpE3UB8GU49zz2/ZHYQrf5zAJw58hi8gbgfjay2EWMR2Hla4aVwLIoQKR80dT+GEg66bO6gEeMfJOfJDDgiBEi2gJXK7oAzVNYEsBXaBSFKMBJpe2WQyw6AUXJb3Q47JGcBAJKLyeXLTfQsecl1ctRwG1qZok2kRGwug/csFY/VHLAzNaoYVoCd4fL7F+FAzcVwmXEMc0pKchbVwbJS1stIbgjg7IhdGus725Ei6Qnkl/AcBj+bd8O/zXIwX918JHdUBtl5Atk2jqSvAgAt6701fP+9DmkbtS41PwFW33q00aYka/MUeB2r7tEKVXZz/RpXWVT98OIODaVB84BX6s98sOfagmwKylwlTuURYgHYDqscb3OHY/mQNNO8ANwR/t4d/s4K4MYebfeB5fWmHou0vR52yJwNl+WsD2dV1Nsc0u2V6Pe28Hdy89gQzm8J2Bv/PLyoXQvgzUO4xnMC+CGAW8JrbwATEm1vbF7XauyRtrPDDoEqC+OrAGabBvfQvnDJWbc2G4TX4XDGzNawIXYegMMSn7kE3vDOEl5bA7hkSGM+H54Ezgqvx2FD4A8Atmy0vRKeNF4Db5ZvgtUkmn2OhyVeR3WdZ4E3K8vB2V6pdrHnaS8An0q0XwZ2CnwZJprtNoYbAcxdez83LNk5pvfciK7vJQDmr71fAMDFXdovHnsl2r4NdprdBzs7pwCYMoQx35TZbjyAn4R/zwVgnh7t14Dr+wFvog5MnVsfY94uzIN/rF2by4bUd9a9HO7jWWrvk+tCwXdPREu2eEr4959gVZ0vJz6zJ2wcXxPGvEjhd948jOs2PbzgEoF5au/ngctEYm0nwxuMu8P1e6zLNb4LwGvRsgU+CODoMT6374RxTAqvOwF8a0h9zwlniRwV3r8NwLqJtldEXpdP69++x/lVttua8Fq8Tmregx2/98E8Yq9v/N+tA4zhrpxj4fiSAGYP/14L3jjPn2j7WtjJfzu8WTwIwGuHdN3ugx2MD4bnZDIStmRhvzsD+Fd47nr2CzsDvhJe7+7Sbid0rr9fGtK1uDPn2IDfMR5WnEv9/96wFHdOX7vWXt+BS5eOSbR9Bt50vwSXQT4D4OkufT8Al78uHO6/1zbvOQAHhb/nosUjOPWV6PdsAO+ovV8WtnmWGORap571RNsvw8Gch+C93J4APlTw+XFI2LNwSVxzfbp6mPdQxvj2zTnWZ9/HR56/1D33PzAv0Ylw4sHDAD6WaLsabO89G+7RV7rdnwXjXQO22X+Plr0VtanhfeTDMFfq8aHtVkXfN5Y/9BjcSDeHv1fDG8oFu1y882D+mykA5ocdD6nFb2V4U7go7HU9G8BqjTZbdXsl+u3pUOr3/GqfydkYLQR7ai8AcHn1GsLvcTRMMno3bMAdCuCIRNuJOcfG4B5aH04FfwGJhSfcA8lXot+sxTrcDxsXjvli1DZZcPT1Yti5c0+jbbVh+AKA74d/p5xxI9uIwdGpzQB8rnol2v0i3O87h9eV4Tr/FmFBr7U9BMB7C8YwcgNqWr0S5xZ1bDfaLAwT9C4Gc3PF2lwL4MPhuV4c3vz9ING2ZAH+MZx5uTpsYK8Il5TE2mY7NtCK2q0AR813AnDVsK4zXMpad25PHkK/TcfBvEg7Dh4E8JrGNU6uI4Xj2LmPzywPBwceAHBpos1raq8F4SzWgccMq6D1PDbqV7jPWHs/DsDtibaL115vRM2JF2l7a/h7FyweASTslhGf34qwc3AiumyA++j3NADfQFi3YEfQwHMyvCE8BC2nxMEYklOi9L4If3MCjh0bawQnzIBjuB2Wq67eL9Hl3rwTDtK8Fd6U7A/ggkTbS2Cn3VvCa4/U89/HmBePvRJtszeU8KY66z4onJOL198wb68CK4q9H8D7E+2uQ21dhMuBbhjCNT4lnFPFb/RXWC461rZy0CTt5C7fMzuAK4d0X/R0pAB4T/j7gdgr8Zl7UscGmY9QZuN8Ha5ASa4HPb5raQAPJf7vwfpcEn6T6PqLSCCreQztwYv6K+lIjc05qbZ9nHvHs9bt+YNtkHXDa8Eu7W6F58I7YOfoNgD2GcJ4ezowG+1fB/OyfQrA60q/b2Yr9apqjPdA7xrjjWFP308kPUny9TAZcgck3RL++SwSZQpqlFiQnNeHu6b8T4QNm6/AWQwfhB1FKWSfH8kfAdhP7YRVu0qKlb6cDBtc68D1+FvBhMKxfnsSYdVQkjq6FTrZ6reOHBs1DoQn5ckKT1gT6i+dMIvrRK5z/gas6JWLN0n6Z+39Y+HYv0k2eQBmCff6xmipaqUwEl4EkifCkcQ70Up3FeLKUMsDWEOtcqzD4YyCNeFFpY7bAOxBcmk4Wvorter1Y3iO5IrV+ZB8D2zIzAyIpa9G7+fw/yUlJD1LX2tYXp310++OtAPKVOTuIPlbWFGrfm/GarhfliSSnwLwM0lHk9w2MYZSjERaF84mrBOFPhs5VuHH8PW4AnZwvR8FUqI98CrJ+Rvz/aaSokokAY/BxK6PI03uWi9xeBmOWg3jNzkLndfoTAxJJKAArK8fYV5P2VuvB3CvWpw589By1DdF2j5Jcm7YGX4yycfQTg49JlCmWEIfWFLSJiQ3Dd/zPNlOKMj+OOh+BV+zDcP7zeF1be3hDT0LfyN5JKxquC+tVpUq798bDsbVcQPic0AJdkO+tP2rsvjC+gAOlXQoA+F8BK+XVLfv9ia5ySADrXhGkCihT+Aj6JRO/3jkGAA8gnx+wZI5eXxYHwVMLSNPCk2Q/AK8H1gUtotWg3/r2No3EcAZJB+Ff7/XARjoOgcsK3O6bA7zVO4Oz9P7NxtqMP6SjlJWFpJi19Cz3FNSRWnxLnVKoU9ETdGphvuCrfmr8H6TcGx2tEjM+0G2jSOpqGyKLTLhqhTrH4jf84Bt7ZsbZYvNPWyJEmFP7ptavzvCojJLkKyT7c8DOzWHgXEkF5DL+yourW7+jtnhqolZACxLEkooSkt6iOT4sCc5NsyHHVQCJPdVoxwydiyghK8O8L3+d7ikbymSS6XGG8NM5fhRq8b4avSoMZb0PICzSS5McrFw+IFYW1phajd01pF2PKwkV4IzEubxWz4J4PO1yac+hp4OpUb77PMD8HFJ36599gmSn0Cc8+S1YTM0UUHajuQtkXaAvaHNjVzUuA/X+DsAvhMWvrlkMrapCAbeZjD5c13KeR60ahjHEo/Anv2emzeWKRuUcJ1cSvLr6HS4pK7HlSTPQ0tWesNwbC4ATzba/gB2wF0n6RaSS8AlYTGMihdhJdjIyNkgLwA7OCvjbC44u+EVNuRWg/P1+DDJbwgb1otJelui710wGgNqesB3AFxL8ir43N4H4Itd2u8FG5uXSno3yQ/CZVExvEgrcPyB5JfhUtK5E22zF2CV8SNMgB0L9XtRiJP3PUMTPW8J4H1h7MMijr+Ko5HWzXYcSDqWlkWtjMpvSvrHEMYAANtJ+nntu54guR0iErQkvwQ7lBeC56LtJEW5hiQllSD7AcllYCflfGznPJsXY0yCHDCF5FfQUpj5EhIKMujcPD4XOVbhU7D4wFdh58V8SJBGTy8Ijv4vS3oqvF8czvr7cKT5SzRPVrVhXhKdKk79cNAN3SnRJ3oGHEm+Dt5YzRGc5PUN15yDDiA47XOl7f8bbLSt0CJOTc2dvyP5WbSCVp9BgqS8AE2ekboTUKjZwX1uKKfAttL5aHccxJyHJc7ciwCcFpx8gIU0Lkq0BezMWRnOZP1gmM9+1DEA29Hvg8uA67/fIM6ICrPSxN+fhgMk/yXZZqORXEbSAylHTcxBwzyi+4oUewJsH94F/9bLw1kWqyfGXBIsKgkubwXfS5UC6XVwBs5/4QB9Xyi0cUr7zp4PJf2QJjXvptRVVyKs/64dSoSqcd+E+WsV+He4JWKLnAI7FveBnYsVnumyxynFAQBuoNUQAfMe/jDWkFbn2wQu96y4fQTvs5t4nlZXvZNWRvw70o77Egd0Nl9doZM4CubtvWY+sDPCvRiAByR1RLhJ3gWrHrQRcsWcOWHB2UnSNeH9mjDvy6AkqUUI41i5WtCDMXVr4vxulLQayYvhdOhHAZwpaclI29sArN/IJPi1pI6FgJaP3wG+ZrfAhsvBkvavtVkcTgvumATgtL8xJfwkeRxsTFyIHoYAyevh7JPmfXFWo914ACdIyiK4pAl2m5DSam+EHR0VcfR1AM7KdKx0G8cSshpZ12N99HsGgK8oEKb1aLst7Ky8Eq1shh/BmVOTZHWl5mdWgSfyTwG4X1JKTa1SOBm2ATVdgOSCaEmo3qgucqQkb5W0Upjr3h2M2rskrRBpuzKcDTQ/7DCaF8D+km6MtP0crKzVtgBLOjHSdhH4t32DpI+TXBbA6pKObrYtQTBENoONkGuCo38tSbEMs9K+x8GZKh+F78+LAfxyCM/e2fA9X3ccfFDSpxPtF4DLaetqj9kRoC7jmAxnbdUj13cn1pF9YHLnOwf93lLQ2VyfBrAeWoSVgNeRX0lKCRCMajwLw2vph2Aj8jIAuygix0vyTknvahy7e6xthlGB5Pawo+prsENjNzj7uMNBSvKjsNN6WZinbg14U3LFgGM4ECYvrTslVpH09fSnpg1IbgVvSFdCu8DFMwCOUzyrsaT/CfB8siZ8b14Dl+D/J9J2WdiGu0HSqbQ4x8aS9o20fQZ2ylW20Hi0AldS/zLGqfNoc8TQqksLoGBDSXLP2HFFRFpK5uSwLnwRrYyyS+B1IUroS/IWSSuTvBMuH3uR5L2JefZmSavE+hkEwVH9Tdjpsg68JzpJ0vtqbX4h6Yt0dmkTSgTDF6+97Up0H67xngok3iSXg+28vsU/2Aour4l29aN54Iy2pmrleDgANnQnTbhH94TtWMDZRj+onOJD6H95dAaio/NFOM9FGm07SM1ZoEQYnBLfg+lCCJfT/UDSMYn2K6I1D10Xc3T0izB3Vffj5akgFMkHYRsn5fyut10cwD/h7L2vwoGXw1QT+ak5oJeES0krzANzLnXsAwufp8loOYnfVTmJJTVFftLn8X/Y8XMXfFO0RbgldaSbk7xNUlaqOMk7ZDnC+rHbY46RUYLkN+EIzbHh0DYwkdl+kbbrwhPim+CslHlh/pffRtr+D8y70pZJIKkjslMZtHTq6IoIqaPTs0FbaAh0GOxd+r0WJmZ7qWfjEYLOXjsc5gRaLiwU60naO9K2474teRa6jOEKOJvoZrQ716KSjyEqWhk6t0hqKhVU7faDOZr+CKfo/ka17LRI+40AXKRMhZWZGSQvhTfO+8D1zo/BjuNBlUJKFuAL4fnqO5JWCNHUOyS9M9L2eJgDrF6CdIDi2XbVgl0pb80JYLwGV94aGQodB9EIUMxo6GMc+8OZrvXI9SOSdh2071GA5OqSbpjW4yhB4YZyA1iAYGF4/a3U94a6qR42QgDsCphI993qkpFGK1mtBp9bh8Oa5CHdvktSh3LPWDslhgGSGzaDSEPq93TYiVQpqG4G87BtNOzvGhZI/kDS92rvxwE4MRVMG8WGsnBOXra5zpFcS9KVib5/Ddvou4T+n4BFLz4RaftTOOtqqCX4iXHNknLSZHy2WKo+5uxKOcDC//V0pPQTXCZ5GYANhuWQqfV7FoB70Cqr2hLACrFNO52x/0IIwi0FZ3ldmApOkjwGzpBqy1yJ2UQkd4av2z/hOTGp4konDeyIPEfxgzDP5uPh/WthZ8fSkbbfhTMgK8fUpwGcEduLjBLB7txI0rM9G+f1V+yALuw/20mc7OP/sOOnJMI9Cd4I/RrtG9XYxHUQLK94KvyQbAKnZp8UPjNmm0qSH4dJWAErSA2adlv1m5VJQPJeeIN/Cpw6elXqGs+IILk3PKk16/BjbU8A8HY4Gl1frGMpxVWkY1m0R/CjGQokV4Mddm+HPdHjATwXM2bp0p/dABxZOShJ3iNpuVqbqmxiP7Snoc8Lk/0NJB1M8gOx4wqy0JH2b0RnmWVHNgMdWT4rdT9G2t8tafmwKdkLJjj/nqRVe3x0pkMwMv4DGwBVCcnJ1QI+RmOoFrQ7avdm1LmacLB3HAvHt4Ojr6+RtCRd5nBEM9LX55ibcrKVATVsOeNuYxg4AtSl73Gws2fqOoIuketpDZaV347i+w9FF46nhFOiZEP5EIBPSrp/aIMeMUhuCXMR7glvTD4GZ/HcFWl7WfO5bB4PucAtAAAgAElEQVSjM2IAZwMtC2+AAWcT3idph+GfxdiBLQ6jXRG5l1I2Q0H/90lattexcPxPiTGM2fwWxnEsgN9L2ofmWTkdDgpMirTtuaEkeZCkXUiei/j5RYNQBeO9B+ZR2R+24fYDsJKkVMlS/bMfgNffi2KBwpLsgFKQXAe2/ep2Z7SUlOR70TnPnlD7/+reIZw99ET49/wA/qJIuS/JU2H7uHJKbg4rr26aGEO2I6UEJM8B8G54vavb67ly8Kl+Y9mdKRvnNjiwvgCcxX8LgJe6ODujz3Ci7UOw06CnfVfiKKarINaq7lu6LOrKWAAxOIlWqBxIwcF0Z8xJNEqEe2gFeN2t7+9ja3XT3qvadsyHYV9W5+6bF1Zn7eDuy3Fg1tpmO4lTmKk4fuiyjR3RfvGOSHhIS0gSK0Ojvgluqy+uoXJqNDNH3o1G7SnJReENe92TOlHSX2ODKDw/yGRRPQmjSqPnyCfCOhLAn+HU0auD5/3pXuOZlggbh2+gc/FLkex9m+RLaBG+paKHfwyvcejBTUBnHa0FG7UXwHWh1yJOfgy43vazcDnNSrBK1lKJtnNKupntfJnNiMfScF39/GjV9QOe/LfrNvYcpBw8MbCs/vYoAJvR5Wg/oEt6Xifp5kT31eZ1HVg++PzgzPs/B0n1ue/4ZMPR4rkQIarKilZDmnizhLxvJzhj7CYAkPQHJnjJ+sDRcMpvW7nnGOM/kv5DEiRnlzkYhmI8hYDIcXCm1oPD6BOYakDdKek5mvR+RbgM+OEeH+2Fc+B19FJMm9+jG5l8FMHB89nM5v+ckZw+ARsCWDOc56nBcD0eLQ45sIBIVEFIg06pX7OK2JM8Au2lHDMqKg6jFG/aoLid5GoKpbkkV0X6vq1zp0yAnWtdMzlGhM/Ddvq3YI6VCyQdlGi7Bdo3lD+GMyHra3tValxEoFuAVeHMvOthe+9ktErxowgBqLfJnG0Lwfd9R9m/RsQTE56fOeHr+0u4HDJqOzFDoKNy7JA8CqaDuCC8/zjsjIthG3iPMzG8vxqtTMgYlpS0Ye3990MmRGzM2QFS2Gk4UEllAi+QXFPStWFMayAtKEKZ3H5buJxov9S5BdzASKZZAiWk5ss1HEpXkGxms1VE+w8BuCk4zgTTLdT5tup4FJ5Tqsyh2WG+yLHGb9FeHt4NJfZeCRn8MbADc+Pwfks4+73DgSlp/fDPScEJPB+684d1QkOQTpteXvBkdTzsXPlQuHC/TLSdC96EzwI7dr6CiHxaaLPJiMZ7CTzRzRJeW8OZOcM4vw1g0t6n0ENuEQXSd/Bi9mcA58MkpufCJWS55xyVJsT0I+f+O5iz4364PvUYJKRA++x/XtQkQRNtJof77q7wfpEe90Ul8Xt37Vjq97sQXrBvD+8/A6ePxtquPtbXPzKGNtnJHm0PB/BzmNcHcKTkli7tz4Odk1NgJ9fsmAbSyCO6bktW1w12In4FNVn1SPvs+WKEY14Rjmw9Ff7+Hq69jrX9HEzGv1d4PQBgy0Tbm8LfSkp5FgxPNrSnnOwYXLdfh/t3Emwon4OE5HIffa8XnsE/hffvKpnvu/R7N7y5XwGWRt0JwFVD6Hdg6e8x/N0OhTN9oq9G2w3C62A4w2XT2rENpvW59HHuszXeT4Q3uS+G+fhP4XUXTAwd6+NBOIuver8AEpLE///Vdt3uh4Mofw6vV8OxpPRy4/O3jeFYV6y9VoUdDT9HdxnsK+prXZgbL0+0fU/k2LpDGPdscLbPnfBm+LM92u8J29K/D+/fAJeoxdouAm9ALwzvlwWw7RDGfHfj79wArulyDzGz38k5x/oc8w2w87d6vwYS0vYolOKGKzeWHsY4a32uEOa06tm7A2kb5w6Y1PpGAO/odd3g/cpTYV7sJaV+NBxM/hbMvfY1AF9LtD0JwGq196vCnKXN+zf5SvT7G9jRcxy8l/0r7GzrWP/6uM4de7bYsXD8kwDGZfabbe8hYot0+T1ibUdmy8xUGT8wJ0W9jOhyupSrA2pFuF9Flwi3HPHcDa104q4IEes90criuRZO2Yql1C0k6dja++NI7hJpVyH7/ODU0tyU8JLo+afhybAnEVboqyN1FHEVkq0wfci5lyicgSYJrzKwrpR0XqJdXe0NJJ9CQu0Nrbrel0N64GMw/1IKJUzzO8EcTcuQ/BtsXKfUm9any/VegD3KywP4qqSTEu1HgSlwPXvO/baqpBUZ5GZlBaKkhCoyFFZmYJwFYCWSb4V/73PgkstUOmj2fME4x8ZTsAPynD7HC0m3hzT3pWGnQJJsW9IJJG9FK4NyA6UjXVdxNMpbQIEaw6igYUSA0tgTzpa6MnzXnTTB66B4WZJoUuafhTl3GHLu55H8hDLKb0eJELH/JjrLdeuZoyXZQfXMy+dhMvGp3WI00emhIGTzbItOO2BqRrEss3wwC4hEAfwYwB3hnq+I/ycNZdDTEIn5dSo0YLkJvObljqUenR4HZwBFbUOSJ0rastexQhzQeP8E/EwdgLR601MA7iV5SWjzEVi6+hCg4/odRfJzku4J490ULqGI2nEFuAVec1eGOfOOoDmbUjxK68NVAbeHMT5KMpUZfhwCF154/3t4fzKQCAJamSfPk3wDrJr5+kTbe2AV1J4CHQAepTkU6+VbKZ7G7FKagB1hJdf5wvsn4H1DFMqX4v4knA02G4C3kHwXvI/ruwSQJlPeUuYvnDeMp1sFxC5hbL+WdC+twHtFl/ZHw5kik9HKjk/hL+E1W3h1w3sAXE+yIn5eDMCDDGptkpZXhAM1A78OrwpX9tFHCiWKWpsAOCiUfB0jKaruHVBi75Uoe5Zkgg2Mmc3x8wrJJSX9EQDCgzKMdO8See1fwVHXKv1w8/C5tSNtHw9p7qeG95vCk20KJedXkhKeLX2Hgo14Tuoopz8592qj+ffgtHoUidTmkEK8MpzGCwATSa4hqWMhgTOHvqR2tbdjYWdKE7eSnB8uXboNThHsRli6JRzB+DKchvgmtO6/NsiKXGvTnC7j1J3g9qOSvkFyfTg6sQF8bw/s+AkOmWVgw+xBpUmvn4cdWj3rb2Hp2fFolQothC4LoKTnAZxNcmG6LAxw5sjMgFclvRx+u0MlHVo5xBIomS8mwL9dNV9sCDsQVyD5QUndnNdJMKI2QzJKIggAwdGTk9a8O7zxnAzz1VwAz0fDQImcbDZqqdN1PAVH25Pp3iooo8zEfyU9xfbSUA2h32dC2cYWAN5PcwmlZKJLUJXfvgjP5dOK/PhkeN1fB1ZF2grA/9YbKJQs5UDSNkMd3djiRHhe/Rgc9NkczhjoQJinsvjt5JKYC9F6Br+pLqTRwFQ+pXq/HSo20wFiwaChQWXllHXHy8uwHbBxvCmapLzj4U1j31B/ZU0lG8rPADiT5GYwn8rn0O5UnYrgXJgU2gHdFZm2lVQ5dv8O4FM011UKLwVHeGW7zNWl7YKSTg/zJ8I6P4x9znnB7twfdkAJ6XVyQQD3kcwR6NgUDiD8Gq0y/ShnDwpLp8NamOtIKQmQTkJnwGMgXitJrwS7v9c4q/ZV4HnO8H4KnLmdwv8qIsiT6LvEUVPiKM6myihZ/wq+v1LUWoJWtq4wD5xF3gFJW4T7Z1M4+ULw3uzUyP6oxN7bAc5e2gMt7r4vJoZed2AS3vtunWg7MGYqcmeSH4Z/sCnwxVsczqq4fMB+/xQ5rJgXmg2i3HBssuLKNIvD6d6rwzfG9QB2lvRIYhzZ50fyYNgj/xu0T8wpab9c5Z0SIqyKPLf6OzecnlqXh1wcmK7k3EsUzu4G8C5Jr4b34+Fykhgzfl9qbyTfDGBeSak62SwkNpJTobhc/b2S3kHylwDOlHQRh0DOHRxqR8CcR4R//+1lTqpm260S4+1YNGj1uE3gNPDjYaNuD0lnNNuG9uvBhu0b4KyqxQA8oAHJq6cHkLwJwEFwVPCTkv6UmJuqGuIPIHO+IHkjgDVC1Ay0+tY1sMNmsjIJBiP9DlVthoEUluS+kmKRnukWJE+BjYsqM2ldOH37zTBRaYc644jGcTQ81+8OO/i+AhMJDkSgS/J18O97i6RrguN1rdgGf0YEg/oha7LsDOTlkbY52UEzLKq1r2YHzAqXkKwWabsnIvx2Ssg503xAb0P7dYsR/zfn+sXhkuAZfq6f1ggOiG/DpTHPV4cBvARz5+2e+mzBd8wOzz9vRnsmSJR4uLDvpeB17y8A1pcUjbSzTJGpEknI4hsMgeW3wZkK+8DZcKcokv1G8kr4WlwiZzivBpexfKDgtLsiXO8JCacWWCjQUfC9N6lAXIPkjwDsp3Z+0l0l7RFpuzh6SHHX2t4oaTW2C01Mncv7BcnDYe6mM9CeRBCzs1aHHWFzS1qM5AqwnfylRN+HwWWN56K3DVfCZYrw3dW+7RpFiPlDu9/BAY+voxbwqNtfJE+XtDFDxlCzj0GuMQdQ1KKrdbaEM63uh8sCD4k9g6NCpgNz8O+ZyRw/s4d/VsSWDwKAMsuShjSGA+GsltPDoc8AWEXS1yNt15B0Xa9jtf/LPj9aBaEJaUB1k8KN+E2SVg0bxQ3gbKZ7Jb010ffrYC+74A1B1+jdtEZw/KxVTSh0idyVCcdPT7U3tqdVd0CJ8hFmKG+wJVO/NJylVDmyPgngZkkd5V50RtOn4ZTDVeBF5byShTkx3gfgOvqHwvslAZwvaZkB+hwHK839G1YgIoDL1CWLhS6T/BCAS8PG5IMAtpA0jJKTaYrgyN0Brnc/lS7P2VjSvo12sXmiQnS+oNUYVqmMwrDY3ixp6ZiDs2DM2Wozuf0B+AJsPG0GoD1tZTgSv4sA+BGAN0j6eLjuq0saKO2e5NUAPqEgMRqc5ufD0bfb+r0mfYxjTth5+FH4+l0MYC8lsrCmNRgnjT5IY5zZUds4XAxH/R6FnedLRtr2NJZnZJC8WdIq4Z7+EoB/wPNFLHA2GYH3SS6JWATASZI+Emn7BTjDa1GYS2U1eL7r2LzMSHM9R6w4VTiWErWZfRTPdh7GOC5CyHhELRNE0gG1NtkbykibhUP/Lzbb1j5Tosh0OJxt/CFJbw8Oid8lHL+E7+FlUJtnJV2SuBYrwkHJ5WBH1EIAPjOE4OCcAHYFsJik7Wj1y6WVpi9YHCajvjR8dry6Z5DnjOHHcPZ6Vul0zN5gIqDKmjx6eD8e5kF8PtJ2VAGP7H1ZCN59BubUiyrwDtB39ppDciIs6lI5kNYH8IuEU7JnwIPk6yX9Pdw/sQEPKvBQfc94mA+r7ijusANCUGAb2NFzAoDjJT0W7un7JL251ranvUfyGzIRd1ThU7UkCbYUHKOBeQ2o4JjCzFbqdUN44KdOgCRvR41FO7UoVEhM+HPC5FeLSfpijwlxO9hjWEWtx8FqNdujM+X8UHQyfMeOZZ9f7TxGkhou6Xhadm8x9VZ5yU4dpfkd9gRwObzwHUryB5KOGd7o00g9pBUULy3aB50cA6noVo7aW7OevW0ISJeP9FTeUEjtDMb3impJDE6CN5SdXyjtTqfEPiWnqT4Ps/QPimfUHmWZAmd6TEVpVEDmRPp5WCBzy7X+K+lxkuNIjpN0RXDQzfCQdB/Jb8JZTJD0J5iYvdmun3liPzhd+kq07vsfBcPq0r4HXaY2k4PvwTLSiwJoLqADl2MFHIfR8C0sjPZy2v8CWETSC3QZ05ggGMXfQev8BgLJayWtSfIZdD7Xgh23+0s6rM+vOBxO/V8B3sT8Ei41Glo0PBN7h03zrmhljn410TabV47kW8Kz3PXYdIZfhI3vHnDAYW74uYyhhN9uIhzEuFHSB0kuAxvlMcxIc/2oFadKkK02A5MYT0XYeO2h/vg/mlhUUq+Sk0oJat2M/nLaNFHCw5HNNyhJJC+QqwKizp5G+2wuvEIcCzvWKsn5v8GZKR37HJLbwWUrr4GFJN4IZ3F/eMAxlJZOj6eVLF8M45oDFumI4TKYcuPZ8H4OWMylQ2ocwM7wmvciHKy9GBaRGAil9pakR9heZh0tfwvP2uOKJBgkUMJlui18Pz8XvmtfmHoilgnTkyojOH3GAzhOo1Oo+zJcrvdPtKsBx7KJNgTwUzUyRdVSVKvjOPS296pgc47tWpV0xvi8RpaVM1M4fuhMkTfC5J3vBtqkQOdsNK8m/J3C32qR3bzLV1QTYjVBJCdESV2lusN4Vw99LdTw9M0Le7ub7UvOr/rMUrARvIik5UguD2A9SQPJVbOA9ExSNVGeRfI8dEkdhdMO361Agk2n3V0PGx5jgX5keE8Nm9/Km53kGMiZ4PqdBNVJHH4QydvgjW8Ti8Ap2BVeCsdSff+79u/nUEtPHQC3krwAzooT7Ki6haHsSE5NLTHiKlxGckMAZ0tZqYxP0pkUV8NSsY9hOOc3zVHynIb2x8MqevWU6QNi0aJgMFwAZ4EBwLclVWSNg5Bj9yQRLOlM0pkwf8N3a3PRsDEqvoWT0ZJFBZyZd0pwruXwGg0EJrINKqTuo16QVHEcRNfJ2rzfr+NnVKTRRagFhZ6COe66IZtXDiZtbwZ6zsSAXCojxmWycMTVAJYA7KxKtC3ht/uPpP+QRNj8PUBy6UTbGWauVxB8kHQV87nwRoVsuWwAHw7r77YAXgvbzcPiHLue5DslTU41KNlQ1rMKGMkMSGAHACewnUg4mgGPQr5BOOixsqSkkEhtvEVceAVYUtImNO9mtfFlou1O8Pp/U2j7B5o/ayD0YQOfDNt9VbbLNkiL9UxQyKAN3/UsA39OZBxDDXhUCPfBdugsWYxVYjxC8r0ARJfHTkSaG+2V4IjMRcmaQ7Q7nF5BI3u6hqyARxjvqyTn67InHAS7wMkZ3ThzK3wJwYEb9s3LwJQk/5V0WaNtT3tP0rnhb08OI0lHhn9eqkj1T8bY+8JM4fiBSQO3hiO7B6B1Uz4N1x5PRTXhk/yI2lMEd6ezZ2IZGyUTYrVp6lZ3Phsc9ZoF7Z6+p+HUvr7Pr4aj4E3YkeH776Z5IwZy/KCA9IydqaOLkXyf4plSj6M96+MZdCe6HipyHtLE5/6OVtnUUEDyc4nvivJfsEB5A05lvJlkRX74adiLPZaYAHviqyj8/8LRl0/ChszZ4bqWpn1uD2fmvUKyMoKkNLHrp+AJ/6uw43c+xBXnZkRMQhk54fKV0ye0fyI4mVMYB/9uswB4K8m3NiMmfSCbRDAHJJeRFRrOZ6SMUsNR3nouOCsqA381eLM/ECTtRZc3VMGGHdQiC+0WpBgWpkm2QcjKWGuALkZFGl2EEkcqMoxlOpvlHQDmY4uXC6FtXSlrekSWsyrYVPuEa3ZEuP+78dv9NTiJfgPgEpJPAEitFzPcXM8IFx7JKBfeCJGd5SJpM5KbwCT6zwHYrLmZGQBrAtiaLmt/EZhK2t7M/C3aUJLcGc7CzskMeFoNRaYuDsxDYDLjhUn+EIFvsMtQVgWwOcmH4WsXPb+AE2D7uMq42AwOYPfFhVfDS3TGTLWWLYm0iMuLkl6qtkE0z180UFDo7Kju+yb3TPRZlbQvXcZZiefsJenixJifI7lite6TfA8S9zKtwvvtyJgH4viBld6ugTOjewWIdoBVjd8IJxv8DnZSpHAnLZDTkz8IZRmpx8JBqPqeIZrRXBjweBbAZFp9rz7eQVULAeAR5NthVwN4X1ijfwcr8m2CuJ3V097rM2hWWv0zEGYKx0/YtB9PyyWelfkxssanEzyrKYb37AmRibpz1FIV1UqtOy5nY9vn+c0p6eaGf2oYRMkxlZdUJCM7dRROE64i3IINtbsZMqI0olrH6RT1OvAJcPrs7fCCH0O28oakH9JKKBVR2zaSuqk9DR3KSHdlvBSk3keHMyeVRdAF2wM4TdLfkI4SzagoeU4BYBzJBUJkHjRfVUq2d194YbwX7cbyQI4fSQ8zk0QwE1+D09FjJZTDKvX6Guz4XZLkdQh8C0PoF/Az/zeE34HkYhojrhrVSDqZX9o7rO/OkQhOYRN4I7StpH/QpKr7D2dkRch2pGYay0vD2Y/zo13a/Rl4UzXdodRZFTK1LgDwzvD+z936l7R++Ockutx6PgAXJZrPiHP9AQA+qAYXHoCxdPxkq83QFAgTYUff2wFsSXOwdHCo9IGPF7Qt2VBORH5mwFlwmXydeDWabSfpZDrruuIb/LS6q2Z+LOP7Kyyndo63K2g+u0GxJ/z8vInkyQDWQFpZ6CqS34arED4COyTOTbTNdnYwQwk4gvvhTM9LSc5Jch7FuYZ2AXAGyUfh3+R18HoRw8lw4DxHGr0Ecyqfu21pSW3Oh+B4TTlTJ8DB8rpdI7S4eVoHCxw0kg6kKxvWDIeSewaaGmJv2KF2EexA/aqkmBLw2bGxDQlTAFxJ8ny0c0XF9pFUq6zrMJmfJ5XVmGPvVUGzDeB7rDr3TWEHc+uLC6t/hoWZwvFTocApAjgd9Ri20jafhJn0Y5iEzgkxtXnNrjsvzGYoPb9/BUOhclZ9BpYvHBT30tKX48NC/xU4NT+GkkypP4ZXharEoXQzP8ND0s719yGq+asu7YvSY0PEo2e2A4MqUq9jpaBTlbdFZ1Tn87V/zxPa7gXftyfCi/XmAF7fpe8NUEuBlvSbLkOZB8DvSP4brtM9Q9I/u7SfkVDynALeZNxAslJA2wjADxNtPw0bJUPlmmEnieBJJKMkgjmQ9MXwdyR15KHvkfAtNCLRVWp1KhI9MrCwZHBaQy61PbD2/i9IO8xHiRJHas/sIEnnADiH5OqSUqVP0xv6cVZll7wAAC2P/DZZ2n0hODoe4zuaEef6nlx4o4bK5LLPBfDlsAEnvEm6BQ2Z9z7H8XDkt5470bxkQ9kzM6DUgVkb8wPI5Bss3AsMmwuvGsMldNXDavB6M1HSvxLNd4dtuMmwU/UCSUcl2pY4O96rlhLw90kegC6OThZwDUm6JfyWU8VxuqzV2dLohTiP5CckXZDRtigLJCeg2i9y9wwAPirpGyTXhwPQG8ABwZjj50y4XLdShx2PND9TKf4SXrOFVzcwOGA2h+9pIOF0ybH3qqAZyQMk1bmqziXZfE5Lq3+GgplK1asfVI6fXmmhIb2rmhBvTE2IDAzmwWO4qqQXGWSxhz32HuNdAsAvYG/iE7AxtHmpsynSb13lBTDp2d6K1BeTvB6egK+Tie6WBHCqpFWabacXsFxprcMYUYRok4WKCY3PzgrgHklR/gLGGeGfgtV/Up7rbt83AY66XAFL69Y5pS7SAOpbof8zYINoMzjdfnNYWndipG2HfHzsWDh+GMzMf2o4tAmAP0raqdm28bnlQ9sNAfxV0trd2s8ISDyne3Vz1tAKBVW06HJJ0SginTG2kWr18sMArZK3ulokgnPBhPYDOzvojM43oz1te2CHACN8CwAG5lsg+RC8foxZuWtiHLfB98SVaimLTJaJSAftu64KMweAWRKR2py+UqTRVclEqtxzJKDLdb8NZ7gCwZEq6cRI25gyTVQdj4VlE9MDSpxVtOLjW+GSra4lL7RS5UrwOroUyTfADp0kN8KMMNfXnAsfgWXn61x4f1FCznlEY4nxBEZLb0jO23QMkVxK0u+HMI7s3zqsGx0bSqXVm5aGM6mimQE0X9inAayH9rL+ZwD8SlK3gMrQQfJ+eMxtXHhwtnf0WenRX7GaLE0KfHCvY+H43gCuz3F2sFwJ+E4ErqHU+kTyQ5Iubzjt6ucXkzv/MJyhcRl6SKPnoLYuESb0fRHm2elYn9jKAtkFwE9r3cwLYP2Y7Rs+tyjsGKqeiWtg591f+xlzP2BQHSP5S1jF8qIu9vqNANZWu3Lp7yTFyLb7Hc+csee+0eb9sLrZdXLp4BIAdlEkQ7DE3gvP6TqSpoT3b4EdpG+PtF180L15CWaqjB/W2N27HQvHs2V42cpyOD9yrInsuvM+nAxZ5xcWui9JWjssguP6Nagj/Z4vR9BzSM+yU0fpmtrvwIbOMGtqS5HtZa8bI3BZ26ywZztmeJYoJtRrRMcBWBY2/lJYKbyqVNt1YeW3HUieIWm/Lp+NYXt40XkD2r38TwP4WWFfMbxV0kYkPyWrxJ0CT6AxPEdyczjjSfBinCLl/BCAtyt4s+lI+r0Z43kMlhh+HFZTmhmwjqQ2ckKSG6G1Ee1AcPTkpIw/D9eTN42iQWuzS0gE8zslT4SjgXfW+heGkwkyKr6Fkhr1USJWMjhwtCgSqV0UA6jCqAdp9FhD0gkhulc5UjdIOVJRkB2EMo6I6QUP0WUhb0ZvZ1VJycv6sCrm7aG/R0n2+v1nhLm+nh0V48IbS9TX2gmwbZEqWZqD5E8BvFHS/1Q2Nax8MyhKfusS9aaemQGa/rLthsqFh/7UZLeC+Wfq2DpyDHAVxLdJvoQWqXDKGR9TAk5lEgF5XEMfgNWCP4lOREuh4IqOZWCb/tUebXuicF3qNwvkWACnoGV7bBGOfaTZkKNThzwvOO9fALBjCFSkgmDZZNulCM6zo+HruBhNIbB9zGkuc1NeXXs/Bc6Qj6HE3vsqXG42BbZjF4f3VjH8kuRGas/6/ZWkkvUwGzOV4wfm0mlu0GPHgAxZtlrmw4Lhh6hnPrwxNgCV1Z0Xy7lH/q/jmExwVxnCQ1OuUCFxnspSR0dVU5sF9ldrWWKMlJS91YlVXwbwcA+v/aJw7XnlOd8TdlK+H3Y2FTl+QtTmYJI7q88ymx6oFv8nSS4HG+IpI3wz2Jg4GF54rwvHYngIjn5VjtY3oSExWwfJL8FcSAvBDpHtumzOZjR8C51OntixfvBbDJnQPCCbRLAQKwFYtnIIDhmj4lsoqVEfJUpLBnMxElWY6QkFjtSSMsuSsonpBdnOKpWV9LwkSSQrR/9ciXYz1FyvEZZslEJSm1OA5E/g7NEYjkNvqeN+kf1bo0y9qURq/ovBYd3sY0yz7YadGaCCUuhgv24Gl/3WbYB5Yf6nWP/ZTg+VKQEDGVxDkvYMf0ueq5WVyLAfBHQJ1OXVOQUn16dgHjIAACAASURBVFqqURKokAO2hoUkHVt7fxzJXRJti9Uh6XLPuuO+4/eWtDvN8/NU2C8+B/O1xpBNtt0HDoKDCL8N47orZPZ0IKwx30An9UTM4Vli710BizxVFRLdyj4XVCcn4MjsoZnC8cM+5M6RJ8Nbz3y4rdZvVuaDagSZjfGOXM4dwB3MZ3gvQU/iPHamjlbcQovRBKWxWtFR1dTmoh8ve4kxkk0QnrpvumDhRl//BbCIpBdIFvOwMKTGAvgbI+mxQ7iHfhEcqXvAE/PcAL4baygTfKYWjibmAXA/yZvh67wKLA9cTf5NXpI3wSmdxeVw0ytIfhzAJwC8keQhtf+aF8Mhd6/I5ocOFZAIFuIemGRvGBxnTYyEbwFlNeqjxM7wRu5FuITyYgB7df1EHrJVYWZ2FGYHlXBETC/IdlYVZtGeTvJIAPOHDfnnkc4OmOHmemZw4U0DzAkHmmLIsan7RfO33hYm/42h54aS5EGSdmFCgSdiKwDt2dkT4MDfo+WnMn2CeWUs18Pr6IJozxR6Bs4yT/W9HhyIBFw23JPiQK5k6GW/dnANoXFfME6FUP+eWDDlepLLjsA5vKekKrAFSU+GOS/GRfk8yf2R55AAgMdJboEW1cGmaCgjsw++KpLbA/g+nLlTPSsCsEStTUc5XSOuHdszlJBtF0PSI40xpOaik2EH9bqwktpWcHZlDCX23g2SVgQwVaAkJELEEjteZU28gy6DH5k9NFM4ftAud15/iLvJnfeUZRth5sMgcu6555fN8F6IHOK8flJH96TrQodSU1uKmJedlgKeW2lCwxLDs6TsLaZo9RQ8wewaUhHrOBktRTTAKa2nBEdUPwtXP6mxWQjX9Gm5rOFq1BaPRPtjETfMYsZvlI8gBUnfCt+xMNoX1jFRThoRHoXvk/VgZ3WFZ5CW68wCydMlbUxyMuK/ycBlmconESzBggDuCw7B+tzSN0Fx7RrMChuJfwnvF0cmoWc3FEaiRwa5Pv47AL5Dl/rOpQH5iwJ6Rmr/L6EgO6heNvESphGHUSFKnFVZWbS0RX8aHE19GnYUfU/SJbFOZ9C5/kR4LvkYalx4YzmAxlw/Hs6YikprI8Om7heSfhLmiacBLAVgD0mXJprnbCgrrq2fIBNqiKuQPBXAtbmfnwHQs4wl2MYPk1wbwAuSXiW5FPwcTo51SvLHsODNyeHQRJrS4luDDljSq7DNfRRdIrtoJLO3n/Lf1eBy9j/BNkOSa6wQMeXo1D68xCEBeP9xKMwLJNhJ18xy6odw/+twpkuqWgPoY8+gMrLtUjxC8zqK5kidiPTc+VpJR9McVdU+MCUu8B607D0g8GtV86RMTN5PosZ3AFxL8qrQ/n1wKfxIMFORO7NA7jxEAQ4BsBwcEV4IJiyNygdzBOSgDIROzCCgCu1L5NxHBo5A3pfkSfDi0SYRPdbRLZpvZgfYO3wL/LAeLCkqCRyMkY/CD+vFKcMztM0lCN8LwF/hel0C+CzMg3E7gB0lrRX5zMpo1bBfJ2kYWQcjAclb1c52363thrW3U6NsGpxPBrRi0YFwRt9j8Kb9fo0xEfsoEBa7WTDE55Tk6yX9PUQjOlCYljxmoFUYOtBHZl29z+g1qPXd17XoMxI9MpTOhwX9joMjtVPnTgC/jBjt/fT9OjjbTwBukZW+xhSxaDHJtSRdOdZjmdYIgYyuhKa1tjdLWoXk7bIgRJLgnQUk4zPiXM9A8E0rHC0f5vRrJK02hmOoz3MvA/inpGjmKJ3pfSgyberM72+SttdD+K/C5UX7Szqs8blZ0WNDGRzZJ6ghmV0wtqVhzsso8fCMBpL3qb2MJXosHL8N3pwuAJff3wJnwHdcS1q04V3BSVNd9zuGESiiM4TXg22d2+Bn+3pJgwa5RmLjkDwGVpD+eTi0E4DXSNo60vY2Sf+PvTOPt3as9//784iESNJoOEgkZAzlnCjVqVCKZCwNpAGV6nQ0IKdSdJJ+xurJWAgNypx5yCwUJ3GaDxGSBkOf3x/f6372vde+77XXvea99/V+vZ7Xfu57Xeta1157rfu+ru/1/X4+6xff/3TuWtsbtrQ72PbHFBoxHZXyq5ng/jlEFuq0a9QmKDQnz7H9sKRPEJkwB7m6IqRp388g5CG2IK4Z5wF7uaI8TdLVtjeWdC4RE/g9IUy9SkXbaed9kt5GbOpvwORsoIeBb9YlM6QxF9f22vVhP5htgZ9nE7Xx0wo2p/ZPogMbXtWIg/a6+FRJgMp2WwGq1L7j3y9F4Y8kSn7WVLhZbG37oB7HvMDe1/ZKamPvq2YK6Hd4ADW1TZF0k+11FILC6xGppNd3e5NSd44JVU5WxbjqFPIXAp7F5MBkV7uZ6i41tkn/nwfuI3YzyuWClTXiLc+dB1zuPij/S7qZyD67IE2wNwd2tv3OaZ469jT5nnbR92ttn91y7j22j+q177mOpPVtXz+IYFWX4+nr9XDQSHoXkfn3Y+K+/nLic/+NIY/jVmK3/AtEwPoLwAa2N2n7xOn7FZH5sZLtz0haHniO7Wt6HfM4IGlfQhfhVcDniF3sk12Rca0Q7/+qO7B+n4nX+lIQ7FJiHvV/wDW222bJDmAcHc0tJD2ZmB8vmFMTxiKNy80bjG0ZYqG/WulcxwtKSZcDr7D9aAevVQ4+mfh7fHzYm7FqnhHeab8nEt+nchnL+2zvWtG2CMx+AHiK7S8U94qKtj8ldGz+lI6fTpR7rV1q03ienJ5XBEffBSxv+9PlQElL25WJYMDGxPt3FfDBbt+vbkiB7E8SAQmA84nP5hQt1k4DEopsk7WJ+3Lb97H0nI4z6RVZK/MJPb5KM49u1gylgPamRPn4IUTW5kad/A7tUAPjJElbEmvT5YnA9ZLAAa6RHknr9H9Nh5fVBbbVMFFD0vOYam50af0zume2lHoVzKdDcTlJvyR2Co4qnTvL9pYV/Q5KHLRjAapEx78fkf74EeDo1PdPFbu3PQV+gP2J3dSLU783pQtqFU0U0AdVU9uUhdNu0RuJm+BjSho+rSjqWQ8mNHZE9U5mN2Vvf5X0FkJsDaL87++l57SO4wNEKdk9TDghmbgZdMOgnXGKtOuyzfqkmuE2rEr/3Fges32/pHmS5tm+SNKX+9T3qNmfqd/TlfrU9ycl/cOhA4WkjwKbE65MY4ei5OBw4IVEme1CwCMew/KYFPRZCNi9253oPtPx9bATVFMmWNCHgNJHgHVt359ebxki5X2ogR9gI+LecCVxPS3Ke3vlCCLT4RXEZPkvxO7xhu2eNEoknU7MUc4pdv3r8OSSnrblW8R7vJOkaa3fmZnX+kIL75NMo4U3KFrmFmV3o6r3uNC0WOCkqXpNi76Q/qabtZz+pO3T0oLylcSC8kji89LKXcAVCh3A8ibUlIWqx8QxkFg31GWEfwPYrMt+py1jKbVV2rjeicjchHoTlM8RmqMXpfH+G7GBUKaYJy9KrLduTm3XJgJadQHzJ0l6DiHcPp3T8MnEtbIw4HkroYfTc6ChU1KAp/V3r+MgSUsBH2YiIFGVyXQO8ACwhKQ/MzH/b1cG3ESv6mhiI6Wd8U7x3ViNuBcVQZOtgLpNiSKJ4vXAsbZ/KKnX9WlBx8ZJntCbeoiYx9YiaW+iJK7I2jlR0jHljYlyEKwqIFYTBDuYWBdNqnih5DbWT2Zb4KeJuNxjwOYpqr1HivhXOnUxQHFQdy5ABc1+v8VsX9PSdz/EXavsfesuBk0U0AdVU9uUo4H/JW48lypS++o0fr4AbGW7tu7eDRwTSuxE7EwcQXz5rwZ2VpTYvb+i/d7AasVip1c8eH2RF7ZmfSmyw6ZQs8tWKRSaslx+ON3iosSDkpYgov0nSbqXeqv4mcZAbLgTWxO6HR8hrGVXp3MB7lHwVWKSdxoxqdyV0IkYSxxuGCtKWqSTnegB0+R62AnFxkoR9C20NnamP5/P+4nNhoKHaRG4HBKPEYKyTyEm1nc3uC61Y6O0034jULh/jFL8uxOOJLQmDlc4l813m/LTFOipLZku0cTqdsZd620XIrWX0NmmyCCYdm6h7jQt+obt1nl5kwXlL9O/eXSw4ZUCcasyWSdqIIuzNmztyVnfx6Rsm48pdNO6pYlN/D6ES+iZtm9Lm78XVTW0/S1FSVYRnP6YW8pvi3mypDMIh9pb0vGaxCZWHQcSZcKXOzRjVgZ+UdN2MdsnlI5PTHOYoaGoxNiXqbIhUzaAOw1I2P4I8BFJ37Pd0TysNRNF7fWqFrY9XUbPAamfS4m/38PpeH/CYbiK3yk0Ul8FHKzIGKzSQOoYdefO3IR3EvfgR9LrHUxkjpUzUrsJEL+RuM4OLDuyzGwL/DQRl/urw177o8BlivTQuoln38VBE00EqKDZ73efwjmqaLst/QlcNbH3baKA3uSmMzBsf4VIqyz4lSItvIp72gV9yqhB2VtKPa0SSYPqi/Nv6JOIYhkNLjX2SqZG3qvONd1l2x74ctph/obt6UR2tyYyqfYmFp5LEu4Fs4FB2XBj+z6FS8cFRF39tgPIhuwrtu+UtJDtJ4D5aeHcs7jkAOl4J3qQtF4P025wN8Hsor9COP9VttctPfSxlB3Q6W5oHXcyIXRvIiD502ISOMT371rCxnxDYv5wlCL1uyrbtQmPpYyw4r6+LPUbL2OBQ4T3grR7vUP6/2+IrOQTXSqx7zCLtui3id7GjLvWp7ne/kSmWDFn+Ey/Nng6pJO5Rdl85FAmAj/tzEcGSccLyiabXIpyor2J3/MmYl50FdVZ24OkUUZ4pzT5PjmJ4JaO7yLmGFOQ9DLgJtvfV7hOfVTSYTWvt1oR9En93irphW3GcRqxoVMex5trmp8t6T+AbxPv0/bAjxSlZx1JDfSB04jM6K/RfpO/KGXd28nmOwUdD3WN7mmnQZ8a2mXSny1pd8J8obz+rXq/nkWYDhQ8ms5V8RZi3XeIw93sOUTGbi90487cBDH571ZUWCygy43zuwijkKEEfmabxk+VuNy2tqfYDCrVhqb/b0HsCj/d9pQPvwakt6BqAaq9627sDX+/lYFjiOjnA8DdwE4NJ0tVY1iMSKl8dTp1LlGjWqXb83Mi9W9S6iiReTQlmyel5q5qe36a0C5h++5extsUSc8CPktnOkqHEZlg32UaJzJJpxK7zyemUzsCT6taCKTf/d1M3RWovOBL+jrxPv+wZRy9avFcTaTGFvaQbwU+4C5rcEs7gycSv395Z/Ao26uX2nZb870ksbjYjbi5zwe+VexApDatgpGUxlIrGDmTaPmeFuK5n6n6njboszX7ahHSd5n6lOKRk3ahtiAmW/9HBMDf7gqtrHFBYfE6hSFk4w0FSTcR+hFXpOOXAke4QiOiYb+V71vBsN4/SRu4RWBf0i4tO87d9LsTsWBZDziOmMx+wh2Keo6KFMTYGdiFKCk4idgEWcslswJJdzJNFm3D152x13pJ5xOp/sWcYSdCK2WL+mf1fQwdzy00PuYjixELylts/yItKNeyfV5F244zMBQlTxsSwqvrKByJPmv7Ta1tB0lpQ24TJjLCPwj8Dljf9sCcxtSF+YBC4+fFRNnWfKLs8y22p6yrUubJI0z+zC9he4c+jL3dWsIegnaWkmBzh20XrFHbnetyHB3rVdW8b5Xvl6T9iIBOYVn/RuAU259rM5a+Oy0qGSf12k9Fvx8i3NXKv983bU8pG1YzHaXTie9Iq6t1zyY2VcyqwA+AOhds3sr2D0rHKwJvs11nVTkWTPf7KSzpDlMSslKIic0rL3y7fN0TbO9S9N/hczp2vkkT9g2IiP8LJD0XOM12P3QROkbS2SQdJdsvTu/3ja5wD0lf7FZc88Vu4phwJbG7dz2l6HLdpGpQi0RViOSpRly6w/7KavfXMjEBn6J2r6gHr8NVE7PSc5chFhj7EBl0zwe+4gqB0DbPnyQYmZm5pOvQvcSOygeBpYggw50jHVgHKMpTsP2XUY+lnyhcNb9B/C1EbE68oy6gO1MZ0KR2dUK7RMCF/QqSDApJZxJzlhOI6/wfSo9NcniUdMUw7/njfK2XdKvtNVvOdexk1qcxzNgAdCffPYXo91FMnWtdX9H2WtsbpqD1Rrb/Iek2j7EzXL9RF+YDmhCC/hTwO4d19g2uECJWZMbvSegAQQQ+j+xlw2qcUJQ+3UsEDtpmz6TP5ma2H0jHTwcuGeb3vxvSpm0hfnyp7Rtr2m1NZAgWTosrALf34/vUMKDb8WZ/ar8esWkBIe5c9/t17Eic1kZTsH1c1flemVWBH3Wg5i9pddu312UUtLSt2i0C2opmNRnvFwix5b8RAl1rE6U0J9a07+T3K1xYKi+sPYz1Z8Su+dmEeFxreltPaZLpZroucIMnMrEq1fkHSenmXs4Iq3QraNhvE8eEnl+vHyjqVx9gcmrs0sAXofu/+aB2BiW9gQgsPZ8QFj/O9r1pB/Bntv+lQV/P8VTtgBlDkxtfF31vA/zY9kPp+GnEBOW7vfadCRTaBicAT0+n7gN2tX1b/bNmHoryH4rPUh/6G9jnvuE4BmIfLukrwLdt96VscxhI2tx2u0B+uW3HWbT9Ylyv9ZK+RAijnppObQu8xPa+oxvV+NNkQdkwA+NMIpN4H6K86wFC/+R1/Rp7h+NolBE+aiRdQqxv3kEEBO4Fbh52AEMN5BYGOIYm2TO7EqWSpxHrrW2B/3JN1mja5P+bk5acwgF3UdfYsKfvSRFgu9gTmkJVbdcE1mByIPX4uvadoAE6LTYM6Ha82d/jmPrmSNzzWGZZ4GdaeziFAvfuNRkFbTMJBjDeIkizDSF6+SEiQlqZUdHh7/ctIqPiuYRo3YKH6EEsWdJeRCR+Zaaqv1deuBr2X1iXFrsDixMuEcMO/FxM1Aifn8axMXCwq9NSlyNK74odysuIUr3fVrTtuOxNIUR4pe0fdTjmZYGPAi9i8oW5p89yzU2q1H13f3OFMv58ItPnWJJNtKtTsd8HnOTJdc47uCI1X9I3CW2fKWKLkl5p+8JuxjsTaXLj66LvKYFJ9SkFeRAo7Do/w4RVZl8C94MkZf3tVyyYFc41nx32pEGhudLKQ0QZxb099LsU4RZUTDwvIWzXewoADfJz38U4+j6pTTuD2xP3kjOJIFCdbt5Y0MmGValtx1m0sxVNLsNYnAkNp3nAX4Zx3VJ3JT1Pdos4adW5QdPJdy9lT0Do0nSUgdHyGi8nshXP8ZAF+NUwI7zPr93YlVFR4r8DcK3tyyUtT2wUTQlgKPSA9meqrfXKLe26sQ/vWG5hXEjZJ8U8/sdu43qskGbYwik7WJEtfF7VnEHS54myxZPSqeLvM0WTS5H1txkR+PkR8FoigNGTZo5Stmf6vq5r+5/qoaKgpe8mAd2BbPZXvM5qhPnM8yseu5vq6+xAyg9nm7jztGr+tndPP7sWqOwjxfv/eqKsqdWFp5VOfr8d0oX2XELQsC84iXxKOtL2nv3qt8SpCkG+p0l6N7E78LVpnjMIPkRYEa4i6QqSjlJN2/mERWRx49g5nXtVRdsm4tV7A/8p6R+EO8x0C9WTgFOI4OF7iBrUPzZ4vUps98v+u5V3OMoRXwMUZVknEBpXrbzb9v8rjemB9PmYFPhRCJ6uWBX0Sc+bM0GfxOO2jxxQ31VCmeN8L/ky8CYiWDFTdjoWL2dJ2L44BcOHzTsJLYliLJsRC46VJB1Yt/vYAd8gdOreko53Ia6dveplDPJz34SB2Ic7Ur+PSwvXNxPitSvYXrXnEQ+Osr32FkTGaKW9tu3dhj24ccPjYRtefK8PafCcq5hq0FB1btB08t27nongGkwWlTXTuKi5R33PHlnMdqWz6RDYcvomgVLFBCWXLUkLdGUkHchUfa2vE+XYk4JaFXTzHWniMjwQJF1P/I4nF5uZFW2WtP3ndI3/P2KNUTz29DZByUVdKgm3/RdFtnsVrwPW8UR20HHAjVSLsW9L6M/caHs3RWlUZVVKQwqnxUvpv9PiDyS9l84Cuk1MkzpGDRyJiWSNgkWJNeXTa9r2zDhP1ruhkT2cQlDyX5gcWe4pfa0hZ0m6nSj12jNlbrRLO+zo93NYJb5YYf+9gttYpzbF9p6aLML8DOCp7lGE2fYhkl5FqK+vRmQydWLp2lds35B2c6bViQKWtV3eofympH1q+v1VylZZnsmftym7nl1M/JZx1E3vnSYkl0i6tmEfU6jZqf2Ma2pam3Sdfr4OON5hB1oX8VxIkooFewrwTLEvdlhg/1PSUr1mDcwSmtz4mnKdogyhCMi9j5iojSu/AW6dQUEfgLskfZLJdue9uul1w5OAF9q+Byjq4Y8nFu2XlsbXlFVsl2vgD1CU+3ZFaQd/kJ/7JgxyUgtRzro6qYSsj/0OgvKG1TFVG1YFilK9I4Fn2V5T0tqEfXWdHfesRg3KMfpJKUNuHbdoOioydi8pHY/Uzr2Cab97A9zUGgZnSXqdO8wI7ydu5vy1afpZOZ9Ni+0rmbyJ95DtszvouxuNqSYuw4Nie6Jc8DpJ1xGbHee1zE1OJgJsRXCyoAgg1AUlH5G0XrGmUOjo/a3NWJ5GiNtDZK/V8beUjfO4wjzlXmId0ytvSOP7ICHivRTQL43dQjOnk4Buk83+jmmyjvNUQ6cvpyDhp3odRxWzrdSriZr/CcAqhC1jMTGxB6SiXUeasD6UFq6LE0GU/6tp2+T324rYrVnE9kqS1iFS6XvKAtKARJglHdy6i1F1btCoQR2wpAtJrlHp1A7AbrZfWdH2M4T+zC+ZuJjbNeVYKUi0KpNLtyqzWSRdbXtjSecS1su/B75je5Vpf+E2aHJp4UHETu2k0sIu+51PTBRXInYSFiImtVNSMyV9kVjcHJ1O7QH8xvaHK9p+j9CJOp/JFthD/U6PA2pQS95F34sDnyR27yHe74Ns93Nh2zckbUiUel1CH13vBkn6/h9ASUQQ2N9J6HGI45gkQJ8CtLfZXkM9lPdJugr4iJMDjSLF/xDbm3TZX5EqXRVA7svnvuF4FicmtfOYmNSe2GsASqELuA1xH/k28N26neNxQdJZhOPQq4jNg78B17gipV+hB/IR4GhPpN1PETmeC6i6HOM62x8f4himaEW2fu812bShvJCeYtowDGq+eydVLK4Gubk1MFImweLEvayTjPCxRS36WukzvxBwBpPv1Te0PO8r7fqtmvOpocvwIFHovWxJBLmfINYRh/Vyf0jznG8T838RWmnbu1rXZgfg80Qmr4jg8n/YPqWi7RFEJtBbgQ8DfwFu6jU7U1Gud4rt3/XSTz9Qh6ZQXfTb0TpOkzWH5xHX0j2r7pF9GddsCvw0IV0E1phhu8Adk6KFryAW1MUEqmdHCA1IhLlmgjEKcecmtusrEho/ha3mlcBernaPuIMI0k1bDy7pXUS513JEYHJjQu+oLki0JbEwXD6NZ0ngANvfn+61phnHjY4a+c8RwcaTe1nslfqdB6wD3GX7wbTz8zzbP61puzuTgwxfsz0lDVhDVsYfV9J7tl3VTbzPr/NUYsI01o5Tks4jJiu3MKGXMSOcaUZNmvStQAhMQpQX/ZZYnJ/lLkumJb2YyBwqXL3+BLzd9s09D3oMGNRGhqQ9gNNt39fTAIdIww2roegtzAQUNtjlcoyFiHKLgc+J0sJwRyLwfFnpoacC/6zZ3BoXO/eOF5SD2tzKdIc61F6tm+uVnjBlzqcGLsODJGUx7kZkvJ9LBHY3BXZxaL62LY1sDYK19L0wEcCAaQIY6Tq8YTq8pirhIG30LGf7N+n4X4Alq+bqTUlJBG8h7v2nEAkE9/Tab+p7YSa7w11MbCZMeT+abPY3HEPH67iWz/3jwN3Aoe5jtc6k15ulcY9pkXQasUif1s1BIXB5MPBMYpI69hH2UhZIeQLVjwBNX0WYJe1JfOlWAcr2yk8lBI536mW8XYynY9v1hv2eTkRwpxVEVQjobQhcnW4EqxPCrr3qXzSiyU5tw34vbJ04Vp1L59dwi6CdpM1sX1zTd9/LG2ciarFJ7nPfaxGL9rLj1Nts3zqI1+uVmZgxoPFxpxIR7CkyOq8gAg99mTgoUsex/ec+9Tdy55Y0joFsZKSg7o7AyrYPlLQC8Gzb1/TS77igcFh5P7EIWE/StsA7bb92xEMbOinws1mRBaDIDr94SIGfFYmM3M8B/1F66GHgp7YfL7Xd2faJkj5MtUDpUDMrmywoB7W5NQjUwJF4SONZhCg3NRFkGKrIdctYloDQtemg7TOZnIExZaN2UKQN+QcJnZ/TXRI+l3SG7TfVBL8KqoJgr7D9Y1UbMVQ6Iqb1yNeJbLd/Tn3WpLY9JwxM0//aRAncm4Hf2t5imqd00ufXgIWBIgC4C/CE7XdVtB2I6Hcn6ziFPMdhkjZ1yn4eBrNN46cJzwB+JukaJqcUVpVCfQHYyva419KXuU3SjoRGyqqEe0E/LGCrRJiP7aG/kwmL+CkTDA9flwEa1AGrma3m54AbJd3K9J+3v9v+uyQUrhi3KxThW1//cNo7LPRa4vQWYqf2kJSZ8xwm18w2Ii3KFgOeoUiBLGsBPK/maadKOp7YiVuU+C5uQGRZtfa/oLwRWEl9Km+coVwgaV9i4lsue+vHd+po4EOe7Dh1DDBym8oafiTp1VUZBmPMaYQ71ddoL3I5UFKA5zvpX99Q6NO9mXTtVJL4st1rjf/xxCTu8HS8I6FDNBTnltJGxspp4V7wVCJo1iv/j8haewWhh/AwcDoTO7cznfcR15LVJf2O2PncebRDGhmfJeYMk8oxhvHCKfvhV1TcZysoROeXGNyIOidlch5QWlBeIqluQdlIF3TEfIjIfj604jEz4f40cCS9nrg//ZL4bK4kaQ93oM/TYd+tDrWV9wWFzfgJxCaUJP0R2NX2bRVttybeu+cSOjWFPtqLeh1zA7azXanVVwQEusiifTnwY2Crqm6JsrlWjiSyjg5PSRDz22yW3iBpQ9s964bWcC8h+gFhHgAAIABJREFUfHw/kVzRDzZs2aD+scI9rIpBiX53so7bDTiMkOgYmgj+XA787N+g7T2DCPo0yXxIjzVJH/8AsB8RZDiZSCnsSSAx7f6eQkT5+yLC7BDifUjS462plpJOsL1LL2PuFE3YVC4MXCnp1+l4ReD2mqd9j9hRvoDpF2fHEVljk8pNavitpKcB3wXOl/QAMQlrZaDCdLb/SummkbLjps2Qa8MewD7Ejbe8O/Vn4Ks1z9mIeN+uJBZPJzGRfdDK/sBLiLRObN8kaajaHmPE9unn+0rnpnUr6ZBxcZzqlD2BfdW5S944MBbuVAPMdv0e4ZxxPaVAeB8YtXPLoDcyNkqZMDfCApfDKWL3M5W0KNoiXU/m2X541GMaBSmz659EeUAR1PuYa/QfBziOab//to9OP8etdLaTBWVfN7cGiZMjMfDa1gzGtKk2TA4FNrd9Z3r9VYAfEte+rpF0FLE5uDmx6bEt0C6b8RimbkIdS/Um1GeI79MFKctrc4YfVN5KoXH5MPH7rUto61SVvXaUvWr70+m/B7rFZEdSpYi57QuIzcGlCO2wCyT9hnjvTvTkkqiNgJ0k/YrYRCyuAb1mr76X+P4tS2x0vbs1u78HnpC0iu1fptdamfo12qBEvztZx/1c0i+A57ZsFPXlPa5jzpZ6NUHSYYRQ1neZnK3RlWhdKfPhIsIet5z5cI7t1Wue11H6uKIW/IIuIsedjH0gaX+tv5tCbOun7rHEqsHrN67/VQPtASXtgi7G9XJCB+OcUabS9hNJH7B9+PQtF6QT/xexI7cE8Anb365pO5DyxsxkJJ1JBO7KjlPr295mdKOaXUjan1i4jNSdStKdDCDbVQMqv5N0IvDVlknc+2zv2u/XGgWSfkIsaq5NAaBlCVeYsStN6QaFPksrDwHX2+7a9W0mogGW6zYYw7Tff3UhtDtIKhaUp/ZxQTlyatYBU84NeAyT5rNpU/iabua4Lf0WmkvFzyWAs23/a037m1syOyrPpfPX2d4gZX6s63Cqqmw7KIrXk/QaYiP0k8AJVX87NSxBqvlcXO8K05T02DLE3G0XQhC60Bpay/ZmpXaVa6OqNVETFOWVpwziui7plYRg9l3E+npFwnhnShmd+iz6LWmligBc7TpO4Yp4LjClMqHX97iOOZvxo1DGb416PURE+j7cko63JPBX4NWlc3UpdJ3QKPOhafq4B2tt3de0P0kfJxTjnyKp0HkQ8CgRzR8KXX7BmthqXpYudN+njVtBxbguaff4DOVoSXvRgfAacC2RHbAhUZ55lEJEsurmN6jyxhmHQlD1Q4Te0e7p/VjN/bEDfgfhOFVc/y5L5zL9o4kd6SAZSLYrkVW5lu1b+tzv+kxkbEKaxBUZnbMgCPwVIhj4TEn/ReyIf2K0Q+orG6R/P0jHWwI/Bd4j6TTbXxjZyIbPIMt1O6WT73/hGvQyYA1ivBDllaMIuCwP7DPbAoVpgfg8Yq68LpM3jBcb8nCuk/Qj4FTivrQdcG3KEOt6U5wJ+/G/KhyD7wee06b9XZI+yeRNqMpSKuDBFEi6FDhJ0r2UvldDovibvY4I+NyWgmZVdJS9qtCOeRGwlCbr/CxJqVyu5TlnEsGOE4jAbpHJf4rCZr5MVdZlz5mYTu6EGoDmku0LizlvOnWHS3pKLfx7r6/XwneA9VWq3mm3jktZnEMLPsIczvhR2Gv/lkjNFmFVtwoRiNmzHPEc4Bg6ynxI6XhL0yB9XAOytpZ0O/B8ImWtn2l/n/MQbUp7oRQ0FFHf/ihRQgI1ZRDq0K1gLqBmwmsb2L6u5dwutk+oaLsYUd74auJvcy5hzTpUYddxQNIpxIR8V9trpvfmyk4z1DIZ6H+2a6nfnxH3kbtTv/26j4yFc8sgSRP9VxLv2YUDCsyNBEmXAq9zEmlNC7UfEpPz64eVATwOSLq74rRtDy342+T7L+lqYFMn4WeFs85ltjce0nBbxzMyEd9BoHCyejsRGC3PiR4GvtnrNbnhWOa3ediu1rnspN9PEvpsryT0zAwca/tTNe2XJjahyuVQB9h+oKLt4kRgaR6wE5GBcZLt+7sZazek9+15wErEYn8hQrB9SlZOp9mrkt4AvJHIGCk7+T4MfNv2lM1PSZtXZb/UjPl/iWDqA8Q952lECeU9RHnWFLv4DvvdCvgSLZpLtnvWXJL0PuJv+2A6XhrYwfYRpTZL2v6zQjR/Ct0G2BVl2KcREgP/XdHvUMXuq5jLgZ+qFMGbHOrbkx5TlGa9k6mCYz3tcivKWN5DZ5kPxXNeDBRpj5e5xv5WA7K2HlTaX+p7aWBVJr/Hl/bab2a8aJieK+ImPStdbAZFKa25XPbWl7RmSRsQWXr/wmRR85meTTE2qNqh4yHCeWZaZ8A+jqNqgt/1xL7U78DuI5mZS9pYWquYAynEdm+2vbrG1G1pNtPk+y/pDmATT7iQLU042kwxphgkg1xQjgMp4/n0UY9j0KTv/qJ1VQsaoKTFoFBod60D3OXQlFoGeJ4r7NGbliBJ2sT2VQ3GsiaRoVdebx1f0e5Y4Du2z03HryaMGeYDh9neqNPXbOn3ZkKQfJLmku13dtNfS99TZDha7x+SzrK9ZQqwFxv5BV0H2BUCzm8kqnqOan3cY6CFNmdLvYhUwrcw4VayLVBkBrRGw04gBH5fQzhp7ESowffKEUTmQxGF3IVQW5+S+QCQymN2Z6LE4kRJx1RlDfUa4KljUBNzSe8C9gaWA24iRNiuYohOBd2QFmgLdhtsf7dN247dChqO4Thg75bo9qG9Ls4GSBPhtSPo0MUmZVVV2cmO9WdoQDyqsLY3gEJ8sV8iuicRJUidCJWPjLqdnIIhl0w05Z2Eo06xK7cZkcG1kqQDqzLeBoHt3QbUbw7wZKo4CfhJyliGcKo5Oe3Wzxqdlk5Qh+Kug6Th9//zTHUh238Q45qGgxi9iO/AsH36oOaSnSJpOSIzpzDauIyYg/62X6+RSnNq5ywerKTFQHDoCt0NvEDTC3I3LUG6MWW6TJugIOnTxJxiDeBHwGuBywlXzFY2tv3uUn/nSTrE9h4pONctj9m+X9I8SfNsXyTpyz30V2YhSXLKbElBwkkmCLa3TD8rBbC7xeGOdrBCp6qR2LmkxRymOgNlLgd+diJs1Iqgy1XAzmmx9P6Wts+3vZ2kN9g+TtLJxIWuV5pYzkEEhDay/QiApIPTuDsSyh1z9iYW81fb3jyls392xGNqi6QjiHKFb6VT75H0Ktvvq2jb1K2gCWsXQR9Y4PQyzjujHyHqlScJr9W0beJis2/p/4sSuxKP92nMM41PA+cAy0sqnNDe3qe+/2j7+9M3GznXM7GTswKTU5V/TaRbjytPAl5o+x4ASc8iJmUbERoFAw38SPqo7S9IOpzqYOpQBVszcwPbn5F0NhMLyvd4otR3pxENa1QcT2x0FPO7HYnvfaW46yBossC3PT/97YoMgKG7kCUGuaAcOQOeS3bKfEImo/gs7pzOvWrI4/gLcIukvkpaDIqGG9yV5ThtShabJChsS5Sa3Wh7tzS/OLGm7R8kfQwoTFW2B+5JwZReNv4KzaXL6L/m0jmEXtHR6XiPdG4Kkk4g5lSX2a5zcO6GGyR9HXiu7ddKWoPIiPx6xRheSnyXlwBWSJU9e9h+bx/Hs4A5G/hxiDdvVfPw5S3HRenVgyk97v+ot4dsQpPMB4hFS/nxJ5icnjaT+bvtv0tC0pNt355S5saZVxCLsyKqfBxwW03bl3rCreAASYfSo/VliXmSlnaqa06ZDmP73XYz4bXH0g2meI+XpeZm46m1xldImpMlYbbPl3QDMbEQMVm/r0/df1qh03QhfdR96TfFTk5KVT7TSYRd0muJVNxxZvki6JO4N537k6TaUuA+UkwY+2Frmsl0TAr05M9dh+KuA6bjBb4kAVtQKsuW9JIRlGUPckE5DgxyLtkpy9oulwF+U9I+vXSYPj/L2f5Ng6edQfcmO6OgyQb3D5nYuFqU2Ki6g8joqaJJgsLfUvbR45KWJM0vatruSGwkFtUMV6RzCxHued2yNVFlszdxXVmS0GvqBx8jgj17puPzicBKFd8g5FMOT5nxNwKX2j6sxzF8k7hW7peO/4cQvp8S+CG0gF5D0miyfbOkf6to1xfGdnE4aBqmKh6Tymc+QfxhliBs+HqlSeYDxIfoJwpFdojFS9WHCEnb2T5tunNjxG8lPY24uJwv6QFCQHqcuZPIJCjGuXw6V0VTt4ImHApcJan4225HWKCPJQrRxz0oaVtJqtO26tjFpqW0Zx7h8LNU3wY+83g5E2UCCxPvYz/YDVg99VkE4XpxORw0ranKZ0sad3egiyWdRYgEQmSvXZxKXh6sf1p/sP2D9HMgJcOZTGZabpC0sSeLuw47INZkgd9xWfaAGeSCchwY5FyyU+6XtDMT2e47pHF0jW0rnMLWavC07xCbxk/AgpKeyvIjSS8jSg9XJNa+haHAMJ0yO97gtj3pfZC0HlH6WUeTBIXr0nrrWCIz+i9E5lHVOO4DPiDpqXEYwvuJuvVOLZIut70pIQ5dZDUVCQwHSfoT8EWXhJibYvufhGzKkR20vUhhKrAhkUX3HiK41mvg5xm2T1U4V2P7cUm1iR22f6PJBm/tkkB6Ys4Gfmiwk2G7iBReSh/tdBtmPmD7S5IuYSJYtZvtG2uaf5yJRUO7c2OB7W3Sf/dX1IgvRU1q3hjxVODnKavEwEuIC2oRtd261PasdKH9IuEcZ+oj0I2wfbzCgrFIF32T7XHWQziSDrWtbJ8k6XomXGze6HoXm3Jpz+OEY1DPQnEzkYoyxD0kbVFVhtgFG3rIgp098ntJn2AilXkn4PcjHE8nvI8I9hTX+uOB01N24dDELCW9gCih/BcmC3nPRd2sTGaYrA9cKWmSuKukW+iDA16HNFngNynL7jvDWFCOCQObSzbgHcTG+X+n17+S/pSS3yBpQ9vXdtj+QiLLrAhGPAU4D3hpRduvAx8k5okDW1RPQ9cb3LZvSMHfOjpOUCiVEB0l6RxgSVcITANIWouYfzw9Hd8HvM32rZ2Mu+K1N00/n1rzessQn6fG39Pi2tjmtadcMyVdSLgzX0UkgGzo/hhoPJJ+l6JaYWPCoKOK36RyL6eN8b3pj45wJXPZ1atK9XvKuSGM46VMnVRXCWwV7RcCntXS/telx18LvI5IwTul9NQlgTVsv6RfY5/rSHp5u8dtX1LzvLZuBbMdNXD1ynSHwh2nXIY4D7jN9gv70Pd8YgI9zsHFBaRMsE8zkWF2KWH5Os7izmOBQnPuKFomyxVllZlMpo+oxvmuwEMQSE9jOJwQmi8W+HtV6YxI+gmx4L42BYCWBc7zmDixFQvKGbZp0ZZRzSUlvcz2FdOd66Lf24kNq18R5XlFVk5lkLPJOk7ST9ylA9UgSOuHpYBzbD9a8fiHSofzgPWAZWy/pqLtPGBb26dO85rrtXvc9g0Vz7kS2M/J/l3SZsBnbVcF1/qCpOfY/kMXz2t8zZT030SQ/R9EGdulwFW2/9batuFY1iOunWsCtwLLEn+jKge3ZxAZRlsQn/nziAqknrLo6pjLGT99T1VsikJUahVC5KuYVJtqZXUkfYBYwNzDhL6PgfJF8fdEOvDWxGS94GEi2p3pE3WBnSokbUdc4B8mSvzWk/SZNhlbs5mm2lYdoWoL7AWMmwbNgGlShtiUjYGbFO4U/2CaydmoSQGevUc9jk4odq0lPczknaviPV5yyEN63Pa06dKZTKa/DCOw0+EYtp62YdBxWfYocAg+bzbqcXRLu/mNpGHPbw4nAhHTnWvKlKDGNDwiab0iYCFpfSZK4Vq5SNIXiZL0sjbhlGDHMOhg/VDOiHmc0Pw5vaavf0r6KNA28EPIQtQOiWqR6cWLoE96raLkfGB0E/RJz2t8zbT9QYBUyvZ2ovLn2dSUDDbo94YU3FuNmL/dUSNnUZTTDc28YC5n/HS8kzHAMfycyMLp6I8g6U4inXbaAJVCsOuR1tpXD8EqbrbTzeJMIcS3tqRNCbvRLwKfGqcdiGEh6ZXExXWStlX55tJlvz8kdhx/nE5tTnyv/0j8XcbV3r5vSPoB8ZlciqhZLsoQNwKusb1ZH16jcldlHBYqZSR92fY+pfdkEi2lmJkKJO1PCD+eyeTJcs6WymRmOQrDir2dXENTKcmhdfdShVhtUZZ9YZuy7ExDUqZtHUOZ30jahJhj7UOUeRUsCWzTj6ztNEde1eEStyywhO27a9puSLhN/Z74zD0b2L4qIzVJSLTi2VK2LOnzwH1EpUfZ4ayne7VCU/YGJpxEdwbWL8lzjA0ta7KizLOQf6hbm72fEHdeH/hfotzrMts/bm3b4Rgab0Cnz/m7mVr9M5Dv9JwN/DShnK2RtCLWAw7qNVKsEOPdq9PoZrpwvcr2tBbVkq4GtnAS4lK4HJw3yPS8TD2SbrS9rqTPAbfYPrk4N+qxjYKUotyRtlWDPs8jao//kI6fA3yzKjV2ttJt+eFsRNL6tq+ve0/m0nvRLSmrqxV7uIKYmUxmBFTNUebyvGWuk+6lmxECuEeVHnoY+IHtX/TY/6eBDYDVbL9AIV59mu2XtXnOwkyeSw7D9XJg1G1UFdRtWDW5V0taDPgQsILt3ZW0Zm2fVdF2aUIcvTAKuYwolX+gk99n3JG0L/E7Xd/J2rqD/ooA7TOp2Ii2vWXFc64sxsDkkvrKDK9embOlXg13Mj5p+7QUid6CyNY4kthF74VnAD9TiAOXd1PrdqLvIpxdftjS/ksVbRd1SX3d9l/Slz0zGn4n6WhCPPzgFPiYN+IxjQRJixLuBAtuJJKOsv33HrteviWIeg9R7jRnyMGMCYpdP9uXKERGX5AemvGTw2Fhe6VRjyGTyYyMeZKWLhZ5SS9tzq4bxgFJn6o6b/vAQb92ml9cIumbRYZv0pdZwvaf+/AS2wDrEhkm2P59KsGppLQpf2uxKS+pclNe0lJM1vq7BDjQ46e1eUj6+SYig6kwpdiBmNNW0vBePZ8IMhSJAL8jjH8mBX5SpcgZtodmKNEvWjLHngE8tSpzzPYhU5/dPbZ3S69/HlHRM2kjuuZpi9n+WD/H0Y65fAFfuwj6wAIHgrpdjCIC93rgGNs/lHRQH8awf8P2v07/Fkn/2tGk9jUzeN4C/DtwiO0H00XgIyMe06g4ntghOjwd70ikkW5X+4zOuFDSuUzodr0VuKDHPmckLSmvixAuao+MQCNm5CRdh+OINF4By0t6m+1LRzmucUbSK2z/uC5tech6EplMZjQcClyVstMh7tH/NcLxZEplPMCiwJYM0AGohs9Jeg+xNroWWFLSYba/2GO/j9q2pMKUYjotmfKm/CuJoEndpvw3CJHdt6TjXYgASNvSnGFTbN5JOtT2BqWHfqBw751El9qWq9jeXtIOqc1fpcle4un8E5L+KWmpMQyQ1VLOHCP+xosQAbTazLEB0GQj+ixJr7P9oyGMa04HfprsZAwkW6Pp7rztAxo03wc4TdKk2tcmr5fpH0lb6YzS8R+ArgTMZgFr2l6jdHyRpJ4domy/X9I2RL0uwFG2v9trvzMRl6wy0w39DYQo81zkUODVtu8AUFiUf4uo6c5U83IiRXmrisdM6VqWyWRmJ7aPT4vNQgflTZ4hbo6zFduTBHolHQKcO+RhrGH7z5J2As4G/oPIIOk18HNqWms9TdK7Cdv4Y9u0L2/KHzvNpvwqtt9cOj5A0k09jneQLC5pZdt3AUhaibAdb6W4R1eWFlF9r35U0lOYsBpfhVIVSQt/AW6RdD6TtYP2avbrDJVGmWMDonUjentaNqJLG7QC/lPSP4DHGLCZx1wO/DTZyZhx2Rq2r01Ce7Om9jUza7hB0sa2rwaQtBHhRNcVmiq2XexcvFvSP4E/EfbjR/Q68JmIQ8jtu2kX5D9GPZ4RsHAR9AGw/T9JFyBTg+1Pp5+7jXosmUxmdKRATw72jC+LAcsN+TUXTvfQNwJftf1YkaXTC7YPkfQq4M/E2uVTts9v85Qmm/J/k7Sp7csh7OcZ7yqIDxLSHmUTlN1bG3VZWvRp4Bwi+/kkIhPm7TVtz2DmbfQ0zRzrO6WN6KK08BjbZ7a0GXYwCpjj4s6S1mBiJ+PH0+1kSHomkVoJgIfoANaUknjXirbf3U68K5MZBpJuIQIzhRjfr9PxisDtLVlA/XzdZQhRtdWmbTxLaEn/nUekvb7c9iYjGtLISGJ7TzBRK78TsFCNnlumRPrufJoJPa7LCV2EaZ0lM5lMJtNfSvMogIWAZYlr8leHOIa9gI8BNxPZNisAJ9r+17ZP7P84FiM25W+x/YsU7FjL9nkVbV9MyAwslU49QJiB/HRoA25ICmStng5vdxsTFEk/t/3C0vE84LbyuZb2yxBZ4AKudliK1/W9SBqHiSSCRxv/MkMkCTavSgQEP0dkjp1s+/C2TxwRKUD046KcTtLTgM0GVbEw1wM/HdkGStqayBB6LmFtuwLxJXxRH8bwFEJZ/Y5pGzfr9xQi9XJX22umC+SVttfp5+tkMp2iGhvwAg/QDlzSc9yhe95sQJOtXx8n9G2OtX3vaEY0OtLk6X1E8ALCPeGIdpOoTJDSuy9lctBsM9tbjG5UmUwmMzdpmUc9DtzjPrgR9UIqJ1+o13G0aBMWPERkhH+4KHvqot+FgINt7ytpSYA+iVGPDZK+SgQ7yqVFd9r+QE37tZlqH15lNf464Gjgl0SQaCVgD9tn93P8/SZljr2aGPO502SOjRRJN7WuzTVA98Q5G/hRA9tASTcTmUEXOCy5Nwd2tv3OHsewFSFGtojtlSStQ0Tu6+z6XkAIlz0rBXPWBra2PaWmVdJ1tjcof3gk3Wz7xb2MOZPJjD9KVuYt57acaxl/acJ3m+3Vp22cmYKkW22v2XLuFttrjWpMmUwmM1eRtDFxT3s4HT+VKPH5yWhH1juSPgP8FjiZWLC/FViF0GrZ0/ZmPfR9te1ZrXOYMr2LrKtLW0uLSu2+AawN3Ab8M512VRa0pNuBLW3fmY5XAX44znOqpIf0Byen4JRg8Szb/zvSgdUg6ae21245N7B51py0k05sA2xNEquy/Xugrt7usZTaPk/SPNsXEUGjXtkfeAnwYBrDTUQ0tY5jgY8T4k+kFMW31rRtIt6VyWRmF8dKWrBgl/RW4JMjHM9IsP0EcIekOjeFTHvOk/RWSfPSv7cwfCHRTCaTyQRHEoK7BY+kc7OBrW0fbfth23+2fQzwGtunAEv32PeNkr4vaRdJbyr+9WHMY4PtM2x/MP2rDPokNra9ge232d4t/asrfX+4CPok7iJceceZ05gIaEGU+p9W03YcuE7SlyStkv59iajYGQhzWdy5ifjTg5KWIFLeT5J0L5MtFbvlMdsPabKLXrsUrMVsX9PSvi61sol4VyaTmV1sC3xH0o7EDtCuRNrrXGRp4DZJ1zDZlaIyszIzxW1iHyZKveYRi459RzS0TCaTmcvIpVIN2/+UNFvWcn9NmwvfScfbAn9P/++1PGVR4H4mdF2LPsdSuFjSehWnHwJ+VVVSlzLBDgdeSNiXLwQ84mpnqKskrTGdrm3iOkk/Ak4l3q/tgGuLoFlVedgY8KSyDpHtR5NO0dBI4uH7ExqmT2LCqWvliuYfIDZmT0nH5xPyBANhtlwsuqGJbeAbCPX3DxIaB0sBB/ZhDLelhdlCSXx5L8J+r477UuZOEazalhpLcNvnS7qBCfGuvduJd2UymdmD7btSls93CRHtV9seZweLQTLnMp16ZVRuE5lMJpNpy11JXLnI8nkvkYUxNJJm6IcJfdJ+msfsBBwGHEGsc64Gdk7VC+/vse+v2b6ifCItzseVI4D1gJ8Sa7g1idKspSTtWSFg/VWiAuQ0oiJlV+AFNX0fTwR//o+oBCmCEmtXtF0UuAd4eTr+I/AUwkZ+XANnf5S0te3vA0h6AzDs9e/XiZjB9UTGUS22H2GIjrtzUuMnCZEtR6iUj0z8KV0892NiJ/5c4KCiLrGi/crAMcBLCUX6uwmtof8ttVnd9u010WIDfxqkiG4mkxkdLY4fAM8kdon+AVBzY89kaulUBDKTyWQyg0XhLvwVInPFwIXAPsM0bpiJ5jGSbrC93nTnxgVJZwCftH1bOl6DSDj4KHBGhRhwoeu6QC+mTiBY0p2E6/MtlEqiZsvaMCVInEQYMkHoRu1i+5dDHMNPbG/UYdtlib/ri5jsHP6K2if1Mra5GPiBmS1QmcrS5hXibi2PHWN7d0kX1Tx9GeBm27sMdJCZTGbojNI5bdyocQhZQE0KdKZEExHITCaTycx+BmUeI2lR4J1MXQB3fb+RtAmxWb4P8N+lh5YEthlXw5saY4VbU6CtygXqUmAL4GvA/xHVIG+v+v0kXWV7kwEOfyxIEi3Y/st0bQfw2p8nyu3OoKSva/uGirbnEWVe+wLvAd4G/NH2xwYxtrlc6nWDpA1tXzuqASiscrez/WA6Xhr4tu3X1LT/LPCFlvYftv2Joo3t3dPPzdu8bmuKYCaTmQXMpcDOdBTlSskp5A/ACUR2507Ac0Y4tJnExrbXGPUgMplMZi4j6XDab2TsNcThDMo85gTgduA1RHbLTsDPe+xzEWAJYr1bLmH+M6EhNK7cJulI4NvpeHvgZ5KeTDL4aWEXItDwfqLEaHngzTV93yjpZOAHTA5KzKpM3lEEfEoU2T5lIygzWWOqYBnbX5e0t+1LgEskDSw2MZczfm4Hng/8ihD8bFfjOKgxTEnDq0vNa9O+MlUxRc7fC2xKfNguA46qKyPLZDKZ2UjVTmQ/difnApK+DhzaoQhkJpPJZAaApLe1e9z2cUMcy6uATwBrAOeRzGNsX9xjvzfaXrcoV5K0MHCZ+2DDLmnFmbQxlgJrxRoO4ApC9+fvhNFP10ENSfMrTldm8kpayfbd053LdI+kq21vLOlcoozz98B3bK8yiNd5nY34AAAgAElEQVSbyxk/lVk1ZSr0MibRhyDRPyWtYPvX6fVWbPd6hAj0k23/I7V/CvDkmrbHE5Z7h6fjHYlo+nY9jjmTyWRmEo9I2onYOTOwA/1xZZwLNBGBzGQymcwAGGZgZzoGaB5TZLI8KGlNomTpmX3oF+BrkjqusBg1yYzj0PSvlSlBH0l3U7F+rHKRsr1bg6GcTohMl/kOsH6DPuYckpYi3LX/LZ26BDjQ9kMVzQ9K7T9MrNmXJLK2BsKcDfx0GPndMv0sbNVOSD936tMw9gMul3QJcfH8V2D3Nu1PAi4sRWt3A+puBmu2pOhfJCnv2mYymbnGjoRTyGHExOiKdC4zPV8nUsgniUBmMplMZvgkIdiPEdk2AxeCrRnDy4CbbP9Q0s7Af0o6rA8ZNcekgMwnge8TJVqf6rHPgmcUQR8A2w8koeyxpMIOHKgO5CTKJUWLEpv8T6/pe1otJUmrp8eXKqzbE0uWnzOOSLqcCLRcBlxRpYc7BL4B3Aq8JR3vAswH3tTasOSG9xBQK9PSL+ZsqVcTmpRYddH3M4ioOcDV00XNJb0WeGU6PN/2uTXtTgS+avvqdLwR8D7bu/Y65kwmk8nMfuaKCGQmk8nMBIYtBFszhp8CLyaE/+cTGwRvsf3ytk8cIZKuJ8ScyxUWZ46xq9ftVNiB276/QR/X256SmSPpNEJLaUdKWkq29y61eQPwRmBrIghX8DCRKXVlo19oiEhaiUik+Fdiff0PomRwYFk0FWOoEuCeci6dP47Imitnox06KBONOZvx0xBJepntK9LBS4F5fer7ycCfiL/FGpKwfWldY9tnA2e3GWhRnrYwcKWkX6eHViC+6JlMJjNnSDuk72aqJXl2ppqeOSECmclkMjOEoQrB1vC4bafgwP9L43lnr50m4eI3M/VefWCvfdO8wmLUPJTWex0hqRzAmkdkANWt8Z9veztJb7B9XLrHX1ZuYPt7wPckbWL7qqaDHyW275b0d+DR9G9z4IVDHsbfJG1q+3JYkMH1t5q2a1dko1Vq/faDHPjpjHcC30g1eAAPAj0vGiQdTCi1T7LKBSoDPynd7mCi5lVM6C2UbYm3rHpuJpPJzFG+R0xqLqC0c5bpiKcQAZ9Xl86ZsCjNZDKZzHApdHD+IOn1hBBsZUnPAHlY0seBnYF/kzSP2Gzule8R5S7X0x+XsAXYPicFR4oKi336pEs0KC6S9EU6sANPlLWAHgf+l4kyo1aaaCltI+k2ImhxDpHl9UHbJ3byS4wCSb8E7gNOJrLRPmB72KXq7wGOT3EDEQkeb69pO0/S0rYfAJD0dAYYn8mlXg0oAj814kzd9HcHEenr6AIn6U5gK9sd2RtKejER1YZIc7u5u5FmMpnMzKQuvTaTyWQymZmEpC2JjYzlmRCCPcD299s+sb9jeDZRJnSt7cskrQBsZvv4Hvu91faafRnk1L5FlDStbPvANOZn275mEK/XK5Iuqjjtfmg5SXoXIdq8FvBNQkvpk7aPrmh7k+11JG1DJBZ8CLh0nF1RJe1NuKEtT1S6XEKM+ZcjGMuSALb/3KbNrsB/AqelU9sB/2X7hLrn9DSmHPiZHknPAj4LPNf2ayWtAWxi++s99ns2sF2ntnySrrD9sg7b7k2UNxQ7s9sAx9g+vP5ZmUwmM7uQdBBwpe0fjXosMw1JyxGLi+K+cxlRi/7b0Y0qk8lkMrMNSccAh9u+ZQB9H0lUVrzC9guTjsp5tjfs92uNAkkfave47S+ldvOAbW2f2mG/t9l+kaSvERbj50i6eZwDPwWSliBMkPYFlrO90BBfu1HZYoorFEG9H9semBlTDvx0QArQzAf2s/1iSU8CbrS9Vo/9nk4IpF3I5FS+vWraHwY8G/gu0+gtJPG1TWw/ko4XB67KNryZTGYuIelhYHHimvkY1SWymQoknU+kSxc7TzsDO9l+1ehGlclkMnOTYQvB1oxhY2JD4IXAIsBCwF9sL9X2idP3+zPg+cDdxP26uFf3vG4pDHnKZj3jGMCQtLPtE+sCOUUAp+J5JwMbMiHEvBVwDfCL9LwDSm2vs73BlE6q+/08IfL8N+AlwNOAs2xv1NlvNHwkHUpk/CwBXAlcTlS93DXEMZzDRNliWZz70NonDYms8dMZz7B9aqppxfbjkvqhFfF9JqulT8eSwF/pTG9BTNazeCKdy2QymTmD7aeOegwzmGVtzy8df1PSPiMbTSaTycxthioEW8NXgbcSpSkbALsCL+hDv6/tQx91PCZpIWLNVJg+DFv3pRMWTz+bzluWA9YrrMsl7Q/80PbOFW0vkLQv4Q73SHHS9p9aG9r+D0lfIMSmn5D0CPCGhmMbNlcBX7B9zwjHsJztfx/h69eSAz+d8YikZZi4YGxMRPJ6IqmpPwVYwfYdHbTfrUH384GfSDozHb+RELnKZDKZOUXaFV0VWLQ41849MbOA+yXtDHwrHe8AdGwnm8lkMpm+MlQh2Dps3ylpIdtPAPMl3Qh8vMc+fwUg6ZmU7tV94ivAmcAzJf0XsC3wiT6/Rs+UdHaOsP3HBk99FuFgVfBoOlfF9unn+8ovDazc2lDSwkyIeEPo5RzVYFyj4AxgR0kr2f7MiPScrpS01iDKFnsll3p1gKT1iYvGmsCtwLKENk9PYsmStgIOARaxvZKkdYADbW9d0/4FwJHAs2yvKWltYGvbB9W0X49Id4NIc7uxl/FmMpnMTCMJGe5N7IjdRLh6XNUPkcTZjqQViZT+TYiJ4ZXAXrZ/PdKBZTKZzBxk2EKwNWO4FNgC+BrhCPUH4O29lk1J2ppwp3oucC+wIvBz2y/qbcQL+l8deCVR/XBhp0Y5o0DS/xDOXKcAZxSBvjbt9yNcvMqb/afY/lypzXa2T5O0cqdlT0nbZ2HguHRqF+AJ2+9q8vsMk1HqOUm6hZgrPYnYbLyLPpct9koO/HRI0vVZjfjj3WH7sWme0kmf1xNiTheXak5rVe0lXQJ8BDi6k/aZTCYz10k34g2Bq5M7xerAZ22/acRDy2QymUymEcMUgq15/RWBewh9nw8CSxEZKnf22O/NxO91ge11JW0O7Gz7nT302dbqvqq8aVyQ9BKipO6NwM+Ab7ezUU+b/YWT86Wtm/0lnaMbbK/X4Rim6CCNozZSmVHqOaXvRi1FVtsoyaVeHSDpl8AXbR9VOneW7S177Pox2w+l9LmCdjWni9m+pqX94z2OIZPJZGYzf7f9d0lIerLt2yWtNupBzQTGQUg0k8lkMhOkQM9Qgz0tr/8rSYsQjkVnEJvhj7Z/Vkc8Zvt+SfMkzbN9kaQv99jn9UQGRnnhVBxXljeNC6k06RpJnwW+RGTd1AZ+bN8A3NCmy/slnQesJGmKvmxNtckTklZxskKXtDKT9WPHkZHpOZXKFU+wvUv5MUknEBlTIyUHfjrjMWBzSRsBe6QL3PP60O9tknYEFpK0KrAXkUpfx32SVmHiw7wtkWKZyWQymWp+K+lphBvi+ZIeAEa+6zJDGAch0Uwmk8mMCZJeT+i8/JIIoKwkaQ/bZ/fY9YMKC+5LgZMk3UtJfLgbbK/U45hGgqQlgW2IjJ9ViBKul/TY7euB9QiXzk7dpT4CXCTpLuJvvSJhkT7OjIOe06TyxBSIWn/IY6gkl3p1QClt7KPAm4ma2u92mirXpt/FgP2YcOk6FzjI9t9r2q8MHAO8FHiAsDzcaRxSxzKZTGbckfRyIi39nD7tUM5qUur9Zi1CopfYXmu0I8tkMpnMKJB0O7BlUdqVNqR/aHv1HvtdHPg7EWDYibhXn2S7Z0MBRanETsAoBX87RtLdxGbVqbav6nPfyzYRjpb0ZELqBCK76x/9HM8gGJWek8L9+z+BpxAu3KQxPAocY7snAfR+kAM/HdBSJ7gFYWX4dNvP7KHPhYg61s0btD/Y9r7p4jivsO3LZDKZTKbfjIOQaCaTyWTGB0nXloVyU1Dlmn6J56ZslwUVKf3Q4Rml4G83SJJtpwwobP9lRONYGNgT+Ld06mJCZ7Znndt+I2lJ23+u03Uapp6TpM+NQ5Cnilzq1RmfKv5j+wJJrwHe1kuHtp+Q9E9JS9me1ho+td80/b+n1MdMJpPJZKbD9vGSrmNCSPRNwxYSzWQymczokVQYIlwn6UfAqYT0xHbAtX3ofw/gACLr55/0V4dno0LwFxaULS/Sh34HxYuSJszTiTjQH4G32b51yOM4knD1OiId75LOjaOr18nAlkzoOhWMQs9pNUmvI7LLh6Iv1Ck546cNklZPQqCVJV1JSKuX/r8HrAucT6mO1fZeNe2PJLSFTmtpf0Yv48hkMplMJpPJZDKZKiTNb/OwexX9l/QLYBPb9/XST03fPyFkMq5NAaBliYyfsdSsk3QlsJ/ti9LxZoQb6UuHPI4Z5+o1DqTqoN2AjYk1+3zbd4x2VEHO+GnPh4DdqRbBMhO7oN1yRvrXKYsC97e8rhv2kclkMplMJpPJZDIdYXvQor6/ZEIXpd+Mg+BvExYvgj4Ati9OMh9dI+lwJmfCTKIm6WDGuXolx7JvAd+zPajPU1tsXwBcIGkpYIf0/98AxwInjrJULmf8jBhJTwFWGJdIYCaTyWQymUwmk8m0IukFRLnPs2yvKWltYGvbB/XY77rAfOAnwAIB4boqiC76H4ngbzdIOpOwZi/09HYG1re9TQ99tpUosX1cxXNeSfxNJrl6lYNS40Yy8diecDG7Fvg2cFadcdIAx7EM8XfbBfg9cBKwKbCW7c2GOZZJ48qBn86Q9FLgX5gsOHZ8j31uBRwCLGJ7JUnrAAfa3rqm/UAutplMJpPJZDKZTCbTDkmXEDbfR5eMb261vWaP/V4DXA7cQmj8ANUBidlOEp8+gAgUAFwG7F84bA55LDPO1QsWmCK9Ang38O+2lxzia59JvGcnAN+0/YfSY9fZ3mBYY5kythz4mZ4ksLUKcBMTKW7uNQot6XriQ3lxJxfPQV1sM5lMJpPJZDKZTKYdhatXi+PxTbbX6bHfBf1lBoekDYD9iOydcjLD2hVtFwXeSwSgTASgjhp29kxTUjXNVkTmz3pExs8Hhvj6m49rVlTW+OmMDYA13P8o2WO2HwonxAW0U/9ezPY1Le0f7/OYMplMJpPJZDKZTKaV+yStQtKLkbQt8If2T+mIsyXtDvyAyaVeQ7PhHhdScOY/mVppMiU40wUnEUkEkzKrajgeeBg4PB3vSGSxbNeHcQwESacCLwHOAb4KXDICZ62rJH2IiYDZ5cCR4xAwy4GfzrgVeDb9ubCVuU3SjsBCklYF9gKubNN+UBfbTCaTyWQymUwmk2nH+4BjgNUl/Q64G9ipD/3ukH5+vHRu2Dbc40KT4ExT/mj7+x22XdP2GqXjiyT9rM/j6TdfB3awPUoR6rENmOXAT2c8A/hZqj8tR6ErtXga8AEi3e4fwMnAuUA7vZ5BXWwzmUwmk8lkMplMppKkm/Je21skl6l5th/uU/cvbM2ISKVGc5EmwZmmfFrS14ALmbymrXKIvkHSxravBpC0EXDdgMbVE5LeVDp8Q0t1TN3vNyjGNmCWAz+dsX8/O5N0gu1dgHfb3o8I/rRrv7ftw4DnDOhim8lkMplMJpPJZDKV2H5C0qbp/4/0ufsrCT2W6c7NBZoEZ5qyG7A6sDAT2UQGFvQt6ZZ0bmHgSkm/TscrArf3YQyDYKv085nAS4Efp+PNic/RMAM/Yxswy+LOIyBF/bYAzgY2IyzyFtBaz1qIpkm6wfZcvABmMplMJpPJZDKZESLpSOB5wGnAguBPt0EJSc9O/Z1IlMQUa6IlCSHh1Xsa8AxE0olEcOY2SsEZ2+/oQ9932F5tmjYrtnvc9q96HcegkHQ+sGvhpCXpOYSz1muGOIafE65ev06nVgDuIHR53Setpq7IGT8dIOlhkq5OiYeI6N2Hbd/VsMujiCjuysANLY9V1bP+XNIvgOdK+ml5aIz4A5TJZDKZTCaTyWTmBIsC9xOuxAWTMkYa8hrg7cBywJdK5x8mBI7nIhtOF5zpgSslrWG7tvRonAM7HbBc2T4duIcIvAyTfx/y63VMzvjpAEmfAX5L6PAIeCth734DsKftzbrs90jbe3bY9tmEBtAUXaEZ/gXNZDKZTCaTyWQycxRJb7Z9+qjHMQ5Img98sV1wpoe+f06sYe8myshmVRKBpK8CqwLfSqe2B+4cpp37OJMDPx0g6WbbL245V5RfTXmsYd+bAqvani/pGcBTbd/dpv1TgBVs39Hta2YymUwmk8lkMplMJ0j6qO0vSDqcqVUQ2N6rx/6fDLyZqRbmB/bS70xkkMGZujKu2ZREkISe/zUdXmr7zFGOZ5zIpV6d8VdJbwG+k463BQrl+a4jZ5I+DWxA1AHOBxYhalxfVtN+K+CQ1G4lSesAB/bBXSyTyWQymUwmk8lkqiiyTwYlUvs9QkbjekqCxnOUQZYKrWr7gvIJSW8Djhvgaw6VpDc1TDHnGUMO/HTGTsBhwBHp+Cpg55R98/4e+t0GWJek82P795Ke2qb9/sBLgItT+5skrdTD62cymUwmk8lkMplMO7YHzgKelpyG+81ytsdWG2WY2P5VS0XIssASfer+U5LeDOyb+vwaEWib0YEfSZfb3rRCl7fIllpyREMbK3LgpwOSePNWNQ9f3kPXj9q2JAMkm/Z2PGb7IWmyCVgPr5/JZDKZTCaTyWQy7Vhf0nOBd0g6nmkcibvgSklr2b6lx35mPBUVIQvTpiKkIS8HPgzclI4/ZftbbdrPCGxvmn62S6CY88wb9QBmApKWk3SmpHvTv9MlLdeHrk+VdDTwNEnvBi4Ajm3T/jZJOwILSVo11dle2YdxZDKZTCaTyWQymUwVhSPx6kQ5VvlfP8q/NgWul3SHpJ9KuqXFyXgusQ1h5vMIREUI0K+AxtJE9cgviUyfFdWSUTBTkbSQpNtHPY5xJos7d4Ck8wlHrxPSqZ2BnWy/qoc+RVgXrg68moicn2v7/DbPWQzYL7WHcPk6yPbf656TyWQymUwmk8lkMr3SxJG4Yb+zXnS4UyRdY/slkm6wvV6qCLmqT+LO/wN83vY3kmTJwcAGtl/aa9/jgKTvAf+/vbuPlbwszzj+vSDACsqb0b4pKFShCCgodVetb6mSWqXG1ioi2qDYWCqobdKY+JJo0iamNrW0arUNFWirttaqtFYxVXYrIsjyllhoNQqxrRoVKEIAi1f/mN/RlRyWObtzzm9m9vtJTuY8z8yZc3FCNpk7z3Pfr2l709hZ5pGFnymsTPC6v71deN/r2h435Wv3Bj7d9hm78zslSZIkaZ6s1tdmZ5OOl1WS32UykvxZwB8AZwB/0/bcGbz3YfcuiiR5atutu/ve8yDJVib9cy9nODEF4CCkCXv8TOc7SV4KrNyBPBX4zgzed3uSk9pecX8vbHtPkh8kOajtrTP43ZIkSZI0qnXua7MwhhshH2RyI+R/mfw93ryzGyFrsdpJmGUp+gzeNHaAeeaJnykMxw/PBbYwaaZ8KXD27h4jG+4h/ixwI5Oq5Ern8VWP8g3H104ALubHq5hn704OSZIkSRpDkqsZJh23PWHYu3YW15sWzVpuhEhr4YmfKQz3S9fjiNjJa3z9PwxfkiRJkrQM1jrpeJlNfSNEP26Vce4AtzJpQP47w6TuPZYnfqaQ5P3AOW1vGdaHAO9oe8a4ySRJkiRpca1nX5tFs9YbIVO+56E7e77td3f1vedJkrcBX2cylCnAi4Ejge3Aq9s+fbx047PwM4UkV60cO9zZniRJkiRpbZI8iyknHS+z9ZhwluSrTE7CBDgMuHn4/mDgpraP3NX3nidJrmn72HvtXd32cas9t6fxqtd09kpySNub4YdVU/92kiRJkrSb2l6c5AsMn7GSHLosJ1HWYj1G2K8UdpK8D/hI238e1r8EPH/Wv29EdyT5deDvh/WvAXcO3+/xp132GjvAgngH8PkkbxuOkF0KvH2jQyR54TR7kiRJkrQIkvxmkm8A1zLpx3Ll8KjZ2rxS9AFo+wngSSPmmbXTgNOBbwHfHL5/aZIHAL89ZrB54FWvKSU5BnjmsPzXtl8aIcP2tife354kSZIkLYIk/wlsafvtsbMssySfBLYBFw5bpwFPbbvWgUNaQF5Xmt6hwO1tz0vykCSPbPvVjfjFwzG85wA/k+RPdnjqQOD/NiKDJEmSJK2DrwB3jB1iD3Aq8BbgI8N667C3FJJsAl4BPAbYtLLvQKYJCz9TSPIW4AnAUcB5wD5MKqVP3qAI/83kuOMpTI4+rrgNeN0GZZAkSZKkWXsDcOnQ4+eulc22Z48XafkMPZPOGTvHOroAuB44GXgrkxNN/z5qojniVa8pJLkaOAHYvjLJK8m1uzNWbxdzHMjk1NE9w3pvYL+2VsglSZIkLZwklwP/BlwH/GBlv+37Rwu1RJL8cdvXJvk4qzQ5bnvKCLFmbmXq9srn9CT7ANvabh472zzwxM907m7bJAVIcsBIOT4F/CLwvWH9gGFvmZpySZIkSdpz7NP29WOHWGIXDI9/OGqK9ff94fGWJMcC3wAeOmKeuWLhZzofSvLnwMFJzgTOAN43Qo5NbVeKPrT9XpL9R8ghSZIkSbPwiSSvAj7Oj1/12uPGua+HtlcOj5ck2Rd49PDUDW2/f98/uXDem+QQ4I3Ax4AHAm8aN9L88KrX/UgS4GHA0cCzgQCfbHvxCFk+B7ym7fZh/XjgT9tu2egskiRJkrS7kqw2MKdtj9jwMEssydOB9wNfY/KZ9uHAy9tuHTHWzKw2fGkjBzLNOws/U0hyXdvj5iDHScAHmDR7DvCTwItWqriSJEmSJN1bkiuBl7S9YVg/Gvjbto8fN9lsJNne9sR77V25LP99u8urXtPZnuSktleMGaLtFUmOZjJdDJbveJ4kSZKkPUiSF6yyfStwXdtvbXSeJbbPStEHoO1/DA2QF9rw+fgxwEH3+n/pQHYY676ns/AznScCpyW5EbidyWmbjjDVa3/g9cDhbc9M8qgkR7W9aCNzSJIkSdKMvALYAnxmWD8duBJ4ZJK3tr3gvn5Qa3Jlkr8ALhzWpwFfHDHPrBwFPBc4GHjeDvu3AWeOkmgOedVrCkkOX22/7Y0bnOODTP4RfFnbY4dC0KVtH7eROSRJkiRpFpJ8ksnnm28O658AzgdOBba2PXbMfMsiyX7AWcBThq1twLva3nXfP7U4kmxp+/mxc8wrT/xMYaMLPDtxZNsXJTkVoO0dQ/NpSZIkSVpED18p+gy+Nex9N4ltLWYgyd7ANW2PBv5o7Dzr5KokZzG59vXDK15tzxgv0vzYa+wAWpO7kzwAKECSI9lh5KEkSZIkLZjPJrkoycuTvBz46LB3AHDLyNmWQtt7gBuSHDZ2lnV0AZPhRycDlzCZzH3bqInmiFe9FkiSZwFvBI4BPgU8GfiNtp8dM5ckSZIk7YrhBsOvMvlsA/A54MP1g+pMJdkKnABczqRvLQBtTxkt1AwluartCUmubXv80Lh6W9vNY2ebBxZ+FkySBwObmTSYvqztt0eOJEmSJEmaY0mettp+20s2Ost6SHJ5258fCly/BXwDuLztESNHmwsWfhZAkqPbXp/kxFWeLvDdOepDJEmSJElTSbIZOBf4OWBfYG/g9rYHjhpMCyXJK4EPA8cD5wEPBN7c9j2jBpsTFn4WQJL3tn1Vks/cx0sezKRZ1+kbmUuSJEmSdkeSLwIvBv4OeALwMuDRbd8warAlkeQ2hh6xq7HAtmdwqtcCaPuq4fEZ9/WaJJ/auESSJEmSNBttv5xk76EJ8XlJrgIs/MxA2wcBJHkb8D9MmiAHOA34qRGjzUSS1+/s+bbLOsVsTSz8LJAkm5jcV3wKk6rtNuA9be9s++xRw0mSJEnS2t2RZF/g6iRvZ1KccPr07J3S9rE7rN+d5BrgzWMFmpEHDY9HAScBHxvWz2PSyFp41WuhJPkQk5F0Fw5bLwEObvvC8VJJkiRJ0q5JcjjwTSb9fV4HHAS8q+2XRw22ZJJcCvwZ8AEmhwhOBc5q+6RRg83I0NT5l9veNqwfBPxT26eOm2w+WPhZIEm+1PaY+9uTJEmSpHmXZG/g/LanjZ1l2SV5BPBO4MlMCj+fA17b9mvjpZqdJDcAx7e9a1jvB1zb9qhxk80Hr3otlu1JNre9DCDJE4EvjpxJkiRJktas7T1JDk+yb9u7x86zzIYCz6+MnWMdnQ9cnuQjw/r5wF+NF2e+eOJnASS5jklVdh8mdxdvGp46DLjeEz+SJEmSFlGS85mMcv8YcPvKvk15ZyvJQ4AzgUewwwGQtmeMlWnWkpwI/MKw3Nr2qjHzzBNP/CyG544dQJIkSZLWwVeGr734UaNezd5HmQwH+jRwz8hZ1kXb7cD2sXPMIws/C6DtjSvfJ3ksP6pibmt7zTipJEmSJGnXJLmg7enALW3fOXaePcD+bX9v7BAah2PyFkiSc4C/Bh46fF2Y5DXjppIkSZKkNXt8kp8GzkhySJJDd/waO9wSuijJc8YOoXHY42eBJLkW2NL29mF9APD5tsePm0ySJEmSppfkbODVwBHAfwHZ4em2PWKUYEsqyW3AAcBdwPeZ/L3b9sBRg2lDWPhZIEOT55Pa3jmsNwFXtD1u3GSSJEmStHZJ3t321WPnkJaZPX4Wy3nAF+41ou4vR8wjSZIkSbvMos/GSXII8Chg08pe263jJdJG8cTPghlG1D1lWG5zRJ0kSZIkaWeSvBI4B3gYcDWwmUnbkGeOGkwbwsKPJEmSJElLbKVtCHBZ28clORr4/bYvGDmaNoBTvSRJkiRJWm537tArdr+21wNHjZxJG8QeP5IkSZIkLbevJzkY+Efg4iQ3AzeOnEkbxKtekiRJkiTtIZI8DTgI+Je2d4+dR+vPwo8kSZIkSdKSssePJEmSJEnSkrLwI0mSJEmStKQs/EiSJEmSJC0pCz+SJEmSJElLysKPJEmSJEnSkvp/RIO1sNMAAAADSURBVCULtZ+zVGEAAAAASUVORK5CYII=\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"3NP9IAx0nIkw","colab":{"base_uri":"https://localhost:8080/","height":578},"executionInfo":{"status":"ok","timestamp":1620090856875,"user_tz":-120,"elapsed":296973,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"17da7f61-af6f-4ead-9d37-0fcb72d27db6"},"source":["keyword_pipe['yake'].setMinNGrams(1) \n","keyword_pipe['yake'].setNKeywords(3) \n","keyword_pipe['yake'].setMaxNGrams(5) \n","keyword_pipe['yake'].setWindowSize(6)\n","keyword_predictions = keyword_pipe.predict(df['Title'])\n","keyword_predictions.explode('keywords').keywords.value_counts()[0:100].plot.bar(title='Count of top 100 predicted keywords with new parameters.', figsize=(20,8))\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":10},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"h6kgf8LpvoVC"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/dependency_parsing/NLU_typed_dependency_parsing_example.ipynb b/examples/colab/component_examples/dependency_parsing/NLU_typed_dependency_parsing_example.ipynb
deleted file mode 100644
index 315301be..00000000
--- a/examples/colab/component_examples/dependency_parsing/NLU_typed_dependency_parsing_example.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_typed_dependency_parsing_example.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"s4ljYpQNp50r"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/dependency_parsing/NLU_typed_dependency_parsing_example.ipynb)\n","\n","# Typed Dependency Parsing with NLU. \n","\n","\n","Each word in a sentence has a grammatical relation to other words in the sentence. \n","These relation pairs can be typed (i.e. subject or pronouns) or they can be untyped, in which case only the edges between the tokens will be predicted, withouth the label.\n","\n","With NLU you can get these relations and their types in just 1 line of code! \n","# 1. Install Java and NLU"]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619905346261,"user_tz":-120,"elapsed":106923,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b151cfb1-176d-4bb5-9e47-89f0cdf5e11f"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:40:39-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \rInstalling NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-01 21:40:39 (58.0 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 78kB/s \n","\u001b[K |████████████████████████████████| 153kB 43.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 18.5MB/s \n","\u001b[K |████████████████████████████████| 204kB 50.3MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"kHtLKNXDtZf5"},"source":["# 2. Load the Dependency model and predict some sample relationships"]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":291},"executionInfo":{"status":"ok","timestamp":1619905429836,"user_tz":-120,"elapsed":190482,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"29989a7d-1474-4dfe-c67f-550c7cc2159a"},"source":["import nlu\n","dependency_pipe = nlu.load('dep')\n","dependency_pipe.predict('Untyped dependencies describe with their relationship a directed graph')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dependency_typed_conllu download started this may take some time.\n","Approximate size to download 2.3 MB\n","[OK!]\n","dependency_conllu download started this may take some time.\n","Approximate size to download 16.7 MB\n","[OK!]\n","pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
token
\n","
pos
\n","
unlabeled_dependency
\n","
labeled_dependency
\n","
\n"," \n"," \n","
\n","
0
\n","
Untyped dependencies describe with their relat...
\n","
[Untyped dependencies describe with their rela...
\n","
[Untyped, dependencies, describe, with, their,...
\n","
[NNP, NNS, VBP, IN, PRP$, NN, DT, JJ, NN]
\n","
[ROOT, describe, Untyped, relationship, relati...
\n","
[root, nsubj, parataxis, det, appos, nsubj, ns...
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... labeled_dependency\n","0 Untyped dependencies describe with their relat... ... [root, nsubj, parataxis, det, appos, nsubj, ns...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"5lrDNzw3tcqT"},"source":["# 3.1 Download sample dataset"]},{"cell_type":"code","metadata":{"id":"gpeS8DWBlrun","colab":{"base_uri":"https://localhost:8080/","height":602},"executionInfo":{"status":"ok","timestamp":1619905438457,"user_tz":-120,"elapsed":199097,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"6a71ba58-34ed-4920-f3c8-7eed133943f3"},"source":["import pandas as pd\n","# Download the dataset \n","! wget -N https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv -P /tmp\n","# Load dataset to Pandas\n","df = pd.read_csv('/tmp/train-balanced-sarcasm.csv')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:43:48-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.89.238\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.89.238|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 255268960 (243M) [text/csv]\n","Saving to: ‘/tmp/train-balanced-sarcasm.csv’\n","\n","train-balanced-sarc 100%[===================>] 243.44M 54.9MB/s in 4.3s \n","\n","2021-05-01 21:43:52 (56.3 MB/s) - ‘/tmp/train-balanced-sarcasm.csv’ saved [255268960/255268960]\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
label
\n","
comment
\n","
author
\n","
subreddit
\n","
score
\n","
ups
\n","
downs
\n","
date
\n","
created_utc
\n","
parent_comment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
NC and NH.
\n","
Trumpbart
\n","
politics
\n","
2
\n","
-1
\n","
-1
\n","
2016-10
\n","
2016-10-16 23:55:23
\n","
Yeah, I get that argument. At this point, I'd ...
\n","
\n","
\n","
1
\n","
0
\n","
You do know west teams play against west teams...
\n","
Shbshb906
\n","
nba
\n","
-4
\n","
-1
\n","
-1
\n","
2016-11
\n","
2016-11-01 00:24:10
\n","
The blazers and Mavericks (The wests 5 and 6 s...
\n","
\n","
\n","
2
\n","
0
\n","
They were underdogs earlier today, but since G...
\n","
Creepeth
\n","
nfl
\n","
3
\n","
3
\n","
0
\n","
2016-09
\n","
2016-09-22 21:45:37
\n","
They're favored to win.
\n","
\n","
\n","
3
\n","
0
\n","
This meme isn't funny none of the \"new york ni...
\n","
icebrotha
\n","
BlackPeopleTwitter
\n","
-8
\n","
-1
\n","
-1
\n","
2016-10
\n","
2016-10-18 21:03:47
\n","
deadass don't kill my buzz
\n","
\n","
\n","
4
\n","
0
\n","
I could use one of those tools.
\n","
cush2push
\n","
MaddenUltimateTeam
\n","
6
\n","
-1
\n","
-1
\n","
2016-12
\n","
2016-12-30 17:00:13
\n","
Yep can confirm I saw the tool they use for th...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1010821
\n","
1
\n","
I'm sure that Iran and N. Korea have the techn...
\n","
TwarkMain
\n","
reddit.com
\n","
2
\n","
2
\n","
0
\n","
2009-04
\n","
2009-04-25 00:47:52
\n","
No one is calling this an engineered pathogen,...
\n","
\n","
\n","
1010822
\n","
1
\n","
whatever you do, don't vote green!
\n","
BCHarvey
\n","
climate
\n","
1
\n","
1
\n","
0
\n","
2009-05
\n","
2009-05-14 22:27:40
\n","
In a move typical of their recent do-nothing a...
\n","
\n","
\n","
1010823
\n","
1
\n","
Perhaps this is an atheist conspiracy to make ...
\n","
rebelcommander
\n","
atheism
\n","
1
\n","
1
\n","
0
\n","
2009-01
\n","
2009-01-11 00:22:57
\n","
Screw the Disabled--I've got to get to Church ...
\n","
\n","
\n","
1010824
\n","
1
\n","
The Slavs got their own country - it is called...
\n","
catsi
\n","
worldnews
\n","
1
\n","
1
\n","
0
\n","
2009-01
\n","
2009-01-23 21:12:49
\n","
I've always been unsettled by that. I hear a l...
\n","
\n","
\n","
1010825
\n","
1
\n","
values, as in capitalism .. there is good mone...
\n","
frogking
\n","
politics
\n","
2
\n","
2
\n","
0
\n","
2009-01
\n","
2009-01-24 06:20:14
\n","
Why do the people who make our laws seem unabl...
\n","
\n"," \n","
\n","
1010826 rows × 10 columns
\n","
"],"text/plain":[" label ... parent_comment\n","0 0 ... Yeah, I get that argument. At this point, I'd ...\n","1 0 ... The blazers and Mavericks (The wests 5 and 6 s...\n","2 0 ... They're favored to win.\n","3 0 ... deadass don't kill my buzz\n","4 0 ... Yep can confirm I saw the tool they use for th...\n","... ... ... ...\n","1010821 1 ... No one is calling this an engineered pathogen,...\n","1010822 1 ... In a move typical of their recent do-nothing a...\n","1010823 1 ... Screw the Disabled--I've got to get to Church ...\n","1010824 1 ... I've always been unsettled by that. I hear a l...\n","1010825 1 ... Why do the people who make our laws seem unabl...\n","\n","[1010826 rows x 10 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"uLWu8DG3tfjz"},"source":["## 3.2 Predict on sample dataset\n","NLU expects a text column, thus we must create it from the column that contains our text data"]},{"cell_type":"code","metadata":{"id":"3V5l-B6nl43U","colab":{"base_uri":"https://localhost:8080/","height":291},"executionInfo":{"status":"ok","timestamp":1619905453827,"user_tz":-120,"elapsed":214462,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"749446d8-b24f-4d8e-dbe3-580b4e87d396"},"source":["dependency_pipe = nlu.load('dep')\n","dependency_predictions = dependency_pipe.predict(df.comment.iloc[0:1])\n","dependency_predictions"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dependency_typed_conllu download started this may take some time.\n","Approximate size to download 2.3 MB\n","[OK!]\n","dependency_conllu download started this may take some time.\n","Approximate size to download 16.7 MB\n","[OK!]\n","pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
token
\n","
pos
\n","
unlabeled_dependency
\n","
labeled_dependency
\n","
\n"," \n"," \n","
\n","
0
\n","
NC and NH.
\n","
[NC and NH.]
\n","
[NC, and, NH, .]
\n","
[NNP, CC, NNP, .]
\n","
[ROOT, NH, NC, NC]
\n","
[root, cc, flat, punct]
\n","
\n"," \n","
\n","
"],"text/plain":[" document sentence ... unlabeled_dependency labeled_dependency\n","0 NC and NH. [NC and NH.] ... [ROOT, NH, NC, NC] [root, cc, flat, punct]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":4}]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/dependency_parsing/NLU_untyped_dependency_parsing_example.ipynb b/examples/colab/component_examples/dependency_parsing/NLU_untyped_dependency_parsing_example.ipynb
deleted file mode 100644
index b549db67..00000000
--- a/examples/colab/component_examples/dependency_parsing/NLU_untyped_dependency_parsing_example.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_untyped_dependency_parsing_example.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"s4ljYpQNp50r"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/dependency_parsing/NLU_untyped_dependency_parsing_example.ipynb)\n","\n","\n","# Untyped Dependency Parsing with NLU. \n","\n","\n","Each word in a sentence has a grammatical relation to other words in the sentence. \n","These relation pairs can be typed (i.e. subject or pronouns) or they can be untyped, in which case only the edges between the tokens will be predicted, withouth the label.\n","\n","With NLU you can get these relations in just 1 line of code! \n","# 1. Install Java and NLU"]},{"cell_type":"code","metadata":{"id":"SF5-Z-U4jukd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619905368532,"user_tz":-120,"elapsed":113840,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"7f6cba80-6299-4235-ad53-af9d63d087b3"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:40:55-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \rInstalling NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-01 21:40:55 (47.6 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 73kB/s \n","\u001b[K |████████████████████████████████| 153kB 40.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.5MB/s \n","\u001b[K |████████████████████████████████| 204kB 43.7MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"kHtLKNXDtZf5"},"source":["# 2. Load the Dependency model and predict some sample relationships"]},{"cell_type":"code","metadata":{"id":"7GJX5d6mjk5j","colab":{"base_uri":"https://localhost:8080/","height":291},"executionInfo":{"status":"ok","timestamp":1619905446121,"user_tz":-120,"elapsed":191423,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"d30778e2-5b46-48e1-8015-2ced31845230"},"source":["import nlu\n","dependency_pipe = nlu.load('dep.untyped')\n","dependency_pipe.predict('Untyped dependencies describe with their relationship a directed graph')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dependency_typed_conllu download started this may take some time.\n","Approximate size to download 2.3 MB\n","[OK!]\n","dependency_conllu download started this may take some time.\n","Approximate size to download 16.7 MB\n","[OK!]\n","pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
token
\n","
pos
\n","
unlabeled_dependency
\n","
labeled_dependency
\n","
\n"," \n"," \n","
\n","
0
\n","
Untyped dependencies describe with their relat...
\n","
[Untyped dependencies describe with their rela...
\n","
[Untyped, dependencies, describe, with, their,...
\n","
[NNP, NNS, VBP, IN, PRP$, NN, DT, JJ, NN]
\n","
[ROOT, describe, Untyped, relationship, relati...
\n","
[root, nsubj, parataxis, det, appos, nsubj, ns...
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... labeled_dependency\n","0 Untyped dependencies describe with their relat... ... [root, nsubj, parataxis, det, appos, nsubj, ns...\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"5lrDNzw3tcqT"},"source":["# 3.1 Download sample dataset"]},{"cell_type":"code","metadata":{"id":"gpeS8DWBlrun","colab":{"base_uri":"https://localhost:8080/","height":602},"executionInfo":{"status":"ok","timestamp":1619905454159,"user_tz":-120,"elapsed":199454,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"5b5c5414-f419-48c1-f9d4-ac3f6c512560"},"source":["import pandas as pd\n","# Download the dataset \n","! wget -N https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv -P /tmp\n","# Load dataset to Pandas\n","df = pd.read_csv('/tmp/train-balanced-sarcasm.csv')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:44:06-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.238.197\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.238.197|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 255268960 (243M) [text/csv]\n","Saving to: ‘/tmp/train-balanced-sarcasm.csv’\n","\n","train-balanced-sarc 100%[===================>] 243.44M 96.8MB/s in 2.5s \n","\n","2021-05-01 21:44:08 (96.8 MB/s) - ‘/tmp/train-balanced-sarcasm.csv’ saved [255268960/255268960]\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
label
\n","
comment
\n","
author
\n","
subreddit
\n","
score
\n","
ups
\n","
downs
\n","
date
\n","
created_utc
\n","
parent_comment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
NC and NH.
\n","
Trumpbart
\n","
politics
\n","
2
\n","
-1
\n","
-1
\n","
2016-10
\n","
2016-10-16 23:55:23
\n","
Yeah, I get that argument. At this point, I'd ...
\n","
\n","
\n","
1
\n","
0
\n","
You do know west teams play against west teams...
\n","
Shbshb906
\n","
nba
\n","
-4
\n","
-1
\n","
-1
\n","
2016-11
\n","
2016-11-01 00:24:10
\n","
The blazers and Mavericks (The wests 5 and 6 s...
\n","
\n","
\n","
2
\n","
0
\n","
They were underdogs earlier today, but since G...
\n","
Creepeth
\n","
nfl
\n","
3
\n","
3
\n","
0
\n","
2016-09
\n","
2016-09-22 21:45:37
\n","
They're favored to win.
\n","
\n","
\n","
3
\n","
0
\n","
This meme isn't funny none of the \"new york ni...
\n","
icebrotha
\n","
BlackPeopleTwitter
\n","
-8
\n","
-1
\n","
-1
\n","
2016-10
\n","
2016-10-18 21:03:47
\n","
deadass don't kill my buzz
\n","
\n","
\n","
4
\n","
0
\n","
I could use one of those tools.
\n","
cush2push
\n","
MaddenUltimateTeam
\n","
6
\n","
-1
\n","
-1
\n","
2016-12
\n","
2016-12-30 17:00:13
\n","
Yep can confirm I saw the tool they use for th...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1010821
\n","
1
\n","
I'm sure that Iran and N. Korea have the techn...
\n","
TwarkMain
\n","
reddit.com
\n","
2
\n","
2
\n","
0
\n","
2009-04
\n","
2009-04-25 00:47:52
\n","
No one is calling this an engineered pathogen,...
\n","
\n","
\n","
1010822
\n","
1
\n","
whatever you do, don't vote green!
\n","
BCHarvey
\n","
climate
\n","
1
\n","
1
\n","
0
\n","
2009-05
\n","
2009-05-14 22:27:40
\n","
In a move typical of their recent do-nothing a...
\n","
\n","
\n","
1010823
\n","
1
\n","
Perhaps this is an atheist conspiracy to make ...
\n","
rebelcommander
\n","
atheism
\n","
1
\n","
1
\n","
0
\n","
2009-01
\n","
2009-01-11 00:22:57
\n","
Screw the Disabled--I've got to get to Church ...
\n","
\n","
\n","
1010824
\n","
1
\n","
The Slavs got their own country - it is called...
\n","
catsi
\n","
worldnews
\n","
1
\n","
1
\n","
0
\n","
2009-01
\n","
2009-01-23 21:12:49
\n","
I've always been unsettled by that. I hear a l...
\n","
\n","
\n","
1010825
\n","
1
\n","
values, as in capitalism .. there is good mone...
\n","
frogking
\n","
politics
\n","
2
\n","
2
\n","
0
\n","
2009-01
\n","
2009-01-24 06:20:14
\n","
Why do the people who make our laws seem unabl...
\n","
\n"," \n","
\n","
1010826 rows × 10 columns
\n","
"],"text/plain":[" label ... parent_comment\n","0 0 ... Yeah, I get that argument. At this point, I'd ...\n","1 0 ... The blazers and Mavericks (The wests 5 and 6 s...\n","2 0 ... They're favored to win.\n","3 0 ... deadass don't kill my buzz\n","4 0 ... Yep can confirm I saw the tool they use for th...\n","... ... ... ...\n","1010821 1 ... No one is calling this an engineered pathogen,...\n","1010822 1 ... In a move typical of their recent do-nothing a...\n","1010823 1 ... Screw the Disabled--I've got to get to Church ...\n","1010824 1 ... I've always been unsettled by that. I hear a l...\n","1010825 1 ... Why do the people who make our laws seem unabl...\n","\n","[1010826 rows x 10 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"uLWu8DG3tfjz"},"source":["## 3.2 Predict on sample dataset\n","NLU expects a text column, thus we must create it from the column that contains our text data"]},{"cell_type":"code","metadata":{"id":"3V5l-B6nl43U","colab":{"base_uri":"https://localhost:8080/","height":291},"executionInfo":{"status":"ok","timestamp":1619905469031,"user_tz":-120,"elapsed":214321,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f21619d6-1658-4465-e591-0e766431c7e2"},"source":["dependency_pipe = nlu.load('dep.untyped')\n","df['text'] = df['comment']\n","dependency_predictions = dependency_pipe.predict(df['text'].iloc[0:1])\n","dependency_predictions"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dependency_typed_conllu download started this may take some time.\n","Approximate size to download 2.3 MB\n","[OK!]\n","dependency_conllu download started this may take some time.\n","Approximate size to download 16.7 MB\n","[OK!]\n","pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
token
\n","
pos
\n","
unlabeled_dependency
\n","
labeled_dependency
\n","
\n"," \n"," \n","
\n","
0
\n","
NC and NH.
\n","
[NC and NH.]
\n","
[NC, and, NH, .]
\n","
[NNP, CC, NNP, .]
\n","
[ROOT, NH, NC, NC]
\n","
[root, cc, flat, punct]
\n","
\n"," \n","
\n","
"],"text/plain":[" document sentence ... unlabeled_dependency labeled_dependency\n","0 NC and NH. [NC and NH.] ... [ROOT, NH, NC, NC] [root, cc, flat, punct]\n","\n","[1 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":4}]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/matchers/NLU_date_matching.ipynb b/examples/colab/component_examples/matchers/NLU_date_matching.ipynb
deleted file mode 100644
index 088a76fa..00000000
--- a/examples/colab/component_examples/matchers/NLU_date_matching.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_date_matching.ipynb","provenance":[{"file_id":"1svpqtC3cY6JnRGeJngIPl2raqxdowpyi","timestamp":1599400881246},{"file_id":"1tW833T3HS8F5Lvn6LgeDd5LW5226syKN","timestamp":1599398724652},{"file_id":"1CYzHfQyFCdvIOVO2Z5aggVI9c0hDEOrw","timestamp":1599354735581}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"NYQRU3pRO146"},"source":["\n","\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/matchers/NLU_date_matching.ipynb)\n","\n","# Date Matching\n","\n"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ"},"source":["import os\n","! apt-get update -qq > /dev/null \n","# Install java\n","! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null\n","os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n","os.environ[\"PATH\"] = os.environ[\"JAVA_HOME\"] + \"/bin:\" + os.environ[\"PATH\"]\n","! pip install nlu pyspark==2.4.7 > /dev/null\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Gph8XOL1Pzpl"},"source":["# NLU makes Date Matching easy. \n","\n"]},{"cell_type":"code","metadata":{"id":"pmpZSNvGlyZQ","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1604903819898,"user_tz":-60,"elapsed":96605,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"196ebf48-489e-4830-a716-395ae0bdefe5"},"source":["import nlu \n","\n","example_text = [\"A person like Jim or Joe\", \n"," \"An organisation like Microsoft or PETA\",\n"," \"A location like Germany\",\n"," \"Anything else like Playstation\", \n"," \"Person consisting of multiple tokens like Angela Merkel or Donald Trump\",\n"," \"Organisations consisting of multiple tokens like JP Morgan\",\n"," \"Locations consiting of multiple tokens like Los Angeles\", \n"," \"Anything else made up of multiple tokens like Super Nintendo\",]\n","\n","pipe = nlu.load('match.datetime',verbose = True)\n","pipe.predict(\"Jim and Joe went to the market next to the town hall\")\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Setting default lang to english\n","For input nlu_ref match.datetime detected : \n"," lang: en , component type: match , component dataset: datetime , component embeddings \n","Searching local Namespaces for SparkNLP reference.. \n","Found Spark NLP reference in language free aliases namespace\n","Starting Spark NLP to NLU pipeline conversion process\n"],"name":"stderr"},{"output_type":"stream","text":["match_datetime download started this may take some time.\n","Approx size to download 12.9 KB\n","[OK!]"],"name":"stdout"},{"output_type":"stream","text":["Extracting model from Spark NLP pipeline: document_67de075e1018 and creating Component\n","Parsed Component for : document\n","Extracted into NLU Component type : document\n","Extracting model from Spark NLP pipeline: SENTENCE_97da4bd8012c and creating Component\n","Parsed Component for : sentence\n","Extracted into NLU Component type : sentence\n","Extracting model from Spark NLP pipeline: REGEX_TOKENIZER_5211aea6ebb7 and creating Component\n","Parsed Component for : regex\n","Extracted into NLU Component type : regex\n","Extracting model from Spark NLP pipeline: MULTI_DATE_76982f2e0107 and creating Component\n","Parsed Component for : multi\n","Extracted into NLU Component type : multi\n","Inferred Spark reference nlp_ref=match_datetime and nlu_ref=match.datetime to NLP Annotator Class [, , , ]\n","Adding document_assembler to internal pipe\n","Adding sentence_detector to internal pipe\n","Adding regex_tokenizer to internal pipe\n","Adding date_matcher to internal pipe\n"],"name":"stderr"},{"output_type":"stream","text":["\n"],"name":"stdout"},{"output_type":"stream","text":["Inferred and set output level of pipeline to token\n","Getting field types for output SDF\n","Parsed type=document for field=document\n","Parsed type=document for field=sentence\n","Parsed type=token for field=token\n","Error there are no rows for this Component in the final Dataframe. For field=date. It will be dropped in the final dataset\n","NoneType: None\n","Parsed type=Error for field=date\n","Parsing field types done, parsed={'document': 'document', 'sentence': 'document', 'token': 'token', 'date': 'Error'}\n","Setting Output level as : token\n","Selecting Columns for field=document of type=document\n","Setting field for field=document of type=document to output level=document which is NOT SAME LEVEL\n","Selecting Columns for field=sentence of type=document\n","Setting field for field=sentence of type=document to output level=sentence which is NOT SAME LEVEL\n","Selecting Columns for field=date of type=Error\n","Setting field for field=date of type=Error to output level=token which is SAME LEVEL\n","exploding amd zipping at same level fields = ['token.result', 'date.result']\n","as same level fields = ['document.result', 'sentence.result']\n","Renaming columns and extracting meta data for outputlevel_same=True and fields_to_rename=['token.result', 'date.result'] and get_meta=False\n","Renaming Fields for old name=date.result and new name=date\n","Renaming exploded field : nr=0 , name=date.result to new_name=date\n","Renaming Fields for old name=token.result and new name=token\n","Renaming exploded field : nr=1 , name=token.result to new_name=token\n","Renaming columns and extracting meta data for outputlevel_same=False and fields_to_rename=['document.result', 'sentence.result'] and get_meta=False\n","Renaming Fields for old name=sentence.result and new name=sentence\n","Renaming non exploded field : nr=0 , original_name=sentence.result to new_name=sentence\n","Renaming Fields for old name=document.result and new name=document\n","Renaming non exploded field : nr=1 , original_name=document.result to new_name=document\n","Final cleanup select of same level =['date', 'token']\n","Final cleanup select of different level =['sentence', 'document', 'origin_index']\n","Final ptmp columns = ['text', 'origin_index', 'document', 'sentence', 'token', 'date', 'tmp', 'res']\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
token
\n","
date
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n"," \n"," \n","
\n","
0
\n","
Jim
\n","
None
\n","
\n","
\n","
0
\n","
and
\n","
None
\n","
\n","
\n","
0
\n","
Joe
\n","
None
\n","
\n","
\n","
0
\n","
went
\n","
None
\n","
\n","
\n","
0
\n","
to
\n","
None
\n","
\n","
\n","
0
\n","
the
\n","
None
\n","
\n","
\n","
0
\n","
market
\n","
None
\n","
\n","
\n","
0
\n","
next
\n","
None
\n","
\n","
\n","
0
\n","
to
\n","
None
\n","
\n","
\n","
0
\n","
the
\n","
None
\n","
\n","
\n","
0
\n","
town
\n","
None
\n","
\n","
\n","
0
\n","
hall
\n","
None
\n","
\n"," \n","
\n","
"],"text/plain":[" token date\n","origin_index \n","0 Jim None\n","0 and None\n","0 Joe None\n","0 went None\n","0 to None\n","0 the None\n","0 market None\n","0 next None\n","0 to None\n","0 the None\n","0 town None\n","0 hall None"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"if5mQWqRxDst"},"source":["## Configure the date macher with custom parameters"]},{"cell_type":"code","metadata":{"id":"j2ZZZvr1uGpx","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1604903822555,"user_tz":-60,"elapsed":99233,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b91c39ac-5885-4b58-dad5-c96d41690ca7"},"source":["pipe.print_info()\n","# Lets set our Chunker to only match NN\n","pipe['date_matcher'].setReadMonthFirst(True) \n","\n","# Now we can predict with the configured pipeline\n","pipe.predict(\"2020/01/01 was a intresting day\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The following parameters are configurable for this NLU pipeline (You can copy paste the examples) :\n",">>> pipe['document_assembler'] has settable params:\n","pipe['document_assembler'].setCleanupMode('disabled') | Info: possible values: disabled, inplace, inplace_full, shrink, shrink_full, each, each_full, delete_full | Currently set to : disabled\n",">>> pipe['sentence_detector'] has settable params:\n","pipe['sentence_detector'].setCustomBounds([]) | Info: characters used to explicitly mark sentence bounds | Currently set to : []\n","pipe['sentence_detector'].setDetectLists(True) | Info: whether detect lists during sentence detection | Currently set to : True\n","pipe['sentence_detector'].setExplodeSentences(False) | Info: whether to explode each sentence into a different row, for better parallelization. Defaults to false. | Currently set to : False\n","pipe['sentence_detector'].setMaxLength(99999) | Info: Set the maximum allowed length for each sentence | Currently set to : 99999\n","pipe['sentence_detector'].setMinLength(0) | Info: Set the minimum allowed length for each sentence. | Currently set to : 0\n","pipe['sentence_detector'].setUseAbbreviations(True) | Info: whether to apply abbreviations at sentence detection | Currently set to : True\n","pipe['sentence_detector'].setUseCustomBoundsOnly(False) | Info: Only utilize custom bounds in sentence detection | Currently set to : False\n",">>> pipe['regex_tokenizer'] has settable params:\n","pipe['regex_tokenizer'].setCaseSensitiveExceptions(True) | Info: Whether to care for case sensitiveness in exceptions | Currently set to : True\n","pipe['regex_tokenizer'].setTargetPattern('\\S+') | Info: pattern to grab from text as token candidates. Defaults \\S+ | Currently set to : \\S+\n","pipe['regex_tokenizer'].setMinLength(0) | Info: Set the minimum allowed length for each token | Currently set to : 0\n",">>> pipe['date_matcher'] has settable params:\n","pipe['date_matcher'].setDateFormat('yyyy/MM/dd') | Info: desired format for dates extracted | Currently set to : yyyy/MM/dd\n","pipe['date_matcher'].setDefaultDayWhenMissing(1) | Info: which day to set when it is missing from parsed input | Currently set to : 1\n","pipe['date_matcher'].setReadMonthFirst(True) | Info: Whether to parse july 07/05/2015 or as 05/07/2015 | Currently set to : True\n"],"name":"stdout"},{"output_type":"stream","text":["Getting field types for output SDF\n","Parsed type=document for field=document\n","Parsed type=document for field=sentence\n","Parsed type=token for field=token\n","Parsed type=date for field=date\n","Parsing field types done, parsed={'document': 'document', 'sentence': 'document', 'token': 'token', 'date': 'date'}\n","Setting Output level as : token\n","Selecting Columns for field=document of type=document\n","Setting field for field=document of type=document to output level=document which is NOT SAME LEVEL\n","Selecting Columns for field=sentence of type=document\n","Setting field for field=sentence of type=document to output level=sentence which is NOT SAME LEVEL\n","Selecting Columns for field=date of type=date\n","Setting field for field=date of type=date to output level=token which is SAME LEVEL\n","exploding amd zipping at same level fields = ['token.result', 'date.result']\n","as same level fields = ['document.result', 'sentence.result']\n","Renaming columns and extracting meta data for outputlevel_same=True and fields_to_rename=['token.result', 'date.result'] and get_meta=False\n","Renaming Fields for old name=date.result and new name=date\n","Renaming exploded field : nr=0 , name=date.result to new_name=date\n","Renaming Fields for old name=token.result and new name=token\n","Renaming exploded field : nr=1 , name=token.result to new_name=token\n","Renaming columns and extracting meta data for outputlevel_same=False and fields_to_rename=['document.result', 'sentence.result'] and get_meta=False\n","Renaming Fields for old name=sentence.result and new name=sentence\n","Renaming non exploded field : nr=0 , original_name=sentence.result to new_name=sentence\n","Renaming Fields for old name=document.result and new name=document\n","Renaming non exploded field : nr=1 , original_name=document.result to new_name=document\n","Final cleanup select of same level =['date', 'token']\n","Final cleanup select of different level =['sentence', 'document', 'origin_index']\n","Final ptmp columns = ['text', 'origin_index', 'document', 'sentence', 'token', 'date', 'tmp', 'res']\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
token
\n","
date
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n"," \n"," \n","
\n","
0
\n","
2020/01/01
\n","
2020/01/01
\n","
\n","
\n","
0
\n","
was
\n","
2001/01/01
\n","
\n","
\n","
0
\n","
a
\n","
None
\n","
\n","
\n","
0
\n","
intresting
\n","
None
\n","
\n","
\n","
0
\n","
day
\n","
None
\n","
\n"," \n","
\n","
"],"text/plain":[" token date\n","origin_index \n","0 2020/01/01 2020/01/01\n","0 was 2001/01/01\n","0 a None\n","0 intresting None\n","0 day None"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"2z5D3cPrEhu9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1604903824401,"user_tz":-60,"elapsed":101040,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"441b2834-362d-440e-ee2c-724c72e690da"},"source":["pipe['date_matcher'].setReadMonthFirst(False) \n","\n","# Now we can predict with the configured pipeline\n","pipe.predict(\"2020/01/01 was a intresting day\")\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Getting field types for output SDF\n","Parsed type=document for field=document\n","Parsed type=document for field=sentence\n","Parsed type=token for field=token\n","Parsed type=date for field=date\n","Parsing field types done, parsed={'document': 'document', 'sentence': 'document', 'token': 'token', 'date': 'date'}\n","Setting Output level as : token\n","Selecting Columns for field=document of type=document\n","Setting field for field=document of type=document to output level=document which is NOT SAME LEVEL\n","Selecting Columns for field=sentence of type=document\n","Setting field for field=sentence of type=document to output level=sentence which is NOT SAME LEVEL\n","Selecting Columns for field=date of type=date\n","Setting field for field=date of type=date to output level=token which is SAME LEVEL\n","exploding amd zipping at same level fields = ['token.result', 'date.result']\n","as same level fields = ['document.result', 'sentence.result']\n","Renaming columns and extracting meta data for outputlevel_same=True and fields_to_rename=['token.result', 'date.result'] and get_meta=False\n","Renaming Fields for old name=date.result and new name=date\n","Renaming exploded field : nr=0 , name=date.result to new_name=date\n","Renaming Fields for old name=token.result and new name=token\n","Renaming exploded field : nr=1 , name=token.result to new_name=token\n","Renaming columns and extracting meta data for outputlevel_same=False and fields_to_rename=['document.result', 'sentence.result'] and get_meta=False\n","Renaming Fields for old name=sentence.result and new name=sentence\n","Renaming non exploded field : nr=0 , original_name=sentence.result to new_name=sentence\n","Renaming Fields for old name=document.result and new name=document\n","Renaming non exploded field : nr=1 , original_name=document.result to new_name=document\n","Final cleanup select of same level =['date', 'token']\n","Final cleanup select of different level =['sentence', 'document', 'origin_index']\n","Final ptmp columns = ['text', 'origin_index', 'document', 'sentence', 'token', 'date', 'tmp', 'res']\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
token
\n","
date
\n","
\n","
\n","
origin_index
\n","
\n","
\n","
\n"," \n"," \n","
\n","
0
\n","
2020/01/01
\n","
2020/01/01
\n","
\n","
\n","
0
\n","
was
\n","
2001/01/01
\n","
\n","
\n","
0
\n","
a
\n","
None
\n","
\n","
\n","
0
\n","
intresting
\n","
None
\n","
\n","
\n","
0
\n","
day
\n","
None
\n","
\n"," \n","
\n","
"],"text/plain":[" token date\n","origin_index \n","0 2020/01/01 2020/01/01\n","0 was 2001/01/01\n","0 a None\n","0 intresting None\n","0 day None"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"n3p0gLbvEodo"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/multilingual/chinese_ner_pos_and_tokenization.ipynb b/examples/colab/component_examples/multilingual/chinese_ner_pos_and_tokenization.ipynb
deleted file mode 100644
index 2f287704..00000000
--- a/examples/colab/component_examples/multilingual/chinese_ner_pos_and_tokenization.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"chinese_ner_pos_and_tokenization.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"E6qlUniWPXLL"},"source":["\n","\n","\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples//colab/component_examples/multilingual/chinese_ner_pos_and_tokenization.ipynb)\n","\n"," \n"," # Detect Named Entities (NER), Part of Speech Tags (POS) and Tokenize in Chinese\n","\n"]},{"cell_type":"markdown","metadata":{"id":"qMM7GQjNRDIC"},"source":["# Install NLU"]},{"cell_type":"code","metadata":{"id":"NyzSofTuC6Wl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619905738286,"user_tz":-120,"elapsed":107846,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"30393a2f-4fd0-404f-cd32-3f5e8cd06a3f"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:47:11-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-01 21:47:11 (1.47 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 74kB/s \n","\u001b[K |████████████████████████████████| 153kB 34.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 21.6MB/s \n","\u001b[K |████████████████████████████████| 204kB 50.6MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bY8e6Wr5RG5N"},"source":["# Tokenize Chinese"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":581},"id":"9lKSMrKmCwSa","executionInfo":{"status":"ok","timestamp":1619905777058,"user_tz":-120,"elapsed":146609,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"8f3d8994-11dc-4ea3-d211-61ea1b10e3ac"},"source":["# Tokenize in chinese\n","import nlu\n","# pipe = nlu.load('zh.tokenize') This is an alias that gives you the same model\n","\n","pipe = nlu.load('zh.segment_words')\n","\n","# Chinese for 'Donald Trump and Angela Merkel dont share many opinions'\n","\n","zh_data = ['唐纳德特朗普和安吉拉·默克尔没有太多意见']\n","df = pipe.predict(zh_data, output_level='token')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["wordseg_weibo download started this may take some time.\n","Approximate size to download 1.1 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
words_seg
\n","
\n"," \n"," \n","
\n","
0
\n","
唐纳特
\n","
\n","
\n","
0
\n","
德
\n","
\n","
\n","
0
\n","
朗
\n","
\n","
\n","
0
\n","
普
\n","
\n","
\n","
0
\n","
和
\n","
\n","
\n","
0
\n","
安吉拉
\n","
\n","
\n","
0
\n","
·
\n","
\n","
\n","
0
\n","
默
\n","
\n","
\n","
0
\n","
克
\n","
\n","
\n","
0
\n","
尔
\n","
\n","
\n","
0
\n","
没
\n","
\n","
\n","
0
\n","
有
\n","
\n","
\n","
0
\n","
太
\n","
\n","
\n","
0
\n","
多
\n","
\n","
\n","
0
\n","
意
\n","
\n","
\n","
0
\n","
见
\n","
\n"," \n","
\n","
"],"text/plain":[" words_seg\n","0 唐纳特\n","0 德\n","0 朗\n","0 普\n","0 和\n","0 安吉拉\n","0 ·\n","0 默\n","0 克\n","0 尔\n","0 没\n","0 有\n","0 太\n","0 多\n","0 意\n","0 见"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"jjKH8L9PRIIO"},"source":["# Extract Chinese POS"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"Z5soJqWwQHeq","executionInfo":{"status":"ok","timestamp":1619905794670,"user_tz":-120,"elapsed":164216,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"52c7185d-3d7c-4894-bc39-f9a3baba1b61"},"source":["# Extract Part of Speech\n","pipe = nlu.load('zh.pos')\n","zh_data = ['唐纳德特朗普和安吉拉·默克尔没有太多意见']\n","\n","df = pipe.predict(zh_data, output_level='document')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pos_ud_gsd download started this may take some time.\n","Approximate size to download 3.3 MB\n","[OK!]\n","wordseg_weibo download started this may take some time.\n","Approximate size to download 1.1 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
pos
\n","
\n"," \n"," \n","
\n","
0
\n","
唐纳德特朗普和安吉拉·默克尔没有太多意见
\n","
[PROPN, PROPN, PROPN, NOUN, CONJ, PROPN, PUNCT...
\n","
\n"," \n","
\n","
"],"text/plain":[" document pos\n","0 唐纳德特朗普和安吉拉·默克尔没有太多意见 [PROPN, PROPN, PROPN, NOUN, CONJ, PROPN, PUNCT..."]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"mGi5xPJGRLcc"},"source":["# Extract Chinese NER"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":291},"id":"Zuc7qS_pDYsG","executionInfo":{"status":"ok","timestamp":1619905831242,"user_tz":-120,"elapsed":200782,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2d988925-cd47-420a-c542-629f87d2b121"},"source":["# Extract named chinese entities\n","pipe = nlu.load('zh.ner')\n","zh_data = ['唐纳德特朗普和安吉拉·默克尔没有太多意见']\n","df = pipe.predict(zh_data, output_level='document')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["ner_msra_bert_768d download started this may take some time.\n","Approximate size to download 19.2 MB\n","[OK!]\n","bert_base_chinese download started this may take some time.\n","Approximate size to download 367.6 MB\n","[OK!]\n","wordseg_weibo download started this may take some time.\n","Approximate size to download 1.1 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
word_embedding_zh.embed
\n","
entities
\n","
entities_class
\n","
\n"," \n"," \n","
\n","
0
\n","
唐纳德特朗普和安吉拉·默克尔没有太多意见
\n","
[[-0.049358122050762177, -0.47514015436172485,...
\n","
[唐纳德, 安吉拉]
\n","
[R, R]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... entities_class\n","0 唐纳德特朗普和安吉拉·默克尔没有太多意见 ... [R, R]\n","\n","[1 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"2dFXYx8GROR0"},"source":["# Translate Chinese extracted named entities to English"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":131},"id":"UHyNj4l3GXgn","executionInfo":{"status":"ok","timestamp":1619905889665,"user_tz":-120,"elapsed":259200,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"89267b23-e54e-4c11-ef07-d5aa2df978f1"},"source":["# Translate Chinese extracted named entities to English\n","translate_pipe = nlu.load('zh.translate_to.en')\n","en_entities = translate_pipe.predict(df.entities.str.join('.').values.tolist())\n","en_entities"],"execution_count":null,"outputs":[{"output_type":"stream","text":["translate_zh_en download started this may take some time.\n","Approx size to download 396.8 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
translated
\n","
\n"," \n"," \n","
\n","
0
\n","
唐纳德.安吉拉
\n","
[唐纳德., 安吉拉]
\n","
[Donald Angela.]
\n","
\n"," \n","
\n","
"],"text/plain":[" document sentence translated\n","0 唐纳德.安吉拉 [唐纳德., 安吉拉] [Donald Angela.]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"aNo9Yi9OOQWE"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/multilingual/japanese_ner_pos_and_tokenization.ipynb b/examples/colab/component_examples/multilingual/japanese_ner_pos_and_tokenization.ipynb
deleted file mode 100644
index 1e82dcf6..00000000
--- a/examples/colab/component_examples/multilingual/japanese_ner_pos_and_tokenization.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"japanese_ner_pos_and_tokenization.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"E6qlUniWPXLL"},"source":["\n","\n","\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples//colab/component_examples/multilingual/japanese_ner_pos_and_tokenization.ipynb)\n","\n"," \n"," # Detect Named Entities (NER), Part of Speech Tags (POS) and Tokenize in Japanese\n"]},{"cell_type":"code","metadata":{"id":"NyzSofTuC6Wl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619905759814,"user_tz":-120,"elapsed":131097,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"87d13877-4391-4ebf-a69e-5687f6e43791"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:47:09-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-01 21:47:09 (63.9 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 64kB/s \n","\u001b[K |████████████████████████████████| 153kB 45.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 20.8MB/s \n","\u001b[K |████████████████████████████████| 204kB 48.6MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"3D9XCZohRcei"},"source":["Tokenize Japanese"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":641},"id":"9lKSMrKmCwSa","executionInfo":{"status":"ok","timestamp":1619905834231,"user_tz":-120,"elapsed":205504,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"e81ceb11-acd4-468c-bb9f-2d188cd72255"},"source":["# Tokenize in japanese\n","import nlu\n","# pipe = nlu.load('ja.tokenize') This is an alias that gives you the same model\n","\n","pipe = nlu.load('ja.segment_words')\n","# japanese for 'Donald Trump and Angela Merkel dont share many opinions'\n","ja_data = ['ドナルド・トランプとアンゲラ・メルケルは多くの意見を共有していません']\n","df = pipe.predict(ja_data, output_level='token')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["wordseg_gsd_ud download started this may take some time.\n","Approximate size to download 979 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
words_seg
\n","
\n"," \n"," \n","
\n","
0
\n","
ドナルド
\n","
\n","
\n","
0
\n","
・
\n","
\n","
\n","
0
\n","
トランプ
\n","
\n","
\n","
0
\n","
と
\n","
\n","
\n","
0
\n","
アンゲラ
\n","
\n","
\n","
0
\n","
・
\n","
\n","
\n","
0
\n","
メルケル
\n","
\n","
\n","
0
\n","
は
\n","
\n","
\n","
0
\n","
多く
\n","
\n","
\n","
0
\n","
の
\n","
\n","
\n","
0
\n","
意見
\n","
\n","
\n","
0
\n","
を
\n","
\n","
\n","
0
\n","
共有
\n","
\n","
\n","
0
\n","
し
\n","
\n","
\n","
0
\n","
て
\n","
\n","
\n","
0
\n","
い
\n","
\n","
\n","
0
\n","
ませ
\n","
\n","
\n","
0
\n","
ん
\n","
\n"," \n","
\n","
"],"text/plain":[" words_seg\n","0 ドナルド\n","0 ・\n","0 トランプ\n","0 と\n","0 アンゲラ\n","0 ・\n","0 メルケル\n","0 は\n","0 多く\n","0 の\n","0 意見\n","0 を\n","0 共有\n","0 し\n","0 て\n","0 い\n","0 ませ\n","0 ん"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"fhaCyjFORgf_"},"source":["# Extract Part of Speech in Japanese\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":237},"id":"u8GGpELAP-ct","executionInfo":{"status":"ok","timestamp":1619905856388,"user_tz":-120,"elapsed":227655,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"cbf4402f-1176-4215-96a8-1ff66a248d90"},"source":["# Extract Part of Speech\n","pipe = nlu.load('ja.pos')\n","ja_data = ['ドナルド・トランプとアンゲラ・メルケルは多くの意見を共有していません']\n","\n","df = pipe.predict(ja_data, output_level='document')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pos_ud_gsd download started this may take some time.\n","Approximate size to download 2.5 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n","wordseg_gsd_ud download started this may take some time.\n","Approximate size to download 979 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
pos
\n","
\n"," \n"," \n","
\n","
0
\n","
ドナルド・トランプとアンゲラ・メルケルは多くの意見を共有していません
\n","
[PROPN, SYM, PROPN, ADP, PROPN, SYM, NOUN, ADP...
\n","
\n"," \n","
\n","
"],"text/plain":[" document pos\n","0 ドナルド・トランプとアンゲラ・メルケルは多くの意見を共有していません [PROPN, SYM, PROPN, ADP, PROPN, SYM, NOUN, ADP..."]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"YMXf6Ic2Riuc"},"source":["# Extract named japanese entities\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":291},"id":"Zuc7qS_pDYsG","executionInfo":{"status":"ok","timestamp":1619906142517,"user_tz":-120,"elapsed":513779,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b65adfeb-db68-454a-af2d-b777e6d2c5d5"},"source":["# Extract named japanese entities\n","pipe = nlu.load('ja.ner')\n","ja_data = ['ドナルド・トランプとアンゲラ・メルケルは多くの意見を共有していません']\n","df = pipe.predict(ja_data, output_level='document')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["ner_ud_gsd_glove_840B_300d download started this may take some time.\n","Approximate size to download 19.2 MB\n","[OK!]\n","glove_840B_300 download started this may take some time.\n","Approximate size to download 2.3 GB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n","wordseg_gsd_ud download started this may take some time.\n","Approximate size to download 979 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
word_embedding_glove_840B_300
\n","
entities
\n","
entities_class
\n","
\n"," \n"," \n","
\n","
0
\n","
ドナルド・トランプとアンゲラ・メルケルは多くの意見を共有していません
\n","
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
\n","
[トランプ, アンゲラ, メルケル]
\n","
[G, RSON, RSON]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... entities_class\n","0 ドナルド・トランプとアンゲラ・メルケルは多くの意見を共有していません ... [G, RSON, RSON]\n","\n","[1 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"koweiN6cRkyx"},"source":["# Translate Japanese extracted named entities to English"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":131},"id":"UHyNj4l3GXgn","executionInfo":{"status":"ok","timestamp":1619906221824,"user_tz":-120,"elapsed":593081,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"54af13b6-4501-4503-93bf-3257a749dc32"},"source":["# Translate Japanese extracted named entities to English\n","translate_pipe = nlu.load('ja.translate_to.en')\n","en_entities = translate_pipe.predict(df.entities.str.join('.').values.tolist())\n","en_entities"],"execution_count":null,"outputs":[{"output_type":"stream","text":["translate_ja_en download started this may take some time.\n","Approx size to download 380.5 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
"
- ],
- "text/plain": [
- " translation sentence\n",
- "origin_index \n",
- "0 Asia, Asia, Asia, Asia, 아시아 있습니"
- ]
- },
- "metadata": {
- "tags": []
- },
- "execution_count": 24
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "wnkmr3hv_TdJ"
- },
- "source": [
- ""
- ],
- "execution_count": null,
- "outputs": []
- }
- ]
-}
\ No newline at end of file
diff --git a/examples/colab/component_examples/named_entity_recognition_(NER)/NER_aspect_airline_ATIS.ipynb b/examples/colab/component_examples/named_entity_recognition_(NER)/NER_aspect_airline_ATIS.ipynb
deleted file mode 100644
index 56732c29..00000000
--- a/examples/colab/component_examples/named_entity_recognition_(NER)/NER_aspect_airline_ATIS.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NER_aspect_airline_ATIS.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"NYQRU3pRO146"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/named_entity_recognition_(NER)/NER_aspect_airline_ATIS.ipynb)\n","\n","\n","Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:\n"," \n"," \n","\n","#Content\n","ATIS dataset provides large number of messages and their associated intents that can be used in training a classifier. Within a chatbot, intent refers to the goal the customer has in mind when typing in a question or comment. While entity refers to the modifier the customer uses to describe their issue, the intent is what they really mean. For example, a user says, ‘I need new shoes.’ The intent behind the message is to browse the footwear on offer. Understanding the intent of the customer is key to implementing a successful chatbot experience for end-user.\n","https://www.kaggle.com/hassanamin/atis-airlinetravelinformationsystem\n"," \n"," \n","\n","|Tags predicted by this model | \t\n","|------|\n"," | O|\n"," | I-depart_time.end_time|\n"," | B-arrive_date.date_relative|\n"," | I-fromloc.state_name|\n"," | B-depart_date.date_relative|\n"," | B-fromloc.state_code|\n"," | B-meal_description|\n"," | B-depart_time.time_relative|\n"," | I-fare_amount|\n"," | I-fromloc.city_name|\n"," | B-booking_class|\n"," | I-arrive_time.end_time|\n"," | B-return_date.today_relative|\n"," | B-fromloc.state_name|\n"," | B-round_trip|\n"," | B-depart_date.today_relative|\n"," | I-return_date.day_number|\n"," | I-depart_time.start_time|\n"," | B-period_of_day|\n"," | B-arrive_date.day_number|\n"," | B-flight_stop|\n"," | B-depart_date.day_name|\n"," | I-stoploc.city_name|\n"," | I-return_date.today_relative|\n"," | B-class_type|\n"," | B-stoploc.state_code|\n"," | B-economy|\n"," | B-depart_time.end_time|\n"," | B-return_date.date_relative|\n"," | I-fromloc.airport_name|\n"," | B-arrive_date.month_name|\n"," | I-flight_mod|\n"," | B-toloc.airport_code|\n"," | I-depart_time.end_time|\n"," | B-airline_code|\n"," | B-flight_mod|\n"," | B-cost_relative|\n"," | B-state_name|\n"," | B-fromloc.city_name|\n"," | B-depart_time.period_of_day|\n"," | I-city_name|\n"," | B-depart_time.period_mod|\n"," | B-city_name|\n"," | B-meal|\n"," | B-return_date.day_number|\n"," | I-airline_name|\n"," | I-restriction_code|\n"," | B-airline_name|\n"," | B-restriction_code|\n"," | B-flight|\n"," | B-transport_type|\n"," | B-time_relative|\n"," | B-arrive_time.time_relative|\n"," | B-fromloc.airport_code|\n"," | B-time|\n"," | I-toloc.city_name|\n"," | B-toloc.state_name|\n"," | B-meal_code|\n"," | I-arrive_date.day_number|\n"," | B-depart_time.start_time|\n"," | B-month_name|\n"," | B-fromloc.airport_name|\n"," | B-flight_number|\n"," | B-days_code|\n"," | I-meal_description|\n"," | B-fare_basis_code|\n"," | I-cost_relative|\n"," | I-time|\n"," | B-return_time.period_of_day|\n"," | I-depart_time.time|\n"," | B-depart_date.day_number|\n"," | I-economy|\n"," | B-arrive_time.start_time|\n"," | B-return_date.day_name|\n"," | B-return_time.period_mod|\n"," | B-airport_code|\n"," | B-stoploc.airport_code|\n"," | B-flight_time|\n"," | I-transport_type|\n"," | B-depart_date.month_name|\n"," | I-toloc.airport_name|\n"," | B-today_relative|\n"," | I-arrive_time.period_of_day|\n"," | B-day_name|\n"," | B-toloc.city_name|\n"," | B-connect|\n"," | I-round_trip|\n"," | B-depart_time.time|\n"," | B-airport_name|\n"," | B-arrive_time.period_of_day|\n"," | B-stoploc.airport_name|\n"," | I-class_type|\n"," | B-aircraft_code|\n"," | I-return_date.date_relative|\n"," | B-toloc.country_name|\n"," | I-flight_number|\n"," | B-state_code|\n"," | B-or|\n"," | I-depart_date.today_relative|\n"," | B-toloc.airport_name|\n"," | I-arrive_time.time|\n"," | I-flight_time|\n"," | I-state_name|\n"," | I-airport_name|\n"," | I-depart_time.period_of_day|\n"," | B-arrive_time.time|\n"," | B-depart_date.year|\n"," | I-flight_stop|\n"," | I-toloc.state_name|\n"," | B-arrive_date.day_name|\n"," | B-compartment|\n"," | I-depart_date.day_number|\n"," | I-meal_code|\n"," | B-arrive_time.end_time|\n"," | I-today_relative|\n"," | I-arrive_time.start_time|\n"," | B-toloc.state_code|\n"," | B-day_number|\n"," | I-arrive_time.time_relative|\n"," | I-fare_basis_code|\n"," | I-depart_time.time_relative|\n"," | B-return_date.month_name|\n"," | B-stoploc.city_name|\n"," | B-arrive_time.period_mod|\n"," | B-fare_amount|\n"," | B-mod|\n"," | B-arrive_date.today_relative|\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619906511912,"user_tz":-120,"elapsed":116260,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"bc3415c4-cb8d-4d61-a0be-dbcb347c4896"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:59:56-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-01 21:59:56 (48.5 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 75kB/s \n","\u001b[K |████████████████████████████████| 153kB 54.5MB/s \n","\u001b[K |████████████████████████████████| 204kB 21.6MB/s \n","\u001b[K |████████████████████████████████| 204kB 39.4MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Gph8XOL1Pzpl"},"source":["# NLU makes NER easy. \n","\n","You just need to load the NER model via ner.load() and predict on some dataset. \n","It could be a pandas dataframe with a column named text or just an array of strings."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":762},"id":"pmpZSNvGlyZQ","executionInfo":{"status":"ok","timestamp":1619906885198,"user_tz":-120,"elapsed":299542,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"991ce847-7757-4de3-bd18-127dc120d3ef"},"source":["import nlu \n","import pandas as pd\n","! wget http://ckl-it.de/wp-content/uploads/2021/01/atis_intents.csv\n","df = pd.read_csv(\"atis_intents.csv\")\n","df.columns = [\"flight\",\"text\"]\n","ner_df = nlu.load('en.ner.aspect.airline',).predict(df[\"text\"],output_level='chunk')\n","ner_df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 22:03:05-- http://ckl-it.de/wp-content/uploads/2021/01/atis_intents.csv\n","Resolving ckl-it.de (ckl-it.de)... 217.160.0.108, 2001:8d8:100f:f000::209\n","Connecting to ckl-it.de (ckl-it.de)|217.160.0.108|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 391936 (383K) [text/csv]\n","Saving to: ‘atis_intents.csv’\n","\n","atis_intents.csv 100%[===================>] 382.75K 688KB/s in 0.6s \n","\n","2021-05-01 22:03:06 (688 KB/s) - ‘atis_intents.csv’ saved [391936/391936]\n","\n","nerdl_atis_840b_300d download started this may take some time.\n","Approximate size to download 14.5 MB\n","[OK!]\n","glove_840B_300 download started this may take some time.\n","Approximate size to download 2.3 GB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
word_embedding_glove_840B_300
\n","
entities
\n","
entities_class
\n","
\n"," \n"," \n","
\n","
0
\n","
what flights are available from pittsburgh to ...
\n","
[[-0.038548000156879425, 0.5425199866294861, -...
\n","
pittsburgh
\n","
fromloc.city_name
\n","
\n","
\n","
0
\n","
what flights are available from pittsburgh to ...
\n","
[[-0.038548000156879425, 0.5425199866294861, -...
\n","
baltimore
\n","
toloc.city_name
\n","
\n","
\n","
0
\n","
what flights are available from pittsburgh to ...
\n","
[[-0.038548000156879425, 0.5425199866294861, -...
\n","
thursday
\n","
depart_date.day_name
\n","
\n","
\n","
0
\n","
what flights are available from pittsburgh to ...
\n","
[[-0.038548000156879425, 0.5425199866294861, -...
\n","
morning
\n","
depart_time.period_of_day
\n","
\n","
\n","
1
\n","
what is the arrival time in san francisco for ...
\n","
[[-0.038548000156879425, 0.5425199866294861, -...
\n","
arrival time
\n","
flight_time
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
4975
\n","
does continental fly from boston to san franci...
\n","
[[-0.13562999665737152, 0.3321700096130371, -0...
\n","
san francisco
\n","
toloc.city_name
\n","
\n","
\n","
4975
\n","
does continental fly from boston to san franci...
\n","
[[-0.13562999665737152, 0.3321700096130371, -0...
\n","
denver
\n","
stoploc.city_name
\n","
\n","
\n","
4976
\n","
is there a delta flight from denver to san fra...
\n","
[[-0.08496099710464478, 0.5019999742507935, 0....
\n","
delta
\n","
airline_name
\n","
\n","
\n","
4976
\n","
is there a delta flight from denver to san fra...
\n","
[[-0.08496099710464478, 0.5019999742507935, 0....
\n","
denver
\n","
fromloc.city_name
\n","
\n","
\n","
4976
\n","
is there a delta flight from denver to san fra...
\n","
[[-0.08496099710464478, 0.5019999742507935, 0....
\n","
san francisco
\n","
toloc.city_name
\n","
\n"," \n","
\n","
16693 rows × 4 columns
\n","
"],"text/plain":[" document ... entities_class\n","0 what flights are available from pittsburgh to ... ... fromloc.city_name\n","0 what flights are available from pittsburgh to ... ... toloc.city_name\n","0 what flights are available from pittsburgh to ... ... depart_date.day_name\n","0 what flights are available from pittsburgh to ... ... depart_time.period_of_day\n","1 what is the arrival time in san francisco for ... ... flight_time\n","... ... ... ...\n","4975 does continental fly from boston to san franci... ... toloc.city_name\n","4975 does continental fly from boston to san franci... ... stoploc.city_name\n","4976 is there a delta flight from denver to san fra... ... airline_name\n","4976 is there a delta flight from denver to san fra... ... fromloc.city_name\n","4976 is there a delta flight from denver to san fra... ... toloc.city_name\n","\n","[16693 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"STc7iOwtljGo"},"source":["## Lets explore our data which the predicted NER tags and visalize them! \n","\n","We specify [1:] so we dont see the count for the O-tag wich is the most common, since most words in a sentence are not named entities and thus not part of a chunk"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":920},"id":"UDSAYjadlfdK","executionInfo":{"status":"ok","timestamp":1619906886167,"user_tz":-120,"elapsed":299994,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"6c1e0af0-4c33-4bc7-9610-7df90aef4414"},"source":["ner_df['entities'].value_counts()[0:50].plot.bar(title='Occurence of Named Entities in dataset', figsize=(20,14))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":4},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"YO6d6VYi4aJQ"},"source":["## Most occurding `fromloc.city_name` tagged entities"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":386},"id":"rlcEvP9tOSiy","executionInfo":{"status":"ok","timestamp":1619906887261,"user_tz":-120,"elapsed":301034,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"7d3a0944-e8fc-4cad-ec9e-c81abdf9c99a"},"source":["ner_type_to_viz = 'fromloc.city_name'\n","ner_df[ner_df.entities_class == ner_type_to_viz]['entities'].value_counts().plot.bar(title='Most often occuring fromloc.city_name labeled entities in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":5},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"6R2-0v5Z4hMJ"},"source":["## Most occurding `flight_time` tagged entities"]},{"cell_type":"code","metadata":{"id":"ks6NDXg7RXG3","colab":{"base_uri":"https://localhost:8080/","height":368},"executionInfo":{"status":"ok","timestamp":1619906887263,"user_tz":-120,"elapsed":300979,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"4118ee15-755f-4584-fdef-9830b37d4819"},"source":["ner_type_to_viz = 'flight_time'\n","ner_df[ner_df.entities_class == ner_type_to_viz]['entities'].value_counts().plot.bar(title='Most often occuring ORG labeled entities in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":6},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"67MNeUed5W0y"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_CONLL_2003_5class_example.ipynb b/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_CONLL_2003_5class_example.ipynb
deleted file mode 100644
index e72a9d2f..00000000
--- a/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_CONLL_2003_5class_example.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_ner_CONLL_2003_5class_example.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"NYQRU3pRO146"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_CONLL_2003_5class_example.ipynb)\n","\n","\n","Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:\n"," \n"," \n","[ORG **U.N.** ] official [PER **Ekeus** ] heads for [LOC **Baghdad** ] . \n"," \n","\n","https://www.aclweb.org/anthology/W03-0419.pdf \n","CoNLL-2003 is a NER dataset that available in English and German. NLU provides pretrained languages for both of these languages.\n","\n","It features **5 classes** of tags, **LOC (location)** , **ORG(Organisation)**, **PER(Persons)** and the forth which describes all the named entities which do not belong to any of the thre previously mentioned tags **(MISC)**. \n","The fifth class **(O)** is used for tokens which belong to no named entity.\n","\n","\n","\n","\n","\n","|Tag | \tDescription |\n","|------|--------------|\n","|PER | A person like **Jim** or **Joe** |\n","|ORG | An organisation like **Microsoft** or **PETA**|\n","|LOC | A location like **Germany**|\n","|MISC | Anything else like **Playstation** |\n","|O| Everything that is not an entity. | \n","\n","\n","The shared task of [CoNLL-2003 concerns](https://www.clips.uantwerpen.be/conll2003/) language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task will be offered training and test data for two languages. They will use the data for developing a named-entity recognition system that includes a machine learning component. For each language, additional information (lists of names and non-annotated data) will be supplied as well. The challenge for the participants is to find ways of incorporating this information in their system.\n","\n","\n","\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619906485409,"user_tz":-120,"elapsed":104456,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b13d203a-ca47-40b5-c607-924443b753bf"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:59:41-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-01 21:59:41 (36.2 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 74kB/s \n","\u001b[K |████████████████████████████████| 153kB 46.7MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 43.2MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Gph8XOL1Pzpl"},"source":["# NLU makes NER easy. \n","\n","You just need to load the NER model via ner.load() and predict on some dataset. \n","It could be a pandas dataframe with a column named text or just an array of strings."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":341},"id":"pmpZSNvGlyZQ","executionInfo":{"status":"ok","timestamp":1619906537307,"user_tz":-120,"elapsed":156336,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"03b9ab18-7a3f-46d0-d326-83d45b0edbcc"},"source":["import nlu \n","\n","example_text = [\"A person like Jim or Joe\", \n"," \"An organisation like Microsoft or PETA\",\n"," \"A location like Germany\",\n"," \"Anything else like Playstation\", \n"," \"Person consisting of multiple tokens like Angela Merkel or Donald Trump\",\n"," \"Organisations consisting of multiple tokens like JP Morgan\",\n"," \"Locations consiting of multiple tokens like Los Angeles\", \n"," \"Anything else made up of multiple tokens like Super Nintendo\",]\n","\n","nlu.load('ner').predict(example_text)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["onto_recognize_entities_sm download started this may take some time.\n","Approx size to download 160.1 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
token
\n","
word_embedding_ner
\n","
entities
\n","
entities_class
\n","
\n"," \n"," \n","
\n","
0
\n","
A person like Jim or Joe
\n","
[A person like Jim or Joe]
\n","
[A, person, like, Jim, or, Joe]
\n","
[[-0.2708599865436554, 0.04400600120425224, -0...
\n","
[Jim, Joe]
\n","
[PERSON, PERSON]
\n","
\n","
\n","
1
\n","
An organisation like Microsoft or PETA
\n","
[An organisation like Microsoft or PETA]
\n","
[An, organisation, like, Microsoft, or, PETA]
\n","
[[-0.4214000105857849, -0.18796999752521515, 0...
\n","
[Microsoft, PETA]
\n","
[ORG, ORG]
\n","
\n","
\n","
2
\n","
A location like Germany
\n","
[A location like Germany]
\n","
[A, location, like, Germany]
\n","
[[-0.2708599865436554, 0.04400600120425224, -0...
\n","
[Germany]
\n","
[GPE]
\n","
\n","
\n","
3
\n","
Anything else like Playstation
\n","
[Anything else like Playstation]
\n","
[Anything, else, like, Playstation]
\n","
[[-0.029784999787807465, 0.08645900338888168, ...
\n","
[Playstation]
\n","
[PRODUCT]
\n","
\n","
\n","
4
\n","
Person consisting of multiple tokens like Ange...
\n","
[Person consisting of multiple tokens like Ang...
\n","
[Person, consisting, of, multiple, tokens, lik...
\n","
[[0.3870899975299835, 0.3262900114059448, 0.64...
\n","
[Angela Merkel, Donald Trump]
\n","
[PERSON, PERSON]
\n","
\n","
\n","
5
\n","
Organisations consisting of multiple tokens li...
\n","
[Organisations consisting of multiple tokens l...
\n","
[Organisations, consisting, of, multiple, toke...
\n","
[[-0.19327999651432037, 0.6523399949073792, -1...
\n","
[JP Morgan]
\n","
[ORG]
\n","
\n","
\n","
6
\n","
Locations consiting of multiple tokens like Lo...
\n","
[Locations consiting of multiple tokens like L...
\n","
[Locations, consiting, of, multiple, tokens, l...
\n","
[[0.06345599889755249, -0.042142000049352646, ...
\n","
[Los Angeles]
\n","
[GPE]
\n","
\n","
\n","
7
\n","
Anything else made up of multiple tokens like ...
\n","
[Anything else made up of multiple tokens like...
\n","
[Anything, else, made, up, of, multiple, token...
\n","
[[-0.029784999787807465, 0.08645900338888168, ...
\n","
[Super Nintendo]
\n","
[PRODUCT]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... entities_class\n","0 A person like Jim or Joe ... [PERSON, PERSON]\n","1 An organisation like Microsoft or PETA ... [ORG, ORG]\n","2 A location like Germany ... [GPE]\n","3 Anything else like Playstation ... [PRODUCT]\n","4 Person consisting of multiple tokens like Ange... ... [PERSON, PERSON]\n","5 Organisations consisting of multiple tokens li... ... [ORG]\n","6 Locations consiting of multiple tokens like Lo... ... [GPE]\n","7 Anything else made up of multiple tokens like ... ... [PRODUCT]\n","\n","[8 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qgGdEUgkMika","executionInfo":{"status":"ok","timestamp":1619906542469,"user_tz":-120,"elapsed":161491,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"397a4f68-4127-424d-d86b-4fac1d85e9ca"},"source":["text = [\"Barclays misled shareholders and the public about one of the biggest investments in the bank's history, a BBC Panorama investigation has found.\",\n","\"The bank announced in 2008 that Manchester City owner Sheikh Mansour had agreed to invest more than £3bn.\",\n","\"But the BBC found that the money, which helped Barclays avoid a bailout by British taxpayers, actually came from the Abu Dhabi government.\",\n","\"Barclays said the mistake in its accounts was 'a drafting error'.\",\n","\"Unlike RBS and Lloyds TSB, Barclays narrowly avoided having to request a government bailout late in 2008 after it was rescued by £7bn worth of new investment, most of which came from the Gulf states of Qatar and Abu Dhabi.\",\n","\"The S&P 500's price to earnings multiple is 71% higher than Apple's, and if Apple were simply valued at the same multiple, its share price would be $840, which is 52% higher than its current price.\",\n","\"Alice has a cat named Alice and also a dog named Alice and also a parrot named Alice, it is her favorite name!\"\n","] + example_text\n","ner_df = nlu.load('ner').predict(text, output_level= 'chunk')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["onto_recognize_entities_sm download started this may take some time.\n","Approx size to download 160.1 MB\n","[OK!]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"5nhKQZPpSRxv","executionInfo":{"status":"ok","timestamp":1619906545257,"user_tz":-120,"elapsed":164273,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"92d844a6-b894-457e-8584-d7af6082ea67"},"source":["ner_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
word_embedding_ner
\n","
entities
\n","
entities_class
\n","
\n"," \n"," \n","
\n","
0
\n","
Barclays misled shareholders and the public ab...
\n","
[[0.044123999774456024, -0.47940999269485474, ...
\n","
Barclays
\n","
ORG
\n","
\n","
\n","
0
\n","
Barclays misled shareholders and the public ab...
\n","
[[0.044123999774456024, -0.47940999269485474, ...
\n","
about one
\n","
CARDINAL
\n","
\n","
\n","
0
\n","
Barclays misled shareholders and the public ab...
\n","
[[0.044123999774456024, -0.47940999269485474, ...
\n","
BBC Panorama
\n","
ORG
\n","
\n","
\n","
1
\n","
The bank announced in 2008 that Manchester Cit...
\n","
[[-0.03819400072097778, -0.24487000703811646, ...
\n","
2008
\n","
DATE
\n","
\n","
\n","
1
\n","
The bank announced in 2008 that Manchester Cit...
\n","
[[-0.03819400072097778, -0.24487000703811646, ...
\n","
Manchester City
\n","
GPE
\n","
\n","
\n","
1
\n","
The bank announced in 2008 that Manchester Cit...
\n","
[[-0.03819400072097778, -0.24487000703811646, ...
\n","
Sheikh Mansour
\n","
PERSON
\n","
\n","
\n","
2
\n","
But the BBC found that the money, which helped...
\n","
[[-0.05707800015807152, 0.3987399935722351, 0....
\n","
BBC
\n","
ORG
\n","
\n","
\n","
2
\n","
But the BBC found that the money, which helped...
\n","
[[-0.05707800015807152, 0.3987399935722351, 0....
\n","
Barclays
\n","
ORG
\n","
\n","
\n","
2
\n","
But the BBC found that the money, which helped...
\n","
[[-0.05707800015807152, 0.3987399935722351, 0....
\n","
British
\n","
NORP
\n","
\n","
\n","
2
\n","
But the BBC found that the money, which helped...
\n","
[[-0.05707800015807152, 0.3987399935722351, 0....
\n","
Abu Dhabi
\n","
GPE
\n","
\n","
\n","
3
\n","
Barclays said the mistake in its accounts was ...
\n","
[[0.044123999774456024, -0.47940999269485474, ...
\n","
Barclays
\n","
ORG
\n","
\n","
\n","
4
\n","
Unlike RBS and Lloyds TSB, Barclays narrowly a...
\n","
[[-0.32710000872612, 0.4879100024700165, 0.416...
\n","
RBS
\n","
ORG
\n","
\n","
\n","
4
\n","
Unlike RBS and Lloyds TSB, Barclays narrowly a...
\n","
[[-0.32710000872612, 0.4879100024700165, 0.416...
\n","
Lloyds TSB, Barclays
\n","
ORG
\n","
\n","
\n","
4
\n","
Unlike RBS and Lloyds TSB, Barclays narrowly a...
\n","
[[-0.32710000872612, 0.4879100024700165, 0.416...
\n","
2008
\n","
DATE
\n","
\n","
\n","
4
\n","
Unlike RBS and Lloyds TSB, Barclays narrowly a...
\n","
[[-0.32710000872612, 0.4879100024700165, 0.416...
\n","
Gulf
\n","
LOC
\n","
\n","
\n","
4
\n","
Unlike RBS and Lloyds TSB, Barclays narrowly a...
\n","
[[-0.32710000872612, 0.4879100024700165, 0.416...
\n","
Qatar
\n","
GPE
\n","
\n","
\n","
4
\n","
Unlike RBS and Lloyds TSB, Barclays narrowly a...
\n","
[[-0.32710000872612, 0.4879100024700165, 0.416...
\n","
Abu Dhabi.
\n","
GPE
\n","
\n","
\n","
5
\n","
The S&P 500's price to earnings multiple is 71...
\n","
[[-0.03819400072097778, -0.24487000703811646, ...
\n","
S&P
\n","
ORG
\n","
\n","
\n","
5
\n","
The S&P 500's price to earnings multiple is 71...
\n","
[[-0.03819400072097778, -0.24487000703811646, ...
\n","
500's
\n","
DATE
\n","
\n","
\n","
5
\n","
The S&P 500's price to earnings multiple is 71...
\n","
[[-0.03819400072097778, -0.24487000703811646, ...
\n","
71%
\n","
PERCENT
\n","
\n","
\n","
5
\n","
The S&P 500's price to earnings multiple is 71...
\n","
[[-0.03819400072097778, -0.24487000703811646, ...
\n","
Apple's,
\n","
CARDINAL
\n","
\n","
\n","
5
\n","
The S&P 500's price to earnings multiple is 71...
\n","
[[-0.03819400072097778, -0.24487000703811646, ...
\n","
Apple
\n","
ORG
\n","
\n","
\n","
5
\n","
The S&P 500's price to earnings multiple is 71...
\n","
[[-0.03819400072097778, -0.24487000703811646, ...
\n","
$840
\n","
CARDINAL
\n","
\n","
\n","
5
\n","
The S&P 500's price to earnings multiple is 71...
\n","
[[-0.03819400072097778, -0.24487000703811646, ...
\n","
52%
\n","
PERCENT
\n","
\n","
\n","
6
\n","
Alice has a cat named Alice and also a dog nam...
\n","
[[0.28501999378204346, -0.4355500042438507, 0....
\n","
Alice
\n","
PERSON
\n","
\n","
\n","
6
\n","
Alice has a cat named Alice and also a dog nam...
\n","
[[0.28501999378204346, -0.4355500042438507, 0....
\n","
Alice
\n","
PERSON
\n","
\n","
\n","
6
\n","
Alice has a cat named Alice and also a dog nam...
\n","
[[0.28501999378204346, -0.4355500042438507, 0....
\n","
Alice
\n","
PERSON
\n","
\n","
\n","
6
\n","
Alice has a cat named Alice and also a dog nam...
\n","
[[0.28501999378204346, -0.4355500042438507, 0....
\n","
Alice,
\n","
PERSON
\n","
\n","
\n","
7
\n","
A person like Jim or Joe
\n","
[[-0.2708599865436554, 0.04400600120425224, -0...
\n","
Jim
\n","
PERSON
\n","
\n","
\n","
7
\n","
A person like Jim or Joe
\n","
[[-0.2708599865436554, 0.04400600120425224, -0...
\n","
Joe
\n","
PERSON
\n","
\n","
\n","
8
\n","
An organisation like Microsoft or PETA
\n","
[[-0.4214000105857849, -0.18796999752521515, 0...
\n","
Microsoft
\n","
ORG
\n","
\n","
\n","
8
\n","
An organisation like Microsoft or PETA
\n","
[[-0.4214000105857849, -0.18796999752521515, 0...
\n","
PETA
\n","
ORG
\n","
\n","
\n","
9
\n","
A location like Germany
\n","
[[-0.2708599865436554, 0.04400600120425224, -0...
\n","
Germany
\n","
GPE
\n","
\n","
\n","
10
\n","
Anything else like Playstation
\n","
[[-0.029784999787807465, 0.08645900338888168, ...
\n","
Playstation
\n","
PRODUCT
\n","
\n","
\n","
11
\n","
Person consisting of multiple tokens like Ange...
\n","
[[0.3870899975299835, 0.3262900114059448, 0.64...
\n","
Angela Merkel
\n","
PERSON
\n","
\n","
\n","
11
\n","
Person consisting of multiple tokens like Ange...
\n","
[[0.3870899975299835, 0.3262900114059448, 0.64...
\n","
Donald Trump
\n","
PERSON
\n","
\n","
\n","
12
\n","
Organisations consisting of multiple tokens li...
\n","
[[-0.19327999651432037, 0.6523399949073792, -1...
\n","
JP Morgan
\n","
ORG
\n","
\n","
\n","
13
\n","
Locations consiting of multiple tokens like Lo...
\n","
[[0.06345599889755249, -0.042142000049352646, ...
\n","
Los Angeles
\n","
GPE
\n","
\n","
\n","
14
\n","
Anything else made up of multiple tokens like ...
\n","
[[-0.029784999787807465, 0.08645900338888168, ...
\n","
Super Nintendo
\n","
PRODUCT
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... entities_class\n","0 Barclays misled shareholders and the public ab... ... ORG\n","0 Barclays misled shareholders and the public ab... ... CARDINAL\n","0 Barclays misled shareholders and the public ab... ... ORG\n","1 The bank announced in 2008 that Manchester Cit... ... DATE\n","1 The bank announced in 2008 that Manchester Cit... ... GPE\n","1 The bank announced in 2008 that Manchester Cit... ... PERSON\n","2 But the BBC found that the money, which helped... ... ORG\n","2 But the BBC found that the money, which helped... ... ORG\n","2 But the BBC found that the money, which helped... ... NORP\n","2 But the BBC found that the money, which helped... ... GPE\n","3 Barclays said the mistake in its accounts was ... ... ORG\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... ORG\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... ORG\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... DATE\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... LOC\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... GPE\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... GPE\n","5 The S&P 500's price to earnings multiple is 71... ... ORG\n","5 The S&P 500's price to earnings multiple is 71... ... DATE\n","5 The S&P 500's price to earnings multiple is 71... ... PERCENT\n","5 The S&P 500's price to earnings multiple is 71... ... CARDINAL\n","5 The S&P 500's price to earnings multiple is 71... ... ORG\n","5 The S&P 500's price to earnings multiple is 71... ... CARDINAL\n","5 The S&P 500's price to earnings multiple is 71... ... PERCENT\n","6 Alice has a cat named Alice and also a dog nam... ... PERSON\n","6 Alice has a cat named Alice and also a dog nam... ... PERSON\n","6 Alice has a cat named Alice and also a dog nam... ... PERSON\n","6 Alice has a cat named Alice and also a dog nam... ... PERSON\n","7 A person like Jim or Joe ... PERSON\n","7 A person like Jim or Joe ... PERSON\n","8 An organisation like Microsoft or PETA ... ORG\n","8 An organisation like Microsoft or PETA ... ORG\n","9 A location like Germany ... GPE\n","10 Anything else like Playstation ... PRODUCT\n","11 Person consisting of multiple tokens like Ange... ... PERSON\n","11 Person consisting of multiple tokens like Ange... ... PERSON\n","12 Organisations consisting of multiple tokens li... ... ORG\n","13 Locations consiting of multiple tokens like Lo... ... GPE\n","14 Anything else made up of multiple tokens like ... ... PRODUCT\n","\n","[39 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"STc7iOwtljGo"},"source":["## Lets explore our data which the predicted NER tags and visalize them! \n","\n","We specify [1:] so we dont se the count for the O-tag wich is the most common, since most words in a sentence are not named entities and thus not part of a chunk"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":392},"id":"UDSAYjadlfdK","executionInfo":{"status":"ok","timestamp":1619906545258,"user_tz":-120,"elapsed":164268,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"c99390e2-f5da-4c8f-de4e-6494fe31704f"},"source":["ner_df['entities'].value_counts()[1:].plot.bar(title='Occurence of Named Entity tokens in dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":5},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":310},"id":"rlcEvP9tOSiy","executionInfo":{"status":"ok","timestamp":1619906545259,"user_tz":-120,"elapsed":164264,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"bb35185e-2890-41f7-f62e-b4005c91997b"},"source":["ner_type_to_viz = 'LOC'\n","ner_df[ner_df.entities_class == ner_type_to_viz]['entities'].value_counts().plot.bar(title='Most often occuring LOC labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":6},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"ks6NDXg7RXG3","colab":{"base_uri":"https://localhost:8080/","height":392},"executionInfo":{"status":"ok","timestamp":1619906545260,"user_tz":-120,"elapsed":164260,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"9a851eab-c5ff-4c5f-af31-ba57a0baec91"},"source":["ner_type_to_viz = 'ORG'\n","ner_df[ner_df.entities_class == ner_type_to_viz]['entities'].value_counts().plot.bar(title='Most often occuring ORG labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":7},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"BDe5P8ByVOU_"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_ONTO_18class_example.ipynb b/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_ONTO_18class_example.ipynb
deleted file mode 100644
index e5f1ca01..00000000
--- a/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_ONTO_18class_example.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_ner_ONTO_18class_example.ipynb","provenance":[{"file_id":"1CYzHfQyFCdvIOVO2Z5aggVI9c0hDEOrw","timestamp":1599267946314}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"NYQRU3pRO146"},"source":["\n","\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_ONTO_18class_example.ipynb)\n","\n","# Named-entity recognition with Deep Learning ONTO NOTES\n","\n","Named-Entity recognition is a well-known technique in information extraction it is also known as entity identification, entity chunking and entity extraction. Knowing the relevant tags for each article help in automatically categorizing the articles in defined hierarchies and enable smooth content discovery. This pipeline is based on NerDLApproach annotator with Char CNN - BiLSTM and GloVe Embeddings on the OntoNotes corpus and supports the identification of 18 entities.\n","\n","\n","Following NER classes can be detected by this model\n","\n","\n","\n","\n","|Type | \tDescription |\n","|------|--------------|\n","| PERSON | \tPeople, including fictional like **Harry Potter** |\n","| NORP | \tNationalities or religious or political groups like the **Germans** |\n","| FAC | \tBuildings, airports, highways, bridges, etc. like **New York Airport** |\n","| ORG | \tCompanies, agencies, institutions, etc. like **Microsoft** |\n","| GPE | \tCountries, cities, states. like **Germany** |\n","| LOC | \tNon-GPE locations, mountain ranges, bodies of water. Like the **Sahara desert**|\n","| PRODUCT | \tObjects, vehicles, foods, etc. (Not services.) like **playstation** |\n","| EVENT | \tNamed hurricanes, battles, wars, sports events, etc. like **hurricane Katrina**|\n","| WORK_OF_ART | \tTitles of books, songs, etc. Like **Mona Lisa** |\n","| LAW | \tNamed documents made into laws. Like : **Declaration of Independence** |\n","| LANGUAGE | \tAny named language. Like **Turkish**|\n","| DATE | \tAbsolute or relative dates or periods. Like every second **friday**|\n","| TIME | \tTimes smaller than a day. Like **every minute**|\n","| PERCENT | \tPercentage, including ”%“. Like **55%** of workers enjoy their work |\n","| MONEY | \tMonetary values, including unit. Like **50$** for those pants |\n","| QUANTITY | \tMeasurements, as of weight or distance. Like this person weights **50kg** |\n","| ORDINAL | \t“first”, “second”, etc. Like David placed **first** in the tournament |\n","| CARDINAL | \tNumerals that do not fall under another type. Like **hundreds** of models are avaiable in NLU |"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619906478228,"user_tz":-120,"elapsed":104818,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"f2f732f2-d47c-4189-c60e-126bb4a7c680"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:59:33-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \rInstalling NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-01 21:59:33 (51.7 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 79kB/s \n","\u001b[K |████████████████████████████████| 153kB 43.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 22.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 60.5MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Gph8XOL1Pzpl"},"source":["# NLU makes NER easy. \n","\n","You just need to load the NER model via ner.load() and predict on some dataset. \n","It could be a pandas dataframe with a column named text or just an array of strings."]},{"cell_type":"code","metadata":{"id":"pmpZSNvGlyZQ","colab":{"base_uri":"https://localhost:8080/","height":641},"executionInfo":{"status":"ok","timestamp":1619906534563,"user_tz":-120,"elapsed":161145,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"560d1f4c-62d9-4914-f74c-9007b1833259"},"source":["import nlu \n","\n","example_text = ['People, including fictional like Harry Potter.',\n","'Nationalities or religious or political groups like Germans.',\n","'Buildings, airports, highways, bridges, etc. like New York Airport',\n","'Companies, agencies, institutions, etc. like Microsoft',\n","'Countries, cities, states. like Germany',\n","'Non-GPE locations, mountain ranges, bodies of water. Like Sahara Destert',\n","'Objects, vehicles, foods, etc. (Not services.) Like the a or playstation or Playstation',\n","'Named hurricanes, battles, wars, sports events, etc. like hurricane Katrina',\n","'Titles of books, songs, etc. Like the Mona Lisa',\n","'Named documents made into laws. Like the Declaration of Independence',\n","'Any named language. Like English',\n","'Absolute or relative dates or periods. Like every second friday',\n","'Times smaller than a day. Like every minute',\n","'Percentage, including ”%“. Like 55% of workers enjoy their work',\n","'Monetary values, including unit. Like 50$ for those pants',\n","'Measurements, as of weight or distance. Like this person weights 50kg',\n","'“first”, “second”, etc. Like David place first in the tournament',\n","'Numerals that do not fall under another type. Like hundreds of models are avaiable in NLU',]\n","nlu.load('ner.onto').predict(example_text)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["onto_recognize_entities_sm download started this may take some time.\n","Approx size to download 160.1 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
token
\n","
word_embedding_onto
\n","
entities
\n","
entities_class
\n","
\n"," \n"," \n","
\n","
0
\n","
People, including fictional like Harry Potter.
\n","
[People, including fictional like Harry Potter.]
\n","
[People,, including, fictional, like, Harry, P...
\n","
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
\n","
[Harry Potter.]
\n","
[PERSON]
\n","
\n","
\n","
1
\n","
Nationalities or religious or political groups...
\n","
[Nationalities or religious or political group...
\n","
[Nationalities, or, religious, or, political, ...
\n","
[[-0.02076599933207035, 0.5784800052642822, 0....
\n","
NaN
\n","
NaN
\n","
\n","
\n","
2
\n","
Buildings, airports, highways, bridges, etc. l...
\n","
[Buildings, airports, highways, bridges, etc. ...
\n","
[Buildings,, airports,, highways,, bridges,, e...
\n","
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
\n","
[New York Airport]
\n","
[FAC]
\n","
\n","
\n","
3
\n","
Companies, agencies, institutions, etc. like M...
\n","
[Companies, agencies, institutions, etc. like ...
\n","
[Companies,, agencies,, institutions,, etc., l...
\n","
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
\n","
[Microsoft]
\n","
[ORG]
\n","
\n","
\n","
4
\n","
Countries, cities, states. like Germany
\n","
[Countries, cities, states., like Germany]
\n","
[Countries,, cities,, states., like, Germany]
\n","
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
\n","
[Germany]
\n","
[GPE]
\n","
\n","
\n","
5
\n","
Non-GPE locations, mountain ranges, bodies of ...
\n","
[Non-GPE locations, mountain ranges, bodies of...
\n","
[Non-GPE, locations,, mountain, ranges,, bodie...
\n","
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
\n","
[Non-GPE, Sahara Destert]
\n","
[ORG, LOC]
\n","
\n","
\n","
6
\n","
Objects, vehicles, foods, etc. (Not services.)...
\n","
[Objects, vehicles, foods, etc., (Not services...
\n","
[Objects,, vehicles,, foods,, etc., (Not, serv...
\n","
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
\n","
[Playstation]
\n","
[PRODUCT]
\n","
\n","
\n","
7
\n","
Named hurricanes, battles, wars, sports events...
\n","
[Named hurricanes, battles, wars, sports event...
\n","
[Named, hurricanes,, battles,, wars,, sports, ...
\n","
[[-0.3515700101852417, -0.1662600040435791, 0....
\n","
[hurricane Katrina]
\n","
[EVENT]
\n","
\n","
\n","
8
\n","
Titles of books, songs, etc. Like the Mona Lisa
\n","
[Titles of books, songs, etc., Like the Mona L...
\n","
[Titles, of, books,, songs,, etc., Like, the, ...
\n","
[[0.5689799785614014, -0.38422998785972595, 0....
\n","
[Lisa]
\n","
[PERSON]
\n","
\n","
\n","
9
\n","
Named documents made into laws. Like the Decla...
\n","
[Named documents made into laws., Like the Dec...
\n","
[Named, documents, made, into, laws., Like, th...
\n","
[[-0.3515700101852417, -0.1662600040435791, 0....
\n","
NaN
\n","
NaN
\n","
\n","
\n","
10
\n","
Any named language. Like English
\n","
[Any named language., Like English]
\n","
[Any, named, language., Like, English]
\n","
[[-0.2367600053548813, 0.15658999979496002, 0....
\n","
[English]
\n","
[LANGUAGE]
\n","
\n","
\n","
11
\n","
Absolute or relative dates or periods. Like ev...
\n","
[Absolute or relative dates or periods., Like ...
\n","
[Absolute, or, relative, dates, or, periods., ...
\n","
[[-0.0853630006313324, -0.5337499976158142, 1....
\n","
[second]
\n","
[ORDINAL]
\n","
\n","
\n","
12
\n","
Times smaller than a day. Like every minute
\n","
[Times smaller than a day., Like every minute]
\n","
[Times, smaller, than, a, day., Like, every, m...
\n","
[[-0.29739999771118164, 0.1302099972963333, 0....
\n","
NaN
\n","
NaN
\n","
\n","
\n","
13
\n","
Percentage, including ”%“. Like 55% of workers...
\n","
[Percentage, including ”%“., Like 55% of worke...
\n","
[Percentage,, including, ”%“., Like, 55, %, of...
\n","
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
\n","
[55%]
\n","
[PERCENT]
\n","
\n","
\n","
14
\n","
Monetary values, including unit. Like 50$ for ...
\n","
[Monetary values, including unit., Like 50$ fo...
\n","
[Monetary, values,, including, unit., Like, 50...
\n","
[[0.3520300090312958, -0.1374099999666214, 0.2...
\n","
[50$]
\n","
[MONEY]
\n","
\n","
\n","
15
\n","
Measurements, as of weight or distance. Like t...
\n","
[Measurements, as of weight or distance., Like...
\n","
[Measurements,, as, of, weight, or, distance.,...
\n","
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
\n","
[50kg]
\n","
[PERSON]
\n","
\n","
\n","
16
\n","
“first”, “second”, etc. Like David place first...
\n","
[“first”, “second”, etc., Like David place fir...
\n","
[“first”,, “second”,, etc., Like, David, place...
\n","
[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,...
\n","
[David, first]
\n","
[PERSON, ORDINAL]
\n","
\n","
\n","
17
\n","
Numerals that do not fall under another type. ...
\n","
[Numerals that do not fall under another type....
\n","
[Numerals, that, do, not, fall, under, another...
\n","
[[-0.2671700119972229, 0.7479100227355957, -0....
\n","
[hundreds, NLU]
\n","
[CARDINAL, ORG]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... entities_class\n","0 People, including fictional like Harry Potter. ... [PERSON]\n","1 Nationalities or religious or political groups... ... NaN\n","2 Buildings, airports, highways, bridges, etc. l... ... [FAC]\n","3 Companies, agencies, institutions, etc. like M... ... [ORG]\n","4 Countries, cities, states. like Germany ... [GPE]\n","5 Non-GPE locations, mountain ranges, bodies of ... ... [ORG, LOC]\n","6 Objects, vehicles, foods, etc. (Not services.)... ... [PRODUCT]\n","7 Named hurricanes, battles, wars, sports events... ... [EVENT]\n","8 Titles of books, songs, etc. Like the Mona Lisa ... [PERSON]\n","9 Named documents made into laws. Like the Decla... ... NaN\n","10 Any named language. Like English ... [LANGUAGE]\n","11 Absolute or relative dates or periods. Like ev... ... [ORDINAL]\n","12 Times smaller than a day. Like every minute ... NaN\n","13 Percentage, including ”%“. Like 55% of workers... ... [PERCENT]\n","14 Monetary values, including unit. Like 50$ for ... ... [MONEY]\n","15 Measurements, as of weight or distance. Like t... ... [PERSON]\n","16 “first”, “second”, etc. Like David place first... ... [PERSON, ORDINAL]\n","17 Numerals that do not fall under another type. ... ... [CARDINAL, ORG]\n","\n","[18 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"qgGdEUgkMika","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619906539857,"user_tz":-120,"elapsed":166433,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"d95750e5-df16-43d7-fde8-c0130e043aac"},"source":["text = [\"Barclays misled shareholders and the public about one of the biggest investments in the bank's history, a BBC Panorama investigation has found.\",\n","\"The bank announced in 2008 that Manchester City owner Sheikh Mansour had agreed to invest more than £3bn.\",\n","\"But the BBC found that the money, which helped Barclays avoid a bailout by British taxpayers, actually came from the Abu Dhabi government.\",\n","\"Barclays said the mistake in its accounts was 'a drafting error'.\",\n","\"Unlike RBS and Lloyds TSB, Barclays narrowly avoided having to request a government bailout late in 2008 after it was rescued by £7bn worth of new investment, most of which came from the Gulf states of Qatar and Abu Dhabi.\",\n","\"The S&P 500's price to earnings multiple is 71% higher than Apple's, and if Apple were simply valued at the same multiple, its share price would be $840, which is 52% higher than its current price.\",\n","\"Alice has a cat named Alice and also a dog named Alice and also a parrot named Alice, it is her favorite name!\"\n","] + example_text\n","ner_df = nlu.load('ner.onto').predict(text, output_level='chunk')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["onto_recognize_entities_sm download started this may take some time.\n","Approx size to download 160.1 MB\n","[OK!]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"STc7iOwtljGo"},"source":["## Lets explore our data which the predicted NER tags and visalize them! \n","\n","We specify [1:] so we dont se the count for the O-tag wich is the most common, since most words in a sentence are not named entities and thus not part of a chunk"]},{"cell_type":"code","metadata":{"id":"UDSAYjadlfdK","colab":{"base_uri":"https://localhost:8080/","height":392},"executionInfo":{"status":"ok","timestamp":1619906540401,"user_tz":-120,"elapsed":166972,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"7170936b-db59-4a80-adb1-78e3bde3e875"},"source":["ner_df['entities'].value_counts()[1:].plot.bar(title='Occurence of Named Entity tokens in dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":4},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"],"text/plain":[" document ... entities_class\n","0 Barclays misled shareholders and the public ab... ... ORG\n","0 Barclays misled shareholders and the public ab... ... CARDINAL\n","0 Barclays misled shareholders and the public ab... ... ORG\n","1 The bank announced in 2008 that Manchester Cit... ... DATE\n","1 The bank announced in 2008 that Manchester Cit... ... GPE\n","1 The bank announced in 2008 that Manchester Cit... ... PERSON\n","2 But the BBC found that the money, which helped... ... ORG\n","2 But the BBC found that the money, which helped... ... ORG\n","2 But the BBC found that the money, which helped... ... NORP\n","2 But the BBC found that the money, which helped... ... GPE\n","3 Barclays said the mistake in its accounts was ... ... ORG\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... ORG\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... ORG\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... DATE\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... LOC\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... GPE\n","4 Unlike RBS and Lloyds TSB, Barclays narrowly a... ... GPE\n","5 The S&P 500's price to earnings multiple is 71... ... ORG\n","5 The S&P 500's price to earnings multiple is 71... ... DATE\n","5 The S&P 500's price to earnings multiple is 71... ... PERCENT\n","5 The S&P 500's price to earnings multiple is 71... ... CARDINAL\n","5 The S&P 500's price to earnings multiple is 71... ... ORG\n","5 The S&P 500's price to earnings multiple is 71... ... CARDINAL\n","5 The S&P 500's price to earnings multiple is 71... ... PERCENT\n","6 Alice has a cat named Alice and also a dog nam... ... PERSON\n","6 Alice has a cat named Alice and also a dog nam... ... PERSON\n","6 Alice has a cat named Alice and also a dog nam... ... PERSON\n","6 Alice has a cat named Alice and also a dog nam... ... PERSON\n","7 People, including fictional like Harry Potter. ... PERSON\n","8 Nationalities or religious or political groups... ... NaN\n","9 Buildings, airports, highways, bridges, etc. l... ... FAC\n","10 Companies, agencies, institutions, etc. like M... ... ORG\n","11 Countries, cities, states. like Germany ... GPE\n","12 Non-GPE locations, mountain ranges, bodies of ... ... ORG\n","12 Non-GPE locations, mountain ranges, bodies of ... ... LOC\n","13 Objects, vehicles, foods, etc. (Not services.)... ... PRODUCT\n","14 Named hurricanes, battles, wars, sports events... ... EVENT\n","15 Titles of books, songs, etc. Like the Mona Lisa ... PERSON\n","16 Named documents made into laws. Like the Decla... ... NaN\n","17 Any named language. Like English ... LANGUAGE\n","18 Absolute or relative dates or periods. Like ev... ... ORDINAL\n","19 Times smaller than a day. Like every minute ... NaN\n","20 Percentage, including ”%“. Like 55% of workers... ... PERCENT\n","21 Monetary values, including unit. Like 50$ for ... ... MONEY\n","22 Measurements, as of weight or distance. Like t... ... PERSON\n","23 “first”, “second”, etc. Like David place first... ... PERSON\n","23 “first”, “second”, etc. Like David place first... ... ORDINAL\n","24 Numerals that do not fall under another type. ... ... CARDINAL\n","24 Numerals that do not fall under another type. ... ... ORG\n","\n","[49 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"rlcEvP9tOSiy","colab":{"base_uri":"https://localhost:8080/","height":392},"executionInfo":{"status":"ok","timestamp":1619906542447,"user_tz":-120,"elapsed":169007,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"181c2498-43d1-4b21-addf-552b5cf641fc"},"source":["ner_type_to_viz = 'ORG'\n","ner_df[ner_df.entities_class == ner_type_to_viz]['entities'].value_counts().plot.bar(title='Most often occuring ORG labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":6},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"ks6NDXg7RXG3","colab":{"base_uri":"https://localhost:8080/","height":364},"executionInfo":{"status":"ok","timestamp":1619906542448,"user_tz":-120,"elapsed":169002,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"aa63967d-6b87-4c45-c341-6a7fa8e33a3c"},"source":["ner_type_to_viz = 'LOC'\n","ner_df[ner_df.entities_class == ner_type_to_viz]['entities'].value_counts().plot.bar(title='Most often occuring LOC labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":7},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/named_entity_recognition_(NER)/aspect_based_ner_sentiment_restaurants.ipynb b/examples/colab/component_examples/named_entity_recognition_(NER)/aspect_based_ner_sentiment_restaurants.ipynb
deleted file mode 100644
index cb6650f2..00000000
--- a/examples/colab/component_examples/named_entity_recognition_(NER)/aspect_based_ner_sentiment_restaurants.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"aspect_based_ner_sentiment_restaurants.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"9ayP-N_Cqr9K"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/named_entity_recognition_(NER)/aspect_based_ner_sentiment_restaurants.ipynb)\n","\n","\n","\n","\n","Automatically detect positive, negative and neutral aspects about restaurants from user reviews. Instead of labelling the entire review as negative or positive, this model helps identify which exact phrases relate to sentiment identified in the review."]},{"cell_type":"code","metadata":{"id":"NqnAGVadANyZ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619906502725,"user_tz":-120,"elapsed":104244,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"ec001e53-1299-46cf-de30-c55132bed144"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 21:59:58-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\r- 100%[===================>] 1.63K --.-KB/s in 0.001s \n","\n","2021-05-01 21:59:58 (1.36 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 77kB/s \n","\u001b[K |████████████████████████████████| 153kB 40.4MB/s \n","\u001b[K |████████████████████████████████| 204kB 23.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 49.3MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":357},"id":"c-9dIJVco9Xf","executionInfo":{"status":"ok","timestamp":1619906610022,"user_tz":-120,"elapsed":67625,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"1aa478f7-78ce-4a86-b585-4a3ecfa91037"},"source":["import nlu\n","pipe = nlu.load('en.ner.aspect_sentiment')\n","data = 'We loved our Thai-style main which amazing with lots of flavours very impressive for vegetarian. But the service was below average and the chips were too terrible to finish.'\n","df = pipe.predict([data], output_level='chunk')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["ner_aspect_based_sentiment download started this may take some time.\n","Approximate size to download 21.3 MB\n","[OK!]\n","glove_6B_300 download started this may take some time.\n","Approximate size to download 426.2 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
word_embedding_glove_6B_300
\n","
entities
\n","
entities_class
\n","
\n"," \n"," \n","
\n","
0
\n","
We loved our Thai-style main which amazing wit...
\n","
[[-0.05083499848842621, 0.2482600063085556, -0...
\n","
Thai-style main
\n","
POS
\n","
\n","
\n","
0
\n","
We loved our Thai-style main which amazing wit...
\n","
[[-0.05083499848842621, 0.2482600063085556, -0...
\n","
flavours
\n","
POS
\n","
\n","
\n","
0
\n","
We loved our Thai-style main which amazing wit...
\n","
[[-0.05083499848842621, 0.2482600063085556, -0...
\n","
vegetarian
\n","
POS
\n","
\n","
\n","
0
\n","
We loved our Thai-style main which amazing wit...
\n","
[[-0.05083499848842621, 0.2482600063085556, -0...
\n","
service
\n","
NEG
\n","
\n","
\n","
0
\n","
We loved our Thai-style main which amazing wit...
\n","
[[-0.05083499848842621, 0.2482600063085556, -0...
\n","
chips
\n","
NEG
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... entities_class\n","0 We loved our Thai-style main which amazing wit... ... POS\n","0 We loved our Thai-style main which amazing wit... ... POS\n","0 We loved our Thai-style main which amazing wit... ... POS\n","0 We loved our Thai-style main which amazing wit... ... NEG\n","0 We loved our Thai-style main which amazing wit... ... NEG\n","\n","[5 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":77},"id":"WFtrCQSnp_Ie","executionInfo":{"status":"ok","timestamp":1619906612521,"user_tz":-120,"elapsed":69663,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b596d2bf-ee9f-4a6e-b045-cfa60c5ba9d0"},"source":["df = pipe.predict([data], output_level='document')\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
word_embedding_glove_6B_300
\n","
entities
\n","
entities_class
\n","
\n"," \n"," \n","
\n","
0
\n","
We loved our Thai-style main which amazing wit...
\n","
[[-0.05083499848842621, 0.2482600063085556, -0...
\n","
[Thai-style main, flavours, vegetarian, servic...
\n","
[POS, POS, POS, NEG, NEG]
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... entities_class\n","0 We loved our Thai-style main which amazing wit... ... [POS, POS, POS, NEG, NEG]\n","\n","[1 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":107},"id":"GCFVSTRKqIgi","executionInfo":{"status":"ok","timestamp":1619906614943,"user_tz":-120,"elapsed":72051,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"1e62f6da-74a7-4ef0-bb64-14b4ad4b7d1e"},"source":["data = 'We loved our Thai-style main which amazing with lots of flavours very impressive for vegetarian. But the service was below average and the chips were too terrible to finish.'\n","df = pipe.predict([data], output_level='sentence')\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
word_embedding_glove_6B_300
\n","
entities
\n","
entities_class
\n","
\n"," \n"," \n","
\n","
0
\n","
We loved our Thai-style main which amazing wit...
\n","
[[-0.05083499848842621, 0.2482600063085556, -0...
\n","
[Thai-style main, flavours, vegetarian, servic...
\n","
[POS, POS, POS, NEG, NEG]
\n","
\n","
\n","
0
\n","
But the service was below average and the chip...
\n","
[[-0.05083499848842621, 0.2482600063085556, -0...
\n","
[Thai-style main, flavours, vegetarian, servic...
\n","
[POS, POS, POS, NEG, NEG]
\n","
\n"," \n","
\n","
"],"text/plain":[" sentence ... entities_class\n","0 We loved our Thai-style main which amazing wit... ... [POS, POS, POS, NEG, NEG]\n","0 But the service was below average and the chip... ... [POS, POS, POS, NEG, NEG]\n","\n","[2 rows x 4 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"Yao4hlfyqQNg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619906614944,"user_tz":-120,"elapsed":71982,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"a3b265dc-677f-4ecd-954b-8ada55667df4"},"source":["nlu.print_all_model_kinds_for_action('pos')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('nl.pos') returns Spark NLP model pos_ud_alpino\n","nlu.load('nl.pos.ud_alpino') returns Spark NLP model pos_ud_alpino\n","For language NLU provides the following Models : \n","nlu.load('en.pos') returns Spark NLP model pos_anc\n","nlu.load('en.pos.anc') returns Spark NLP model pos_anc\n","nlu.load('en.pos.ud_ewt') returns Spark NLP model pos_ud_ewt\n","For language NLU provides the following Models : \n","nlu.load('fr.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('fr.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","For language NLU provides the following Models : \n","nlu.load('de.pos.ud_hdt') returns Spark NLP model pos_ud_hdt\n","nlu.load('de.pos') returns Spark NLP model pos_ud_hdt\n","For language NLU provides the following Models : \n","nlu.load('it.pos') returns Spark NLP model pos_ud_isdt\n","nlu.load('it.pos.ud_isdt') returns Spark NLP model pos_ud_isdt\n","For language NLU provides the following Models : \n","nlu.load('nb.pos.ud_bokmaal') returns Spark NLP model pos_ud_bokmaal\n","For language NLU provides the following Models : \n","nlu.load('nn.pos') returns Spark NLP model pos_ud_nynorsk\n","nlu.load('nn.pos.ud_nynorsk') returns Spark NLP model pos_ud_nynorsk\n","For language NLU provides the following Models : \n","nlu.load('pl.pos') returns Spark NLP model pos_ud_lfg\n","nlu.load('pl.pos.ud_lfg') returns Spark NLP model pos_ud_lfg\n","For language NLU provides the following Models : \n","nlu.load('pt.pos.ud_bosque') returns Spark NLP model pos_ud_bosque\n","nlu.load('pt.pos') returns Spark NLP model pos_ud_bosque\n","For language NLU provides the following Models : \n","nlu.load('ru.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","nlu.load('ru.pos') returns Spark NLP model pos_ud_gsd\n","For language NLU provides the following Models : \n","nlu.load('es.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('es.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","For language NLU provides the following Models : \n","nlu.load('ar.pos') returns Spark NLP model pos_ud_padt\n","For language NLU provides the following Models : \n","nlu.load('hy.pos') returns Spark NLP model pos_ud_armtdp\n","For language NLU provides the following Models : \n","nlu.load('eu.pos') returns Spark NLP model pos_ud_bdt\n","For language NLU provides the following Models : \n","nlu.load('bn.pos') returns Spark NLP model pos_msri\n","For language NLU provides the following Models : \n","nlu.load('br.pos') returns Spark NLP model pos_ud_keb\n","For language NLU provides the following Models : \n","nlu.load('bg.pos') returns Spark NLP model pos_ud_btb\n","nlu.load('bg.pos.ud_btb') returns Spark NLP model pos_ud_btb\n","For language NLU provides the following Models : \n","nlu.load('ca.pos') returns Spark NLP model pos_ud_ancora\n","For language NLU provides the following Models : \n","nlu.load('cs.pos') returns Spark NLP model pos_ud_pdt\n","nlu.load('cs.pos.ud_pdt') returns Spark NLP model pos_ud_pdt\n","For language NLU provides the following Models : \n","nlu.load('fi.pos.ud_tdt') returns Spark NLP model pos_ud_tdt\n","nlu.load('fi.pos') returns Spark NLP model pos_ud_tdt\n","For language NLU provides the following Models : \n","nlu.load('gl.pos') returns Spark NLP model pos_ud_treegal\n","For language NLU provides the following Models : \n","nlu.load('el.pos') returns Spark NLP model pos_ud_gdt\n","nlu.load('el.pos.ud_gdt') returns Spark NLP model pos_ud_gdt\n","For language NLU provides the following Models : \n","nlu.load('he.pos') returns Spark NLP model pos_ud_htb\n","nlu.load('he.pos.ud_htb') returns Spark NLP model pos_ud_htb\n","For language NLU provides the following Models : \n","nlu.load('hi.pos') returns Spark NLP model pos_ud_hdtb\n","For language NLU provides the following Models : \n","nlu.load('hu.pos') returns Spark NLP model pos_ud_szeged\n","nlu.load('hu.pos.ud_szeged') returns Spark NLP model pos_ud_szeged\n","For language NLU provides the following Models : \n","nlu.load('id.pos') returns Spark NLP model pos_ud_gsd\n","For language NLU provides the following Models : \n","nlu.load('ga.pos') returns Spark NLP model pos_ud_idt\n","For language NLU provides the following Models : \n","nlu.load('da.pos') returns Spark NLP model pos_ud_ddt\n","For language NLU provides the following Models : \n","nlu.load('ja.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('ja.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","For language NLU provides the following Models : \n","nlu.load('la.pos') returns Spark NLP model pos_ud_llct\n","For language NLU provides the following Models : \n","nlu.load('lv.pos') returns Spark NLP model pos_ud_lvtb\n","For language NLU provides the following Models : \n","nlu.load('mr.pos') returns Spark NLP model pos_ud_ufal\n","For language NLU provides the following Models : \n","nlu.load('fa.pos') returns Spark NLP model pos_ud_perdt\n","For language NLU provides the following Models : \n","nlu.load('ro.pos') returns Spark NLP model pos_ud_rrt\n","nlu.load('ro.pos.ud_rrt') returns Spark NLP model pos_ud_rrt\n","For language NLU provides the following Models : \n","nlu.load('sk.pos') returns Spark NLP model pos_ud_snk\n","nlu.load('sk.pos.ud_snk') returns Spark NLP model pos_ud_snk\n","For language NLU provides the following Models : \n","nlu.load('sl.pos') returns Spark NLP model pos_ud_ssj\n","For language NLU provides the following Models : \n","nlu.load('sv.pos') returns Spark NLP model pos_ud_tal\n","nlu.load('sv.pos.ud_tal') returns Spark NLP model pos_ud_tal\n","For language
NLU provides the following Models : \n","nlu.load('th.pos') returns Spark NLP model pos_lst20\n","For language
NLU provides the following Models : \n","nlu.load('tr.pos') returns Spark NLP model pos_ud_imst\n","nlu.load('tr.pos.ud_imst') returns Spark NLP model pos_ud_imst\n","For language NLU provides the following Models : \n","nlu.load('uk.pos') returns Spark NLP model pos_ud_iu\n","nlu.load('uk.pos.ud_iu') returns Spark NLP model pos_ud_iu\n","For language NLU provides the following Models : \n","nlu.load('yo.pos') returns Spark NLP model pos_ud_ytb\n","For language NLU provides the following Models : \n","nlu.load('zh.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('zh.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","nlu.load('zh.pos.ctb9') returns Spark NLP model pos_ctb9\n","nlu.load('zh.pos.ud_gsd_trad') returns Spark NLP model pos_ud_gsd_trad\n","For language NLU provides the following Models : \n","nlu.load('et.pos') returns Spark NLP model pos_ud_edt\n","For language NLU provides the following Models : \n","nlu.load('ur.pos') returns Spark NLP model pos_ud_udtb\n","nlu.load('ur.pos.ud_udtb') returns Spark NLP model pos_ud_udtb\n","For language NLU provides the following Models : \n","nlu.load('ko.pos') returns Spark NLP model pos_ud_kaist\n","nlu.load('ko.pos.ud_kaist') returns Spark NLP model pos_ud_kaist\n","For language NLU provides the following Models : \n","nlu.load('bh.pos') returns Spark NLP model pos_ud_bhtb\n","For language NLU provides the following Models : \n","nlu.load('am.pos') returns Spark NLP model pos_ud_att\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"FvFwAWm29D0x"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/part_of_speech(POS)/NLU_part_of_speech_ANC_example.ipynb b/examples/colab/component_examples/part_of_speech(POS)/NLU_part_of_speech_ANC_example.ipynb
deleted file mode 100644
index d3b1023c..00000000
--- a/examples/colab/component_examples/part_of_speech(POS)/NLU_part_of_speech_ANC_example.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_part_of_speech_ANC_example.ipynb","provenance":[{"file_id":"1CYzHfQyFCdvIOVO2Z5aggVI9c0hDEOrw","timestamp":1599354735581}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"Qolj9DDIuG1Y"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/part_of_speech(POS)/NLU_part_of_speech_ANC_example.ipynb)\n","\n","\n","# Part of Speech tagging with NLU \n","\n","## Install Java and NLU"]},{"cell_type":"markdown","metadata":{"id":"NYQRU3pRO146"},"source":["Part of speech tags assign each token one of the following grammatical labels \n"," \n","\n","\n","\n","|Tag |Description | Example|\n","|------|------------|------|\n","|CC| Coordinating conjunction | This batch of mushroom stew is savory **and** delicious |\n","|CD| Cardinal number | Here are **five** coins |\n","|DT| Determiner | **The** bunny went home |\n","|EX| Existential there | **There** is a storm coming |\n","|FW| Foreign word | I'm having a **déjà vu** |\n","|IN| Preposition or subordinating conjunction | He is cleverer **than** I am |\n","|JJ| Adjective | She wore a **beautiful** dress |\n","|JJR| Adjective, comparative | My house is **bigger** than yours |\n","|JJS| Adjective, superlative | I am the **shortest** person in my family |\n","|LS| List item marker | A number of things need to be considered before starting a business **,** such as premises **,** finance **,** product demand **,** staffing and access to customers |\n","|MD| Modal | You **must** stop when the traffic lights turn red |\n","|NN| Noun, singular or mass | The **dog** likes to run |\n","|NNS| Noun, plural | The **cars** are fast |\n","|NNP| Proper noun, singular | I ordered the chair from **Amazon** |\n","|NNPS| Proper noun, plural | We visted the **Kennedys** |\n","|PDT| Predeterminer | **Both** the children had a toy |\n","|POS| Possessive ending | I built the dog'**s** house |\n","|PRP| Personal pronoun | **You** need to stop |\n","|PRP$| Possessive pronoun | Remember not to judge a book by **its** cover |\n","|RB| Adverb | The dog barks **loudly** |\n","|RBR| Adverb, comparative | Could you sing more **quietly** please? |\n","|RBS| Adverb, superlative | Everyone in the race ran fast, but John ran **the fastest** of all |\n","|RP| Particle | He ate **up** all his dinner |\n","|SYM| Symbol | What are you doing **?** |\n","|TO| to | Please send it back **to** me |\n","|UH| Interjection | **Wow!** You look gorgeous |\n","|VB| Verb, base form | We **play** soccer |\n","|VBD| Verb, past tense | I **worked** at a restaurant |\n","|VBG| Verb, gerund or present participle | **Smoking** kills people |\n","|VBN| Verb, past participle | She has **done** her homework |\n","|VBP| Verb, non-3rd person singular present | You **flit** from place to place |\n","|VBZ| Verb, 3rd person singular present | He never **calls** me |\n","|WDT| Wh-determiner | The store honored the complaints, **which** were less than 25 days old |\n","|WP| Wh-pronoun | **Who** can help me? |\n","|WP\\$| Possessive wh-pronoun | **Whose** fault is it? |\n","|WRB| Wh-adverb | **Where** are you going? |\n","\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619906803479,"user_tz":-120,"elapsed":117656,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"6215753e-19b2-42e2-b91f-aa397b9242f0"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 22:04:46-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-01 22:04:46 (34.4 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 63kB/s \n","\u001b[K |████████████████████████████████| 153kB 44.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 21.3MB/s \n","\u001b[K |████████████████████████████████| 204kB 41.8MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Gph8XOL1Pzpl"},"source":["# NLU makes POS easy. \n","\n","You just need to load the POS model via ner.load() and predict on some dataset. \n","It could be a pandas dataframe with a column named text or just an array of strings."]},{"cell_type":"code","metadata":{"id":"pmpZSNvGlyZQ","colab":{"base_uri":"https://localhost:8080/","height":394},"executionInfo":{"status":"ok","timestamp":1619906856795,"user_tz":-120,"elapsed":170962,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"69d43673-ce13-4304-fe8e-3b3dfa0fe361"},"source":["import nlu \n","\n","example_text = [\"A person like Jim or Joe\", \n"," \"An organisation like Microsoft or PETA\",\n"," \"A location like Germany\",\n"," \"Anything else like Playstation\", \n"," \"Person consisting of multiple tokens like Angela Merkel or Donald Trump\",\n"," \"Organisations consisting of multiple tokens like JP Morgan\",\n"," \"Locations consiting of multiple tokens like Los Angeles\", \n"," \"Anything else made up of multiple tokens like Super Nintendo\",]\n","\n","nlu.load('pos').predict(example_text)[['pos','token']]"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
pos
\n","
token
\n","
\n"," \n"," \n","
\n","
0
\n","
[DT, NN, IN, NNP, CC, NNP]
\n","
[A, person, like, Jim, or, Joe]
\n","
\n","
\n","
1
\n","
[DT, NN, IN, NNP, CC, NNP]
\n","
[An, organisation, like, Microsoft, or, PETA]
\n","
\n","
\n","
2
\n","
[DT, NN, IN, NNP]
\n","
[A, location, like, Germany]
\n","
\n","
\n","
3
\n","
[NN, RB, IN, NNP]
\n","
[Anything, else, like, Playstation]
\n","
\n","
\n","
4
\n","
[NN, VBG, IN, JJ, NNS, IN, NNP, NNP, CC, NNP, ...
\n","
[Person, consisting, of, multiple, tokens, lik...
\n","
\n","
\n","
5
\n","
[NNP, VBG, IN, JJ, NNS, IN, NNP, NNP]
\n","
[Organisations, consisting, of, multiple, toke...
\n","
\n","
\n","
6
\n","
[NNP, VBG, IN, JJ, NNS, IN, NNP, NNP]
\n","
[Locations, consiting, of, multiple, tokens, l...
\n","
\n","
\n","
7
\n","
[NN, RB, VBN, RP, IN, JJ, NNS, IN, NNP, NNP]
\n","
[Anything, else, made, up, of, multiple, token...
\n","
\n"," \n","
\n","
"],"text/plain":[" pos token\n","0 [DT, NN, IN, NNP, CC, NNP] [A, person, like, Jim, or, Joe]\n","1 [DT, NN, IN, NNP, CC, NNP] [An, organisation, like, Microsoft, or, PETA]\n","2 [DT, NN, IN, NNP] [A, location, like, Germany]\n","3 [NN, RB, IN, NNP] [Anything, else, like, Playstation]\n","4 [NN, VBG, IN, JJ, NNS, IN, NNP, NNP, CC, NNP, ... [Person, consisting, of, multiple, tokens, lik...\n","5 [NNP, VBG, IN, JJ, NNS, IN, NNP, NNP] [Organisations, consisting, of, multiple, toke...\n","6 [NNP, VBG, IN, JJ, NNS, IN, NNP, NNP] [Locations, consiting, of, multiple, tokens, l...\n","7 [NN, RB, VBN, RP, IN, JJ, NNS, IN, NNP, NNP] [Anything, else, made, up, of, multiple, token..."]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"qgGdEUgkMika","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619907105989,"user_tz":-120,"elapsed":7981,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"40591daa-d68c-46fe-b626-6bf5588c0102"},"source":["text = [\"Barclays misled shareholders and the public about one of the biggest investments in the bank's history, a BBC Panorama investigation has found.\",\n","\"The bank announced in 2008 that Manchester City owner Sheikh Mansour had agreed to invest more than £3bn.\",\n","\"But the BBC found that the money, which helped Barclays avoid a bailout by British taxpayers, actually came from the Abu Dhabi government.\",\n","\"Barclays said the mistake in its accounts was 'a drafting error'.\",\n","\"Unlike RBS and Lloyds TSB, Barclays narrowly avoided having to request a government bailout late in 2008 after it was rescued by £7bn worth of new investment, most of which came from the Gulf states of Qatar and Abu Dhabi.\",\n","\"The S&P 500's price to earnings multiple is 71% higher than Apple's, and if Apple were simply valued at the same multiple, its share price would be $840, which is 52% higher than its current price.\",\n","\"Alice has a cat named Alice and also a dog named Alice and also a parrot named Alice, it is her favorite name!\"\n","] + example_text\n","pos_df = nlu.load('pos').predict(text, output_level='token')[['pos','token']]"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"STc7iOwtljGo"},"source":["## Lets explore our data which the predicted POS tags and visalize them! \n","\n","We specify [1:] so we dont se the count for the O-tag wich is the most common, since most words in a sentence are not named entities and thus not part of a chunk"]},{"cell_type":"code","metadata":{"id":"UDSAYjadlfdK","colab":{"base_uri":"https://localhost:8080/","height":314},"executionInfo":{"status":"ok","timestamp":1619907198530,"user_tz":-120,"elapsed":795,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"3c48b8ac-646d-4498-8d81-f73d5fce1151"},"source":["\n","pos_df['pos'].value_counts()[1:].plot.bar(title='Occurence of Part of Speech tokens in dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":7},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"PC83y42kq8vd"},"source":["## We can merge the I-XXX and B-XXX tags for tokens with the same XXX tag for better insight \n","\n","Let's define a dict to rename I-XXX and B-XXX to XXX. \n","We can use the pandas [Dataframe.replace()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.replace.html) function with a dict to add a column which has only the tag ORG, PER, LOC, MISC or O in it.\n","\n"]},{"cell_type":"code","metadata":{"id":"rlcEvP9tOSiy","colab":{"base_uri":"https://localhost:8080/","height":358},"executionInfo":{"status":"ok","timestamp":1619907202559,"user_tz":-120,"elapsed":958,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"ff40b823-ffe5-417a-c495-c1e6a07801ab"},"source":["pos_type_to_viz = 'NNP'\n","pos_df[pos_df.pos == pos_type_to_viz]['token'].value_counts().plot.bar(title='Most often occuring NNP labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":8},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"ks6NDXg7RXG3","colab":{"base_uri":"https://localhost:8080/","height":330},"executionInfo":{"status":"ok","timestamp":1619907205286,"user_tz":-120,"elapsed":819,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"fbb81ee3-3bcb-4bf8-f4e2-16b6c1b8ef4a"},"source":["pos_type_to_viz = 'JJ'\n","pos_df[pos_df.pos == pos_type_to_viz]['token'].value_counts().plot.bar(title='Most often occuring JJ labeled tokens in the dataset')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":9},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"sQ8nKGB7qB29"},"source":["# NLU provides many more POS models!"]},{"cell_type":"code","metadata":{"id":"HmpMeRm_qElf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619907206187,"user_tz":-120,"elapsed":327,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"009edf7a-5140-48dd-b425-af2524a9328c"},"source":["nlu.print_all_model_kinds_for_action('pos')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('nl.pos') returns Spark NLP model pos_ud_alpino\n","nlu.load('nl.pos.ud_alpino') returns Spark NLP model pos_ud_alpino\n","For language NLU provides the following Models : \n","nlu.load('en.pos') returns Spark NLP model pos_anc\n","nlu.load('en.pos.anc') returns Spark NLP model pos_anc\n","nlu.load('en.pos.ud_ewt') returns Spark NLP model pos_ud_ewt\n","For language NLU provides the following Models : \n","nlu.load('fr.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('fr.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","For language NLU provides the following Models : \n","nlu.load('de.pos.ud_hdt') returns Spark NLP model pos_ud_hdt\n","nlu.load('de.pos') returns Spark NLP model pos_ud_hdt\n","For language NLU provides the following Models : \n","nlu.load('it.pos') returns Spark NLP model pos_ud_isdt\n","nlu.load('it.pos.ud_isdt') returns Spark NLP model pos_ud_isdt\n","For language NLU provides the following Models : \n","nlu.load('nb.pos.ud_bokmaal') returns Spark NLP model pos_ud_bokmaal\n","For language NLU provides the following Models : \n","nlu.load('nn.pos') returns Spark NLP model pos_ud_nynorsk\n","nlu.load('nn.pos.ud_nynorsk') returns Spark NLP model pos_ud_nynorsk\n","For language NLU provides the following Models : \n","nlu.load('pl.pos') returns Spark NLP model pos_ud_lfg\n","nlu.load('pl.pos.ud_lfg') returns Spark NLP model pos_ud_lfg\n","For language NLU provides the following Models : \n","nlu.load('pt.pos.ud_bosque') returns Spark NLP model pos_ud_bosque\n","nlu.load('pt.pos') returns Spark NLP model pos_ud_bosque\n","For language NLU provides the following Models : \n","nlu.load('ru.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","nlu.load('ru.pos') returns Spark NLP model pos_ud_gsd\n","For language NLU provides the following Models : \n","nlu.load('es.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('es.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","For language NLU provides the following Models : \n","nlu.load('ar.pos') returns Spark NLP model pos_ud_padt\n","For language NLU provides the following Models : \n","nlu.load('hy.pos') returns Spark NLP model pos_ud_armtdp\n","For language NLU provides the following Models : \n","nlu.load('eu.pos') returns Spark NLP model pos_ud_bdt\n","For language NLU provides the following Models : \n","nlu.load('bn.pos') returns Spark NLP model pos_msri\n","For language NLU provides the following Models : \n","nlu.load('br.pos') returns Spark NLP model pos_ud_keb\n","For language NLU provides the following Models : \n","nlu.load('bg.pos') returns Spark NLP model pos_ud_btb\n","nlu.load('bg.pos.ud_btb') returns Spark NLP model pos_ud_btb\n","For language NLU provides the following Models : \n","nlu.load('ca.pos') returns Spark NLP model pos_ud_ancora\n","For language NLU provides the following Models : \n","nlu.load('cs.pos') returns Spark NLP model pos_ud_pdt\n","nlu.load('cs.pos.ud_pdt') returns Spark NLP model pos_ud_pdt\n","For language NLU provides the following Models : \n","nlu.load('fi.pos.ud_tdt') returns Spark NLP model pos_ud_tdt\n","nlu.load('fi.pos') returns Spark NLP model pos_ud_tdt\n","For language NLU provides the following Models : \n","nlu.load('gl.pos') returns Spark NLP model pos_ud_treegal\n","For language NLU provides the following Models : \n","nlu.load('el.pos') returns Spark NLP model pos_ud_gdt\n","nlu.load('el.pos.ud_gdt') returns Spark NLP model pos_ud_gdt\n","For language NLU provides the following Models : \n","nlu.load('he.pos') returns Spark NLP model pos_ud_htb\n","nlu.load('he.pos.ud_htb') returns Spark NLP model pos_ud_htb\n","For language NLU provides the following Models : \n","nlu.load('hi.pos') returns Spark NLP model pos_ud_hdtb\n","For language NLU provides the following Models : \n","nlu.load('hu.pos') returns Spark NLP model pos_ud_szeged\n","nlu.load('hu.pos.ud_szeged') returns Spark NLP model pos_ud_szeged\n","For language NLU provides the following Models : \n","nlu.load('id.pos') returns Spark NLP model pos_ud_gsd\n","For language NLU provides the following Models : \n","nlu.load('ga.pos') returns Spark NLP model pos_ud_idt\n","For language NLU provides the following Models : \n","nlu.load('da.pos') returns Spark NLP model pos_ud_ddt\n","For language NLU provides the following Models : \n","nlu.load('ja.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('ja.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","For language NLU provides the following Models : \n","nlu.load('la.pos') returns Spark NLP model pos_ud_llct\n","For language NLU provides the following Models : \n","nlu.load('lv.pos') returns Spark NLP model pos_ud_lvtb\n","For language NLU provides the following Models : \n","nlu.load('mr.pos') returns Spark NLP model pos_ud_ufal\n","For language NLU provides the following Models : \n","nlu.load('fa.pos') returns Spark NLP model pos_ud_perdt\n","For language NLU provides the following Models : \n","nlu.load('ro.pos') returns Spark NLP model pos_ud_rrt\n","nlu.load('ro.pos.ud_rrt') returns Spark NLP model pos_ud_rrt\n","For language NLU provides the following Models : \n","nlu.load('sk.pos') returns Spark NLP model pos_ud_snk\n","nlu.load('sk.pos.ud_snk') returns Spark NLP model pos_ud_snk\n","For language NLU provides the following Models : \n","nlu.load('sl.pos') returns Spark NLP model pos_ud_ssj\n","For language NLU provides the following Models : \n","nlu.load('sv.pos') returns Spark NLP model pos_ud_tal\n","nlu.load('sv.pos.ud_tal') returns Spark NLP model pos_ud_tal\n","For language
NLU provides the following Models : \n","nlu.load('th.pos') returns Spark NLP model pos_lst20\n","For language
NLU provides the following Models : \n","nlu.load('tr.pos') returns Spark NLP model pos_ud_imst\n","nlu.load('tr.pos.ud_imst') returns Spark NLP model pos_ud_imst\n","For language NLU provides the following Models : \n","nlu.load('uk.pos') returns Spark NLP model pos_ud_iu\n","nlu.load('uk.pos.ud_iu') returns Spark NLP model pos_ud_iu\n","For language NLU provides the following Models : \n","nlu.load('yo.pos') returns Spark NLP model pos_ud_ytb\n","For language NLU provides the following Models : \n","nlu.load('zh.pos') returns Spark NLP model pos_ud_gsd\n","nlu.load('zh.pos.ud_gsd') returns Spark NLP model pos_ud_gsd\n","nlu.load('zh.pos.ctb9') returns Spark NLP model pos_ctb9\n","nlu.load('zh.pos.ud_gsd_trad') returns Spark NLP model pos_ud_gsd_trad\n","For language NLU provides the following Models : \n","nlu.load('et.pos') returns Spark NLP model pos_ud_edt\n","For language NLU provides the following Models : \n","nlu.load('ur.pos') returns Spark NLP model pos_ud_udtb\n","nlu.load('ur.pos.ud_udtb') returns Spark NLP model pos_ud_udtb\n","For language NLU provides the following Models : \n","nlu.load('ko.pos') returns Spark NLP model pos_ud_kaist\n","nlu.load('ko.pos.ud_kaist') returns Spark NLP model pos_ud_kaist\n","For language NLU provides the following Models : \n","nlu.load('bh.pos') returns Spark NLP model pos_ud_bhtb\n","For language NLU provides the following Models : \n","nlu.load('am.pos') returns Spark NLP model pos_ud_att\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"weRI1oc4qGx2"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/sentence_embeddings/NLU_BERT_sentence_embeddings_and_t-SNE_visualization_Example.ipynb b/examples/colab/component_examples/sentence_embeddings/NLU_BERT_sentence_embeddings_and_t-SNE_visualization_Example.ipynb
deleted file mode 100644
index 04a4a4c4..00000000
--- a/examples/colab/component_examples/sentence_embeddings/NLU_BERT_sentence_embeddings_and_t-SNE_visualization_Example.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_BERT_sentence_embeddings_and_t-SNE_visualization_Example.ipynb","provenance":[{"file_id":"1pgqoRJ6yGWbTLWdLnRvwG5DLSU3rxuMq","timestamp":1599401652794},{"file_id":"1JrlfuV2jNGTdOXvaWIoHTSf6BscDMkN7","timestamp":1599401257319},{"file_id":"1svpqtC3cY6JnRGeJngIPl2raqxdowpyi","timestamp":1599400881246},{"file_id":"1tW833T3HS8F5Lvn6LgeDd5LW5226syKN","timestamp":1599398724652},{"file_id":"1CYzHfQyFCdvIOVO2Z5aggVI9c0hDEOrw","timestamp":1599354735581}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"rBXrqlGEYA8G"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sentence_embeddings/NLU_BERT_sentence_embeddings_and_t-SNE_visualization_Example.ipynb)\n","\n","# BERT Sentence Embeddings with NLU \n","\n","BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture.\n","\n","\n","\n","## Sources :\n","- https://arxiv.org/abs/1810.04805\n","- https://github.com/google-research/bert\n","\n","## Paper abstract\n","\n","We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).\n","\n","\n","\n","# 1. Install Java and NLU"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619907473262,"user_tz":-120,"elapsed":106852,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"56ee34a4-f841-47e0-9a26-141ae089a3c2"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 22:16:07-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-01 22:16:07 (33.9 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 71kB/s \n","\u001b[K |████████████████████████████████| 153kB 46.4MB/s \n","\u001b[K |████████████████████████████████| 204kB 21.2MB/s \n","\u001b[K |████████████████████████████████| 204kB 34.6MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"N_CL8HZ8Ydry"},"source":["## 2. Load Model and embed sample sentence with BERT Sentence Embedder"]},{"cell_type":"code","metadata":{"id":"j2ZZZvr1uGpx","colab":{"base_uri":"https://localhost:8080/","height":184},"executionInfo":{"status":"ok","timestamp":1619907522195,"user_tz":-120,"elapsed":155775,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b0981d96-0eaf-4515-cdef-d70c93187d91"},"source":["import nlu\n","pipe = nlu.load('embed_sentence.bert')\n","pipe.predict('He was suprised by the diversity of NLU')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sent_small_bert_L2_128 download started this may take some time.\n","Approximate size to download 16.1 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
sentence_embedding_bert
\n","
\n"," \n"," \n","
\n","
0
\n","
He was suprised by the diversity of NLU
\n","
[He was suprised by the diversity of NLU]
\n","
[[-1.0726687908172607, 0.4481307566165924, -0....
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence_embedding_bert\n","0 He was suprised by the diversity of NLU ... [[-1.0726687908172607, 0.4481307566165924, -0....\n","\n","[1 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"BAUFklCqLr3V"},"source":["# 3. Download Sample dataset"]},{"cell_type":"code","metadata":{"id":"wAFAOUSuLqvn","colab":{"base_uri":"https://localhost:8080/","height":602},"executionInfo":{"status":"ok","timestamp":1619907530923,"user_tz":-120,"elapsed":164497,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"dfab4c59-6d25-4ed2-ea6b-6dfe24a77d87"},"source":["import pandas as pd\n","# Download the dataset \n","! wget -N https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv -P /tmp\n","# Load dataset to Pandas\n","df = pd.read_csv('/tmp/train-balanced-sarcasm.csv')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 22:18:41-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.18.142\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.18.142|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 255268960 (243M) [text/csv]\n","Saving to: ‘/tmp/train-balanced-sarcasm.csv’\n","\n","train-balanced-sarc 100%[===================>] 243.44M 95.3MB/s in 2.6s \n","\n","2021-05-01 22:18:43 (95.3 MB/s) - ‘/tmp/train-balanced-sarcasm.csv’ saved [255268960/255268960]\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
label
\n","
comment
\n","
author
\n","
subreddit
\n","
score
\n","
ups
\n","
downs
\n","
date
\n","
created_utc
\n","
parent_comment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
NC and NH.
\n","
Trumpbart
\n","
politics
\n","
2
\n","
-1
\n","
-1
\n","
2016-10
\n","
2016-10-16 23:55:23
\n","
Yeah, I get that argument. At this point, I'd ...
\n","
\n","
\n","
1
\n","
0
\n","
You do know west teams play against west teams...
\n","
Shbshb906
\n","
nba
\n","
-4
\n","
-1
\n","
-1
\n","
2016-11
\n","
2016-11-01 00:24:10
\n","
The blazers and Mavericks (The wests 5 and 6 s...
\n","
\n","
\n","
2
\n","
0
\n","
They were underdogs earlier today, but since G...
\n","
Creepeth
\n","
nfl
\n","
3
\n","
3
\n","
0
\n","
2016-09
\n","
2016-09-22 21:45:37
\n","
They're favored to win.
\n","
\n","
\n","
3
\n","
0
\n","
This meme isn't funny none of the \"new york ni...
\n","
icebrotha
\n","
BlackPeopleTwitter
\n","
-8
\n","
-1
\n","
-1
\n","
2016-10
\n","
2016-10-18 21:03:47
\n","
deadass don't kill my buzz
\n","
\n","
\n","
4
\n","
0
\n","
I could use one of those tools.
\n","
cush2push
\n","
MaddenUltimateTeam
\n","
6
\n","
-1
\n","
-1
\n","
2016-12
\n","
2016-12-30 17:00:13
\n","
Yep can confirm I saw the tool they use for th...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1010821
\n","
1
\n","
I'm sure that Iran and N. Korea have the techn...
\n","
TwarkMain
\n","
reddit.com
\n","
2
\n","
2
\n","
0
\n","
2009-04
\n","
2009-04-25 00:47:52
\n","
No one is calling this an engineered pathogen,...
\n","
\n","
\n","
1010822
\n","
1
\n","
whatever you do, don't vote green!
\n","
BCHarvey
\n","
climate
\n","
1
\n","
1
\n","
0
\n","
2009-05
\n","
2009-05-14 22:27:40
\n","
In a move typical of their recent do-nothing a...
\n","
\n","
\n","
1010823
\n","
1
\n","
Perhaps this is an atheist conspiracy to make ...
\n","
rebelcommander
\n","
atheism
\n","
1
\n","
1
\n","
0
\n","
2009-01
\n","
2009-01-11 00:22:57
\n","
Screw the Disabled--I've got to get to Church ...
\n","
\n","
\n","
1010824
\n","
1
\n","
The Slavs got their own country - it is called...
\n","
catsi
\n","
worldnews
\n","
1
\n","
1
\n","
0
\n","
2009-01
\n","
2009-01-23 21:12:49
\n","
I've always been unsettled by that. I hear a l...
\n","
\n","
\n","
1010825
\n","
1
\n","
values, as in capitalism .. there is good mone...
\n","
frogking
\n","
politics
\n","
2
\n","
2
\n","
0
\n","
2009-01
\n","
2009-01-24 06:20:14
\n","
Why do the people who make our laws seem unabl...
\n","
\n"," \n","
\n","
1010826 rows × 10 columns
\n","
"],"text/plain":[" label ... parent_comment\n","0 0 ... Yeah, I get that argument. At this point, I'd ...\n","1 0 ... The blazers and Mavericks (The wests 5 and 6 s...\n","2 0 ... They're favored to win.\n","3 0 ... deadass don't kill my buzz\n","4 0 ... Yep can confirm I saw the tool they use for th...\n","... ... ... ...\n","1010821 1 ... No one is calling this an engineered pathogen,...\n","1010822 1 ... In a move typical of their recent do-nothing a...\n","1010823 1 ... Screw the Disabled--I've got to get to Church ...\n","1010824 1 ... I've always been unsettled by that. I hear a l...\n","1010825 1 ... Why do the people who make our laws seem unabl...\n","\n","[1010826 rows x 10 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"OPdBQnV46or5"},"source":["# 4.1 Visualize Embeddings with T-SNE\n","\n","\n","\n","\n","Lets add Sentiment and Part Of Speech to our pipeline because its so easy and so we can hue our T-SNE plots by POS and Sentiment "]},{"cell_type":"code","metadata":{"id":"9bujAZtOCfRW","colab":{"base_uri":"https://localhost:8080/","height":566},"executionInfo":{"status":"ok","timestamp":1619907572271,"user_tz":-120,"elapsed":205839,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"71097884-8bd2-489f-be91-e207183c065b"},"source":["pipe = nlu.load('pos sentiment embed_sentence.bert') # emotion\n","df['text'] = df['comment']\n","\n","# We must set output level to sentence since NLU will infer a different output level for this pipeline composition\n","predictions = pipe.predict(df[['text','label']].iloc[0:500], output_level='sentence')\n","predictions"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","analyze_sentiment download started this may take some time.\n","Approx size to download 4.9 MB\n","[OK!]\n","sent_small_bert_L2_128 download started this may take some time.\n","Approximate size to download 16.1 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
pos
\n","
spell
\n","
sentiment
\n","
sentiment_confidence
\n","
sentence_embedding_bert
\n","
\n"," \n"," \n","
\n","
0
\n","
NC and NH.
\n","
[NNP, CC, NNP, .]
\n","
[NC, and, NH, .]
\n","
negative
\n","
0.5229
\n","
[-1.059532642364502, 0.9238302707672119, -1.06...
\n","
\n","
\n","
1
\n","
You do know west teams play against west teams...
\n","
[PRP, VBP, VB, NN, NNS, VBP, IN, NN, NNS, JJR,...
\n","
[You, do, know, west, teams, play, against, we...
\n","
negative
\n","
0.4733
\n","
[-0.9636414647102356, -0.046410106122493744, -...
\n","
\n","
\n","
2
\n","
They were underdogs earlier today, but since G...
\n","
[PRP, VBD, NNS, RBR, NN, ,, CC, IN, NNP, NN, D...
\n","
[They, were, underdogs, earlier, today, ,, but...
\n","
negative
\n","
0.5118
\n","
[-0.6074598431587219, 0.13940860331058502, -0....
\n","
\n","
\n","
3
\n","
This meme isn't funny none of the \"new york ni...
"],"text/plain":[" sentence ... sentence_embedding_bert\n","0 NC and NH. ... [-1.059532642364502, 0.9238302707672119, -1.06...\n","1 You do know west teams play against west teams... ... [-0.9636414647102356, -0.046410106122493744, -...\n","2 They were underdogs earlier today, but since G... ... [-0.6074598431587219, 0.13940860331058502, -0....\n","3 This meme isn't funny none of the \"new york ni... ... [-0.6330281496047974, 0.13020245730876923, 0.0...\n","4 I could use one of those tools. ... [-1.3357948064804077, 1.081745982170105, -0.41...\n",".. ... ... ...\n","495 CS 1.6, Source and GO Cities skylines Getting ... ... [-0.33594822883605957, -0.4027842581272125, -0...\n","496 Or a \"Your Welcome\" ... [-1.8244224786758423, 0.797812819480896, -0.53...\n","497 But I want it to charge Super fast! ... [-1.131241798400879, 0.3491775691509247, -0.67...\n","498 Right, but I don't think it makes sense to com... ... [0.10539919137954712, 0.7969365119934082, -0.5...\n","499 Hard drive requirements tend to include extra ... ... [-0.8011002540588379, 1.111019253730774, -0.85...\n","\n","[600 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"_OypFES-8EwY"},"source":["## 4.2 Checkout sentiment distribution"]},{"cell_type":"code","metadata":{"id":"ggbC0PxHgc2t","colab":{"base_uri":"https://localhost:8080/","height":332},"executionInfo":{"status":"ok","timestamp":1619907572598,"user_tz":-120,"elapsed":206160,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"fae91c04-0f6c-4dbd-e487-48631efe9afc"},"source":["# Some Tokens are None which we must drop first\n","predictions.dropna(how='any', inplace=True)\n","# Some sentiment are 'na' which we must drop first\n","predictions = predictions[predictions.sentiment!= 'na']\n","predictions.sentiment.value_counts().plot.bar(title='Dataset sentiment distribution')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":5},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"ZUYHpsHTINsF"},"source":["# 5.Prepare data for T-SNE algorithm.\n","We create a Matrix with one row per Embedding vector for T-SNE algorithm"]},{"cell_type":"code","metadata":{"id":"L_0jefTB6i52"},"source":["import numpy as np\n","\n","# Make a matrix from the vectors in the np_array column via list comprehension\n","mat = np.matrix([x for x in predictions.sentence_embedding_bert])"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pbdi4CY2Iqc0"},"source":["## 5.1 Fit and transform T-SNE algorithm\n"]},{"cell_type":"code","metadata":{"id":"fAFGB6iYIqmO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619907593927,"user_tz":-120,"elapsed":6567,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"23266865-3763-46bb-cfd8-021a3424c652"},"source":["\n","from sklearn.manifold import TSNE\n","model = TSNE(n_components=2) #n_components means the lower dimension\n","low_dim_data = model.fit_transform(mat)\n","print('Lower dim data has shape',low_dim_data.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Lower dim data has shape (571, 2)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gsi0b0XhImaz"},"source":["### Set plotting styles"]},{"cell_type":"code","metadata":{"id":"CsPVw7NHfEgt"},"source":["# set some styles for for Plotting\n","import seaborn as sns\n","# Style Plots a bit\n","sns.set_style('darkgrid')\n","sns.set_palette('muted')\n","sns.set_context(\"notebook\", font_scale=1,rc={\"lines.linewidth\": 2.5})\n","\n","%matplotlib inline\n","import matplotlib as plt\n","plt.rcParams['figure.figsize'] = (20, 14)\n","import matplotlib.pyplot as plt1\n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8tuoCxNPmzbo"},"source":["##5.2 Plot low dimensional T-SNE BERT Sentence embeddings with hue for Sarcasm\n"]},{"cell_type":"code","metadata":{"id":"Fbq5MAv0jkft","colab":{"base_uri":"https://localhost:8080/","height":844},"executionInfo":{"status":"ok","timestamp":1619907813520,"user_tz":-120,"elapsed":1634,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"2ae05693-a9da-410a-ee20-807f6a8196a3"},"source":["tsne_df = pd.DataFrame(low_dim_data, df.iloc[:low_dim_data.shape[0]].label.replace({1:'sarcasm',0:'normal'}))\n","tsne_df.columns = ['x','y']\n","ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)\n","ax.set_title('T-SNE BERT Sentence Embeddings, colored by Sarcasm label')\n","plt1.savefig(\"bert_sarcasam\")\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABJwAAAM7CAYAAAAPkIoEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3gc1d328e/M7mpVVs2ybLnbgDkG0zHggEMNJYQeICGhpfGmkfAkIY2eQBJIeBJ4gNCrCS0JJRB6Cb33dkJxt2XJsiSrS7sz7x8zEuqyilft/lwXV7Rn2pnV0Ya9Oec3ju/7iIiIiIiIiIiIDBV3uDsgIiIiIiIiIiJjiwInEREREREREREZUgqcRERERERERERkSClwEhERERERERGRIaXASUREREREREREhpQCJxERERERERERGVIKnERERES6YYxZaoz5whCd60ZjzPm9bPeNMVuEP19pjDlrKK6bTsaYvY0xK0fCtdq/n9KzvsZlp32fMsZ8e4DXGfCxIiIyekWHuwMiIiIDYYypbfcyG2gCUuHr/2etvbXT/r8GvgMUA1XAc9bar4TbngIWAnOttSvCti8A11prZ4evlwKT210D4EZr7Q+76du5wBlhnwBWAGdaa/8Rbt8beAKo73To/tbaF9r1Jwk0Ak8DPwC+Afw63DcKxICG8PUya+38bvryLeB0YFp4vdeAr1hrazrv2x/GmBuBldbaMwdznqFkjJkNLAHqOm36lrX2jvT3aGCstd8d7j5IwBiTAfwe+ApQAKwD7rHWnjasHRMRERkFFDiJiMioZK1NtP4chkHfttY+1t2+xpiTgBOAL1hrPzHGlACHddqtDjgLOKWXyx7a0zW6cYe19vjw+gcC9xhjnrXWrg23r7bWTu/l+B9aa681xhQAdwJ/stZ+HfhdeM6TCe55UU8nMMbsFe5/kLX2DWPMBODQjez/aFZgrU0Odydk4xljItbaVN97pt2vgAXArsAaYBaw50BOZIyJalyKiMh4osBJRETGg12Ah621nwBYa0uBqzvtcynwM2PMha37DRVr7cPGmBpgc2BtX/t3OrbKGHMPwQyn/toFeMFa+0Z4rvXATa0bjTFx4ALgWCAO3A38j7W2IZyFtRj4M/ALgpldv7bW3mCMOQX4OuAbY04DnrTWHmqMmQr8H8EX8lrgz9baS8NrnQtsTTBj60hgOXCStfbVcPsM4BLg8wRL/m9rnT1mjPkmwSytEuBl4BRr7bL+vhnhrKx6YE54nbeALwO/BE4i+N0c1/p+tb6HxphLgSnAPcD3rLWN4fkOAc4HZgPvA9+11r4dbtsRuA6YC/wb8Dv15XTgJ2H7mZ223Ug4e6y330O4bxFwI7AXYIGHgb2ttYuMMQ7wvwS/q0xgWXh/727EezUf+AuwM9ACXGKt/V04Zi4kGDMQhKG/sNY2dXOOrYC/AjsAq4BfWWvva3ePDQQBzl7A4caY9+l5/GSF5zqcIPi5oa97AA4Ox2deuP8vCP7dtxTYy1r7TnjuScBSYJa1trzTOXYB7rbWrg5fLw3/ab3HXxLMnJxEMJPxDGvt3eG2k8NtLwMnAn81xlxAMGaOJpgx9Q7BzMYGY8xdBOMyi2Bsfs9a+154roOBPwEzgA3he/OnduPjUuBnBOPje0Azwe9vIkFY/bu+3ixjTCFwC7Bb+D49RzCm2y9d3NwY8zIwD3gS+Eb4uYIxZiHBeNuaYKz92Fr7VF/XFRGRsUs1nEREZDx4ETjRGHO6MWaBMSbSzT6rgGuA84bywsYYxxjzJSCDIJTo7/FFwFHAxwO4/EvAgcaY84wxe4RhQXt/ALYkCAS2IFh2d3a77SVAftj+LeByY0yhtfZq4FbgImttIgybXOBfBF+UpwH7AaeFs7taHQbcTvBF+z7gsvAeI8D9BF9SZ4fH3x5uO5xgGeFRBMshnwFuG8B70epYgoBnIsGSxxeA18PXfyf4wtze14EDCcLCLcNjWwOl64H/BxQBVwH3GWPi4TKsewi+vE8A7iIItgiPPYggHNifIJDqq05Ut7+HcNvlBLPzSghCs5PaHXcAQXizZXj8sUBFH9fCGJMLPAY8BEwlGBuPh5vPIFjuuQOwPcHMny7LKo0xMYLx8AhBGHMqcKsxxrTb7WsEgWcu8Dy9j59zCH4HmxP8PtrfZ0+OJJidtBNBUPVNa20zwdg6vt1+xwGPdxM2QfDZ8RNjzPeNMduGIV57nxCERPkEnx2LjTFT2m3fDfiUYDnuBQSh0c7A7gRj4+eAF+77IMF4mEQwJtsvC76OYKlwLrANwZLcViUEgWLr3+814f3tHPbtLGPMnB7eo/ZcgmBuFjCTIBC8rNM+JwLfJAhgkwRBF8aYacADBGHaBILx/Q9jTPFGXFdERMYozXASEZExz1q72BjjE9RAOhdoNMZcZK29sNOuvwc+Dmd3dOceY0z7JTGnW2uv6WHfY8MZMDGCL4O/stZWtds+1RhT1emYadba1vpDlxpjLiaYnfEWwZe8frHWPmOMOQr4PvBjIGqMuZpgtpBHsHxwu3YzFH4H/I1gGREEM1t+Ey4D+ndYN8sQfAnvbBeg2Fr7m/D1p8aYa4CvEsy6AXjWWvvv8Fq3AK11cHYlCDZOb7fk6Nnwf78L/N5a+0G7Pv7aGDOrl1lO6zrmGnyu9XiC2Sqvhee6G/i+tfbm8PUdQOeaXJe1q+t1AcEMnDPD9+4qa+1L4X43hXXCFhLMWooBf7HW+sDfjTE/aXfOY4EbWmcahbO/juvhXqCH34Mx5hWCIGsba2098L4x5iZg73bH5RLMRnm53XvQl0OAUmvtxeHrRoLwEoIA7lRrbVnY9/MIwrbORc4XAgngD9ZaD3jCGHN/eJ/nhvvca619LjzPtvQ+fo4l+F2tB9aHs87ah6PdubDd/n8Jr30twSy/u4wxvwx/PycAF/Vwjt8DleF9/xmoMMb8ylp7E4C19q52+95hjPkVwXi+N2xbba39v/AePYK/44XW2lXh9udbD7bWXt/6czgmKo0x+dbaaoLf5dbGmLestZVhn1q1ABdYa1PGmNsJZm9eEtZpey+cObY9QX2zHllrK4B/tOvDBQSzmNq7pd24PQt4M1yyfDzw79a/b+BRY8yrwMG0m1UpIiLjiwInEREZU4wxM2k3k6i11lNYRPzWcObFEeHPb1prH263b7kx5jLgNwTLdzo7oh81nO5sV8NpNnC/MabaWntVuL2vGk4/Cms4bUsw+2c6wTK0frHWPgg8GM5A2odgto0lWD6XDbzWLpxxgPazvyo61ZypJwgRujOLriFahGBGUqvSTufKNMZECZYJLeuhvs0s4JIwfGvfz2kEM6K6M7GXWjntlzQ2dPO68/2taPfzMoJgrLVfJxljTm23PSPc7gOrwjCj/bGtphIUb+9uW3d6+j0UE/y7XPs+tv1srX0iHM+XA7OMMf8Efmat3dDH9WYQzNzpztRO/W3/nnTeb0UYNrXfd1p3faXv8TOVrr+LvnT7u7PWvmSMqQf2NsasIZjBdV93JwjrSl1OMKssiyAwut4Y87K19gNjzIkESyNnh4ckCGbLddeHiQThc5f3NpzldwFwDMHv1Wt3TDVBsHgm8AdjzNvAL621L4T7VLSrf9X6EIG+xnUXxphsglDtIKB1Bl2u6Vhfq/N7Ggv7OAs4xhjTvkZcjK6BlYiIjCMKnEREZEyx1i6nly9X1toWgtkNvyBYmvJwp13+SLAE5uUh7NNSY8yDBAW7r+pr/07HvmOCx5ZfbozZqVOI0Z/zeMDjxpgnCO77GoIvovPbzbboj879WAEssdbOHcC5VgAzTfdFlVcQzN64tZvj0mFGu59nAq21fFr7dUHnA0xQrH2aMcZp9/uayWdBw5puzjsQ5QTLmqYD/+2mv4Q1kC4N6xTdSTC7rfNspM5WEMws6s5qgnDhvfB1+/ek834zjDFuu9BpZrt+Qscx1Nf4aX3P2l+3L533b9/Pmwhm5ZQCf2+ty9Uba20Dwd/heQSzjeoJ/o72I6iVljLGvEkQiLZqf4/rCGaLbU4wa7G9rxEs+/sCQY2ofIJZTE547VcI6lzFCGbh3Umn3/UQ+CnBDMbdrLWlxpgdgDc63U/ncdtCcF8rCGY/fWeI+yQiIqOYAicRERnzwuK95cDTBPVuDgTm89kyoTY2KNJ9MUFtlZohuv50glkD/+5r3x7cRFAf5jA+W6qzMdc9nKAA8cNAFcGyt72A06y1Xrhk6c/GmB9aa8vCOizbtJ/11Yu1wGbtXr8M1IRB3qUERYu3ArLCL8u9eZkgUPiDMeYcgsLHO4fLra4EfhvORnvPGJMPHNBpKdOm9INwKVg9Qf2iO8L2a4C7jTGPhf3PJljK9jRBXagk8CNjzBUEQeOufDbb407gBmPMzQThwjkD6VgYcPwTONcY822CAOBEwplwxphdCOryvE4w7hsJZ86EfxPnWmtnd3Pq+4H/NUHB7b8SzNzaOlw+eBtwZriczydY1ra4m3O8RPCe/Tz8e9ojfB926eF2+ho/dwK/Msa8BOQQ1ITqy+nh/gmCJaXt63MtJgh9agiW1HUrfA/eDO+nhWBpXS5BEJND8B6Uh/t+gyDM7Vb4N3c9wXt7AsHf0K4Ev59cgppiFQRjqa3Itwlqgh0D3G+trTbGbOCzGVBDKZcghK4ywRMtuxuXx7cbt78hCOtSxpjFwCthza3HCGY3LQQ+7lR0XERExhEVDRcRkfFgA0Hh6eUEwctFBE+AeraH/S8hCD06+5cxprbdP3f3cs2vtO4HvELwxKf2BcmndjpXrTHmy92dKCx0fAl9z0zprJLgKVkfEbwHi4E/tpst9AuCYuQvhl9iHyOY4bAxriOY5VFljLknXHJzCEEx6SUEsx6uJZip0avw2EMJljYtB1YCXwm33U3wVLTbwz6+C3yxj1NWdXpff9LH/r35G0Hh608JZiidH/brVYL39jKC9/lj4ORwWzNBkfOTgfXhvfyz3f0+SPAEsSfC49oXgO6vHxK8x6UERcpvIwguIKj/dU3Yv2UEYcYfw20zCMZkF2Htn/0JfielBONnn3Dz+cCrwNsET1h7PWzrfI7m8PgvEoyFK4ATrbUf9nDNvsbPeeE9LCH4fdzS0xvSzr0ESxffJChofV27660I++7TcdlnZ/XAxQTvwzqCp0V+2Vr7qbX2/XDbCwTh0bb08J628zOC9+0VgrFxIcG/j98c3t8qgiXBneuknQAsDf8GvksQfA21vxAE1OvC6z/UzT63EDwVsZRgeeCPoO39bC3wX04w4+l09F1DRGRcc3x/QDPzRURERGSEMcZcCJRYa3t9ipsx5hGCx9ZvbCHxMSecbbTaWtvlKXsiIiIyeFpSJyIiIjJKGWPmESx5e4dgudq3gG/3dZy19oBN3LURLSzkfxSw4zB3RUREZMzSNFcRERGR0SuXYLleHUF9qYvpR52v8cgY81uCpZl/tNYuGe7+iIiIjFVaUiciIiIiIiIiIkNqPCypixNMMV9D9wVgRURERERERESkfyLAFIKHYTR13jgeAqdd6P3pIyIiIiIiIiIiMjCfB7o8/Xk8BE5rACor6/C84V0+WFSUoKKidlj7IOOHxpukm8acpJPGm6Sbxpykk8abpJvGnAyE6zoUFuZAmLt0Nh4CpxSA5/nDHji19kMkXTTeJN005iSdNN4k3TTmJJ003iTdNOZkELotX6Sn1ImIiIiIiIiIyJBS4CQiIiIiIiIiIkNKgZOIiIiIiIiIiAyp8VDDSURERERERETGiVQqSWVlOclk83B3ZUyIRjMoLCwmEulfhDQiAydjzDnAucC21tp3jTELgauALGApcLy1tmz4eigiIiIiIiIiI1FlZTmZmdnk5JTgOM5wd2dU832furoNVFaWM3HilH4dO+KW1BljdgIWAsvC1y6wGPiBtXZL4GngD8PXQxEREREREREZqZLJZnJy8hQ2DQHHccjJyRvQbLERFTgZY+LA5cD32jXvDDRaa58NX18JHJvuvomIiIiIiIjI6KCwaegM9L0caUvqfgMsttYuNca0ts0knO0EYK1dZ4xxjTETrLXrN/bERUWJoe3pABUX5w53F2Qc0XiTdNOYk3TSeJN005iTdNJ4k3QbS2OurMwlGh1R82tGPdd1+z1GRkzgZIz5HLAA+OWmOH9FRS2e52+KU2+04uJcystrhrUPMn5ovEm6acxJOmm8SbppzEk6abxJuo21Med5Hsmkl9ZrHn30ofziF2eyyy679brfokULuP32u5k+fUa/rzGYYwfL87wuY8R1nV4n94ykyG8vYCtgiTFmKTAdeBjYApjVupMxZiLg9Wd2k4iIiIiIiIiIpM+ICZystX+w1k611s621s4GVgIHAn8Esowxi8JdvwvcNUzdFBERERERERGRPoyYJXU9sdZ6xpgTgKuMMZnAUuD44e2ViIiIiIiIiEhH77//LpdccjHLli0hHo+z1177cuqpPyEWi7Xt88ILz3HnnbdRX1/LwQcfxve+dyquG8wHuv/+e7nttluoqKhg663n8/Ofn0FJyZThup1BGbGBUzjLqfXn54Fth683IiIiIiIiIiK9c90Ip576E+bN24ry8jJ+9rMfcffdd3HssV9r2+fpp5/kuutupr6+gdNO+z4zZ87i0EOP4JlnnuKWW27gwgv/zPTpM1i8+EbOPfcMrrzy+mG8o4EbMUvqRERERERERERGs3nztmKbbbYlGo0yZcpUDj/8KN544/UO+3z96yeRl5dPSUkJxx57HI899jAA99zzT0444WRmz55DNBrlxBO/yUcfWUpL1wzHrQzaiJ3hJCIiIiIiIiIymixfvozLLvszH374AY2NjaRSSYzZqsM+kyZNbvu5pGQK69aVA7B27RouueRiLrvsL23bfR/Ky8tG5bI6BU4iIiIiIiIiIkPg4ov/wNy5hnPPvYDs7BzuvPNvPPnk4x32KStby2abbQ7A2rWlTJxYDARB1IknfpMDDvhi2vu9KWhJnYiIiIiIiIjIEKivryMnJ4esrGyWLVvK3Xf/vcs+t912Cxs2bGDt2lLuuus29t13fwAOP/zL3HLLDXz66ScA1NbW8sQTj6W1/0NJM5xERERERERERIbAD35wGhdddAF/+9vNzJ1r2G+/A3jttVc67LNo0V5861snUFdXyxe/eAiHHHI4AHvttQ8NDfWce+6vKS0tJZFIsGDBruy77xeG41YGzfF9f7j7sKnNBpZUVNTiecN7r8XFuZSX1wxrH2T80HiTdNOYk3TSeJN005iTdNJ4k3Qba2OutHQZJSWzhrsbY0p376nrOhQVJQDmAEs7H6MldSIiIiIiIiIiMqQUOImIiIiIiIiIyJBS4CQiIiIiIiIiIkNKgZOIiIiIiIiIiAwpBU4iIiIiIiIiIjKkFDiJiIiIiIiIiMiQUuAkIiIiIiIiIiJDSoGTiIiIiIiIiIgMKQVOIiIiIiIiIiLjwL///S/OPPPnablWNC1XERHpJIWL5/lkRHx8v+v2aDSC73ukUt1sFBERERERGULPf1DDXc9WUlGTpCg3yjGLCtl9q9zh7labVCpFJBIZ7m70iwInEUkrD/hkbYrbnlxDfVOKI3YvYsfNsolHgmCpKeVgVzXx+JtVzCiOs/d2+RQn6DaUEhERERERGaznP6jh+kfX0ZwMvnRU1CS5/tF1AEMSOi1atIBTTvk+Tz/9FNXV1fzgBz9i7733A+DFF5/nqqsuw/M8CgoKOf30XzN9+gxef/1VLrnkTxizFf/9r+U73/kef/7zRRxwwBd57bVXKC8v47vfPZWqqvU8+uhDbNiwgV/96mx22GEnkskkP//5aVRXV9PU1MTWW8/n9NN/TSwWG/S99IeW1IlIWq2q9Pjtrcv4eHUDqyuaueJfa3h7aQOOA67r8MKHdfzp7yt54+Na7nuhgrNuWkpl/XD3WkRERERExqq7nq1sC5taNSd97nq2csiukZOTw7XX3sxZZ53HX/7yJwAqK9dz/vlnc/bZ53PTTbez//4Hct55Z7Yds2TJpxx22JHceOPf2GOPzwPQ0tLCVVfdwAUXXMRFF51PJBLlmmtu5pRTfsBVV10OQCQS4Zxzzue6627hllvuIJVK8cAD9w7ZvWwszXASkbRxXYe3Pq3r0v6vF9ez0+bTaW7xueM/ZR221TV6LC9vpmBmRrq6KSIiIiIi40hFTbJf7QOx334HAjB//rasW1dOU1MT7733LptvviVz5mwGwMEHH8bFF19IfX3wnWn69Blss812nc6zPwBbbjmPxsZG9tvvAADmzduKVatWAuB5HrfdtpgXX3wez0tRU1NDZmbmkN3LxlLgJCKD4rgudc2QkwG+5/W6r+/75GZ1XXdckIgScXwcBxycrtfo2iQiIiIiIjIkinKj3YZLRblDF5lkZAT/Ab21DlMqlerzmKys7D7P0/radV1SqeAeHn30Id5++02uuOIasrNzuPnm61mxYvngb6KftKRORAasrBaufricX9+wlKseKqestvf9fR+2nZ1NIvOz0Ml14ejPTwTfJzsGX92nuMMxiawIM4o3bnZTRb3D60ubeWNZM+sblFKJiIiIiEjfjllUSEa04/eHjKjDMYsKN+l158/flk8++S/Lli0F4MEH72fuXEN2ds6gzltbW0N+fgHZ2TnU1tby6KMPDUFv+08znERkQOqSLn/+53JWrWsG4Nl3q/lkTQNnfW0miVjPM52KcuD8b8ziwxWNNDan2GpGNlMKHPDB83wWmhyKcmfw1NtB0fA95udRmNV30fDSGjj/1qVsqA/+S8GE3Ci/Pm4mkxJDdssiIiIiIjIGtRYGT/dT6goLCznzzN9w3nlnkEqlKCgo5Oyzfzvo8x500CE888zTfO1rX6awcALbb78jTU1NQ9Dj/nH8sf/op9nAkoqKWjxveO+1uDiX8vKaYe2DjB+berx9XJbiN4uXdWk/+/hZbDFp4x7X6Tg9B0nRqIvn+Rv1dxuLudz4+HoefnV9h/aj9yzmyN3ySaV6X+onQ0OfcZJOGm+Sbhpzkk4ab5JuY23MlZYuo6Rk1nB3Y0zp7j11XYeiogTAHGBp52O0pE5EBiQe6/7jI6OH9u70lncnk95Gh8SO67KyvGtiv7K8kWhUH3MiIiIiIiLppm9iIjIgJYUu++xQ0KFtz+3ymZKf/o+VZEuS3efndWnfdV4+TU1D92QJERERERER2Tiq4SQiAxLD5+hFRey0RYLlZU3MmBRn85I4Mbd/S1cdB6obHVZVNBONOEwripEd7eYcjkNdM0RdyIwGT7xr5Xmww2ZZHLF7EQ+8vB7XcTh89yK2mh4HxvyyYRERERERkRFHgZOIDFhuhs/2MzPYcXYGngcDCXfKa+Hsm5dS2xAU+545Kc4vjplObvyzc9U1OzzwaiUPvVJJIivCtw4qYdsZcVzns31yM+Do3QvZd4cCcByKsj1SKYVNIiIiIiIiw0FL6kRk0LyB1uR2HO5/aX1b2ASwvKyJ95Y34Dhtu/DkOxu4/8X1JFM+VbVJLv77SlZVprqczvM8CjJ9CuIeqa6bRUREREREJE0UOIlIv/iOC24Exx38x0fKd/hkTWOX9hXljThh4tSUdHjs9cou+3y85rNQSkREREREREYWBU4islE832H5eo//vFfL7+5YxWX3r2Vl1eCWrMVcn722y+/Svu2cnLYn1EUjMGVCRpd9ChOxXp9yJyIiIiIiIsNHNZxEZKOsrEzx/vIG/vbE2ra2V/9bw++/NYeS3IFNNfI8n8/Ny6G0spDHXqskFnX4yl6T2GxSBq31oFx8vr7fJM65aRktYU2mmZPibD6lawglIiIiIiIyEM7Hz+K+cjvUVkCiCG+Xr+JvsWhY++R5Ho7jtK3+GG0UOIlInyIRl+VlDTz9TlWH9pQHHyyvZ8o2OQOebZQTg6/vNYHDdp2A40JeJvhex5PNKHS58NtzWLmumXjMYWZxBjkxTW8SEREREZHBcz5+FveZq3GSzUFD7TrcZ67Gg0GHTo2NjZx//jksXfopkUiUmTNncdppP+Pcc8+grq6O5uZmdt99D77//R8DcN11V7FkyafU1dWydm0pV155A++88xbXX381yWQS13U444zz2GKLuZx33pksX76MlpZmpk2bwa9+dTZ5eXksX76UCy44j8bGRjwvxRe/eChf+9oJXHfdVSxfvpS6ujpWrFiOMVtx/PEncdllf6G0dA177bUvP/jBjwf5bn5GgZOI9Mn3fWJRh1ika7Iei7iDXtrm+D55meG1uilA7vswMQcm5rTOalLYJCIiIiIiQ8N95fbPwqaQk2zGfeV2UoMMnF566QXq6+tYvPguADZs2EA8HufCC/9MdnY2yWSSn/zkh7z44vMsXLg7AO+//y7XX38rBQUFLF++jAsvPJ/LL7+GGTNm0tzcTDLZAsCPf/wzCgoKALj66iu49dab+N73TuWf//w7ixbtyQknfKPtmq2s/ZBrr72FrKwsvvnN47nyysv4058uJZVKccwxh3HYYUcyY8bMQd1zKwVOItInz/OZXRJn7x0KWfLQmrb2rAyXeTOyhrFnIiIiIiIig1Rb0b/2fthii7ksXbqEiy++kB133Jndd1+E53lcccUlvPPO24BPRUUFH33037bA6XOf26MtSHrllZdYuHD3thAoIyODjIzgP8Q/9ND9PPLIQySTLTQ0NLbts8MOO3LFFZfS2NjITjstYKedFrT1Z9ddF5JIJMK+bcHmm2/Zdr6ZM2exatVKBU4ikl4luQ7RWVmcfuwM3vqklsLcGLuZBMUJVLxbRERERERGr0QR1K7rvn2Qpk2bzuLFd/Lqq6/w4ovPcfXVl3PQQV+ipmYDV199Yzjb6QKam5vajsnKyu7zvG+99Qb33PMP/vrX6yksLOSRRx7ivvv+CcDee+/HNttsx8svv8jixTfywAP3cfbZvwUgIyPedg7XjRCPZ7R77ZJKpQZ9z23nG7IziciY5vtQlAPbzcjgpH2LOHRBLhNzFDaJiIiIiMjo5u3yVfxox4cS+dEMvF2+Ouhzl5WtxXUj7Lnn3vzoRz+lqqqS1atXUVQ0kXg8Tnl5Gc8++58ej99114W8+OLzrFixHIDm5mbq64eZ0WgAACAASURBVOuoqakhJydBfn4+zc3NPPDAfW3HrFy5ggkTijj44EP5xje+w/vvvzfo+xgIzXASkX7xfZ9USimTiIiIiIiMDf4Wi/Bgkzyl7pNPPubKKy8DwPNSHH/8yXzhCwdy1lm/4IQTjqW4eDI777xLj8fPmDGTn//8DM4551ekUh6RiMsZZ5zHwoW788gjD3LccUeRn1/ADjvs2BYsPfHEozzyyEPEYlEcx+HHP/7poO9jIBx/7E9PmA0sqaioxfOG916Li3MpL68Z1j7I+KHxJummMSfppPEm6aYxJ+mk8SbpNtbGXGnpMkpKZg13N8aU7t5T13UoKkoAzAGWdj5GS+pERERERERERGRIKXASEREREREREZEhpcBJRERERERERMaUcVA+KG0G+l4qcBIRERERERGRMcN1I6RSyeHuxpiRSiVx3Ui/j9NT6kQkrSIRp8en3DV7DmurUzS3eJQUxsiJbVyS7uNQVuNRWZvCcR021CUpSESZXhQlK6L/siEiIiIiMp5kZSWoqamioKAIx9E8m8HwfY+amkqyshL9PlaBk4ikRXWjw39XN1K6vpk5JZnMnpxBol2gVN/icP2jZbz8YfB0jPycKOccP5OJOX2f+5OyJLc8tpbtNktw7/Pr2tqP2GMiBy/IJ1Ohk4iIiIjIuJFI5FNZWc7atSsBfRcYHIeMjEwSifx+H6nASUQ2ucaUw//dt5r/rmxoa/vqPpM4YMdcouH/ASwpa24LmwCq65Lc9XQ53z14Ek4va4aTvsv1D61iz+0LuP3JtR223fv8Onaem2DWBP1XDRERERGR8cJxHCZMmDTc3Rj39C1MRDa55eXJDmETwN3PrmNdTRAkOQ6sqWjuctyHKxto7mPpdUvKZ92GFjzPx/M6bvN9qKlPDarvIiIiIiIi0n8KnERkk2tq8bq0NSc9kmEtJ9+H2SXxLvvsNi+XeB/zMLMz4As7FdDQ5FGY23HnvOwIkwpjA++4iIiIiIiIDIgCJxHZ5GYUZ5Ad7/hxs2BuLhPzPnvSwcyiGF/Zq5hIuNvWM7P50q4TgjSqF77nc/CCQlx8vrr3JGZPzgzONynO/3x5BsUJZ2hvRkRERERERPqkGk4isskVZvmcfcJs7niqjOVljew6L4/9dyok0/1s5lNGxOdLC/LYfetcWlI+E3Jcos7GFfhLZPgctXsB9c2ww+YzqGv0yMpwyM3w8TwVCRQREREREUk3BU4issn5PkzNg/85ooTGFsiO+3jJboIg36cwC8Chv0+T8D2frChAiqxsgK41nURERERERCQ9FDiJSPp4HpkR8PooBC4iIiIiIiKjm2o4iYiIiIiIiIjIkNIMJxFJK8d1aElBLBIsg+tJU8qhrDqF5/tMLoiSGVEtJhERERERkdFCgZOIpM36BnjwlfW8u7SOXU0e++2QT168a5BU0+Rw6b1rsCvrAZg1Oc7PvjyN/Mx091hEREREREQGQkvqRCQtGpIOv79tBQ+/Wsmqdc3c/dw6/nr/GpK+02E/x4G3ltS3hU0Ay9Y28fwHtbiu0/m0IiIiIiIiMgIpcBKRtCitSrK2qqVD23vL6qmo7fgouUjE5f3ldV2Of/OTWhxHgZOIiIiIiMhooMBJRNIiFukaFjkORDvNWkqlPHbcIrfLvp/bKhevl5pPIiIiIiIiMnIocBKRtJiUH2HnuYkObQfvOoEJOR0DJ9+HrWdkstd2+W1tu5gEC+Ym8P1+BE6OQ02zQ22Lg6NPOhERERERkbRS0XARSYsM1+fbB01mr+0KWFLagJmezWaTM3DoGiLlxHy+8YWJHLZwAr4PRQmXiLPxYVN9i8O/X63igZcqiEYcvrLXJPacnyBDT7oTERERERFJCwVOIpI2OTGfHWZlsNOceLg8rucAyMWnONE6+2njgyLHgdc+qee+FyoASHk+Nz+2lqlFGWw9LTaI3ouIiIiIiMjG0kITEUm7TVqLyXF58s2qLs2vflRDJKKPPBERERERkXTQty8RGVMcfDafmtmlfdakTDzP6+YIERERERERGWoKnERkTPE8nwN3LiSRFWlrKymMscNm2fSn5riIiIiIiIgMnGo4iciYU5yAP3xzNivXNRNxHaYXxcjJaJ82OVQ2+NQ3eRTlRshUMXEREREREZEhpcBJREa0qOMT8RpJRjJJeU7fBwC+D3lxv12R8M8CpZTv8OwHddz06FqSKZ+J+TF++ZUZTEpsgs6LiIiIiIiMU1pSJyIjVk5TGdHnr8W743RiL9xATnP5oM+5dkOK6x4qJZkKQqh11S1c+cBqkv7GhVkiIiIiIiLSNwVOIjIiZXp1NP/r9yTffRy/Zh3Jtx+h+YGLifv1gzpveXVLl7aPVzVS3zSo04qIiIiIiEg7CpxEZERya0rxK9d0aPPXLSVSWzao805IxLq0zZwUJyuuGU4iIiIiIiJDRYGTiIxITjSj+/ZI18CoP6YURDhmz+K214nMCN8/dCoxxxvUeUVEREREROQzKhouIiNSc04J0a33Ifn+k21tse0PpDl70qDOG3V9vrQgj93mJahr9CjOi5Ib9/H1oDoREREREZEho8BJREakFmK4C79GfIvd8NctxSmeQ7JoC1oY3AwnABefSQkHEhEgDJtcF8+DiON1CZ8cBzY0OpRWtZAVd5mcFyHmKqESERERERHpiQInERmxmiK5NE3eAadkR/yNnILkOA41TVDX5JGf7ZIZ6f04H4flFSnuemYtG+pTHPa5IradmUm83XGlG+C8W5ZS25gCYNE2eZy4bzGZUYVOIiIiIiIi3VHgJCIjRgsuVXUeOXGXRIaHF5ZV2tiwCQfeW9XMZfesprYxxaSCGD87ejoleT0XBF9T7XHOzUvxwktcevcqfnTENHbZPI7vg4fDLY+XtoVNAM++u4G9tytgyxJ9hIqIiIiIiHRHRcNFZEQorYE/3L6Kn171KWffvIwP16Rw+/kJVVEHf7xzRVs4VFbVwp/+vpKmVPeBk+M4vLu0ri1sanXP8+tI+sHFm5Lw0cqGrtfa0NK/zomIiIiIiIwjCpxEZNg1plwuu3cVH60Ogp2yqhYuvGM5pRv6d5511UlSnR42V1bVQnV9T0+g88nKiHRpzcl0ccOMKisGu8zL7bLPlKLun6InIiIiIiIiCpxEZARYV5NieVlTh7aWpM+a9f2bRZSf0zU8SmRGyI53/1Hn+zB/VhY5mZ9tdxw4dq9JOH7bej6OXjSRudOyAIhFHE7afzLTC7WcTkREREREpCf6xiQiwy477pIdd6lv6jgTKS+7a4DUm+Jcl+P3m8Tix8sAiLhw6hFTyY3T5clzrYpy4PyTZ/PO0jpqGzx22CyHaYXB0+taFWT6/PLYqVTWemTEHAqynJ5PKCIiIiIiIgqcZHRxHZ/MpgpoqsHPnkBjNF/f+8eASbkOJx84hSvuW9XW9oWdCpk6IQr0tByuq4jjs9/2uWy/WQ5VtUmK82NMyOm96LjvQ1E27DM/B8dx8Dyf9mETgOs6ZLoOk/Pc4FwadCIiIiIiIr1S4CSjRgSPzJWv0PTIFZBswsnOI+fQX1CXv7m+/49yyaTHLpvHOf/k2ZRWtlCQiDKjKELc3fiwyXEcXNeBlMfkXIfJubFgw0aOjSBH6rpzdaPDc+9v4O0ldXxuqzwWbJFDToYGnIiIiIiISG8UOMmoEalbS9ODl0BYW8ev30DTA/9L/CsX0ugmhrl3MlgRB2ZOcJk5Id7vY6saHV77qJalaxvZbV4ec6fEiPdvNV63GlMOF921ghXlzQC8v6yeD+fn8e0Diok4Cp1ERERERER6oqLhMir4jsP6NaVtYVNbe20FbmPVMPVKRoK6Fofz/7acmx5dy3/eruaiO1fw1Lu1OK2PmRuE0spkW9jU6rn3NlDZ41PvREREREREBBQ4ySjR0AKlzV1nMTnZefjxro+sl/Fjxbpmyqo6Ps3urv+UU9M4+HM73WRWjgODj7JERERERETGNgVOMipkROCZlbnU7voNcMJhG4vj7H8ajdH84e2cDKvu6nd5PdRj6q+Sgihzp2Z1aNtn+wIm5OijU0REREREpDeq4SSjQtTxOWDHXC79z3y+f8hFRJqq2RAppNEtZlrKIeqqns54Nb0oRn5OhOq6VFvbIbtNIDfTGfTT5OIRnx8fOYU3Pqnng+X17LxlLvNnxHE2thK5iIiIiIjIOKXASUaFDKeFOa9fyrcWfYuzH2hkXXUW0Ais4IeHT2W3LbKGZEaLjD65cTj3+Fk8/mYVH69uZJ/tC9h+Ttagw6ZWeXHYe342+22XIJlU7SYREREREZGNocBJRo/GGtaW17KuumPzzY+uZbtZc8iMKnAar4py4Lg9J+ADvucNefjo+/QaNrkuuK6rQEpERERERCSkwEk2KQ+H9XU+yZTHxNwI0QE+Sr7Zj5Gz8BiSax3otJypqcXD0xKncS+V6jvsiUQcfJwwlOq4zcelKeWTGaVfs6PW1sJbn9ZRWdPCDlvkstnkGDEUPImIiIiIyPimwEk2mYYk/OO59TzyWiUAW83I5geHTSEvPsDQadJ8ZscbicfW09Ty2Rf6wz83kZwM8PUdf9RbV++wvKwJ14VZk+IUZvY9VppTDivXJ6msTVKcH2NqQaTbml7r6+Gpt6uwKxvYc9t8dtwsi+xY8NS5tTWw+PFSPl7VwC4mwVF7TCR/I65dXgcX/G05VbVJAB54aT0/Pmo6u2yWgafxKCIiIiIi45gCJ9lkPl7T3BY2AXywop4n3qrmyIX5+F7/Q6cWJ4P8wjjnn5zHPc9XsGpdE/vvVMiCudkDOp+MLGs2wPl/W0pNfVD8uzg/xi+/OpPinJ5/tynf4e4XK3ngpfVtbScfMJl9t010mKVU2+xw3q3LqKwJgqEPltfzpd0m8NVFhdQ0wXmLP7vuk29Vs6qimV8eM7XPGXmfrGlsC5ta3fFkGfNnziTTVeIkIiIiIiLjl57tLZuE6zrYlQ1d2l+2NSQ9Z8Dn9X2fybnw3S8Wc+7Xp7Pn1tlkqXbTqBeNujz6emVb6ANQXt3Cqx/V4Lo9j5fyGq9D2ASw+PEyKus7jolVFc1tYVOrB19ZT3UjlFa1dLguwH9XNrC+tu/AKJnqOvYamj02YnWfiIiIiIjImKbASTYJz/PZfEpml/bt5uQMuI5Te77n4TL0xaFlePi4rChv6tK+sryJaLTnj6n6pq7JTjLl09jcsd3pJrSKOEE9sMyMruePRhwyYn0Ho3NKsohFOu73pd2KyO869EVERERERMYVBU6yyWw5LZNdtky0vZ5alMGBOxcqJJIufC/JHvPzu7QvmJtLc3OqmyMCk/KjJLIiHdqmFWVQlNuxbfqEGFMmZHRoO2KPieRnOkzOi7D7/LwO2766dzEFWX0HTjMK4awTZrPTFglmT87kmweVsMfWCT2tTkRERERExj1nHHz5nw0sqaioxRvmOj/FxbmUl9cMax/SrcVzKNuQIpnymVwQJTMy5sfbiDHaxtuGJoeHXqvi4VfW47oORy6ayN7b5JEd6zm8cRxYU+1zzYOlfLy6ge03y+Gk/Usoyu46zqob4bWP6/hkdQO7zsvDTI2TGS7HbEjC8vIWyqqSTJuYwYyiKLFuCo/3KOKSSjnE3NS4LhY+2sacjG4ab5JuGnOSThpvkm4aczIQrutQVJQAmAMs7bxdgVMa6Y9Y0mk0jjfHdVlf7+MARTk+qZ4nN3WQ9B2akpAVA5ee/84dx8F1HVIqsrRJjMYxJ6OXxpukm8acpJPGm6SbxpwMRF+Bk55SJyIjhu95FIb1jzY2bAKIOj7R2Eac3/dJdVPoW0RERERERIaWAicRGfUiDmQkq/GdCE2R3CGrE5bp1RKpWgleCq9gOg3RrnWmREREREREpCsFTiIyqmWmNuC8+yAtr92PE88me6+TaZq+gCQbMeWpF9ktFbQ8cBHJ8mVBQ2IiOUedRV188hD0WkREREREZGzTU+pEZNRyXQf34+doefluSLXg11fT9OAlZFYtGdR5HQecFW/it4ZNALXr8N57jIjb99PrRERERERExjsFTiIyasW8BpLvPNKl3Vv5Ho4z8GDIdV1SpZ90Pe+qD3CdfhSXEhERERERGacUOInIqOU5MZzCqV035BYPqo5TKuURmbNTl/bIvD1JepEBn1dERERERGS8UOAkIqNWix8httsxEM1oa3MKpsDUrQZ97uTkecR2ORLcCOAQnb8v3pzdhqwguYiIiIiIyFimouEiMqrV5c4h+7iLYP0KiMTwimZTHy0Y9Hmb3ATRHY8lY+v9wPdoySyixdfsJhERERERkY2hwElERjXf96nLLIGpJUN+7qTvkMyYGF5oyE8vIiIiIiIyZmlJnYhILzxcUr7DIGqQi4iIiIiIjDua4SQio1aL51Dd4JOd4ZCTQbf1lRwHqhsd1qxvJp7hMqUgSjzS93Qlz3f4b2kLtz1ZRnPS46g9JrLD7CxiG3GsiIiIiIjIeKfASURGpfI6+MvdK1lR1kRedoQfHj6VeVNjXZa+rdkA5968hPomD4CdtsjhlC+WkB3rPThaXpHkd7ctb3v9f/eu5qdHT2f7mRm9HCUiIiIiIiKgJXUiMgq1+A5/uXsVK8qaANhQn+IPd6ygoq7jfh4utz6+ti1sAnj94zqWljX3ev5IxOGlD2u6tD/w0nocVx+bIiIiIiIifdE3JxEZdarrvbawqZXnQVlVS4e25qTPp6WNXY5ftyHZ6/k9DwoSXSeATsiNqpaTiIiIiIjIRhhRS+qMMfcAcwAPqAVOtda+aYzZErgJKAIqgBOttR8NX09FZDhlZbjkZkeoqU91aM/PiXR4nRmD3bfO4+FXKzu0z5zU+7I43/fZeW6Cu59b1zY7KuLCoQsn4KW8Xo8VERERERGRERY4ASdZa6sBjDGHA9cDOwFXApdbaxcbY44HrgL2Hb5uishwSmTADw+byoV3rsAL858j9yhiUm6EDkWcfJ9Dd5tAWVULb3xcSzzmcPx+k5lWGKVLsadOihNwwTdmY1c20Jz02WpGFpPznL4OExEREREREUZY4NQaNoXyAc8YM4kgdNo/bL8NuMwYU2ytLU93H0Vk+Pm+z7ypMS4+ZTPKqlrIz44wKS9C1O2aBuXFfX582GSq6icRizjkZfn4Xt+pke9DUTbsvmUWjhO8VtgkIiIiIiKycZzuHiM+nIwx1wIHAA5wEJAJ3Gytnd9un/eB4621r2/EKWcDSzZBV0VGBc/zKasKimRPKsjAdVWESERERERERIbMHGBp58YRNcMJwFr7bQBjzAnAH4GzhuK8FRW1eBsxq2FTKi7Opby865OvRDaF4uJcVpbW8sTbNfzjmWAy4JF7TGS/7fPIjI6soFnGBn3GSTppvEm6acxJOmm8SbppzMlAuK5DUVGi5+1p7Eu/WGtvAfYBVgLTjDERgPB/pwIrhrF7IqPCByubuO3JMpqTPs1Jnzv+U877K7o+tU1ERERERERkKI2YwMkYkzDGzGj3+lBgPVAGvAkcF246DnhD9ZtE+vbMu9Vd2p56u5poNNLN3iIiIiIiIiJDYyQtqcsB7jLG5AApgrDpUGutb4z5LnCTMeZsoBI4cRj7KTJqbDYlk1dsx6mxm0/NxGt9tJuIiIiIiIjIJjBiAidr7VpgYQ/bPgR2S2+PREa/z83L5ZFXK6msTQKQnxPl8/Pzhr2e2WjnulCXdPF9SMR8vZ8iIiIiIiKdjJjASUSG3oRsOP+kWaysaMYHphfFyIsPd69GDseBuhaHDfUeuVkuiQyfvh7c2ZRyeP2jBv7xTDmeD0fsPpFd52aRqU9TERERERGRNvqKJDLG5cZ9tpoaG+5upEWL77KmKkVTi8fUCTFyoj0vHXQcWLrO4+J/rKSqNkledoT/OWo6W0yO9Bo62dXN/PVfq9teX/vgGnIyp7PznIyhvBUREREREZFRbcQUDRcRGYyaZocbHyvnzBuW8NvFyzj/b8spq3V63L+2yeHCO1dQFS433FCf4sI7V1Dd2PMx8XiUZ96t6tL+1FtVxOPK70VERERERFopcBKRMeGjNU08885nT+Vbta6Zf71UERRc6sb62hS1DakObY3NHhU1yR6vkUp5TMzrOpNpYn5UdZxERERERETaUeAkIqOe6zosLW3s0v7BsjoaWro/JjfbJSPacTZTxIX87EiP10kmPT6/TT7Z8c8+OuMxl/12LKSlJdXjcSIiIiIiIuON1oCIyKjneT5zSjK7tG89K4esGNBNKafCLPj+YVO59O5VeH5Q0+mUL01hQrYD9DxbaXohnHfibD5e04jv+2w+JYtpBY5mOImIiIiIiLSjwElExoQtpsTZc7t8nn47WFY3bWKcQxYWgdd94XDfhx1nZ/KnUzZjfU2SwtwIRTm9h00QnG5yLkzOzWzXprBJRERERESkPQVOIjIm5Gb4nLTfRA5cMIHmFo8phTGye3lKHYCDz8QcmJijj0IREREREZGhpG9ZIjJmxByfGQUOEKHbdXQiIiIiIiKSFioaLiIiIiIiIiIiQ0qBk4iIiIiIiIiIDCktqRMRGYSaZocV5c0ATJ+YQV5cBcRFREREREQUOImIADhQ3QgNTT6FCZe423dwtL4ezl28jKraJAD5OVHOPWEmRdmburMiIiIiIiIjmwInERnzfBwq632q6lJMSEQoyOq43fPh1U+auObfq2lq8ZlSmMHPjplOcaLnc7quw/MfbGgLmwCq65I8+94GjtwtH8/TTCcRERERERm/FDiJyNjmwBtLGvm/e1aR8iAWcfifL09n2xkx/DATKqvxuezeVW2HrKls5vJ/rebMr04j6nQfHLmuw5LSxi7tn6xuxHUL8bzUJrkdERERERGR0UBFw0VkTKush8vuXU3KC163pHwuuXsVVe2yovLqli7HfbqmkZrGnmcpJZMee26T36V97+3ySSYVNomIiIiIyPimwElExrSquhTJVMfgqKnFY0Od1/a6MNF1sufkghjZGb1/RM6bHufr+00iHnPIiDoct88ktp6ROTQdFxERERERGcW0pE5ExrQJiQgZUYfm5GehU3bcpSARAYK2kvwIX/78RP7xzDoA4jGXHx4+jXjE6+6UbeIRny/umGDR1rkAJOLgq3aTiIiIiIiIAicRGdvys+CnR0/nL/9cRUOzR06my0+PnkFenLYaTlHX55AF+exmcqlpSDEpP0Z+5sYFR54HObFgX7/3fEpERERERGTcUOAkImObD1tPi/HH78yhpj5FXk4kDJs6BkoRx6ckz6EkL0rrzCcREREREREZGAVOIjLm+T7kxX3y4i7g4ytPEhERERER2aQUOImIbCKxlg141WW4mdmkEpPxiAx3l0RERERERNJCgZOIyCYQr12F968LcGrX4+PgLPgy7naH4kXiw901ERERERGRTa73Z36LiEi/RWkh9dQ1+LXrwxYf59W/41YuH9Z+iYiIiIiIpItmOImIhFwXlq/3+XhVA5GIw9xpWUzND55E1x9OSx3+mg+7bqgph4lzh6azIiIiIiIiI5gCJxGR0JJ1Pr+5ZSktqaCqeE6my9nHz2ZKXv/O48dyYMpWsOaDjhtyi4eopyIiIiIiIiObltSJiACRqMsDL61rC5sA6ho9XvlvDa7bv4/KJDEie38bJzEhbHHwFxyDVzhzCHssIiIiIiIycmmGk4gI4PsOVbWpLu2VNS1EIv1fVteUmEb06AvxNpThxrMhdzKer4xfRERERETGB337EREBvFSK/XYs6NK+21Z5tLT0M20KJWO5eEWbk0xMIaWwSURERERExhF9AxIRCW07O4tvHlTCpIIY0yZmcNpR09l8UsZwd0tERERERGTU0ZI6EZFQdtRn321y2M0kcB3Iinp4/V1LJyIiIiIiIv+fvTuPk6yq7///PvfWvnT1vkyvwwA1LCMM4IBsKiCyKKjgFsANNcbk68+fS2LiFqPGxCWJydfEJXFXXEAUo6AiLiC4gIAiWDAwW/f0TO9LdVfXds/3j55paGsGpmequ6q6X8/HwwfW6a7p01W3b9V91+d8DoETADyR51mFXbvv/y/fz7HGaK4ghVwjI0ItAAAAAKsLgRMArLDxjNHXfz6s+7amdVxPRFed16rmaKVnVTlFazQ87Wk8XVBrwq/GqJGRfeo7AgAAAKhaBE4AsIJyntFHru9X/3BWknTPI2k9NjinD726VxH/2gtZitbo1t9N6ys/HpIkOUZ665VdOqk3KM9be48HAAAAsFrQNBwAjtBs3uih3Xnd/VhWg1NWkjno945MFxfCpv3G0wXtnSws8yyr00jaWwibJMmz0idu2q3JTAUnBQAAAOCIUeEEAEdgNm/0rzfuVqp/PiExRvq7l/Vo4zqf7AEKdEJ+R46ZD1aeKBxYm/n/5EyxZGw262km6ykePHhwBwAAAKC6rc0rHAAok10juYWwSZKslT79/UFliwc+vTZEpCvPbVk09pxTG9QcX5un45aET353cbDUWu9XQ3RtPh4AAADAakGFEwAcgdls6Q5zY9N55YtWBypaMpIuPDmu43siGhzPqTXhV0+zXz6zNvsVNUSkv3lZt/7j2wOanCmqoyGg//+KTgXdtfl4AAAAAKsFgRMAHIF1TQE5juQ9IXc6f3ODogHpYButBVzpqBZXR7WE940cXrhijDSRMRoYzcnnGnU1+xXx1VhQY6Vku1//9Jo+zWY9JcKOAoRNAAAAQM0jcAKAI9BWZ/Tuq3r1PzcPamgir/M21+uy0xt1wAZOR8gYaXLOaHAsp6DfUTzq07s+t00zc/NpV09rUH/94i7VBWsrsLHWKuqXon6jww3fAAAAAFQXAicAOBJW2tDi6n3X9KhQtAr7tSxhkyTtnZbe+8XHA6bjeiN61kkN+t6vRiVJO4eyemhXRqcfHVqWnw8AAAAAh4qurABQBn7jKeyzyxY2WWN03U+HFsImSXpox6zqYz45TziTD4xmis6mEAAAIABJREFU5Tjs7gYAAACgsgicAKAG5ApGj+2eKxmfzhQV8j9+Kt/UF5XnsSwNAAAAQGUROAHACjKOo6EZadeEVc4e+ik45LM664S6kvHe1qDm8p5CAUeveW67+lpYKQ0AAACg8rgyAYAVMlc0uvWeSX3rjhEVilbHdob1huevU3PkqSuSrLW6+LQG7RnP6e6H0wr4jK46r00nrw/r39+wQY4jxYN2SSv6jJEK1pEjych7yu8HAAAAgENF4ARgEWOkdM7IWikW1LL1JFqLtg3l9Y2fDS/cfnggo2/9YkSvv7BFsk8d+MSDVn/1vDZNZFrld4zqwpL1PPn39QhfylOVLRrdvy2jm345qnjY1Uuf1aLeJp8Mu8QBAAAAKAMCJwAL8kWjOx6a0XU/HVKhaHXJlkZdclpCEX+lZ7Y6DIzkSsbueySt9LOaFTvEx9iRVWNYkuyhZFQLrIyms5LPlWIB6b6tGX3ipt0LX//7L+7QP75mvdYlaDgOAAAA4MgROAFY8OhQTp/74Z6F2zfdNarWhF/PPCEqS6XTEWtJlJ5y13eEFPE7korL9nOns0Zf+emw7npwStGQq9de3KHfPppe9D2elR7cMavOk6IUtQEAAAA4YjQNByBJcl2j325Nl4z/5P5JWVH1Ug5HtQX19GR84XY84urPzmuTs4xhk4zRTb8a051/mJK1UjpT1L99q18nHxUr+dZw0FnVYZMx0nBa+vWjc7p3e1YTpZv+AQAAACgTKpwASJI8z6q7JVgyvr49KMdYeas4iFgp8aDVay9q0yVbGpXJeupsDqgpYuUtY7/uTF76+e8mS8Y9T3IdqbjvZ8fCro7rDi/fRKrAwITVuz+/Xfni/MGciPr0D6/oUcPq/rUBAACAiiBwAiBpvuH00/qi6mgMaHBsvtdQLOTq4i2N8kibyibsetrQ4kpyJS1v2CRJAZ9RZ0tAWwcWl/M01bn6wKv69Pvts4qFXB3fE1ZT9MkbjxsjTc5JkzOe6qOuEmHVzLFhjKMbf7F3IWySpMmZgu7fNqtnnxBZ1ZVdAAAAQCUQOAFYkAhZveeqbvWP5FX0rLqaAkqELRfjVcJxpJEZo/6RnIJ+o+7mgCK+J0+sXHl6zYXteu+XdihfmH8iT1wfUU9zQBG/VdfmuKT55/ipwqaHduf1rzcMKJPzFA44esuVXdrY4auJ46NorYYm8iXjo1N5GWPoUQYAAACUGYETgEWifqtkx/5TA2FTNdk1Lv3jdds1PTvf8+mE3oje8LwOJYJP/iR1Nzr6yGvXa/dYTuGAo85Gv0K++fscatAykTH62PX9yubnvz+T8/Sx6/v10df1qa50JWbVcc38rotP3JlPkk45OlYzVVoAAABALaFpOADUAOs4+ubPhxbCJkn6w45ZPbI7+9T3tVJjRDqxK6ANrb6FsGkpJtKFhbBpv7mcp4mZZWx4XkbWSif1hfWai9rVEPOpvSGgt7+4Sz1NfO4CAAAALAfeaQNADZjNSTv2lm6rNjialbMhsOy9oBJRV36fWViWJ0lBv1Ei4pNUGxVCIZ/VeSdGdUYyKteRAo5lKR0AAACwTKhwAoAaEA9Ipx1bVzK+YV34sMMmY8whf299WHrTCzrld+fv43eN3vTCTiXCtRXYeJ5VyLXyG5aLAgAAAMuJCicAqAGe5+mipzdoz1hW9z82I79r9MKzmrW+demn8bmC0fahnB7bM6ee1pCOaptvIP5UTu4N6KOvX6+JdEENMZ/qwzrk4ibHkYamjfpHsgoFHHU1BxTzL3NZFgAAAICKIXACgBrRFLZ60+UdGpn25HOltrgjb4nlTdYY3fSrcf3vr8YWxs48vk7XXtgiv/Pk6ZG1UkNYaggv/aVjx5jVB7+yXbPZ+fke3xPRG57fofqnaHgOAAAAoDaxpA4AaojfeOqok1qiWnLYJEmjaU/f+/XYorE7H5zS8PTyNf/25Oi6nwwthE2S9ODOWT06+NQNzwEAAADUJgInAFhD8sUD9y7K5Zev0iiTl3YNlTY8H57IawltpAAAAADUEAInAFhDmuM+Hd0ZWjTWVu9XW/3hr7B2nCdPjeJB6enJ0obnfe1BGncDAAAAqxQ9nABgDfEbT//f5Z26+e5x3f3wtE7si+qyZzQp7Ft68pPOGz26J6cde+fU3RLUcXZOoQNkT57n6dLTGzU6lde9W9MK+h295JktWt/q1yF3HQcAAABQUwicAGCNSYSsXn5Og150ZqOCrpU9jDKjohzdcMeofnzv+MLYOScm9MoLWhRwSntLNYWt3nRZu0bSnvyuUUvMqlgkbAIAAABWK5bUAcAaZK1VwPEOK2ySpD2TxUVhkyTd/sCkBicKB72PK09tMakxbFVcvh7lAAAAAKoAgRMAYMmyB2kyns1RtQQAAACAwAkAcBg6GnzqbA4uGmtvCKij0V+hGUnGGI1lpPt35vSHgbymc2yBBwAAAFQKPZwAAEsWdj295YoufeeuET2wbUbH90T1wnOaFQ+U9m9aKYNTnt79+R3K5ufn0JLw691Xdas+9BR3BAAAAFB2BE4AgMPSErV63XNbNJNtUSQgtTRFNTw8XZG5GMfRTXcNL4RNkjQ8mdfvt2V07vFhHWarKgAAAACHiSV1AIDDZoueIj5P8ipX2SRJnpX6h7Ml43sncjKGpXUAAADASiNwAgDUPEdWF57aUDJ+8oaoPI/yJgAAAGClETgBAGqetVanHh3RS57ZoqDfqC7i6i8vW6fe5so1MQcAAADWMno4AQBWhbDP6vlPr9N5J9XJkVHYb2Vp3gQAAABUBIETAGDVsJ5VxCdJlkbhAAAAQAWxpA4AAAAAAABlReAEAAAAAACAsiJwAgAAAAAAQFkROAEAAAAAAKCsCJwAAAAAAABQVgROAAAAAAAAKCsCJwAAAAAAAJQVgRMAAAAAAADKisAJAAAAAAAAZUXgBAAAAAAAgLIicAIAAAAAAEBZETgBAAAAAACgrAicAAAAAAAAUFYETsAaki062jNlNTknGf76AQAAAADLxFfpCQAoH2OMXNeoUPBKvjYyI330+p3aPZpTwGd07cUdOv2YsBzZCswUAAAAALCaUeMArBITc9KP7k/r3787pN9uy2psKr/wtYI1+tT3BrV7NCdJyhWs/uu7u7V3qjSYAgAAAADgSFHhBNQAzxrtnS5qdKqgxrhPbXWuXPN4ZdJM3uhDX9ulwbH5QOmXD03pRWdn9YLTE5K1ms1Jqf5Myb87NJFTR11wxX4PAAAAAMDaQOAEVD2jO1Oz+vT3BxdGXvmcNp23KSqz7/busfxC2LTfd+4c0bOfllAiJIX80rqmwEKF034NMf9yTx4AAAAAsAaxpA6ocmMZq8/+YM+isS/duldjM4/ftgdow2Ql2X39mQKO1V9etk6hwON/8pef2aSOenc5pgwAAAAAWOOocAKq3EzGU6G4OFHyrDSVKaopOh8YdTb51Vzn18gT+jZd/PRG1YfNQhrV0+jqo69dr72TecXDrprjRj5Dw3AAAAAAQPkROAFVrinuqj7m00S6sDAWC7lqqfNJ+yqYon6rd1/VrTsemNJDuzI6d1NCzzghIZvLLtzHWqu6kFQX4s8eAAAAALC8WFIHVLlowOpvX9qtzuaAJKmjIaC/+7MexYOLq5MawtLlpyf0ty/p0DOODak5EajEdAEAAAAAoMIJqHbWSh0Jo/dd3a101ioWMAq49oB9mzzPzq+3AwAAAACgggicgBoRcKwaw9L+ZXRAtXJdR55nZQ+UigIAAABYEwicAAAH5DhmvmruEE3O5PXgQF6/Tk2rty2ozRuiqg8t4wQBAAAAVC0CJwBYxbJFo6HJosJBI88aTc4U1FTnU2NEBy2Wy3lG24fySvXPqrMpqGM7Q4oFnjx4Mo50y2/G9Pkf7FkY+169X++7pkdRP5VOAAAAwFpD4AQAq9R4RvrwN3fJ7zp6ejKu628flrWS60hvvbJbm7r9pb3AjNFt90/rqz8ZWhja2B3RW1/UoaB78OBoOmt03W1Di8aGJvLaNZLTxg5/OX8tAAAAADWAXeoAYAUYxyidn/+fccwK/ECj79w1poGRnM7ZlNAN+8ImSSp60n98e0BTc6XzmMxYffPnw4vG/rhrVrvHC0/646yVigdYfud5h/8rAAAAAKhdVDgBwDLLFo1+cu+0rr99Psi54pwWPXtTXCHf8i01yxak+x5NS5LyRVuyeWEm52kqU1Q8uPhzh6Kd//4/lS88+VzrQtLlZzbphttHFsbiEVddzQHR6B4AAABYewicAGCZPdSfXbRE7bqfDKm9wa/NfcFl+5lBn3Tyhphuu29CjmPk95lFoVEi6lND1NWfhkENYUdnHBfXLx+aXhiri7ha1+gv+d4nsp7VC89qVUvCr5/cP6mjOkJ6zuZ61QUJmwAAAIC1iMAJAJaRz+fo57+fKBn/2e8n9fSj21UoLNOaM2t1+TMaleqf1Y/uHtM1F7Tr6z/dq5k5T/Uxn952ZZeiAVvSw8nI0zXntaq7Jag7HpjSMZ1hveDMpqdsGi5JTQm/zkqGddbGqIzskna4AwAAALC6EDgBwDLyPKujOsK6++H0ovGj2kPLHsg0hKW/v6pbw1NFRYKOTn3teqUzRSUi7gHDpv3iQavLt9TpolMS8rtGsoceilkryXo1u4jOGGkiI03MFFUf9SkRtqwIBAAAAA4DgRMALCPPszrz+Lh+eM+4JtLzjbcTUZ/OPqFuRSqAgq5VV8P+Pk1W8YAj6eBh036eJ/kdq6f8xlXEGOnBgbz+5YZ+ZfNWAZ/Rm1/UpRO7/YROAAAAwBIROAHAMmsMSx98Va92jeQkK3U1+1W3fO2bcJgm5qR/uWFA2fx8upQrWP3bt/r10devV32owpMDAAAAakzVBE7JZLJJ0pckbZCUk/SIpD9PpVLDyWTyDEmfkhSWtF3S1alUauhg/xYAVJt4wOr4df5KTwNPYnKmqGx+8fLBXMFqMl1Ufcit0KwAAACA2uQ89besGCvpw6lUKplKpTZJelTSPyWTSUfSlyX9ZSqVOlbSzyX9UwXnCQBYhRJRV0H/4pfFgM8oESNsAgAAAJaqagKnVCo1lkqlfvqEoV9K6pV0qqS5VCp1x77xT0p6yQpPDwCwytWHpLdc0bkQOgX98z2c6sMrPxdjpOms0da9BfVPeMp7ZuUnAQAAAByBqllS90T7qpr+QtJNknok7dj/tVQqNZJMJp1kMtmYSqXGKjVHAMDqYq10fKdfH31dnyZnikpUcJe6PVPS+768XelMUZJ09gl1uub8ZoWr8lUbAAAAKFWtb13/Q1Ja0v+V9MJy/INNTbFy/DNHrKUlXukpYA3heMNKWw3HXHOFf34mW9THvrNtIWySpDv+MKXzT23UGcclKjiz6rMajjfUFo45rCSON6w0jjmUW9UFTslk8qOSjpH0/FQq5SWTyZ2aX1q3/+vNkrylVjeNjqZXZAvyJ9PSEtfw8HRF54C1g+MNK41jrjwyBaNH+jMl43vHsjy+T8DxhpXGMYeVxPGGlcYxh8PhOOZJi3uqpoeTJCWTyX/UfM+mF6RSqey+4XskhZPJ5Nn7br9B0jcrMT8A2M+TNDBhdftDGd23I6fpHD12UB5hv3RasvQTxnWNgQrMBgAAADg8VVPhlEwmT5D0t5IelnRnMpmUpG2pVOqFyWTyGkmfSiaTIUnbJV1dsYkCWPOMkR7endc/XrdrYWxdU0DvenmXYmQCOFLW6sqzm7VnLKeH+zPyu0Yvf3arupp8qkhDKQAAAOAwVE3glEql/iDpgCUCqVTqTkmbVnZGAHBg2aLRZ2/Zu2hs92hOO4byOqHLX6FZYTWpD1m948XrNDbjKeAzqg+b+a7mAAAAQI2omsAJAGpF0ZOmZgsl45mcV4HZYLXyGavW2L7PYQibAAAAUGOqqocTANSCSEC6eEvjojHHkbqbWU9Xbq7ryHV5qQIAAABqDRVOALBE1rO64OSEAj5Ht/xmTC31fl1zfpvaE4ZClDLxrLRjtKj//dWwHCNdenqjeppcPiUBAAAAagSBEwAchqjf6pJTYjrvpLh8jpErj7CpjHaMFvXeL+5YuP2rP07r/a/sU28TkRMAAABQC3jnDgCHyVop6Fi5ondTOfl8jn5wz3jJ+I/vm2B5HQAAAFAjeOcOAKg6AV/py5PfrcBEAAAAABwWAicAQFUpFDxdeEq9HPP4WG9bUOdvblT/WEE5zxz8zgAAAACqAj2cAABVp7PB1QdfvV63PzCp9ka/8gXpXZ/fpkLRqqs5oLdd2a3GCE2zAAAAgGpFhRMAoOoYWXXWG131zAZt6AjrS7fuVaE4HzD1j+T0hVv3yuMlDAAAAKhavFsHAFStYtFqaCJfMn7fo2nNlQ4DAAAAqBIETgCAqtYQK+0WfmxnWEEWhQMAAABVi8AJAFDVuhr9umRL48LtWNjVtRe1yzVeBWcFAAAA4Mnw+TAAoKoFXKsXn9WgZz0toZlsUW0Jv+JBK0vPcAAAAKBqETgBAKqea6za64zmX7YImwAAAIBqx5I6AAAAAAAAlBWBEwAAAAAAAMqKwAkAAAAAAABlReAEAAAAAACAsiJwAgAAAAAAQFkROAEAAAAAAKCsCJwAAAAAAABQVgROAAAAAAAAKCsCJwAAAAAAAJQVgRMAAAAAAADKisAJAAAAAAAAZUXgBAAAAAAAgLIicAIAAAAAAEBZETgBAAAAAACgrAicAAAAAAAAUFYETgAAAAAAACgrAicAAAAAAACUFYETAAAAAAAAyorACQAAAAAAAGVF4AQAAAAAAICyInACAAAAAABAWRE4AQAAAAAAoKwInAAAAAAAAFBWBE4AAAAAAAAoKwInAAAAAAAAlBWBEwAAAAAAAMqKwAkAAAAAAABlReAEAAAAAACAsiJwAgAAAAAAQFkROAEAAAAAAKCsCJwAAAAqxDhGcwVHRZlKTwUAAKCsfJWeAAAAwFo0nTX63m/G9fPfTaq7NaBXXNCurgZH1tpKTw0AAOCIUeEEAACwwqyMvvrTYX3/12NKzxX10M6M3vvF7RqdJWwCAACrA4ETAADACpuak+58cGrRWK5gNTiar9CMAAAAyovACQAAYIX5XCkSLH0bFgrQywkAAKwOBE4AAAArLB6Urr2oY9HY8b0RdTb6KzQjAACA8qJpOAAAwArzPKvN60P64Kv7tHM4q8aYT70tAYV89HACAACrA4ETAABABbjGqrvBUXdDeN8IYRMAAFg9CJwAYJkZY2QcI1krz+OCEgAAAMDqR+AEAMsonTP6zSNp3fXgtE7si+jcTXWqD1V6VgAAAACwvAicAGCZeDL68m3DC1uf/3HXrO58cEp/f3W3Qi6VTgAAAABWL3apA4BlMjbjLYRN++0ezWlwvFChGQEAAADAyiBwAoBlYmRkTOm4c4AxAAAAAFhNCJwAYJk0RI0uOKVh0dgxnWG117OaGQAAAMDqxlUPACwTR1ZXnNmo43siundrWhu7IzppfURB+jcBOADPGu2eKGpwLKe6qKue5oDCPs4XAACgNhE4AcAyivitTl0f1Jajw/I8T9Zy8QiglOMY3fPonD5+48DC2ClHR/UXl7YTUgMAgJrEkjoAWAHFoieyJgAHM52V/ufmPYvGfrt1RgNjbDIAAABqE4ETAAA1pGCN9kxZ9Y97ynl0oF8t8kWr9FyxZDyT9SowGwAAgCPHkjoAqHGzeaO9EwX5fUZtCVd+h1Kq1Wo2L33ptmH94g9TkqSj2kN684s6VR/iOa91dSFHTz82pt88nF4Y8/uMOhr9FZxVdTNGSmeNip4UD0uGMlIAAKoKgRMA1LDRWekDX92h0an5ZTdnHBfXqy5oVcTPhddqlBrILYRNkvTYnjnddv+ErnhGvazHc17LHHl65XPaFI/4dMcDk+puCerai9rVFBXLcQ8g7xn9+pFZffFHezWX93TupoReem6zYgEeLAAAqgWBEwDUKuPoxjuHF8ImSfrlQ9M6d1O9TuyiKmK1cRyjVP9syfhvH0nrBac3yBEX2rWuLmj1yvOb9NJzm+R3JZ+xazpsyhUdjaYLCgUcNUTMouRt12hBn/re4MLtn/1uUs0Jv154ep08ViECAFAV6OEEADUqV7R6cEdpALFrOCtj6O2z2nie1cbuSMn4qcfG5WMZ5aphrFXYZ+UzVrN5o0f2FvTHwbymc2vrb3ps1ui9X9mpv/mf7XrLpx7Tj+6fVn5fzzJjpK27MyX3+dn9k5orrK3HCQCAakaFEwDUqKArbUnG9f1fjy0aP6ojKLuWyyJWsWPXBXXOpoRu//2kJGlDR0jnPS0h70mW0xln/rMlS9lHTZmcM/rnb+xS/0hOkpSIunrvNb1qLs0cVx1Pjr5w6x4N7Pvdi570pVuHlOyKqKfRkbVSa32g5H69bUH5+SgVAICqQeAEADXKWquLn96gxwYz+uOujBwjXX5ms/paAhLLq1aliN/qNRc067IzGlUoWrXUuQocpLrJymjnWFE33Tms2WxRzzujScd2BGgqXwOMkX63bXYhbJKkyZmifnjPhK56ZsOq79c1V7C6/7F0yfjQRF49jUFJ0tEdQR3TGdYjA/OVTuGAo5c+s0WGcx8AAFWDwAkAalgiaPXXV67TaNqT3zVqiBp2alrlXGPVFjeSjJ4sWBycKOq9X9iu/dnEH3bM6m9e2q0TOunvVe0cx9H2vaVLxrYOZGRto1Z7oBxwjY7tDOuPuxY/Bo3xx9+2xgJWb7tinQZG88oVrNY1+dUQpsE6AADVhMJjAKhxvn0BRGOEbcExz3GM7nkkrT8thLnxFyOS4aW/2hWLnk45Jl4y/qynJdZEBY/PeLr2og7FI+7C2CVbGtXVuPhz0rDP6ug2n47v9Ks+RNgEAEC1ocIJAIBVxlopECgNlsJBR0Z2DUQWte/o9oBe/uxWXX/7sIqe1YWnNurUY6Jrpj9be5304df0aWiyoHDQUUvckWvWxu8OAMBqQeAEAMAqY63V5g0xXf/zYWXzj1+kv+is5jUTWNS6oGt1ySlxnX1CXNaT6sJaUyU81krRgNX6lv1VTmvndwcAYLUgcAKAKmSM0UzeyOdYBV27lq4zUSZtcaMPvHq97nl4WrNZT6dvjKuzgeV0tcRaq/j+zdg4BwAAgBpD4AQAVWYmZ3TLbyd0y2/GVB/z69qL2pXs8K+J3i0oH2ut2mLS806rkyR5q3xnM2A1chzD3y4AoGbxUScAVBHjGN16/6S+c+eosnmrveM5fehrOzU4Waz01FCjPM9ywQrUmKms9Kutc/r2ryb16HBRec9UekoAACwZFU4AUEUyeemH94wvGrNW2rE3q3WJcIVmBQBYKTM5o3/82i7tHs1Jkq6/fURvfP46nZkMEx4DAGoKFU4AUEV8jtRc5y8Zj4fdA3w3AGC12TWSWwib9vvij/ZqJneQOwAAUKUInACgiviM1asvbJfzhLNzT2tQfW3Byk0KALBi8sXSKqa5vKcDDAMAUNVYUgcAVaavxdE/X7teAyM5hQKOelsCiga40gCwsorWaGiqqMmZoloSfjVGDZsXrICu5oBCAUdzOW9h7JItjYoHJes9yR0BAKgyBE4AUG3s/Jb2bfHg4wMA1iRjpICXkWMLyrnxFatyKcroh/dP67rbhiRJjiO97cpuPa0nQB+hZdYYMfqHV/TqW78Y0a7hnC7YXK8zNsZkedwBADWGwAkAgMNQtEbD057Sc0W1JHyqDxlZywUhysdVUeHhB5X9yWdVnJ1Q4KSLZE+8SHO+xLL/7OEpbyFskiTPkz5x04A+8tr1igWW/cevadZatdcZvfGSVhU8KeCKkA8AUJMInAAAWKK8J33nVxO66a5RSVI44OjdV/eoq57WiIfKcYym5qRMzlND1JXPsFboT4Wmd2nuxg8u3M7/5kYFXJ+ck66Qt8wP1+RMoWRsZs7TTNZTLGCW94djnrXyGS37cw0AwHLhnTEAVAFjpIk5aedYUdNZI2O4oKtmu8e9hbBJmg9N/vOm3cp7PG+HwrPS3Y/O6W2f2aa3fnqb3n9dv8ZmeexKjDxWMpT/3Q8UKMws+49uqffL5y5+Ttrq/aqP8tYRAAAcGt41AECFWRnduz2nt356m971+R16+39v09a9BUKnKjaRzpeM9Y/klCkdxgEMTVv9240Dms3Ol25s2zOnz9w8qKI45hcJ1ZUMmXizPMe/7D+6MSz9zUu7VRdxJUkdDQG99cXdCjos7QIAAIeGJXVAldq/O9DIVEFNdX611TlyDW/0V6PxWauP39iv4r5lE7NZTx+7oV8feW2fost/XYnD0FJf2sTmuJ6IogEjmrw/tb0TuZKxP+yY1UxWqtvXK79oHU3NeQr5jSJ+q7XYHstr3iDT2CU71j8/YBwFzn2l0lqZJkobO/z652v7NJv1lAg7Crhr8EkAAACHjcAJqEbG6I4HZ/Q/t+xZGHrVhW161olRyhJXodHpwkLYtF86M78VeZSeQFWprc7Rnz+vQ5+7ZY9yBat1TQG97pJ2ufQhOiSJaOnbj46GgIL7AtbxjPTJ7w3ooZ0ZNcR8euNl65Ts8K25LC/jb1D48nfJGd0m5TJSU49mo50r9jhYaxX1S1E/QSoAAFg6AidgGeWt0fBUUX7XUVPMyDnEN+xjM1af/+GeRWNf/NFenbT+KDVFl2OmqKTGuE+uo0WhUyzk7lvKwkVeNXKN1dkbwzqxZ70yOauGmMNSoyVY1+DTRac16Ja7xyVJfp/RGy9bp6BjVZSzEDZJ0ni6oA99bac++rqj1LwC5z9PRqNpT7NZTy11PkUDla2uyvjqpbbNjw9wmAEAgBpB4AQsk4k56eM3DujRwTlJ0kWnNehFZzYq5Hvqq4X0nFdS8eJZKT1XVFPUXY7pooIaI0Z/dXnnfNPpolUo4OgtV3YpHtSaXEZUK6wnJUJSIkT1x1IFHKsXn92oZz4toXSmqNZ6vxoj88fHzKUHAAAgAElEQVT7VMZbCJv28zxpz3hOzdHlXUqW96Sb75nUDXeMyFqpPubTu/6sR62xZf2xAAAAqxKBE7AMjDG65e7xhbBJkm65e1wnb4jp+M6nbsrTFHeViLqanCkujMXCrprjPnFhu/oYWZ16VFAfff16Tc0W1RBzlQjNL2dZqxzHyHGMCgWWqK1Wfseqs96R9i0b3X+4h/xGiahPkzOFRd+/v3n1choYL+r620cWbk+kC/rMzYN6x5WdLJcEAABYIpqDAMsg5xnd/fB0yfhje+YOaeexWMDq717Wo87m+U/zOxoDeufLexQLrt0AYtWzUkNY6m1yVbeGK5uMMRpKW33zzgn9183DemRvQYU1+lisVRG/9MbLOuQ84VR5/uZ6tdcv/2dko1OFkrHUrozmOAgBAACWjAonYBn4HauTNsT0o3vGF433tYUOqWrFWqkjYfS+q7s1k7WKBo0CztrcpQlry3Da6p2f26Fsfr6a5PbfT+rtL+7Wpm6261srrLXa2BHQR16/XnvG8qqLuuqo9ymwAj2ymutKj7PjeyIKcfgBAAAsGRVOWLX2L8mpCGv1vC0N6mh6vN/IOScmdFTb0vqPBByrhrBW5EILqAZ/7M8shE37ff1nQyraCv0toyKMrFqiRpu6A+ptdFfsHNjZ4Orl57UuVFc1J/y69uJ2uSxlBgAAWDIqnLDqGCMNTlr9duu0CkWr046Na129WfHWRw1h6X1Xd2tosqCAz1Fz3JHPcNECPJkDVfF5ViJvwkrwOVYXnRzTlmNj87vUxX0K+ejdBAAAcDgInFCzjJHc2RFpeJskK9PSp2K0VYOTVu/83Hbli/NXrt/6xYg+8Ko+ddWvfEFfyLXqadzf6JawCXgqG7tC8vuM8k/omfOSc1vkN0e2pNSYtdsXC0tjJDVFpKaII4mwCQAA4HAROKFm+aYHpW+/RzYz35zbBiPyvej9uv2B2ELYJM1vp/39X4/pLy5uUbHIFSdQzVrjRh98VZ9uvXdCI1N5PffUBh3THjjsHfuK1mhgvKiHBzJqivt09Lqg4ktb2QpgFcsUjPpH88pkPXU2BdQUE58PAQBQJgROqEl+v6ti6g45mSfsBJedlfeHH6sxfkXJ989mPc1/bs27SFQXx3HkSTLWowJH81VI7XVGrzyvScZIhYKnw/27Ncbo/u1z+rdvDSyM9bYG9Y6Xdinq58EG1rrZgtG/f2dQD+6YlST5XKP3XtOr3kZanAIAUA68oqImGcfImdxdMu5M9GtLMl4yfumWRhWLLI1A9djfa+yTtwzpg18b0D2PZZUt0qhov2LR2xc2Hb5M3uhzP9izaGzHUFa7RnJH9O8CWB12DucWwiZJKhStPnvLHhVoGgcAQFlQ4YSalMsW5B57jsyjdy0at8edr4jP03uv6dWNvxhRoWD1grOadVSrX1Q3oZoMpaV3ff7xXmOp/gH9+aUdOntj5LCXj2GxgrVKZ4ol49k8jy8AaWq29PywezSnfNHI5+M8AQDAkaLCCTXLdhwv+8zXyYTrZEIxmXNeLdu5SY6RNrS4evsL2/WOl6zTxg6fXHaHQ5V5bHBuUa8xSbrh9hFlCxWa0CoUC0jPPa1x0ZjfZ9TdTBMnoJoUrFE6Z1TUylYWdTaVnguedVJCYZbcAgBQFlQ4oWZ5bkjucc+RNpwhWSsvWLdo2Zy1lm2pULVcp/TCyu83Mg69xsrGWj1/S4PiYVc/vndC65oCevl5rWqKcmoAqoEx0u5Jq09/b7ceHZzTib1RveaiNjVHV+bnd9Q7etuVXfrMzXs0NVvQ2ScmdNnpjZwgKsUYTcxaTWc8NcZdxQJHtjspAKDyCJxQ04pFT0V33ztTejShhmzoCCoWdhct+brq2a0KODQPL6dowOp5p8V1weY6+R0jRzy+QLWYzhq9/8s7lJ6bPw8+sGNGH/5Gv97/im4F3eX/Q3UkndQb1Iev7VXBk2JByXCCqAhP0t1b5/Sp/92tfNEqFnb1jpd1q7fR4ZwNADWMwAkAKqAxIn3glb26Z2tao1MFbUnG1dvso3/TMrBWCjpWVI4B1WVosrAQNu23Zzyn0emi1tWvTNcHa63C+98Nc4qomNG01SduGlgIl9KZov71hn596NW9Cq1A+AgAWB4ETgBQAdbOh04XnhSX40jFIoFILTLGyBjJ83jugKWKhkpDJZ9rFA7SYnStGZ0ulFQyjU4VNJXxFIqxayAA1Cpe0QGggqy1+8ImyRqj8Yw0NG1V5PRc9cZmpR//Pq2v3zGubSNFFdlKHViS5pijy89sWjR2zfltagjzt7TWNMZKPwNviPsUP0AoCQCoHVQ4AUAVyBal7989qe/cOSLPSif2RfWGS9tVF6RyphqNZ6R3f3GHpvdtq/7dX47p7S/u1tN6/PQbAQ6Ra6yevyWh046JaXS6oNaEX+31jqj2XHuaY0Z/fmmHPnPzoDxPigQdvfmFnYr4aRwOALWMwAkAqsC2obxu/MXIwu0Hts/oR7+d0JVn1tPXqQpt25NdCJv2+/Jte/WBV/TIb0o3MDDG8DxizTGOUSYvBV0jowNv7BFwpN4mV71N7grPDtXEMdJZGyM6rvsoTc0W1RT3qS4kzpsAUOMInACgwowx2rp7rmT8l3+c1mVnNMhveMNdbfLF0uckm/PkWUlPWA00npEe3JlROlPUCX0Rdda7MlRvYA2Yzhp9/+5x3fHAlNa3h3TVea1qrxPVKjg4a9UYkRojriQqmwBgNSBwAoAKs9aqpyVYMn58T0QBhzfd1eio9pD8rlkUPF1xTrNC7uPP13hGes8Xd2hyZr4Syhjp3Vf16uhWKjmwunky+tyP9uruh9OSpPseTevh/ll9+Nr1qgtxQgMAYK2oqsApmUx+VNIVkvokbUqlUg/sGz9W0hckNUkalfSKVCr1SKXmCQDldnRHUKceE9M9j8xfoDXX+XTZMxpZTlClWuPS+1/Vp5vuGtXQRF6XbGnQiT2hRc/XwwNzC2GTNF/Z8dXbhvTOl3XKPcjyImA1mJi1C2HTfrNZT7vHc6rr8FdoVgAAYKVVVeAk6duSPi7p9j8Z/6SkT6RSqS8nk8mrJX1K0nkrPTkAWC4Rv9UbL23Tnslm5Que2hv8ivgIm6qVtdK6hNFfXNIqT5JjvUWVaMZIs9liyf3SmaI8K7lswoVVzO8aBf1G2fzic1jQz45jAACsJVX1yp9Kpe5IpVK7njiWTCZbJZ0i6bp9Q9dJOiWZTLas9PwAYDn5HavuBkdHtfgIm2qE9TwZzytZ9mittLErIudPgqXLz2xSwOG5xepWF7a6+vy2RWMnHRXVuoZq+5wTtcoYo3TOaChtlSuS4ANAtaqFV/5uSQOpVKooSalUqphMJnfvGx8+1H+kqSm2TNNbmpaWeKWngDWE4w0rjWPucQ2NVh94zVH6yo/3aiJd0AvPatZZJyZUH2NJUblwvFWvi7dEtGFdRI/tyaitPqBkd0Qt9YFKT+uIccxVXrFodddDk/r4Df1KzxXV0RjQ37ysV8nuSKWnVnYcb1hpHHMot1oInMpidDQtz6vsp8otLXEND09XdA5YOzjesNI45kp1JaS/vqJDnrXyGat8Zk7DmdIdCbF0HG/Vr6te6qoPz9/IZzU8nK3shI4Qx1x1GJ2VPnTdDnn7WuENjuX0z1/bofe/ontVVZByvGGlcczhcDiOedLinqpaUncQuyR1JpNJV5L2/XfdvnEAAKqasZ5ctvgGgLIYnsgvhE37DY7lNDnLZgwoZWU0Oitt3VvQ2Oz8bQArp+ornFKp1FAymbxP0sslfXnff+9NpVKHvJwOAAAAQO1LRN2SsXjEVSToSCLZxxMYo3sendP/vWlAnie5jvTmF3XppN4AhwqwQqqqwimZTP57Mpnsl9Ql6dZkMvmHfV96g6T/k0wmH5b0f/bdBgAAALCGtNa5eumzHt87yHWk/3P5OsVqv0UYymxs1uo/v7t7oSKu6En/8e0BTWaocgJWSlVVOKVSqTdJetMBxv8o6fSVnxEA1I68ZzQ56ykUcFQXUsX71gEAUG6usbpoc51OOTqmyZmCWhJ+NUUky7pl/InJmaIKxcXHRa5gNTVbVCJUVXUXwKpVVYETAODwjM1K//6dAT02OKdwwNHrL+3Q5r6gHD7EQw3Ie0aDEwVNZ4pqbwioafVtNgWgjFxj1VFn1FHHrp84uKaYT0G/o2z+8f5ekaCjhpgr1tQBK4NoFwBqXFFG/33LXj02OL/7WSbn6eM3DmhomjdTqH65otFXfzaqd31+h/756/1666cf0yN7CzKEpQCAI5AIW739JV2Kheb7fsUjrt7+km7FgxWeGLCGUOEEADVuNis9sH2mZHzveE7tdbyrQnUbnCjox/dOLNz2POkTN+3WP72mVyGX0BTw+Rx5nmWZNLBE1krJdr8+/Nq++WV0EVexIMsvgZVE4AQANS7olzoaAhoczy0aT0Q5xaMCjFHBkwLuofURm5wtloyNTRc0l7cKlW5GBawZ2aLRw7uz+vF9E+ppCencTXVqic1fRAM4NNZaxQJSLDC/iyF/P8DKYkkdANS4gGP1xsvWKeB7fA3Shac2aF0DgRNWjjHS3mmrT948pHd/aZd+cN+0ZvJPvS6uvcFfsnzuhN6I4jR0xRrmOEZ3pWb0kW/267ePpPXtO0f0ni/s0Him0jMDAODQcTUCAKtAX7Ojj75uvfZM5BUPu2qrc+Vz+BgPT80YKVtwVLBSNGBlD3PZzvis9J4v7FAmN9+c9Uu3Dml0qqCXndPwpCUZLTFHf/2Sbv3Xd3draraojd1hvf6SdrnyDnofYLWbyUlf/+nQorH0XFE7h3Kq7wlUaFYAACwNgROAVcXZty3bWut1Ya1UH5bqw/t37Flbvz8OjyejB3fl9Nkf7FE6U9TzTm/U+ScnFPUv/fjpH80thE373XL3mC7d0qD4k1wfG1md2BXQh6/tUzZvVRdxCJsOg+saGWNUKPDYrRZGdM4HANQ2AicAq4JjpHB6l+y238gW8nI3bFGmrldFy7KcpZrJG+0azilbsOpqCqiZniGr1sBYUR/+xq6F29ffPqJgwNHFm+NLDm39bunfWsjvyD2Ea2ZrrSJ+KeKXRNi0JMZIg5NWP/3dhKZmCzrvpAatb/XJNfzRSrX7IUQ0IL3s2S3675v3LIzFwq56WqluAgDUDgInAKtCOL1L2a//rVQsSJLyd39H4Ze8X+m6DRWeWW2Zzkkf/ka/dgxlJUl+n9E/vKJPnfV80r7aGCM9vLu0IcwP7h7XszfFFVhiVtvV7Fd3S0C7hh9vXn/1+W2KBed3nsPy2DstvfNz25Uvzgcqdzwwpb97eY82dvAWb3DK6jepKUnS04+NqyNRO+cxz7M649ioGuNd+un9k+puDeqs4+vUEOYDAABA7eDdCICa5zhG3iN3LoRNkiTrqXDv/8p33ptVKPLu/FA9OphbCJskKV+w+uptQ3rrFR1yLKlBLfCsNDRtNTSRVyLqal29T3639G/AWqn+ADsZttX75XPNkq9qo36rd7ykWw/tymhwPKcTeiPqa/HXXGVJLTFG+v22mYWwab9v/nxY73zpOjlreGntwKTVOz+3bSHsvPEXI/rAq9ers4ZCp4A7v9z05L42eZ6V57HD1mpmln7aBYCqR+AEoOYZIylXWqlhcxmZNXzBtVTGSGPT+ZLx3WNZ5QtWQbaor3qOY3Tvtqz+9Yb+hbGLn96gK89qlP8ATeSPWRfUusaAdo/NVyX5XKM/O6/1sMPFeNBqy9EhOU54X9DE39/yOvAZbq0/6q7r6NbfjiyqrCt60m33jutV5zerWKyt8PxI+3IZI83mjaYynuIhZ35zgBo4SHKe0VxeigaMXFM9z5kx88dYseiV5XH0JO0e9/TgzlnVRXza2BVSffjI/10AqAYETgBqXrFo5SbPVv7+WxaN+zdfqnSxQpOqQdZKR68rfZd7weYGhXy1cYGy1k3NSZ/6392Lxm7+zbjO3ZRQZ33pGrm6oPSuP+vSjqG8sgVP3S1BtcR0xIkFVU0rw1qrTX0R+VyzqJLzyrOb13R1kzEqaWAvzY+Z2ilwKgtjpO2jnv7l+n6NpwtKRF29+YVdOrrNrdpzujFGO8eK+s/v7tbASE7H9UT0ukva1Ryp9MykdM7o/m2zundrWpuPjumk9RHFAkf2QD66t6D3f2Xnwu2GmE//8IoetRzpZAGgCtBNF8CqkEmsV+jK98nt2yy36wSFXvB3mmveWOlp1ZzOBldvuaJLiahPPtfo0tMb9axNdVV7YYLF5nKeZrOlF9rTmYMnr7GAdEKXX6f0BdUSFeUxNaatzuiDr+rTBafU6/SNcb3rqh4d2+F/6juuYoWCpwtPaSgZf84pDWtuF790zujD39il8fT8kvPJmaL+6eu7NDlXvcnbREZ6/1d2amBkvvLyoZ2z+tg3+5XzKjvngmf0mVv26FPfG9SvU9P61PcG9d+37FXBHv68itboSz8eWjQ2ni5o62D2IPcAgNpyyBVOyWTyXyV9IZVK3beM8wGAw1KUq3RDUv6L/lqyUrrCb0xrlWusNvcF9ZHX9qnoWUUDoqlEDamPulrfHtK2PXMLY37XqK1+bQcQq4VfefkzI5Ks8uEW5eWXrNSRMHr1+c0y5siXX60Wvc0+vefqXn37FyOSkV5wZrN6m9feuuDxdFHTs4sD52ze0+hUQXUt1fl4DE3mNfcnFWoDozmNpz211VXutX142tO9W2cWjf12a1rD0y3qOMx5FayUni39QCBzgA8OAKAWLWVJnSvpB8lkcljSlyR9JZVK9T/FfQBgReWLBE1HylqrkGvnz/plzJpoiLr8fMbTm17QqU9/f1AP7ZxVa71ff3nZOjVGeOxrXag4Jf3yq8o9+FNJku/YM+U76xXK+Oolqeb6Ei0311gd3erq7Ve0z5/G1ugyz1jYUcBnlCs8/vs7jpSIVmfYJEmxcOncgn6jULCyr+/2ICfR+fHDm1vYJ132jCZ99gd7FsYcRzp6Xeiw/j0AqDaHvKQulUq9SdI6Se+QdLKkh5LJ5K3JZPIVyWQytlwTBADUtpm80QP9ed32wIy2jRRVWJvXfSumKWL19ivW6T/euEEffGWPjmqp3l4tODTGSE7//SrsC5skqfDwnXJ23LPmehItlfXsmg2bJKk+LL3xsnVy9h0nxkivv6RDjdHqPXBa444ue0bTorHXXNRR8UbaLXWuTuhd3EjqxL6IWuKHH955ntUZyahed3GHOpoCOqE3ovdd06f2GtpNEQCejDlYWv9UksnkCZK+KmmTpFlJX5P03lQqNVC+6ZVFn6Rto6PpijcxbWmJa3h4uqJzwNrB8XZwjmPkWSOj8uwwg3kHOuYyBaN/+/Z8tc1+f/G8Dp21MVLxczJq21o6x/l8jnw/+pgKW3+1eLz7BBWf917lWUa3Imr1mLMyGp2xGpsuqCHmqilqFgKoapXzpMFxTxPpvFrrA2qrc+SYyr9mTGeNfv1wWnc/ktZpx8S05diY4sEjn5cxRnlPch0js2+X0Fo93lC7OOZwOBzHqKkpJknrJW3/068vaZe6ZDJZJ+nFkq6W9DRJN0h6o6Sdkt4q6eZ94wBQdYyR9k5L3//1qHYOZXX+5nqdsiGisK/yb2JXq91j+UVhkyR94Ud79bT16xWlrRBwSIpFq0DXCdKfBE5Oz0nKraHg1hijnGfkd+yaWSNqjJT3HBU9q7D/8HaANLJqjkrN0drZnDrgSL1NjnqbgvtGquP5jgetLnhaTM/dHFexaA+6zG6prLXyGa2Z4xrA2rGUpuHXS3qupJ9L+qSkb6dSqewTvv4WSZNlnyEAlMl4RnrPF7Yv7OK1dXdGV57TrMu3JMr2phGLZfOlj2sm581v4U7gBBwSa63Ud6qc9p/L27NVkmSae6Sjz1wzlYITc0Y/uGdcdz+c1qb1UT3v9AY1VniJ1XLzZJQayOlLt+7VzJynF5zZrGdsjCrEhyQVZa1VgbXhAHBIlvJRxy8l/VUqldpzoC+mUikvmUy2lWdaAFAeBWuUK0iRgNQ/ki3ZMv47d43q/JMTVNssk45Gv0IBZ9GOQ2edkFAiXNsdxK2MRtKehifzqo/51FrnzH86/f/Yu/M4O6oy8f+fU1V333vfu7MnZCUJS1hDAFllEVlVEBBnc34z4yzf16ijIqjMOON8x6/jrDquiIwoKoqgQkBA1gBhDVk76aQ7ve99t6rz++MmN2lvgE7S3Xd73v9oH7o7p2/VrVv1nOc8jygoE2lFR18KR2saK9wE3cV7zo27KvFe8re4RjpBa+xwA+NGIN/TmhVJx+Ar93ewbV+m++L+gSSv7hrj9g82l3SGake/zRfv2ZP9+n8e7sLtrueMRX5ZJBFCCFEUphxw2rJlyz9O4XvG3+17hBCZFPneMejsS+LzGjRWuDJdwcS0UUqxZ8Dm6w920d6dYN2SMOtOCOd8n8tUKKUolHT9UlPhV9x+Yyt3P9JNe3eCs5dHeM+aaFEHmwxD8eKuBF++ryP7Z1y3vpoLTgxjFkCNEZExFFd88Qd72NeXBKAiaPHpD7UUdVZM3AhAZH6+pzHrekfS2WDTQV39SbqH0rRWFm63teNhGIrNO8dyxh98tp9TFwYw5TNLCCFEESiezdxClJD2Pofbv9NOys7cMJ44z88fXlJf0iu1s61/XPPZb7dnW0H/9tUhlrT4qY666BlMZb/vAxtqCLo1jtTcnRFaa+rDio9fUU/S1nitt28tXSyG4vC1n+6bFDO7Z2MPa+YHqQ1LmlMhUApe3DGaDTYB9I+meeSlIa49I4ptF/c5WG7cloE6QlKkq4TTCrXWxIK5t+lVYVcmsC2nsBBCiCIgASchZllaK/77wc5ssAngxe3jdHSPs6ChiJfeC0zXQCobbDroGw91cdetc3h99zh7exOsXRhiTo1VNjVQ8knh4DGLOrEpayzuMJHMjVAOjdvUhqf+sWqamYfotHQYm3aGYbD99zJiALZ0TKB1DHlaLy4VAcVFayv4xXP92bHTloapCZuU6rHUGpa2+IgFLQZG0wCYBrz/zOrSuJAKIYQoCxJwEmKWpR3F3sNW3Q8aGZ7AaJJW8dPF5zFyxtyWwu9WnLPUj2EEJMtBHJNYwKAm6qL7sEw5l6WoibqYysOvrRU7e1I8+NwAfo/BhWtjNMaMUn1uzgvbdjhpYYjHNk/uZXL28ghaS4Cv2BhorlgXY9W8ANs647TVeplb68Yq8S2sUR987sYWdu5PEE9p5tZ6qAkruVYIIYQoGrlPZEKIGRX0wJmLvTnjDf7xA7WExHSoj1qcujg0aeyWC+sIezOLwxJsEsfKY2r++upmGqvcAMSCFn97XQtR39TOqe37U3zuu7t5bssIj20e4pPf3MW+QTkfp9vCBg9Xn1WNZSoMAy48qYLV8/ySHFKkvJZmcYOL964Ns6zJhd9VHgcy4oVVrR5One+lJiTBJiGEEMVFMpyEmGXptMM1J3sYj9s8vS1J2G/ykTMMWsMJErasvE8Xj6m5+T3VnL8mxuBYmoYKN3URUzLIxLSoDcHtH2xmZELj9yj8Lj2lQIYyDO5/qnfSmOPAc2+NcOUpETk/p5HX0lx2cpizlofQGiI+hZJoU9ZoEnb3pEimHZqrPFQFi2OnVrHXgBNTY2vFvgGbvf1JogGTlip32QQZhRCilEjASYg8iIZ9/PnJHfQtnMCdGqEiFiQZWZXvaZUcnwULai0OXerkZlVMH7ehqQwATC3YlKExzdxMRtOQ7MaZoB1NxHPwC3n/HzSUgC98v4PO/sz2bpeluOOmNhoich6K/DMMxdNbJvjaz/Zlx1bPD/BHl9ThkY6+QghRVGRLnRB5kDR8pBtWUr1wGZFl6xhvWkdK5W6zE0KUFu1oLl9XNWnMMhVrFwQlu0nMmi0d8WywCSCV1tz7WE+mvZ8QeTYch/95qGvS2KZtY+wbSOdpRkIIIY6VZDgJkSeOo4kbgcwXsvIuRNmYW21yx01tPPryIH6PwZnLI9RFpDaLmB1KKXqHUznjnf1JUo7CVeKFuKeTUjCSUHQNpvC5DWojZskXMp8NKVsfsRNoZsyc/QkJIYQ4ZhJwEkIIIWaRoaC10uDW86sAnSlgL8+os04rg1Ra43FlMs9mm1IwOKEYGE0TCZjE/OrQ4oNS9I9pRuM2lSGLoPtotm2+M601J7QEgJ5J4+evieExNY6UEpyy/SNw+3d3MTJuA3D2iggfWF+F15I39PGI+BSr5wfZtG00O+ZxKRoq3MjFUgghiosEnIQQQog8sKVJQF4opegccrj70S52d8c5e0WU806MEHLP3oOsUoo3O1P84//uIZHSWKbiTy5rYPUcL1rDk2+M8Y2HurAdCPlNPnFdC43R6dvu1lxh8hdXNfHNh7oYi9u899RKTlsi2zqPhoPi27/uygabAB7bPMSZyyMsrJXb6+NhoLnlglpiIYunXhumtdbDTe+pI+aThHAhhCg28okohBBCiLIxMKH59LfbiR/YsvPjJ3vpG05x63uqZq2L3UgC/vm+DhKpzL+XtjVf/cle/umjc0mmNf/14KH6NSPjNl/58V7uuKkZtzE98zOVZnWbhyW3tuJoCLik+9vRSqThrY6JnPH+4RRIwOm4hT2amzZUcs2ZlbgthYkj56gQQhQhKRouhBBCiLKxry+VDTYd9NtXhxiamL2H2aFxm/HE5DnYDgyMpo9cX2kgyWh8euentcZravyWlgf5Y+BzwdqFoZzxugp3HmZTorTGZ2lMJBtUCCGKlQSchBBCCFE2PK7crWk+t4FlzF6HtojfJOidXPzYMhUVIYvKsCvn++tjbgIeuWUrKFpzzVmVzKvPdJi1TMWHzquhMSbZTUIIIcRB8qkohBBCiLLRWOFiYaOPt/Ye2g71ofNqCXuZtYLZIQ/85fub+NL/7mE84eBxKT52eSMxP9iOwa0X1vE/D3fhOBD0mS5LviAAACAASURBVPzplY14TMnyKDRRL3ziuib6R2zcLjW58LsQQgghJOAkhBDTycTGO7EfxvohWMWErwZHS2aCmB2Ohp5RTe9wiqqwi+qQgSFdnSbxWpq/uLKB7V0JuodSzKv30lxhzWrBbK0182stvnTbHPpH0oT9FhWBTLc8U2nOOiHAsra5jE5kutSFPNPXpU5ML5dyqA0fyI6TgySEEEJMIgEnIYSYJiY23h0bSfzmvwENysB3yceZaFiLNH8SM02jeGbrOP/2QGd27CMX1XH2CQGUoVBIZ7yDAm7NihY3SrkPxAhm/w2asmHr3jh3P9qN1nDt+mpWzfHiNkChqfRDpd8EJNgkhBBCiOIky+5CCDFNvPFuEo98nezDq3ZIPPRVvMn+vM5rpihDMZpUDCcUWs1e/RtxZIPjk7ubAXzjoS627k/zhXv3cffj/fSO5WlyBSqfgZzt+1P83x/vpXswRc9Qiq/+ZB9b9yXzNyEhhBBCiGkmGU5CCDFdxgZB/14GSSoO8RFwVeRnTjMkaSsef2WUH2zsJpXWnLcmxg0b3LKKkUcjcYe0PTmC4jjw1t4JXm8f5/X2cR7bPMRdt7QR9kjKTD5ZlsFjmwdzxn+1aZCVbXWk0+WbiZbSBhNJjd8NlpLzVAghhChmEnASQohpokPVYLrAPtTWXPkjaH8sj7OaGTt7Unz7V/uzXz/8/ABNVR42LAvMai0ccUhlyCTkNxkZt7Njfo+BfVgQanTCZk9PgqVN0ro9nxxHUx3NPQY1MXfZbp9TCvYMaP79gT3s7k4wt97LH7+3gZpgvmcmhBBCiGMli9FCCDFN4p5KvJf9DcqbeUJSgRie9/4NcSuc55lNL8NQbN4xmjP+600DpB3ZWpcvQbfmb69roSbqAqAq4uLG8+t46PnJWzoNQ45RvjmO5oylYfyeQ7dhHpfBuauiZVtnq3dc8cV7drO7OwHAjs44d92zh4m0nK9CCCFEsZIMJyGEmCaOVoxVr8B7/T+iEiM43ihjVqjkMha01jRVeXPG5zf6MJQ+pvrLyjBQCpwyfdieDlpDU1Tx+ZtaGIlrQl7F01vGGD4s46kyZNFc5SYfRbLFZDUh+MLNbezoiuNomFfvpSqg0KV2wZgC01Rs74xPys4D6B1O0Tdq0xSV9VEhhBCiGEnASQgxq5RSJOzM/7oNpySDMRNWFKzogYH8zmcmaA1LW7201Hiy2QhBr8mVp1eDtt/lpydLOYqtXUkeeLqfgM/gslMraa4wSvJ1my0eU+MJAGjWLQpQFWnm6TeGaa3xsmZBgKBbXtxCoDVU+KFirvewsfI8Nlor0rZGqcmF3E0jc22RC4IQQghRnCTgJISYNUlbsWnnBPc+1oNpKD6woYZlzR4sQx4mik3YA5+8romOvhRpW9NY6WZeo5+enpGj+j1bOpP8ww/2ZL9+bssIX7h5Dg0R2UYzHdymZmmjixUt1TiOLtuAhihsWmsCboML11bw4HOHtoBev6GWqA+JNxUapRhPgtuSwu75ltaK/UM2Y3GH2qhF1FeeWZJCiMIlASchxKx5c2+Cr/10X/brL9/Xwd99oIUFtXIpKkY+Sx927I7+Blcrgx8/0TtpzHHgpe2jNK0NS/HxaVSudYFEcdBaM7/BQ+9IihvPryOVdmio9LCwwU3JpcEWMaUUE47BvY/2sHHzEE1Vbm67uJ6WCkMOUx4kHcWPnuznF88NAJk6cJ/5YAtNMdmCKoQoHHJFEkJMq7Sj2NPvsGlXgj0DTraItGkaPPTCQM73P/HaMKYpl6JypAC3K3N+NFa5ef9Z1Vx+WhURv1WwDy9KQdxWDExkVpaFENMj4NJsWB5k7QI/py8JsbLVjc8q0AtBGRpOKH79yij//rNOYiEX56+JsWt/gs9+p53+8XzPrjzt609ng00AiZTD1362j5Q07xBCFBBJKxBCTBsHxcMvDXPPxp7s2AfPreH8lUFAU32ge9bhqiMuSf8uV9rhqjOqqa8YxmUpfva7XlyWwQ0bakja4Cq0OKSC7T02X71/H73DKebVe/njyxqoDuR7YkKUBqU1US/AsTUfEDMjbiu+9MMO2vdnavZt2jbKaSeEWT4nwCs7x+jsTxFrzP18FzNrcCydM9bRmySeBpc7DxMSBUMrA6U0SKa4KACFdjsvhChi/WOaHzzWM2ns7ke66R8D29ZctDaG2zq08ub3GJy6OCRbp8rYvBqTtjovDz7bTyKlGZ2w+c+fd7KrJ5XvqeUYGIfP372b3uHM3LZ3xvnn+zpIymqyEKKEdQ2ks8Gmg373xjDL5waBzGe5mH010dyo0rK2AH4JNpWtlK14qT3Jnd/fy1d+2s2eASeTTi5EHkmGkxBi2ozF7ZytUI6G8YRDZcCgLmxw161z2N4ZxzRgbr2XSr+U6ChryuDRFwdzhjdtHWVRfayggpHdg2lS6cnz6ehNMjDmUBuSOzohRPk4+Ll98qIQ9TGLmUpJS2tFz4jDRMKhNmIR9Gi5ZzigLmzwx+9t4Ou/7CSR0rTUeLj1wjpMSQ8sS0rBy+1xvnL/3uzYpm0jfPGWOdSH5R5F5I8EnIQQ06YqbBEJWAwdluYdC1lUhjNtrbXWVAWgav7hbcDzMFFRMAylaa52s71zYtJ4XYW74LZahvy5q/h+j3Fgdb+w5iqEKG9JJ9O9zFRQHTFxHUc3ubqoRUuNh93dh7KczlwWYVmrj7OXBvCYM3P9S9jwwyf6s/Ufw36TT3+wlZrgjPxzRcdQmnULvZzQMod4UhMLGLik62/ZSmuD+5/KbcTyWvs4DSsCcr8t8kYCTqVKwdCEYmA0TTRoHqiJIMTMCro1n7qhhf/4+T627YuzoNHHH1xSj18Kv4q3oR3NJadU8sybI0wkM53UqsIuVs4NFFzAqTZscsXpldz/ZB+QWU38g0sbiHgzN3VCCFEIhuKKf7l/L9v2xQE4cV6A2y6qI+g+tmuq19L89fsbeXrLKK/sGGPdCWFWzfUTcM3sNbqjz57UbGR43OabD+3nL99Xh3kcAbRSojWEPRD2KGTho7wpNF537sKYt+AKYopyIwGnEqQUvL43xZfv6yCR0rgsxf93RSOrWt0S3RYzSmuoDcEnrm1kIgU+F1hyUyjeRV0Y7rq1jT09SUxT0VLlJuQpvPPGVJrLTo5y8sIQA6NpamNuqoIU1La/mTSeVuzpSZFIOTRWuakKSIaiEIXGMBRPvj6cDTYBvLh9jFfbJ1i30HvM79mIFy46McQlayLYtj0rCwL9I7m1/LbsHSeRBr/UKBdiEgPNtWfXcMf32rNjfo/BkhaffFaLvJKAUwkaisOX79tLIpW5uqTSmq/8eC//+NE5xHx5npwoC5bShKRopZgirSHmg1jLwZOmcO+MLKVpihk0xcrrBB9NKv7xvr3s6Mw8xLosxedubKMxKnUhxJEpBcMJRWd/Co9LUR+zcMt2nxmnUWzaNpoz/lr7GKcv9mPbx56O6Tgax7GPZ3pHpeYInW1PnB/EK8EmIY5oXo3JnR9u44Wto4T8JqvmBmRxSOSdBJxK0NCYTSI1+YYiZWsGx2xiPjNPsxJCCFGsdnQlssEmyCxkfO833fzVVXUYBRwgFLPDPlDYeSxuUxNxEfZquoY1n/3OLsbimfuR1fMDfPSiWslMmWEKzcmLQrzVMbku3oo5geMKNuVDQ8zig+fW8P1Hu7EdaK7x8IFzquWaI8TbMBS0VBi0rYugNWgtRfZF/knAqQRFAxY+t5GthwKZ1ehYIBNsSmmDeFLj9ygspQuuTooQQojCMjCazhnb25cgZYNH1jHKWtJW3P/MAA883Q9AwGvw+Q/P4fuPdmeDTQCbto2xszvF0sbyizgZppF5+JuFYm+Oo1m3OMRL28d4ddcYAKctDXNCi49Czh49Epehec+qECctCBJPOVSGTMmSE2IKymWrvygOEnAqQWGv5uPvb+LLP+xgIungcSn+7MomYn7YO6j5t5/tob07wcJGH9eeU0PUb1AdlG0RQgghjmxOXW7niQ0nRvG6QBdX0oSYZnsH0tlgE8BY3OGZt0bYflhG3EHdg8myCjjZWrGjO8VPf9eHZRpcfloFLZUmM13CN+TRfPzKOnqGbQxDURU0irfIttbE/ADSDVQIIYqRBJxKkNawuN7iS7e1MTjmEPWbRHyakYTiju8dSm9/a+8E//HAPk5ZHOas5WFqQxJ0EkIIkasxZvLn72vkG7/sYjRuc97qGOeujKBlFbXsDYzkZr89/fow604I88vn+ieNt9aUV8vcHd0p7vje7uzXL2wd4Y4Pt9FaMfNdoyylqY8c/HfkfSrE8VJK0Teu2dOTxDIVLdVuQsfY+VGIciIBpxJ1qE1qZkVIa+geSk9KbwfoHkwR8Jk89foIV62LSAqmEEKIHKbSrJnjZclH2rAdCHqQKqQCOHJh56DP5JKTY3T1J3hp+xguS/GBDTU0V1qUS/DDNA1+/mx/zvhvXx1i7jmVRVdPSYhy1zXs8Hffaid+oGRJdcTF332gmWh5xdGFOGoScCojAW/uiprLVCgFIxNplFKUy42gEEKIo6O1xnfwrkE+KsQBdRGDWy6s4zu/2k/K1jRUurnlgloiHs2fXV7H4LjGMiHqVWVVM1IDXlfufZfXMsrqdRCiFCjD4Ce/68kGmwB6hlK8smuCs5b4pmX9Je0ouoZsBkbTVEdc1ITVjG+/FWI2SMCpjFQFDa46o4r7nujNjl12WhVPvDLEbRfVyWpbgbO1Qd+ojWFAZcBAyROfEEKIPLMMWL80wKq5c4gnNRUBA7eZ+Xwy0VT6M99XbkEWx3a4+OQKnn5jmIPJ45apOH1ZWLLJhSgytqPp6E3kjHf1J1DKf9zXNxvFQy8N84ONPdmxP7msgVMXeqVOoih6EnAqI6bSXLw2wqr5QTr7U9iOZmfXODedX1tWae7FaCSp+M9fdPLyjjGUgvNXx7jq9Ap8VuaYKaUYT2VuZl1KPpmEEELMIq0z20q8kil9uOYKk8/fPIffvTGMy1KcsihEXUTJSyREkTEVvGd1jP/8Reek8RPnB6clgNw34kwKNgH81y86Wdw0l4hXLhiiuEnAqcy4DE1rhUFrhQcHxWmL/CjtIHc/hcswFL99bZiXd2TaG2sND78wwPI5AVa2uBlNKh7eNMhDzw9QGba45cJ65tXIW1uUF8NQkjUghCgoCk1jVHHN6VHgQKtyuUwJUXS01qyZ7+eas6v5yVO9uF0GN55XS2uViyO+qZUingavxZTqHQ5P2DljybRmPGETOUJJFCGKiTyVljEDLUVfi4CtFU+/MZIz/lr7GGvmenn46UHuf6oPgI7eJHd+r527bp1DTfVsz1SI2TeeUmzrTLB13wQLGnwsaPBkM/9EmVGKwXFN2tHEAgamPNmLAiHBcCGKn8/SvPekMBtWhjFQ+Fz6iFvpesfgB4918+aecdYsCHHFaZVE3yVLqSbiwus2JtWIqom6iAVNJEotip0EnIQocIbSrJgTYFdXfNL4ggYfI3HNwy9M7oLjaNjTk2D5vNmcpRCzL60Vd2/s5fFXhrJj61dGuGlDFaaSG7RykrAVD784zI+e6MF2YNW8IB+5sJawR84DUV4sK5MNkU7L9nohppt2NH4LDnYA/33jKcUdd7czMJIG4JGXBtm1P84nr23CZbz9ezLihU/d0ML/u38v+wdTtNZ6+NPLG/Ga8hkmip8EnIQocNrRnLcqynNbRujsTwKwYm6AJc0+LFNREXIxnphcyNDvNfMxVVHkEraib8TG5zGI+VXBZ0D2jjiTgk0AG18e4pKTK6gNqTzNqjiowj+8R6W9J8X/Pn6o/sVL20f5zUte3rcugpbsElEGHA27+x0efK4XU8EFJ1XQUmFKgxEhZtH+oXQ22HTQjs44vaNp6sNvvzVOa01LhcGdN7UynnQIeg2pySpKhgSchCgCUZ/m9g810zWQxjQVtRETt6EBzS0X1nHn99qzXXBaazy0VrvzOl9RfHrH4Ev37qFzIIlpwIfOq+WspQGsAo7bpO0jP0ilbA0U8MTzSKPoHHJ4Y/c4Aa/JoiYvMV++Z3V8lFK8tXciZ/yp14e59OQoLsl2E2Vgd7/Dp7+1K/v1k68Pc8dNbbRUlHf9FxsDrTUu48gZKUJMJ48r997DUOCxpvY+9JgOHh/A0QWbTDPz+6XjuChEEnAS70qj6B/PfEpX+JWsluWJ19S0VR3MXDp0DObVWNx16xz29CTwe03aqt0E3HKMxNTZKL7xyy46BzIZdLYD33x4Pwsa22iOFe7DSk3EpK3Ww679hzL85tZ5qQlLzYO3s6s3zWe/05598IoETD53Y2tRB5201jRXe3LGFzf7cBnIqSBKnmUZPPx876QxrTPbeW49v6osH0JtrdjSmeSeR3tI2Q7vP7Oa5a0e3IX7kSaOggP0jmj6R9NUhiwqg4pCOLQ1YZOzV0R4bPOh7Ov3nVFFLDAzacWOhvZemwee7cFQcOkplbRUSmajKCwScBLvaDwF//tEH4+8NIjWmfoo155VRcAlF7JCodDUhRV1Ye+BkXc/NkplVkNs25EVP0E8Ba+2j+WM9wymaI7lPsgXCreh+fhVjfxq0yCbto2xen6A81dHD2T/id/noLhnY/ek9/zQmM0beyY4bWERR5yA+fUeVs4NZLt5RoMWV5xWCbq8HrRNQ+OJ94F2SHkrSWnZXl0ulJGbWWEWwhN4nrT3pbnrnj3Zr//lx3v566ubWN4sGeDFTqN48s1x/usXnUDmnvZPL2/kpHmevN/TWkpzw/pKTl8apqs/RVO1m+ZKF2qGJtbeZ/OZ77Rnv37mzRHu/LBkNorCIgEn8baUglfa4/zmxcHs2MaXh1jSHOC0Rd68X9TFsRlJKF7aOc7r7WOcOC/I0lafBBDLnMeC+Q1etu2bXJi+IlT4HxFRL1xzeowr18VwGRyxY4zIcHQmwPT7xuJ20dd0Crg0H3tvHfsH0yTTmvoKV9ld1zzOGObmB0k+92NwHKwlZ2KdegMTVjTfUxMzLJ12uGBNjCdeHcq+jw0F56yMlmV2k2ka/O71gZzxXzzbz4rWBrRTfq9JKekf13z9l53Zr7WGf3tgH/M/Ooeo9x1+cJb4LFhc72JxvevAyMx8FlmWwS+f680Zf+TlQW49rzwzG0VhkvCneFumafDMm8PZrw0Fl51WRVprXm5P0jeeCUqJ4pF0FF/9WSf/9YtOnnxtmK/+dB/f39iLI5eCsmYpzW0X1xP0HcqGuPy0ShpixZEdobXGUkduTywOcRnw3nWVk8aUghNa/EUdbDrIY2paKk3m11plF2wCsHreIvnMD8GxAU36jcdR257COELmiyg9LRUmd364jfNWR3nPmhh33txGY5Fcw6eb1ppYMHfBpCJkyVajEjA8ZvP7McNUWjM6UX4BFusIhTYtueaLAlP4y9cib2xbc0JrgOffGgXgitOreXbLMB09mXopLlNx+41tNMXkwlYs9g/ZvLF7fNLY468Mcfm6SqqDma9tFP2jDqahiEnNrrLREFH8w61tdA+lCXgNqoIGphRbLilaa9bO86Murecnv+sj7De5fn0NjVGpeVXsTFPhtL+cM26/+TjWCReQpDwDDwcZBqQdA9PQJdy1UNMcM7j53CrgYPHgUv1b35njaE5aGOInT/UxkcwEISxTcckplTgle/zLR2XYxOs2iCcPBZjCfpNYsLw+y9JphwvX/l5mowHnrJqdzEYbg8ExB49LEfYi7y3xtiTgJN6W1pqTFgR57OVBOnoTeN1GNtgEmU5Qdz/SzV9dVYdRRhf4YvZ2N9qZzBDFSFLx9Yf2s2nrKIaCS06p4L2nxPCacnxLndYQdGuC1bmF6UXp8Jia0xf5OWlBAFPpA9duOdbFznE0Rs2cnHGjcXHZB5uGErBx8zDPvzXK6vlBNqyMEPGW7jkv22gyakLwhVvaeHPPBLYDi5q81IaVXO5KQNgDf3tdM//3R3sZGE1TFXbxF1c1EnSXXyfCppjJ5z/cxuOvDmMoOHN5hMbozJ/ng3HFvz2wlzd2T+D3GNx2cT0nzvHIfglxRBJwEu8o4tV84romuofTvLU3nvPf9/YnSNkKjwQkikJt1Mrp6nXSoiCVIQOlNI+/OsymrZmMNkfDz57u54SWAEubXG/3K4UQRWg8oUnZmlhAYcoTWNHTGnTjCoyauTjdOwBQgRjG8gsp5/hD0jH4yv0dbD1w/9K+P85L20f4xHVNeKS5AACGodC69B7UtYZKP5y+yHeoRl2J/Y3lbG61yRdvaWUs7hDyGvhcpXcOT4VC0xQz+ND6CkBj23rGz3NHKb7z6/28sXsCgPGEw7/8eC933TqHhojsehG5JOAk3pXP0rRWmChyuxidtyqKz+Xk7KUWhcljav7y/Y089fooL20fZd2SEGsXBDHRpLXB714fzvmZ19rHWN4Sk1TZMqYUjCYV3UNp/B7ZblfMUrbiN5tHuPfxHtK2ZtW8IB+5sJawR45nsRt3xfC+9xO4hjrAtnGijYyVecHw3pF0Nth00M6uBD1DNk2x8l6LdztxXP3bcPZsxog1ohuXMe6qfPcfLELlGIgodVqD39L4gwooz2DT4WYzs3E8Cc8dKLdyuK7+JA2Rwu1sLPJHAk5iyhpjJh+/qolvPNTF6ITNBWtjnLMyIoGIIhPxwCVrQly6NozjHCq0bCnN0tYAu7sTk75/br1PjnGZ6xqGO763i+HxTIezy9ZVctnJUdyS2Vh02ntT3P1od/brl7aP8vAmD1efFpWi6yUgbgQhtjjf0ygY5tsUz3278XJhKHBtf4zExv/JjqmKJnxXfJoJM5zHmQkhCp3HUjRVuenoTU4ajwTLe/u2eHvlvbwjjoqpNCe2efiHW9v4f388l2vPiBF0ywNKMXIcjW07kx4wtdZcsDZKdeTQ9rllbX4WNxVAj1mRN7Y2+O8HO7PBJoCf/q6PvQPpPM5KHAulYHtn7tbop14bJmEf4QfErDAMhWXJ7dhMqAoanLU8Mmns1CVhqsPl/Xp7UoMknvz+pDHd34ExsDtPMxJCFAuXcvjDSxtwH9Yh76wVEZoqpPyGODLJcBJHRWudKSBdXo0gykaFD+68qYXO/hSWZVAXMSWL5SgopXBQGKp0OiHFbXhr70TOeP9ImjlVsppVTLSGhkp3zvjCJh9uuabnRf84PPnaENu74qxfEWFxoxevJQdiuphKc8P6KtYuDPHm7nEWNftY1OjFKvMtwUo7kE7m/gc7NfuTEUJMO6Uyny/dQ2lCXoOaiDmt173WSoN/vG0OnQMpAl6D+qiFS+riibchASchxCQ+SzO35uClQT48pmospXhu6xiPvjTI3Hovl5xcQXWw+GtH+CzN8rYAm3eOTRo/PBNOFI+5dR5WzQvw0vbM8Qz7Ta4+s6r4T9QiNJyAz353N4OjmWzBTVtHueGcGi5aEyqZgHUh8Ls0q1rdrJ7jObA9XF7bpDuKa+UFpF56MDumPAF0rDmPsxJCTAelYEePzZ137yaVzlzvLj2lgivXRXFNU3Kn1hD1QdR38F5Qrqvi7UnASQghjpdS/OyZAX7xbD8AO7viPPPGCHfd2lb0xZgNNDdfUMs//KCDzoEkpgE3bKihPiopMcUo4NJ87NI6OgfT2DYE/Sbbu+K8ZWvmN/ioCUnsabZ09KaywaaD7nuihzOWhQhKPHfaSS3CQ9LawDrxCtyRWtKvPYJRMwdr9WWMearksi5EkYunFV/9yb5ssAnggWf6WbckTHNFeW8nFvkhASchhDhOw3F46Pn+SWOjcZuOviQnNBT/k2OlHz53YzN9IzZej0GFX5VuVEJB3xh09afwew0aYhaeEttW6jY1rZUmvWPwqW/uYjyR6W7jshR3friN+nB5F1QWohzErQjG4ouwFp1LWlnEHSXBJiFKwERK0zOUuz12YCxNc0XutnohZpoEnIQQ4jgZCizTwHYmt6W1SqgTksfUNEQPrIyVarCJTBr6Hd9t52CH4dNOCPPh86pLrq6OYSie2TKcDTYBpNKaB5/t5yMXVOHYpfX3FqLmKheRgMXQ2KEsp6vOqCbkAT17Ha5FGXMcTRKXBJqEKCFBj2Jhk4+3OibX36yNFv8CqChOklcnhBDHKeTVXLe+etJYY5Wbpkr5cC8mCUfx7z/rzAabAJ56fZi9A6VXSFcpRd9w7t+VWRUtnUBpIQt54PYbW7j6zCrWLAjy8auaWL9c6jeJwqEUDCXg1Y4kr+9NMZKQa4MQhc5Smj+8tJ6WGg8Afo/BX7yvkeqQvH9FfkiGkxBCHCftwJlLgzRWuXlp2xhNNR5WtPrxu+TBsZgk09A1kNu5aXjcptQ+Lm3b4bQTIvx60+Ck8YtOqsCxjz69RikYiiv2D2Y61tSEp7cjTqmq8MHlp0QwjCjptEMxpJq4VBpXahTb9JBQvnxPR8yg7lH4zLfaGY3bAFSGLD7zoRai3jxPTAjxjqr88Jkbmhgcd/C6DCI+qWMn8qe07qCFECJP3IZmcb2LExpjaK3RJbztrFQF3YqTFoV4bstIdkwpqC/RmgdtVSZ/fXUzdz+6n1Rac81Z1Sxp9HAsQY+9g5rPffdQPagLT4rx/tMqcJdY/aup0koxNKExlSLs4x2zlhxHF82DQCDZTfrxb5LcuQlV0UDwvD9iPLqAIpm+OAqGofjVpoFssAmgbyTN82+Ncf7KoHzGibwzDFU01858cBma6qACNI5s0xZ5JAEnIYSYRnLzU7wUDh86t4ZE0mHzzjHCfpM/uKSemhJNQ7cMWNHiYvEHm0FlgqbH8hCZ1or/+PneSfWgfvncAOuWhJlTZU7nlIvCaFJx72/7eGzzIC5Tcd36Gs5aGiz64JubJKlffQ1n35sA6P59xO/7HL4P/BNjnpo8z05MP8Wu/fGc0d09CQwjhC113kSejCZh674Eu3sSLGryMafGXZDNPZRSKCX3hUJIwEkIURbG04rdPUnGqHNKUgAAIABJREFU4g5NVW5qQgaqCLaviNkV9Wr+4oo6RuIat0sRcFHSK/laZ1ZBD/7/Y5FIw86uRM74wEi67AJOhqF48vURNr6c2aqYTGu+/ev9tNR4WFhX3LdcVryf5IFgU5adgqFOqJGAU6nR2uHcVVHe3D0+afzUxSHsY9h2K/InrRU9ww5aa6ojFi5VvMcvYSv+9WedvNZ+6Ly85uxqLl0bLpiGJkpBzyi8sG2EodE0pywO0VxpYcg95ztSCibSipEJh5DPwGfpQjmk4jgV992PEEJMwVhK8c8/2sdbezMdOwwFn7ihhYW1cgkUuUylifoASutmxzQNtJ7+7Vs+F6ycG+DlHWOTxmti5Vc0P2nDY68M5Yy/2j7G4oZoUa90a8sLHj8kJgcg8ATzMyExo7SGFW1erj27mh8/2Yt1IFtvfr2bYqg1JjJGk4qvP7SfF7aOArCk2c+fXFZHxKsYnIB4yqEiaOAqkpp7XYPpScEmgB890csZS0MFU1usdww+9c1DW8wfeKafT97QwqIiX3SYSUpBe5/DP/+og77hNJVhiz+/som2KrOkF/3KhXSpE0KUvD29yWywCcDR8N+/6CLplOZWKSEOpzW099l88ze9/PCpQbpGNGoaT30Dzc0X1GU74rgsxW0X11EfKc7spqSj6BhwaO+zidtH90K5TJhbn/vU01zlKepgE0DCHcOz4aOTxqwlZ5MKNxzT79MYxG2FOuxkTDqK/SOakYSa1nNUHBufBZeeFOZf/mguX/7oHDYsD+A2ivs8LidKwSu7JrLBJoA39ozzuzdHeXbbBB//zx389X/t5HN3d9A/8Q6/qICk0rnnX9rW2PYRvjlP3tgzMWmLOcA9G7uxp/jYrZQiYSvK6SI4klDcdc8e+obTAPQNp/niPbsZzk2eFkVIQq1CiJI3Hs9NH+8dTpF2wC1hd1HidvTa3P6d9uzXP3+mn8/f0kbdNNamqvBpPn1DEwNjDl6XIupTRbkqOZpUfO2BTl7dlVlBb65289dXNxP1Tu1v0Y7m8nWVbNo6yuhE5globr2Hxc0+ij0rxHE0iaa1eK7/exjqAl+EdLSZ5FF2qlMKOofh7kc62dmV4MxlYS5cGyOR1vzTD/ewry+Jz23wh5fWs7LNMyMrow4wMA6jEzZVYYugu7SyGaeTdjSBA8mKUni4uJimweadoznjL2wdxesyssGb9v0JvvWrbv7ssjoMCvsg11e4iAYtBkfT2bGTFoWIBTLFsfNNKUilc1/DREqjpzC/4YTil88P8OTrw8xv8HHt2dXUhgpmt+CM6R9NT2pQADCecOgfSROqLM7FK3GIBJyEECWvscqNoZjUSem8E6P4XRTC/YkQM0YpxY+e6J00lrI1m7aOcuna8LRm3bgNTe2BIFYxBpuUglfbJ7LBJoA9PUkee2WIK0+JTPm1qgnC39/Syt7+FC5T0VDhwmcV3+txJGks0sFWCLYe8+8YnIDPfGsXE8lD200qwy5++8oQ+/qSAEwkHf75R3v50m1zsufUdLG1YuOro3zn1/txNIT8Jp+8voWGSPlkE4jyYNsOK+cGefK14UnjK+cG+NULA5PGXtw2ykTqUHCxUAVcmk9/oIWf/q6PLR0TnLY0zIYV4YKpj6Q1LGnxYxpweKmzq86owqXeObBt68z2xxe3ZYKEz20Z4Y3d4/z9rW2E3IXx982UkM/EMhXpw5oRmEZmXBQ/WdsXQpS8mpDBpz7QSkOlG5eluPCkCi47taL0l4xE2dNq8g3cQUcaK3eGYbClYzxn/OXtY2imHozQGkIeWFzvYl6NVTLBpunS2Z/KBpsOSqQ1O7pyO6L1DKWm/d/vGrL51q/2ZxcgRsZtvvqTvbLFWpQcrWF5q4+1Cw/VWVvS4mfVvCD9I+lJ3zuv3ovnaNIQlEFaH8PWV6UYTihGEkzaTns0qgJw6/lVfP6mZq48JUzIc0y/ZsbUhRR3fLiNU5aEWNTk4y/f38TyVs+73nIOTuhssOmg0Qmbzv7puQ7q7DErvGtd1Kf46MX12fNJKbjt4noq/IU3V3H0JMNJCFHyFJr5NSZ33NhMyuZAZpM8BIoy4DhceXoVb+zenR0yDFi7MFT0NYWmm207LJ8T4DcvDk4aP3VJCDWlzRBiKjyu3LXOsbhNVdhF7/DkB6toYPpvU/t+70EboKM3yXhC4z663YFCFLyAW/PHl9TRe5aNo6E6ZOBouGBNjIcOZDn5PQa3XVyPNYXC4Qc7sN3/VA9b901w5rII65eHCU4hA2c8BT97ZoAHn+/HVIorT6/i/BPDeMyjv7pqrbFU4W7zbIoafOyS2sznxhQnaRmZGoi/X6fK4zrOoIuC9l6HezZ20T+a5tKTKzlpgR9vAS2GKDQnz/cy97Y59A+nqQhbVBXINklx/CTgJIQoGy6lcVnI55coK/NrLf7uhhYeeLafgNfg4pMraIgqeR8cweJGLxtWRXnkpUzQ6cT5QdYtluDcdKqvsFg2x8+rOw9lkzVVuvnTKxq48+7d2Yet951RRV3EZLpP1Kpw7q1vc42HgEcebmaFMgAtiz6zyFIOdeGDQYvM637tWRWcsyrCWNyhLmoR9k7tkAwnFJ/5zi5GxjP1du59rId9fUk+ckEVxjv8AqXgxR0T/PzZfgAcNPc+3kNLrYcVze7j+vsKlX6HQJNSMJFWpOxMRixaE/Uprl9fw7d/vT/7favmBaiLHt+Na9eQ5jPf2ZWNe/3Xg53YTh0blgcK6rPNUFATVNQEC3xfpzhqEnASQgghSphlwII6i7+6sg6NxrG1PFe/Db9Lc+OGSi49pQLb0VQFTUxVoEvoRcprav7k0np27U/SM5SitdZDU4WF29T8021z2D+UJuw3qQ6pKWVcHK3asMnNF9TxrV914TgQCZh87L0NuKT72oxKOYrXOxL89Kk+fF6Dq86oYk6VrADli6U0DREDIpmMw6nG//b1JbPBpoOeeHWIq8+sJPYOGYLKMHjslaGc8We3jHBiWzW2XU7XWcUb+5L8+wOdDIymOe2EMNevrybs0Zy9LMjcei87uuLUxdzMqXUfUwbY4bbti+ckWf30d32ctiQgjXPErJCAkxBCCFEGyuuG/uhNpBUdfSniSYfGSjfVYYXW8prNhIBLs7TJBU0HV7IzxXSjPoj6ZvbW1FSa9UsDrJgzl7G4TWVIutTNhjc6EvzTDzuyX7+yc4wv3NxGY7Q0n3gNQ2GjMLQuyiYKb8dl5R4vl6Uw3+UwKjQLG328uXtynbx5dd6CyrKZDV3DDl+8Z0/2mvPka8MYhuIj76nCZWjmVpvMqwkc+O/H/9r4PLkHJ+g3MaU8kpglEnASQgghxOxQBvG0xmtRUFtqxlKKf7m/kzf3ZB6GXKbisze20hwrzYfhcqfQVPqh0p/ZsldAp2JJ0srgJ0/1TR7T8MLWUZqPogNkMVAKukfgwef62NmVYMOqKGvm+/G7SuNvbKywWNjo4629E9mxa86uJuJV7xhYs23NhpURXtk5Rn2Fm/2DKcbjNqvnB0oqIDcVnf3JnGvOk68OccP6qmyXwOl8SebXe4gFLQZGD9Wv+9C5NZgzkEEqxJFIwEkIIYQQM24wDj96oodN20Y5ocXPtWdXUxnI96wydnUns8EmgJSt+dav9vO31zRiIllOQhwPhSZwhPbmfq9ZcsG+wQn49Ld3MRbPXDe2d06wf10lV58eRZdAYM1jav78ygbe2hunozfB4mY/bdWuKQWNPJbi9GURfvvKEHPqvFxySgUR7yxMusAEj/BeqI64cOUOT4uIF27/UAtvdsQZHk9zQoufBllMEbNIAk5C5JlhZHJaS2mFT4jjldaKpHQULBlJR/Hl+zrYtT8BwNNvjvDW3gm+cHMr/gLolDM0ltu5bF9vkmRaM8M7vIQofVrzvjMqeXnHaPZy7nUbrJpbetktHX2pbLDpoF8828+Fa6IES6Q2dtCtWT3Hw9p5B7fDTeUYKu57so9fH+gC2r4/zgtbR7jr5tZM0ewy0lTpYtW8AC9tHwMynWP/4NIGPObMZVtGfXDqAi9KFcYtlVKK4XhmLiEfqEKYlJgxchslRL4o6BzSvLhtBIDV84PURaRzlChvSsGeAYdvPNTFnu4kZy4Pc8W6SsIeeWMUs74ROxtsOqh/JE33YJq2qhla1j0KzdW5TzzrV0bwuwrj5lyUJqXeeRtSKWmrtPj8zW28uG0Un9tk5dwA1cHSe38dqZaRZSpUCdbLOZqF0sG4znb/PGhk3GZvf4rF9eXVlcxnaf74krpscLKh0k11aHbeC4XwfkvaikdfHeHex3qwbc35a2Jcsa6CQIlsOxW5JOAkRJ7sG9R88n92crCO7/8+3sMXbp5DfaQE70oESikSdmYly0LjYJByNN4ZXNEqRn3j8Jlvt2dbo/960yBDYzYfu6QWJVubipbbMjAU/P7ziadAWuQ0Rk3+4qomvv5gJ6MTNmetiHDxSbFZDQYolblOSLZr6RuKK97smGBwNM2SFj9NFSbGYatNyjDQGhROCX0+aJqiBi0nR9AatC7Nz76mSje1MRf7B1LZsevPqSn7wvSmUlimIpme/CJYRnne83otzfzawx7Dy+jc2L4/yfd+0539+qHnB2is9HDOstLLeBQZEnASIg9M0+CRl/o4vGmU7cBvXhrkpg2V0k2qxCRseG7bOD9+ohef2+Dac2p4Zccoz701wrknxli/PEzQLR+yAJ19qWyw6aDntozQd04NVf48TUpMYpoGWuujCoxUBBRXnlHFfb/tzY6dsypKdcigEO60DaVZ3eZh8UfaSDtktnjM4o3vSELxavsE7d1xVswJMK/u+Fthi8I0klDc8b3ddA8dCkj8n2uaWdacyfLYO6i5/6n99AymufCkGKvm+ErqXCj1gGrQrfnU9c1s3jnBnt4Ea+YHmVsztRpHpSzsyxQX/+5hgYbWWg+NlS4K4TNAzA7TVLy4fTRnfOPmQc5eFkTJuVCSJOAkRB4oBWNxO2d8LG6XZNp1OVMKNu+K858/78yOfenePdxyYT19zw9w72M9DI/b3HBWrDBynfPM7crNeAl4DeJJB/zy5sintKPY0ZPit68MURVxcdqSMDVT3Aag0Fy0OsLS1gAdvQnqK1y0VrmxCqhLjtaH1WuaxWlNpBVf+uGh+la/eLaf68+p4eLVobJ/SC1Fu7oTk4JNAP/zcBdf+HALQ+Oav/vmLlJ25rj/608nuPXCOs5ZFij5QE0piXjhrBN8GIYf255qjaPSph3N+mUhWqo9bN45RkuNl6UtPnwFUMNPzB7H0bTW5FaKn9/ow1Q6JwtalIbCyGUXosyk0w7nrY7ljJ+3OkY6feTsJhuD0ZTC1vK2zRulGEspUnrqgQ9bKx54pj9nfHd3nOpIZkX7Vy/0M5bI+ZayVBO1OKF1cirT5adVv+37QswOpeDVPQnu/N5uHts8xH2/7eXvvrWLvrGp/w63qZlfY7L+BD+L6lx45UEDgH39qZz6Vj/8bQ/DcXl9SlEilXtcx+I2joad++PZYNNBP3qil/FUzo+IAqc1B4JN4iC3qVnc4OK6M2OsW+gllKfajLY22D+i6RkFB1nImk1aw/I2H83Vhyroh/0mF66JSVC9hEmGkxB50lZl8akbWvjxk72g4MrTqmirsjjSSljPKPz3L/fxxu5xFjf7+MhF9dQEZ3/O5WwkofjfJ3p5/JUh6mJuPnpxPfNq3r2ls2FAZdjFzq74pPGgz2QimQmiBLwmhsQRAYh64cK1FaxZGCaVcvB6DN7YPcaZS4PIKvHxUYZCa4VCH3XmTNIx+P6j3ZPGxhMO2zrjnDyvDPtaT6P0ER5K07ZGdlaXptYaD5apJh33y9ZV4Xdp3Fbuw6/HZRyxELUQxSqfgYWRpOK/f9nFi9sy27rWr4hw3dmVmY64YlaEPfDJ65vZ25fCtjWNVW5CUlaipMlHmBB5YirNwjqL/3N1A//n/Q0srLMwj7C9ZMJWfOGe3byxexyAN/dM8Pm7dzOellWZ2aKV4u6NPWx8eQjHgX19ST73vXa6R6bwAelo3ndG1aQHhpDfJOy3GJ3IbKu85YI6udk5QGvN4kYPtRGLjt442tF84Jxq6V5ynPrG4O7HBrj97r088uooY6mjvX5o7CM8JDhlFBQxTYU5A0/+jZVuIoHJnfrOWRklIltIS1J1CD53Yysr5gaor3Bz83tqOXtZCMeBObWenHPhQ+fV4Dbk+ifE8TIMxTNvjmaDTQAbNw/xRoekmM82v6VZUGuxuMElwaYyIBlOQuTbuzyx9Q7Z9A2nJ40NjKbpHU7TUpH/duLlYDQBT70+PGnMcTJbYaqD7rf5qUOaYoq7bp3D1n1xPJairc5LV3+Sm86vZUGjj8aYKbVaDuO1NCta3Jw4pwbHOfpsHDHZaFJx+/faGRzNXEe2d06wpyfBjesrmWrWmNvQXHNWNV/96b5DY5ZiXn3pZzcplekq+uDz/QyMpLjopAoW1LtxTVMQIOjWfOaDrTz4/ABbOyY4a3mEUxcHUXk6722t6BtzsG1NVdjCpcooqjgbNDTFDP7yynrSDnjMQwX4I164/cZWNu8Yo3c4zZr5QVqqLLkGCjENNIqn3xzOGX95xxgnz/dJwx4hZogEnERBUUrRM6rZuncCgAWNPqqDqqxvtlyu3HbiSoHfI8Gm2eIyIRq0GBiZHPgLeKaY7aChNqSoXeTLDlUH3KgW94EteeV7fr8TufmbHnv7k9lg00GPvDjIZadWEJ1ivEhrWDXXy99c08RDzw9QHXVxwZoYNaHSvz7vH4FPHVbI+eUdY3z8qiZObHNPW53/qgDceE4FjlaZwql52nIykVb84PE+HnlpEIBFTT4+dnk9EU9epjMtLMvAcfL3mr4dpR1cKnfNqcIH5ywLoJQ6MOfCmrcQxUqhWTUvyFsdE5PGlzT7ccopXVeIWSYBJ1FQuoYdPvXNdhKpzIXf41J8/sNt1ITKc2tB3zh89zddXHBSBQ8+e6jw9NVnVVMRUMiN6OzwWZo/uKSev//BnuwD5sq5geNu51viz+miQFhG7vXTNBSGOrpriNuAZU1uVrbVg84EBEs92KRUZhvz7xdyvvfxHpa1NE1rlz3taBT57dKzdV8iG2wC2NIxwcaXh7ny1HDRbZ9MO4rt+1M8+vIgVREXZy0PUxtSRXHd1ZqSf28JMdscR3Pm0jDPvDlM+4FGDcva/Cxv88n7TYgZVDQBp0WLFi0EvgVUAn3AjVu2bNma31mJ6WSaio2bB7PBJsh0c9m4eYjrz4qVXbcPw1A8+doQm3eMYxoGN55fRzLtUF/hZnmLB2OKD4pKSWDjeGkNSxrc/P2tc9jXlyToM2muckk7X5GlFIynFP2jNkGvSdSv0QXygN5Q4aKlxsPu7kN1Kt53RhUR77FdG+wy6hioNRwhXodlKNRRBuwKnWEo3twznjP+3FsjXHpyFJPiOe5KwSu7E/zzjzqyYw+/MMAXb2mj0v8OPyiEKEga6B/PdHSsClkE3PqYPr8iXs2nrmti/2Aaw1DURMxMjTSlmEiBx2LK99dCiKkpmoAT8O/Av27ZsuW7ixYt+iDwH8CGPM9JTCOlFL1Dub1/e4ZSJXdjPxWGobIPiC9uO1TkcPWCICfNqyOdfufXY2gsxVtdafb0JGiodNNW48JXTO/4AqPQ1IUVdeGDe0vK63wU72zfkOauH+xmYCSN21J89JJ61s71HjFYMdt8luZvrm5k864JdnXFWTUvyPw6l6zoTtHiZh9et0E8eSjgsn5llN+8PMLZy4J4zNJ4HR1Hs6DRlzO+am4QyyicAOpUJGyDezZO7qoYTzps2xencn7p1x0TopSkNTzyyih3/6YbR2dKHHzi+hbqQsf2+zympqXyYFkKzcCE4p6N3by4fYzFzT4+dG4t1dIJWohpUxRd6hYtWlQDrAa+f2Do+8DqRYsWVedvVmK6pdMO554YzRk/98QY6TJaUT8onXY4e0UkZ3zDqui7vh4axf/P3n3H2VWXiR//fM85t/d7p/dJQhICKSRA6CChSRUQBREELL/Vre7qrq4FQbGsrlt+/tRdd1UUAUVQARGQLtJ7D4S06b3P7ef7++Mmk0xuApPJlHvvPO/XS2G+cMOZe9r3POf5Ps8vH+zmazdu5/o/dvGNm1u46eE+MroAnn6FKDFJW/Hvt7VN1PhKZTTf+107vWOFE4gIuuD45R4+cnKMQ+scuCX4PGXlfsXXrmji7KNiHLMiyJWnV/PUxmFuuL+LbT35L0kKmVIKyzJQ+7gVLKt1c9TBu57iGipcnLI2hC6w+kfvRrH37D2JsQpRfDoHbW64r3tiufHgaIYf3tE+I3PalK34zq2tPP76CImUzQtvj3HtL7ZPo5PrXihFKqvY5wVXiAWiWKac9UDbxo0bswAbN27MLlu2rH3HeM9U/oBYrDBC1eXl0wzHLxBef5bPfgB+cX8XGrj05ErWHhTE51mYBbLXedN84qwafnF/J1rDhzZUcviyICGf4x0/t6Ujzm8f65009tCLQ5x3TDnLGwrjXBClaSFe47Z0xunoT+WND47ZHNKcH0QXM2eujjeHJ8Pb7Z3EkzaPvz48EbjY0pXk2JWROdmGA9XWm+CxV4d5ZcsoR60IcfiyAOWh/C6b/3CRm5aeJJmspq7MRSTwzvebQvWhkyv57q0tEz+7HAYrmn2Ulx/YmrqFeI1byEbiGbZ3JYgnbWrLXFTH5raCvhxv8ErbQN7Y5s4EtrIoLz+wjMU3to/Rsttyc4ChsQwD45qmJdP/7jd3xPnlQ928vGWUI5YGuOD4chor8zNIC5Ecc2KmFUvA6YD19Y3Oe4eS8vIAPT0j87oNxWBVvYPllzcA4DRsxkfHGR+d542aRycc7GHd4mZQ4HNAajxBz3jiHT8zPGbv9U3uyFhajkExaxbqNc7SirKgg97hydkuAY9akN/HXJnT400pogGLP2+f3FK7OuIoin2cyCq+fnMrW3cUyn1q4wgnrwnzkZNjqL0sDy7b8VyUSSToSbzz/aZQHVLv5PMX13PvswNURJycvDpEyGEf0P5aqNe4hSqegZ8/0Mujr+TOe7/H5CuXNVDhn5mMldGU4q32BJ0DaZbWeWiIWTiMXefj/hxvGkXHUJYtXUkCHpPmShcBZ2mk9IW9+QtyFlW5MXTmgM9HpcEw8rtFmuhp/9nxjOKa67fTvaNMyL3PDvBW2zhfuLguVy+qgMk1TkyHYah3TO4piiV1QAtQu2zZMhNgx19rdoyLEqM1OJSNQ+09aLLQ2LbGY2k85tTbOpcFTBZVTX7rUx5yUBleMDFmIeaMx6H52/NrcTtzt1Sl4PJTKqgILMzMzJKkNRceV0bIt+saunqRj8XVc5vtMF2dA5mJYNNOD744SH8BLfucaQ5Dc3CNg384v4oPnxjd0aGudH9fMfNaejMTwSaA0XiWG+7vxp6Bx6d4RvFvt7Xzb7e1cdOD3Vzz8238+Y2xaa++2tSV5vM/3sp/3dnBd25p5bqbWhjJT7wtSlUhkw+fUjFREzHks/iLs6tnpEtoxKv4wIkVk8ZOXRehPDj9fdw9lJkINu20pTNJ70h22n+mEMWsKJ4+N27c2L1s2bIXgEuAG3b89fmNGzdOaTmdEAuN09D848UN3PJwN8+9NcohTT7ef1xMuqoJMQu0hsaYwXc+3kzPUIag1yDmV9LppsSU+eAbVzXS0Z/GZSkqw1bRFAzf21bmYi+aXMWj0rW3mocaRe+oTfdQmrDPojJoYhV45oGYez3D+TXa3myNk8zoA27C0taf5q32+KSxX9zfzeFLmvHvZ2ZSRhv85J7OSS9p2/tSbO1KsbI+f9lssbEMzamr/Kxb7GcsmSUWsPBPs0vdnhSaU1f7ObjeQ9dAirKQg/qY44CCWU4rP1ilFDit0r7WCrEvRRFw2uEvgOuXLVv2ZWAAuHyet0eIgtZY6eGKk2NcfEIMl4VUSxVilgVdmmCFZDWVMr9Dc1DlzqlT8VxTq8MW9eVOWnp2pTycsDJExGdQTL/HTDAMxfNbk3z31taJ2+JFJ5Rz5rog5gxkTIjSURvLD9YcsSyAx8EBnzbpvXQaTmVsstMo/5HOagZGM3nj8WTpNNxRQMwHMZ8JzEywaSeHAc1lJs071xIf4M4tDxqcuCrEwy8NTYydvT5KbAFeb4WAIgo4bdy48Q1g/XxvhxDFRGuNK3dvFkIIsUC5Lc0/XlTHE2+M8tKWMY5ZEWTNIu+CzMIbTsAP7mif9MB6yyM9HLHUT1VQMhDELrURi8tOqeCmB3vIZDUH1Xq48LiyGXmBVxN14HUZjO8WFDphZYigR+33n+91wplHRLnlT7uaxRgq12VSzD1LaT50Uq6raWtvisYKF43l1l7r5QmxEBRNwEkIMbuUyrXMnu/i+kIIIWZeyA3vXRvg7CNCZDLZBVvPaCxpT3rI32loPEtVUKbFpUSjGIhrRsezRAMWARf7ddw7DM1pqwMcsdRPKq2J+o0ZqRsEEPbAtR9p5JY/9bKlI8FJq8OcuDKImsZ5qW3NyWtCmKbBH57uIxZ0cPmplVSFDMlunyceCw6ucbCi1iG7QCx4cmcVQjCYgFe3xekZSrN6kY+GmCVLC4QQosTYtsa2F3bh2rDXpDrqpKN/1/JCh6moCMmUuJTYGp7aFOe/7+ogk9X43Aafu7iBppixXwEArTVhN+BWzGS6uNZQ4Vd86swK0tlcFuKBvPDzOTRnH+7n5NUBHKbCxJZgUwGYz10Qzyja+tPYNtRGHfhKpGuhKD5ydxVigRtOwrU3bKd3OLf+/7ZHe/n0hXWsbXLKXEUIIURJcZk2//D+Ov7zt21s704SCVj89Xm1RLzI8vMS0jum+cGdu5ZOjiVs/v22Vr5+RWNBNVBRWuM0wJ6Bcku2DW5TIweyGE4ovnVLy0TdvmjA4suXNhD1zvOGiQVJAk5CLHDbu1MTwaadrr+3kxW7Fq54AAAgAElEQVRXNRZNByYhhBBiqir8cPWH6hlO2HidCq9jZosQi/nXP5zJ26d9wxlGEjYev9TqEqVLKXhu89ikJhH9IxkefGmIDxwbJpuVi52YW/l9G4UQC0p6LzeeRMpGSjkJIYQoVQ7DJuYFjyXBplIUDVqoPeJKsaBFwC2PPqK0GYbB5o5E3vgbLXG0lmCrmHty1RVigasvd+G0Jt+AzjumTNZ6CyGEEKIolfkUf3F2NeaOJx2f2+DvLqjD65C5jSht2azN4Qf588ZPXBlC6xlYuynEfpIldUIscOV+xVevaOK2R3vp6E9xxuFR1i3xSrc6MefcmUHM3s0wNoAqbyIRaCCjHPO9WUIIIYqMoeCog7ws+z+Lpt2lTohitbTGxYXHl/G7x/qwbc2p6yIcttgrx7+YFxJwEmKB01pTHVT85VkV2FphKi03JDHn3NkR7Lu/S6bjzYkx13v/jmz90XI8zjGlFH1jmpbeFE5L0VDmlIxHIUTRUWiiHoh6TECWToqFw21pzjsyxEkrQ2g0YY+SroVi3kjASQiRozWGTMjEPDEHt08KNgGkHvpf3JesIG4G52mrFqa2QZsvX7+VVCZ3Magrc/K5D9YTdMnFQQghhCgKWhNy7/p7IeaL1HASQggx/1L5BS51YgylM3v5l8Vs0Urxy4d7JoJNAK29Kd5sz98/QgghhBBCvBMJOAkhhJh/0TowJ9drslacRMoRnqcNWpjSWUV7XypvvG8ojdqz5ZMQQoiCoxQ4LIVhyDVbTI1hKExTwgJidsiRJYQQU5RFMZxQJG2V125ZHJi4uxL3Rddg1CxHeYI41p2LccRFZLTcpuaS29KcujY/yLe8XoqNCiFEoXNnhvC+fT/G7Vfjeu5mfMmu+d6kGZfMKjb3ZHlmc5Lt/TYZuTVNm1KK7lHNrY8P8T/39vLMxmGyWia4YmZJDSchhJiC/rjiR3d18Oq2ccpDDv7yvBoWlZsgE50ZYWsYDSzCedYXMO0UKdNHdgF9txroH4OxZJZYwMLvnJ96aratOXZFgOHxLHc91Y/PbXDFaVXURU3kYBdCiMJlqSw8cwvJl+8DINv2BuqNR/Fc9DXiZmiet25mZLTilkf7uffZgYmxq06v4qRDfVKnaBq6RzVf+MlWkuncd/fgi4N8/uJ6Dq6RDsFi5kjAqUgYhiJjK0xDo6VdvRBzKqMV3/tdG5t21LHpGUrztV9s5zufaCbqmeeNKzEpHGA4FlRsI6PhwZdH+cX93dgaQj6LL1xST1Vwft4y+hyai44N897Dw5iGwmNJ50ohhCh0zkQfqZfvnzSmR3owBtsgVhoBp+7h7KRgE8DP7+tizeJmwu59fEjslVLw2rbxiWDTTr96pIcvXlyHiT1PWyZKjaxVKAJjabj3xVG+fEML/3NvLz2jyHIeIebQ0LieCDbtlMlqugbS87RFopR0Dtr8/L5csAlgaCzD/7ujnfQBLCdUhiKeUSRtY1r3C21rfA6N27Ql2CSEEMVAqb0/IJTQQ8N4Mj8Iks5qkim5T02HvZf7ezY7DxsiSppkOBU6pfjd4/3c/Uwumr+9O8kzb47wrauaCbrl4irEXHA7FT63wVhi8kQn4DHnaYtEKekbyQ9cbutKEk9qHNN4YzueVjzw4jB3PNGHz21yxemVHFLnwlRyzxBC7D+tFPE0uCwlWQ8FLOWK4Vh7Nulnb58YU7F6sqG6edyqmVUZduTNxxorXUT9BgsqNXoGaA0rGrxYpiKzWw2DC48vw1K2rFAUM0YynArccBzufW5y6uhYwqa1P7+LkBBidgRc8Bdn10waO21dhKqwBJzEgSsL5tdKaKp04XHu/59lGIonNo7yq0d6iKdseofTfOeWVlr75ZVlsTMM6TolZo8N9I5pNnZm6B0DTe5YG0wofnhXN3/9/c1865Y2Ood1KSXMlJSMNrBXn4vrrL/HWnYczpOuxHH250iYgfnetBkTdGm+/OFGltZ5MBQcvtTPpy+oxWFIdGQ6KoOKr13RxEmrw6xZ7OOLlzayos4lwSYxoyTDqcAZBjhMg6Q9+Y2SJZNOIeaMbWtWNjj59seb6RpIE/KZVEcsrAPMGNn58GhLXbYFrTJocPkpldxwf9dEDadPnVODRjGWBr+TKS9rS2YV9zwzkDf+2vZxmsoCsjyuCGkUrQNZHn99GLfDYP3yAFVBNSMPBKapsO35KVAvCodG8fSmON+/ox2twVDwV+fVsqbZzX/+dlf9wjda4lxzwza+9dEmgq553mixV0nTT7LmSMz6o3ac26V1cmsN1UHF5y6qIZkBr0OhJOtu+jTUhBSfOL0MDUQjPnp6RuZ7q0SJkYBTgQu4NB96TwU/ubdzYqy+3EldzIGkjgoxdwygMqCoDOxMOzmA809B55DmyY3DpNI2Rx8cpDZiouScXpAsAzas8nPYYh9jySxlAYv2/jTf+XUrfcNpNhwW4byjowSc7358WAZUhB107JEFGwtaJffgsVBs68tw9c+2TQSFfvd4H1+/sonKwPRfPDl0AmffJuytz2FEatD1axh3ls3MBouiMzCu+eGd7RPHmK3hB3e2c92VzXn1C8cSNl2DGYKV8ghRyLLZ0g7CWEpjOUCehWZGJlPax4uYX3K3KHBaw7ErvNSWNfDy1jHqylysqPfgdcgFVohi1Tms+eefbJ1YM3/nk/1c+5EmGqPFtcp557IKiWMcOIUm5oOYz6Rz2OarN26f+F7vfXYAw4APnRB91y9baZsPnlTBa9u2kt5xfFVGHCyr9SAT8+KjDIPfPdY9abenM5qn3xzl3COC08qONAyFc9OfSd7/o13/nVAFnguuIW5FZmKzRZEZHs+yZ3windGMJ20cliKdmXyceVzFda8SQggxfyTgVAScBiytslheE94xuZSHBiGKlWEoHn9teFKBRq3hzif6+JtzKoviraRSuaDZy1vHUQoObfRSFZRlvjOltS+VF1e6//lB3nd0FO8U7tr1EcU3P9pES28Kl2XQUOGcUnaUKEyJVP41IZWe/nXClR4i+egvJo3poW6M/u1QIQGn+aQUDCcU6awm5FWYczTfiwZMPE6D+G7Hms9tUBk0uWxDJT++Z1eW/Umrw1QGTWQuKoQQYiok4FREpM6LEMVPKUju5WExmbaLZvreMaT5wk92ZdA4LMV1VzRRJityZoR/L9kDFSEHDkMxlYc8raHcryj37yyyUixH1sxJ24rOwSwDoxnKQw4qgkZRdunTts15x5Tx6rbtE2NKwfrlgQOYE9iQ2UvjEVsKy8+nrFY88eY4P72ni2TaZlWzj4+/t4rQHHQkDrkV//TBev7ttjaGxjKE/Rb/cGEdfhccd7CPRdVNdA6kiPot6mIOKdAsxAKklGIkCQOjGYJei5BHL8TphZgGCTgJIcQcymY1xx4S5A9P90/KYjlnfQy7CLKbTNPgvuf7JoJNkFt68fDLQ6xcHJzHLSsd9eVOltV52NgaB3IFfD/23mocxuwfH8msomc4i8uhiPkNjCKcTWa14q5nhrj10d6JsU+eU8Mxy9zowj/F8iyptPjnSxq484k+XE6Dc4+KUROefoZJygrjXHce6adu3TXo9KAj9Qe8rUrBaFLRM5zB5zYo8xtSm26K2gay/NedHRM/v7RljFv/3MuVp5ShZnndstaaxRUm37yqkdGETcBj4HPkCk5bBjREDRqi7p3/9qxuixCi8CileLs7w7/8qoXxpI3TUvz1+2pZ3eiUS4J4VxJwEkKIOVYXMbnm8kZuf7yfVMbmnKNiLKosjkYASsHQWCZvfGg0f0xMj8+h+fQFNbT0pBhP2tTEnFQGjVkvljUQV3zrVy209+WyX85aH+W89WHcRTZT6B2xJwWbAP73Dx2sqF80J9kiM80yYHm1xcEXVqMUOwLT0/89shr0oWfgCsTIvHwfqrwBa805jLnLD/gS1D6k+dqN2xgZz6IUXHRCOWesDR5wR8+FoL0/P+vsiddHuPjEsiktpT1QWueuPT5HLpNSavMJIXYaTcG/3trKeDL31iaV0fzHb9r4zieaiXrmeeNEwSuyaaQQQhQ/haYpZvK351YCuWUzxRBsglwnk1PXRXjyjcltc08+TGq/zCSvpVlW7dg1MNtPf0rx6z/1TgSbAH7/ZD+HLfGztMi6UY0k8peGpTKaeMom5C7eWmPanrlltwkzgFq8AceSE8hikZiBS1DaVvzgjjZGxnPfv9bwq4d7WNXso6HIGiLMh4jfzBtrqnThKq7TTwhRgobGshPX9p0yWU3/cIaoRy5S4p3JDEAIIeaJtu0dwabisqjC4p8+WM/iGjeLazx87oP1NJfLhKOYJTLw4ubRvPG23uQ8bM2BqQg5cDsnT2+qI04ifpny7E5rTcq2mKlu2PE0bO3KP176R9Iz8x8ocQ1lTo5cFpj42e00uPK0qjkrHC6EEPsS9Jr43JPvoYYBkYDM/cS7k6NECFFQtDLQWhdl7ZiFwlJwSK2DFZfU7bavZH8VM7cFqxb5efSVoUnjtTHXPj5RuEJu+NKlDXzvd+109KdYUuvmk2fX4JJCx7PK64QltW42tSUmjZeFHPv4hNidx9J8/IwKzjkqSjxlUx1xEPYotKxtEwIAG0U6q3Bbtiz5nGMBF3z6wjq+/atWkmkby1R86pwaot6pNTMRC5sEnIQQBSGjYWN7ml8/0gNKcdHxZSyrcRZlZ6mFQmmbYlmglEXRN2ozlrApD1oEXFKjZBKtef/xMTa1xekcyC2rO+PwCI3lxVFbbHdaa+ojBl+9vJ54CnwupIbQHLCU5pNn1fCNm1voHU5jGnDZKZVUBSWzbKpcpqYxZgK55XUSbBICULC9z+bn93fRPZjm9HURTlwZxOeQ82OuaK1ZVmXxnY830z+aIewziXjV7C/3FyVBAk5CFDilFsYbzs3dGb71y5aJn7/5yxau/nAjiyvy61oIsT/StuKuZ4e49U+5QtJ+t8mXLm2gOlQs4bK5EfXAtZfX0zOUweUwiAWMgl/OoxQMJiCe1ET8Jq7dOvk5DY3T/Q4fFjOu3A/XXdFA30gWr9vIvf0ukfuXUtDRn6SlN0vAYxLzUTQBdyGKWc8IXP2zrexs5HvzQz2kMprz14cWxPy4UGgNIbcm5DZ3DQgxBRJwEqJA2Sha+7O83jJOxG+xvM5D0FW8F3elYCiuaOtP4bQUNVEHHiv3+1iWwR+fHcj7zP3PD7DsrAoyM1VkZD8opchqMA3QdvF+7wI6BrMTwSaA0USWH9zZzpcuqcMhy6wmcZua+ujOIG9hfzfJtM0TbyX40V0dpDKaqoiTz36gjnLffG/ZwuaxNHWRHVlNJfRA0jqgue7GtxhNZDEUfHhDBe9ZGZAsXCFmWUtvciLYtNPvn+zntLVhvJacf0IUOgk4CVGAlILXWlL8y692ZfxUhB1cfWkDgSINOnWNwJev3zLRUvWgGg+fvqAGvzO3tCnky78chfzWvLy9GkspnnxzlIdeGmJJtZszj4xSJg+xRWtvRYu3diVJZMDhnIcNEjNia2ec/3d7+8TPnQMpvn97O1+4uFaW0IkZlbQV//nbVkZ3dEC0Nfzsvm5WNHipCcuSQSFmk9uRf46FfCaWvDASoijIXVKIApTKKn56b+ekse7BNNu6i69jFABKceufeiaCTQBvtcd5qz1XXDabtTnlsDAOc9cCBYelOHFliGx2bicUWil++0Q/P723i62dCe57fpCrf76NkSL96gWU76Vo8fJ6D16HLIgpZp39qbyxtzsSjCblIUTMrPGkpmMvx1vfSGYetkaIhaWh3EltbPLboStPr8JlyrVeiGIgGU5CFKCshtF4Nm88kSnOm2vaVmzuzI/YtPenWNvsRmtNdUjx9auaeGXrOChY2eilMqjmfEXGSJy85X0j41la+9IcXCPdlopRZdDgqtOr+Nl9XWSymoqwg4+fWY2p5n6pppg5sWD++VgVce4IJBbntVIUJp9LUVfuorVn8n2sbC/HoBB7k8goWvvSJNOampiDqHdh1OecCX6n5vMX17O5M8nQWIZF1W5qw0YprdgtCamsonMwQyqjqY46pKi7mCABJzGnTDMXQLClJs478jo05x5dxs0PdU+MWaaisbz4WpQDOA2bE1eF+NXDPZPGl9d5JiZcWkNlQFG1yjfx83xMJpTKfdepPYJ7linZMMXKMuCkQ32sXtRMPGkTDZi45c1o0Wuu8nDhcWXc+miuPpfLYfBX59XglH0rZpjT0Pz1ebkOfIOjGSxTceXplVQGDSS4Kd7NWBr+47cdvNEyDoDLobjm8iZqpHHFlAVdmjWNTkDWwReisZTih3d18uLmMQAifosvf7iBmHeeN0wUBAk4iTmhUbQNZHn01WFMQ3HsIUG50b4D24b3rArgcijueWaAirCDD55UTnmAopzbag0nrQzQNZDikZeHcFoGl55cQUNZfsv1+X5jFfTAB04s54b7dwX76sud1MaKrz282I3WRDwQ8cgDYqnweUzOPiLE+uUBRuJZKkMOQh5pJS9mR01I8Z9/eRAtXXF8HoOoRzLpxNRs6UpPBJsAkmnNDfd385kLqjDkGBIl4K2O5ESwCWBgNMPtj/dz1SkxuScLCTiJudE6kOWLP906EUz4/VN9fP3KZmpCirG0YntPikTKprbMSYVfAlGQ6/Rz6mo/Jx4awDRAabuo57Z+J1x1ahkXHhfDMhRBty7ITDdta046NEB9uYsXN4/RUOHi0EaPdEIRogCZSlMVVFQFLUDPe8BaFC6HqUHnlnhPh9ZQHnZCeueyOjnYxNT0Dec3rmjpSZDOgsvcyweEKCKGoWjpSeSNv9EyTiobYy8138UCIwEnMessy+APT/VOehCwbXj45SEuOCbKt3/dyuaO3IXKNODqy5poisnVCXJLDy2lS2Zeq7Qm7AbQ2AVcPsdpag6ucXBIXQSt5/YhVilIZg2yNnidGl2AQTkhhCgWJlk8g5tIP/1byCTxH/4+kmXLSStZmiPmRnNlfjmEE1eFcVvzn9UtxIGybc1BtZ688aNXBHFaoAt4vi/mhjzVi1mnNaT30mksnbbpGMhMBJsAsjb89N5OslqynMT8s+25DTbZWvHS9hSf+/E2/vaHm7n9qWHG03IuCCHEdHmGtpC45WqyW58n2/oaid9+HVffxvneLLGA1EYt/vLcGnxuA6XghJUhTl8XXnBLjZSC0ZRic0+WrhGNLXP9ktFc4eSi48swd0QWDlviZ8PqkLw0FYBkOIk5kM3avPeIKE++MTJp/KTVYdr30ma4eyBNKgseOTrFAtM6kOXbt7RO/HzLIz14nIrT1gQKcvmhEEIUMtNUZN96LG88/dwdON67knR+M1ixACgFg3FFW18K01DUlc1uRy1TaY5e6uHQxmayNgTcuYzvhaZ9UPPVX2xjNJE78c4/NsZZR4RwSvpD0XOZmnOODHH8oUEytibiMzBLZXmGOGDySC/mRFO5ydWXNfL7J/sxDThrfYy6qIFl5qe0n7I2jNchacZiYVGKSUVFd7r7mQFOODSI05AT4kBZloFtF2btsOlQBoylDAyVq/m20N6WlzKlFN0jmjda46Bheb2HisDCaaPeM6pp60vjdRnUlznwTLOGntaAw503rhweNFL0e6HqHoUvX7+FsURurU9N1Mk/X1JP0DV7x4Nta3yOHT8swMMuoxX/9fv2iWATwG/+3MfaJQEapYxGadCasAeQa6vYgwScxJwwgMXlJp8+rxLIZT0BVIUMPndxPf9zVyeDYxlOWxfhtLULL81YCK0hEnDkjVdFnDhM5N59ADK2YnN3mgdeHKQs6OCEQ4NUBo2ivs7EM/DQy6P85s+9uBwGl59SyWHNbiwJTJaEjmGbL/5kK6lMbn86LcXXrmyiKlD6S1C29tpcc8NWdkwTOLjBy9+eV413Ghkotq0xlhwNz94O2UxuUBlYa88hsZel/jNNKYVhqIk5j5h/ylD8/sm+iWATQHt/ipe3jnPcco+87JwliTRs7swvLN0/kqYxll/jSghROiTgJObUnpMuAzik1sE3r2okY2u8DiS1SSxYS2tdVEWddO5YauowFRefVJHrUCimRSl4tTXJv/5611LFe54Z4JtXNRHzzeOGHQCl4Lm349z0YDcAiZTN//1dG1d/uIHFFXJbL3amqXjoxcGJYBNAKqN54IUhLn9PlEymtK4HhqEmsg4zWvE/93Sw+1Th9e3jbOtJcXBNfkB+KuL+ejwXfwO9+Wl0Jo25+EjiwcZZD+L3x+HZt0Zp601y1MFBFldaRdmtKW0rkhnwOsEogTcftlZs2Uvgo7UniTrYW9QvIgqZx6k4uMHL69snZ3KXh6Z3XgshiofMTMW80xqchp1bwy33ebGAhVzwpQ/Vs707RTJjU1/moiIgMdgDkbYVNz/UM2ksmbZ5sy3O0Uvzu6oUAxuDPz43kDf+3KZRDqqKlMySwYVKKUX/SCZvvGcozVCcXctyilzKhm09Gd5oGacm5mJZrRvDUHTtpbbjaDwLTO8XtzWMeesxVjXkfrZnv/PrcFLxlZ9vY3A0tx8feGGQvzi7muOWe4vm/FQKtvXZ/PDOdlp7U6xZ7OfK0yqJeOZ5+9WOqN00X8QYaE5eE+Z/7+6cNL5mib9o9k0xMrH5+JlV/MsvW+kcSGGZistPqaQqVIRRWCHEfpGAkxBCFJCAU3NI3a4HKwk2HZjc95f/JRbzc4WhoCbmmtThE6Ay4pS38yUgk7HZcFiYJ14fnjR+aJOPFzaPceyy4gyU7k4Zij+9PMr1f+yaGDuoxsNnLqrlPWvC3P305IBqbVl+vcf9NZfBhO09qYlg006/eKCbwxY34THnbDMOyMA4XHvDtolMuxfeHuV7t2f5/AdqsNTcX2eyWrGpK81tj/aiFFxwbBlLKh0Y+7ktWmsOP8hH92CMu57qw2EZXHJSBYvKHchbz9lV5oVrL6+nfySLx2UQ8SqZ5AixAEjASQghRB6lIG0bmIYu6m46TlPzgRMr+Ldbdy2pc1qKZXXF+9CubZtzj47y9MYRkuncW/6yoMWqZt+8BZyUgqG4omMghdtpUB2xpND9AWiqcHDl6dXc/3w/WsNJayI8v2mEww8KoErgGW04Djc/1D1p7K32OO19Kc5ZHyWV1jz04iAhv8UnzqwuuiyI7F6CW5mMBl08xXQ7B9OTlnUCvNUWZ3Dcpsw397XEtvSkue7G7RM/v7ZtO1df1sji8v2P4PkcmouODXPG4WEMBX6nNF2YK25TUxPemaUm33lJUdA9rNncmcBpGSyqdhHO79kgFiAJOAkhhMAwFK5UPyqdoM+s4MFXxnnwxSEaK1y8//gyqkOqKOeGWsPKehefv7ie+54fIBZ0smFNmHJ/cXf8qg4afOujTWzvSeEwFQ0VTgLO+ft9OobhKz/bwngyFwA7YlmAj55WMa1CzyL3UBbxmzRWulEofv1wN+ms5rINFUV5Hu4pa+u8YAZAOqsJODVXbIjx/uNiWObODozzsJEHoKHciddlTJwPABceX4bXYRfN7+Jz5wdyvC4Dt2Pu6x9YlsG9zw7mjd///ADLzqyYXl0zrQk4J/5WzKFkVtHan2ZkPEtVxEll0EAVSSBW7FvrgObL128ls6MhQ8hncu3ljUSK9/3erBlLKbb1pBhP2dSVOakMlPY5IAEnIYRY4CwyuLY/TfL+/0bHmrjV/QnufSkOQNdAipe3jPGtjzbtaHdbfCxDc3CNg5UNVWida15QzMEmyC0LiXoh2rhzqdH8/T42ihvu65z0cP30xhFOXRtmeXWJFByaY1rDIfVOTCPA75/qZ+1Bfs5eH6MyqIolQeYdhbyK4w8N8cjLQxNjfrdJTdQJaNAafxEHA6JeuPYjTdzzTD8tvSlOXRthZYO7qK47VSGTk9eEeeCFXYGej723mqAb7DmuW681+N35WW5+j1WUx8dClswqfvLHHh57LbdkWCn47EX1rKyXJeHFwDQNlCI/yKsUtz3aMxFsAhgay/LylnFOPMQr5+luxlKKb9/ayuaOJJArk/ClSxtZXFEk662nQQJOQgixwLnG2kn+4T8AGGo8ifvui0/65/GUTVtfmnBdcQcPSq27V6FIZsirJwXQO5SBAgk42UDvqKZnKE3YZ1EVMjHnoQ7N/nAYsLLeyarGahRzU+x6riitufjEGDUxJw+/PMTiag/nHxMj5C6+bKa90Roq/PCRk2OAQuviC3I7DM0lJ8Y4YWWIobEMlREnVSFjzgtrj6YUXYMpTlgV5sEXByc6GFqm4j2rQ3ndj8U7S2QULX1pBkZy+7Q2YmLN4fLn9oHMRLAJcufKf/2+nW99tAmvPJUWLKWgY0hz3/N9DIxmOG1dhMUVjoljx9aK3qF03ucGxjIoVdwZ5TNtW09qItgEuZqiP763k69cWo9Dleb1TE5tIYRY6IZ2desxs0nczslLQQCcjrmv2SHmhjIUwwnIZDVhj9rvtG6PA45aEeS+PTrn1ZUfeKHnmaAUvLglyb//pm0imPGBE8t579pgwQedALStSyXONInfCWetC3DqmiAOE9ClEWzaXS44U7y/lMvULCo3YaJO0tz+Lr1jcM0N2xgay1AecvDX59fR2ZdCKThssY+qUGlk/M2VtK248eFeHnpxV2bhladXcfKhM1v/z50dxRzcBqlxCNcR91Zh69wcYiyR/0A9NJYlldEScCpgXcOaL/x0K+kdS6Gf3jjCZy6qY1V97j5vKpsz10f5/u3tkz63ZpF0f9zTWCKbN9Y7mCaT1ThK9BworiqMQgghZp43PPG3wY13cNlRk+94zVUuamOFkakiZlbKhj++OMqnf7iZv/vhZn50by8jqf0MLmrNeUdHWbXIB+SKsl91RhW1kcKYOQ0nFD+4s2NSMONXD/fQM1KabxKLida5TJqSizSJA6aV4leP9DA0luv21zOU5t9vbWVprZsz1waoKpHlpXOpezg7KdgEcMP9XQzG98J6e7cAACAASURBVPGBaXBnh7Hv/TeSt32V5J3/SvLGz+AdeGvin1dHHJh7PH2uXuQj6JaXWoVKKXitJT4RbNrp14/0kt0RStAa1jR5+OgZVUQDFnVlTj73wXrqo6W7TGy66stdqD0O91PXRfCW8DS7MGaDQggh5k06WI916ClkXrkPPdzD0f23Uvuhj/Fau01lxMnSWhdeS2b2pWhrT4af7daa/tFXhqiJOTnn8MB+xQBCLs2n31fF4FjuDV3Io9AF8lZzLGmTSOUHl4bHs1QFZRokRCFKZRQbW/MjIVu7kxxUJeftdOyZuQyQzuhct9MZCviYfVvJtL66a8DOknzwf3Cdfy1J3MR88MVLG/nhHe10DaY5Yqmfy06pxJDoYUHb29Gh9hh3W5r3HOrjqOV+DAVOo/iWEs+FqqDBFz7UwP/+oZP+kTSnrotwxrpwSX9XcsUWQogFLmV4cB71YVyHnAzJUZzBKha5fTRXy7r7UmYYio0t+Q90f3p5iNMPC+YyT/aDiSaWS3IqmGATQMRnUBVx0jmQmhhzWoqKsEyBhChULkuzfnmAu5+evFS3qdIlCXHTVBm28LmNScvaltS4ifhmsPNgciRvSA92YtgpMNy5/2aFyXVXNJLMaPxOhaKwsk2VobC1wkDLHIhc9tKKBi9OS03qLnrRSeUYe+w729a4dswd5KvbF83SSouvXl5Pxgafs7DmTLNBZltCCCFIKTep4KJdA3ri/0SJsm1NTSy/ztLiGg8OU5XMbNFlaj5zUR3/8ds2WrqTxIIWf31eLRHP9H7FtK3oGsoylrSpCluEPRKYFWLGac1ZR0bZ3pXkte3jmAZceHw5DTEHcm+anpAbrv5wIz+5t4tN7XGOWBrg4pPK9/vlwjuK1OYNWStOJG0GJnab1rnsF6cTCm1f9o3Dvc8OsLF1nONXhjhqqR+fs7C2cT5UBBTXXdnEwy8NMTia4T1rwjSXSxjhQDgNjdMAXVjx1lkhR4oQYlaMphRbu5IMjGZpqnJRGzElZVrMCcM0UCDdi6ZgWa2bpbUe3mzLZTr5PSbnHxMruRlQhR++8qE6RhIar0vhsaZXoDqRgZse6ePBHa3iXQ6Dqy9rpC4s9UeEmGkhl+YzF1bTP2bjMBURb+kEwueD1lAVVPzj+6tJZnINH9QMf58Jfz3ucz5L6oEfoceHsZYfh7H2fDK68K+Ro2nFNTdsY3A0Vzdsc0eC7V1JrjglNuPfU7HRWlMZUFxyQgSllHT9FftFAk5CzBHDyL0FXwj3rPGM4ru3tbOpfddynb+/sI7DmlzFnQmgoG8UuofShLwmFcG5bScs3llWKzZ3p7njiT5cDoOzj4rREDX3u+vaQuJ3aj5zYQ3tAxlSGZvamJOgqzSvUw5DE/UCTP/3a+vPTgSbAJJpmx/d1cGXLqnFKoKOd0IUG0tpKvw7ghWleGGaByY7OsLNwteZxWS8eh2ui5dh2ClSjjAZXRw9qtr70hPBpp0eemmQ9x0TJeKZp40qMNlscXfeFPNDAk5CzLKsVmztSfPMm6NURBysWeQr+RtXS096UrAJ4H/v7uTbH2vCbRbnjUopxRvtab75y+3sTJx537Exzj0iLEGnArG5O81Xf7F94uenNo5w3RVN1EUKZ7Ibzyg6BjIoBdVhC3cBFGN3Wztan2NyIMGYhWBwLJM3trUrQTIDVgl3mBFC7J2NQdrWuE25du6kNSQMf64XehF9J6aRn4VlGgpDKYrqFxGiwEjASYhZpJTihS0J/uM3bRNjYb/F1z7SSND17jcvpXIPqPGUJuBWRfMGPZHOT7UdiWdyb0aKtEPqWAq+d3s7u6/S+u2f+1i/LEBtuHACGguVYRrc8UTfpDGt4bHXhrnkhMiOt3IHTilFIpubRzv38wFjMKH41i9baOvLFa9urHDx2ffXEXQXx3ktoDKcH1U6fGkAj5N3fR7RKPrGNGOJLGVBC7+ziB5QlSKRBrcDyTIRgtz8rG1Qc+MDHbT3pTj5sDAnrQzil3o/Ras2atFU5WJrZ3Ji7Pxjywh7S26VuRBzSgJOQsyiRAZ+fl/XpLHB0QzbupOsrM8v1juJgk3dWb73uzb6hjMsqXHzqXNqKPPN4gbPkLqYE4epSO/2kH/KYRH8ruJ9VomnNEN7yW4YGstQG36XfSnmhMPKD/w5LGPGjrlUVvHUpnFueaQHy1BcuqGCVQ3uKWW4GYbisdeGJ4JNANu6kzy9aZRTVvqLe6npAlIdNvnkOTX8+O5Okmmbg2o9XLahAuNd9l/GVvzxpRFufrAbrSHoNfnihxqoCs5vXROloH8cOgfS+NwGVSEL5x5ZqP1x+PWfenlx8yirmn1cdEIZ0RLP0hXi3fSNwZev3zrRtetXD/cwEs9yyfGR4p3oLHBuS/OZC+t4Zds4WzsTrF7kZ3GVo+Q7iAkx2+S1vBCzyAZS6fwbVWYK2RYD4/D1G7fTN5wLcmxqT/Dd29pIF0HhxTI/XHN5I8vrPYR8JucfW8b7jo4W9UN10GvQWOmaNGYoqJRgU0GwszbnHBVD7XZ6WKbiqIMD2DM0WXytNcF//76DgZEMPUNp/v22Njb3pKf0WcNQvLp1PG/89W3jmGbhn9Mix1SaY5a6+e4nmvnuJ5r5/AdqCE8hQ61jKMtND3RPPIcOj2f5/h3tpO353fdb+2w+86MtfOPmFr74021c/0APyeyubUpkFd+6uZVHXxliZDzLn18d5ps3t5LIyjErFrbW3tSkFvEA9z7Tz2hyHx8QRSHo0hy7zMNl74lySJ0Dt6RmCHHAJOAk9otWiuGEYiSlUEomnO/G54ALjiubNOZyKBrKXfv4xC5dg+lJGUIArT1JBsbmJq9XKcVoStE2YDOchP3Z3VpDXcTgny6q4dsfa+KCo0NFn2buUDZ/875a6ity+y7gNfnsB+qJ+ubpPFCKoUTuf/u1c0pYY8zkuiuaOPPIKO87JsbXr2yiOpT/3SiV60azpTdL94jGnkIQ1zAN7nlmIG/8sVeHMc13v5VmszbHHBLMG1+/PFAU3fTker+L1hBwacr8U1/m3DecH5jc2pUkPrV45axIa4P/urOd9G4PzX96eZjW/l0b1TOUpWMgNelznQMpeoayc7adQswH0zQw9lLTZyeXI/+673ObmEVaNkDsojUztgxfCCFL6sR+GEsrfvd4P/c+O4BpKt5/fDknrwrgKtIi0HPBtjXHrfAT8Jrc/cwANTEn56yPUuZX75rtE/Tkz1q8LgOvc/Yf/JRSbOrO8J1bWhhL2LidBn97fi2H1O5fVVwTjWkW39p3Ext3vBOGOsEbJhWoJa1clPtyrdWH4jYep4HfybxkbY2nFQ+9PMyjrw7R0Zfi3KPLOGNdEM8Cv6IrNHURg0tPjKB17vzbW12djiHNV2/cxsh47qH5/GNjnH1EiL08P0z6sysjTl7dNjlLqSLimNIxoDWsWeTlPavDPPRSrsvZqWsjHNLgKejMv8E4vNYSZySe5ZAGLzURQ95UTUNZKP/aubjajXceEyRTGU1rbypvfGg0C+W5i4nLsff7jWsO7kNCzIdco5cMj73WTyzk4MilAcr9+avk6sscNFa62Na1K6XpytOr8Fqyok4IIXa3wB9PxFQppXjmrTHu3vGG385obnqwm4ZyF4fUSWued+K2NEcd5ObIg2oxlEbbekoPmBVBk3OPjnH747lCyErBJ8+pIehWM7ZEaF9GkkwEmwASKZvv3trKv368mXCJ1+5QSuHueIHkHd9mZ7TCsfZsWHsRaeXCYWjKfLmOJfMxqVQGtPVn6B3OsKTGyxmHx7jnmX6aKt0c1iTL++Cd30xmtOK/ft8+EWwC+M2f+1i7JEBjbN+hlGxW894jIvz51SGSO5bJ+t0m65dOfcmez6G5YkOMc4+OohREvApVwE8mgwn40s+2T6pd9qVLGzioUqYO+6sqZHD5KZXc8EAXtg2RgMVfnF0zr40gPE44YlmApzeOTBqviu66jpT5Dc48MspdT/VPjJ15ZJQyX5G1nyoB42lFa1+aVMamNuYk6n33F1di/ygFr7Wk+PYtLRNjtz/Wx9evbCLqnfzveh2af7qolk0dKXqH0xxU46Yuask+EUKIPcisUUyNUjz80lDe8LObRljVWFYUS0Lmk9agsPcrQGEZmvPWhzlqeYDBsQwVYSfl/tkPNgEMjmUngk07pTOa/tEs4b1kXpUSd3qA1B+/z+4PU+nn7sS19DjSgaZ5266dOoY0X79p20S3vIdfGuSj763myTeGOWJJBZmMnIvvJJGGtzsSeeN9I2kaY++81LUqaPCNq5rZ0pXANBTNlS6i3v17m63QxHY+uBT4g8lb7Ym8Qvk3PNDNly+pw0SOs/1hKdiwys/aJT7GEjaxgInXMb/739CayzZUMBbP8tr2cbwug4+fWU3VbstQDaU5/+gw6w7y09aXojbmpKHMwiiSjqmlYiSp+JdbWtnWncumcTsNrr28cd6LzpeatK248cHJjV7GkzZvtSdYv8Sd9+/7nbCm0YlSzh2XczkvhBBiTxJwElOkOajWw6b2+KTR5io3ti0PHrPFYeSWCNVFdr5xnpvJTNBr4HYaJFK79q1pQNhX2sEmAJWOoxOj+f8gPgSBud+e3RmG4s+vDLF7fFdreH7TKMcfGpSaA1PgccKKBi+vbd9jadxeljztSWtNmQ/KFrl3G5vxTSwYiWT+LzcWz5K1NVMoWzXn4hnF9t4Uw2NZamJOqsMmRgE9ACo0US9EvYWTHRR2az57YQ1DcRunpQi6yXup4TLhoEpLMtvm0cb2xESwCXJZx7c+2sunzqoo6CzJYqNRk2qa7fRujV5kFwghxL4V4JRRFCJta05bFybk2zXhrCtzsqrJJzfaEhRyw9+eX4vDyr09NQ34q/NqiXhL/21q1hNBlTVOHjRMCFXNzwbtRilI7WXim8loltcXdi2gQmGi+diZVVSEcwEm04CPnFpJVVhuh3s6qNaNscfXct4xZbgKMO6cyCh+8PtOrruxhf/7u3Y+/+OtvLgtKcXOp8BUNlEv+J16TjJoxf5RStE9kF9ra3t3ksw8dzksNU7D5qITyieNOSzF0tr87CYhhBBTI6+rxJTFvPD1Kxtp60thGoraqGPelwSI2aE1HFLr4Lv/ZxH9IxlCPpOIR6EK5K38bErixv/evyN1z/ewu99G+SK4Tv9Lxl0V871pZLOa4w4JcvfT/ZMCveccFcNrSqbhVJV54WsfaaBvJIvXZeQCqRKsy1MVUlxzeRO/fKibgdEsZx8VZd0i7z4Dm8pQZGyFZeRq1c2ltoEML7w9Nmnsf/7Qwbc/1oRXZjpFTSnQysBQGnuWszjH04rOwQxOS1EZMnEY839d0FqzvN6bN/6eNWFcpkaSzGeO1nBYs4d/eH8df3i6n7KQg7OOiFIRUHKLKGJKKRIZcFnIvV6IeSDTMLFfAk7N8uqdS0/kol2qNIq2gSyPvDKMUnDCoaFcwcwFsstH3dW4zv0SztQwtsPLmOkvmDlKXcTk2subuPPJPtJZzVnroywqtyj1naOUIqsVpgF6Bp6w3KamdmdWU6Hs3EKjoTFq8NkLashqjcPYd1fGwQTc9/wgz28aZd1SPxtWhwm55+57jSeyeWMj41nSGWSmU8TiGXhxS4L7nh+godzFGUdEqQzMzinbNw5f/cU2+kdydcuOPjjIR04p368XaxmtsG1wO/KXJh6IhpjFJ8+u5mf3dRFP2Zy2LsrxK6besEBMndPUrG5wclhzDZALcsotongNxhW/fbyP5zeNcmiTjwuPi+UVgBdCzC6Zhgkh8rQOZPniT7dOTLLuebqf665qpja0cNL3k8oNrh1p9AU02VRoGmMGf3NOJRqwszYFtYGzYDyteHbTOH98boC6chfnHR2lKihvnOeKwsZ6hySwZFbxr7e2TrQHb+lJ8sqWMT73gVqcc5QhUh1z4jAV6d0yYNYvDxDwUOqnR8lShuLBl0a4+aFuAN5sjfPYa8N886PNhGc8mKm49dHeiWATwOOvD3PCqhCH1E6lvhu82ZXh5/d1MZ6wOfeYGEct9eG2ZmY7LUNz7HIvqxc1k7Uh6Np38FfMDFua4RS9lK3419t23Zv+9MoQG1vG+epHGvDM0LkphHh3UrRCCDGJZRnc/czApIdLW8ODLwxiFmKl4AUqm7UXxIRYKcV9Lwzxoz90sLUrwaOvDPGl67fRN/7unxVzo3soOzGh32lTe4Kekfyso9lS5oOvXNZIc5Ubp6XYcFiYyzZUYMhDedEaS8Jv/twzaWw8abO9J7mPT0xf0obXt4/ljbf1JplKGbDWQZvrbtzO9u4kvcNpfnx3J8++PT6jNcRsW+O1NAGnlmCTmHGGobCs0prj9Y7k35u6h9J0D2X28QkhxGyQDCchRJ7MNLq0CDEbxlLwu8f7Jo0lUjatPSmiDc59fErMJcvc+0O1w5i7jEitoT5q8KVLakllweug5JdK7nwBkC3RwLMycsdWMj15P1qzcFy5TDhiWYC7nx6YNN5U5X7Xw0gpxavb8iPgdz7Zz/qlXqyFkxgsitRAHJ54Y4TNHXGOPSTEkmoXQXfx1wdzWkauBpzOHxdCzB0544QQk2QyNmccEckbP3lNuGQfbEThUgpcjvxb1b6CHGLulQcNjlkRnDR20uoQMf/cTzEslcsCKe1gk2L7gM1P7u/lp/f30jpgoynm80ExnlFk9/gdfA64dEPlpLFYwKK+bBYCzVpz5hFRltV7ADAUXHh8GY1lU1lOpwl681s3xoIW5hwGXQ+EUrnsZmnquPCMphTX3rCdmx7s5sk3Rvjura38/plBHnk9TjxT3AdEzKc4e3100tjJa8KUB+XxV4i5JBlOQog8jTGTay5v5K6n+lFKcdaRUeqjJlIMRcw1nxMuP6WS79/RPjFWEXZQX+5EjsfCYCnN5RvKOOrgIG+1jbOszsuSahemkv0zG1oGsnxptxp79z8/yHVXNu0qgl9EhhKKW/7Uy5NvjLC42s1HTqukJpSrz2bbmvUHeSi/pIGn3hyhNubksMU+Aq7ZOa7Cbs0/vb+GvlEbh6mI+BRqioHLFfUewn6LwdHcUh3DgA+cUI7Shf+SZiyleGHLOM+9NcqqxT7WLpq971gUnpbeFH0jk5eY/fGZfs47tpxnNo1zwsH77kxa6BSac9eHWb3Iz/aeJLUxJ80VTiy5NwkxpyTgJITIYyhoLjP5m3Nyb5ezC6AwdbFIZhWtfRn6R9NUhBzURq2SnjzZtmbdYg9f/nADL7w9RlXEycomDwFn6f7OB0Kj6B/TjCWzlAetOSuM6nXAmkYna5tdOzpnFd7+SduKtv4MPUNpogEHdTELl1l42/lOcjX2+vJq7D3wwiBXbCgrqizULAbfv7ON17fHAXht+zjX/Hwb//KxZoI7Ah4OA5ZVW6yoi2Hbs1+7yFKaysCOrI79+G+FPXDt5Q1s7kgST9ksqXEXRWODLIqf/LGbpzaOAPD0myM80ejl7y+owaGK51gS07e3Q3Tn2D3P9HPsch9GAV7Pp8plwtIqi2XV1o7zsXh/FyGKlQSchBD7VEwPLwtBVitue6yfP+xWZ+TDGyo4bc0s9QkvEA5Ds6TCYmlVGK2lRfW+ZLTi3ueH+eXDPWgNkYDFFy9poNw/d9uwe5t2jWIorjEMCHkUei8t3JWCwQS09KQwlKK+zDkr2RUaxX0vjnDTg90TY2ceGeWiYyNFl4m1t++xGEvsDYxmJ4JNO40nbToH0gSrJk9Pi+FeFHbD2mbXxM/FcJ3qHbEngk07vbptnO6hTFFmzIn9V1/mJOSzGBrbleW04bAIT74+RHXUiaF0ScRoiuF8FKJUScBJCCGKRM+IPSnYBHDTg90cvtRP1DNPGzWH7L08aItd2gey3PzQrq5eAyMZfvSHDv7p/bWYc5ytMJJS3PRQL4++MoTTUnzwpHJOOiSAY4+Mou5RuPr6bYwmch3tYgGLqy9rIOye2e0ZGNf88uHuSWN3PdXPe1aHdmW0FIFMxub0w6M8+urwxJhSsOEdauwplSswns3aBfXQ5XQonJYitUeTCo9TAh1zZV/HQyEdJ/PJMBS2VigK69yZSQGn5prLGnj4lWHebo+zosFH91Cazv4UnzqnRg4GIcQBk4CTEEJMkVK5Apt9Ixn8bpOoby/tT2bReDL/gTJrQzJlg0ce0ha6nqF03tgbLXHiGY3/3Wsfzxil4OGXh3n0lSEAUhnNz+/rprHSzdLKXdMOw1D88bmBiWATQN9IhmfeGuPUVf4ZXT6VSP1/9s47MK6rTPu/c+/03tS7HVt23NLtOE4jTu+dQHqBZWm7wC67H0vYJBDCB8uywAfLLuxCIAmBUFJIAum92EmcYmKl2bIsWb1r+r3n+2OksZWR+0gzGp3fP8kcjzRHd84995znvO/zmlNWXIomTPDmGj4XM3VhjZuvauTh9f1owKlHhqgNTu2x50wPom17HaP1dewNKzBrVxCzBGa8z1PhdwquPLmCnz7UmW1bfbCPCr/yC5wpyrw6hy/w8Mq7o9m25loX5XP8OxAiI4Y/9HIfm7sSnHRIgMPmu3BZS/OahFxw4So/cSPI+9vjVIWsnHVUkDKPmDX+TRLBQFQyHDUIeXX8jr3vuxAQSwssmsCiUkkViryjBCeFQqHYSzoGJbf+upWhMQNdg6tPqWTNYveMpeRUBCz4XDrD0R0b9NqIjbB3bm8OFBnK/Lmq0qI6J06LYCbHR8oUPPPmUE57S1uURVX+nSLVBFu64jnv29qdQNO8GHnMEwv7LFSHbXT0JbNtfreFcr+F2XbvaEBjWOMzZ5YDmainqf4Gm0xgPvnfpDa/knnfO8+jzzsC20c+S1LYc94/00hTcswiNw3ljXQOJAl6LNRHrNhmma/WbEYXJtedWs6K+R7WtYxw2EEejlzoxqbt+A40TaALA0PqcybKdDAGN/5iC2PxjPjwfkeMC9ZEOH+lf9YIMPuKaUpswmBx9Y7nyGz5W01g3Xsx/vOBDgwzEyX55UvrmF+u7/FMMJYWPLNxhPtf7MPr1Lny5EqaKy2qYqNCkUfUkbhCoVDsBUlT8P172xkay4g9hgk/e7iT7uGZOw3z2CQ3Xt7AkgYXFl1wxEIPX7q4btLmQJFfDCnoGDR5sy1Jx5DEkMW7Cq0K6Fx6fFl2oRz0Wrj+9KoZT6ezaIL51bk5ntVh+2SPJ2ly0iG50TarFnnz7tlj10z+8eJajljowaoLlja6+erH6/HMYvP5dNocF5umxjrWhTEuNk1gfLAe61jXdHdtr9GFpCGssfIgBwsrLThmyOResQOPDU5c4ub/XFLN2uUefDtpka5UP4637kX74404Xv8drmRv4To6g2zrTWbFpgnuf6GPkcQMfLiArhHJa1sSvPj2ENF08T5zioX+McmP7s+ITQCxpMm//76d0eTur52mCV7cNMqvHutmaMxgW2+SW+/aSvuginJSKPKJinBSKBSKvWAsIWnvTea09w6nqPLbZqQPUkK5B750YRWJNDisoM2SE8jZiASe2jjKz/+yY4N+7amVnLDUXZS+FlZNcvrhPlYu8hKd4Sp1k5Am5x8TZsP7o4zGMgLtghonzTUOdo7EkRKWNzq49Pgy/vBcLxZd8NETyjmoysZ0RB2FXPCZsyuIpzKVi3RR6gb0u/rjSvqPVuwHUkpSKWNSm50E6cd+hNn2FgBG53toH6zDcc5XiQtXIbqZRdMEQzGIJk0CLi3v1SZ1LVeosOiCKZrzzgc9Bjf/qjWbArywxskXLqgu2XS+fNA3nM6Zy4fG0ozGTdzWXX9pCUPw4Id8MQHebotSF/KU+PNBoZg5lOCkUCgUe4HbJnJScgDCvhk0xxlHR+KafZlAs47+KNz+yORokF880snypnmECrvf2iUakogbcBc2zbLcA9+6toGO/hRWi0Z1cOroFacFzjrSxwnLfQjAY59ec3hNjt87c4C0uxytZjFm+9vZNq1mMWl3RQF7pZgt6KNdJMfFpgnM7s1YRjrBN69Avcp49WzYkuBH93cQTZiUB6z8w8V1VHjz9xm1ERsVQStdAzt88S47sRy3bXpF6pTU+NnD7ZP85t5pj9Hak5yU6qaYTNhnRROw86Mj6LHgdWrs7jmoaxDxWegamLyu87stSmxSKPKISqlTKBSKvcCmSz57Xg0eZ8ZgWBNw1ckVVPjUNFqqjMYMPqx9GCaMxgsfbm8CvWPwfneawThQZFkXUoLXDs1VVuaV6btNlZKmxG2VuKxyznjEzAQJ4cRy8mexHXsFes1ibMdegeXkz5IQeS4BqChJhDb1s02Iwj7zesdMvvu7bdkiGt2DKf7999tImvmbBD02yVcuq+O60yo59Ygg/+eyOo5Z5J52ESJtQPdAbiT1h9P7FJMJuwWfObcGqyUzBtwOjS9cWIt7D1FhmjT56Inl6DsN6YjPSnNNfsr+6hYNXS+yh7NCUQDmyDmfQqFQHDi1AcG3r2+kdziNx6ERcmsIFWZUsoS9FrwunZGdTNr9bkvBTdqlhOdaYvzsoe2YEmwWwZcvrWNhpXXWmLwqZoaoNYR28FnoS84iJac3ekxRWiRdFVgWrSG96dlsm954CClPZQF7Bb1D6ZyDgI6+JMMxk4g7f5v7gANOWOJC0zzjnnLTf+84rZKTDg3y4Mv92TYhoCYyM2n7sxWB5Mj5dubf0MRIzCTk0fHa9870vCGkcdt1TWzpSuCwaTRV2PDZD+y7NqRgc0+Kh14ewG4TnD5eSXQm14smAhBoKLFSUXiU4KRQKBR7iZTgtkrc4Yky6mrzVsp47ZKvXFbPD+5tp703SW3ExmfPrcEzzWkVe6J3TPLTh7Zn+5BMS773h3b+73WNuFXWRd4RAsZSgoFRA49DJ+CcXd5PpinVlmO6ENA9ItnancRuFTSW2/Ee4Ga1WEhhRV99JfbGwzDa3kCrWYKsWUaswBFyAbee0+Z3W3DZCJhP/QAAIABJREFUd58+tT9ISd4LGOzpA886KkgqbfLYhkFCHis3nFlJpS//f1upISUEnRAcT6Pblzm6wiuo8OZvXH/QneKWO7ZmXz+/cZhvXNNITWD6owMNKWjpSPLbZ3pBSi46rozmKisWFYyvKCBKcFIoFAqFYgqkhGq/4KbL6xhLSNx2gU0rvNgwMGrk9GEkajASM3Bb1aoy37QPSr5191YGRtPYLIJPnV3NoU125UmgoK3f5MZfbMlWxyoLWLnxY/X4HaUhDsR1H9StRm9cg2maBZ/7AMp9OpedWM5dT3QDYNUFnzuvGre18HNzPvDYJFeeGOaCY8L4vXaMRFxFrs4ihKZx7wt9k9pMCS9tGuGi1YFpjzLd3JPitrvbsq+/dXcbX/14PQsq1JZfUTjU6FMoFArFjCAEDCcEHX1JbBaNql0YSRcbNk1ic0KxnDCHfRY0jUnGsiGvBb+rsKl+pUjCFHzv99sYGE0DmWiy7/+xne/c0ESZR3lzzGUkGr9+ooudA2B6BlO80x7jyPml5ZM1o1E+e0AXklMO8XLYfDdDUYMyv4WgqygLh+43UkrcVgh6rfTE44XujmIfEIBlinKGM+HlZLFoPP7aYE77X14ZYPE5FaTTxXMfK+YWSnBSKBSzFiGEOvnbCX3c+bKYNgc70z0K//rLLVlPpEV1Lj53bhUeW3F+h0IAIuPTVUzeNyGX4O8vqOWH93aQSJn43Ra+eFEtrhI54S8mRqKSzg+Z+EoJPUNpyjwqf3Euk5bQM5TKaR8YTSNEaQkgxYYuJBU+QYVvfBujrrWiSDBNk3OODvPqe6PZNosuWNnsnfZ1hJTgc+du7VXVPUWhUYKTQqGYdSQMQWtPis2dcWojdpoqbLj2UI2klDGBrX0G97/QgynhrFUhmsr04kr5EYI/PNc7yYB7U1uUdzviHNpoL2DHpiaWhtc3x3lswyANFXZOOSxIhbc4NpECySENNv7tE42MxEyCbn3ay3XPVdwOQchroX8kPak95FXLp1JACBiIQkd/CrtVUB2y4tzLqEu7Ljn9yCD/+5euSe2L613qXlQoCoQQgpRJxrOoQDdiY5nOLVc38tQbgzhsGscu8VPpF9MujBqGyQnL/Tzy6gCpdObDrLrgpEMDRXsQqZgbqBWTQqGYVUgheHD9EH94rjfbtmqxlxtOLceqzc1VfsZHpDX7+pV3R/jXKxqYV5ZrrlooUga81x7LaW/vS3JYk6OoItWEJnj89WHufqoHgJa2KM++OcRt1zUSKJJMGSnBZweffd8NUhV7j8sq+fz5Ndx6VxuJlIkQcOXaCsq8ysS3FOgchht/sYVYMrMZW1jj5O/Pr8a9F1GXpilZ2ewhnpLc/0IfHqfOVadUUBMsKqlfsQuEpmXuYFNtxEuFaErw8jtj/PmVfiqDNi46rozagNjt83EsJWjrTZJMSWrLbIRdBx45r5GpfnfNSRFAYhhyxh4XVX7BN69t5M0tUQCWNrio9O3+GigU040SnBQKxaxiYExy7/O9k9pefHuEc1aFqZ2DC31d13hiQ39O+8PrB/jc2eUFydkXWqYcL3KHyazdIjl2qS9TOWUnmmudRSU2AYwmmCRoAkQTJm09SQJ1qjz17hAic8JcTCmIB4KUMK9M5zs3NNE7nMbn0gh7BFoJiU1pKYgmwGHL+JXNFaQQ/PrJrqzYBPBOe4z3OhOsqN+7+9xllZxxmIcTlnnRtUzUU5FNZ4oPIRG09Rvc+3wPI3GDc1aFaK62z9kDq1JBaIIn3thxUNTem+SNzWN867pGIu6p/ZNGEoLbftNGW08mbdpuFdx8VSNVvvz4LRUiqkhKKPcI1i5zZ1+rOUlRaJTgpFDkGSEgnhYk0uCxCzRVkDqvpAzJVHvZZHruPlFtltzFkdUiaO0zGIsZVIeteGYo5bBnFP78aj+tXQlOOiTAiiYnTovENOGE5X629SZ44e0RrBbBJceX0RixUmyRIkJkPBc+PKa0KYxAFTsYisObW2J0DSZZMc9NQ8RaEps4KcHvkPgdxRMxmC96xuDHD7TzXnuc6rCNT59TTX1ILzoReDpIGtDalchp7x5MIhpse71Jk5JsGt4cuGyznu1DmcqCE+uITVujfOniWparw4RZTTQJ932oOlwqLWnrSRJxT52239Iez4pNAImU5DdP9fD5cypm/c08y7uvKDGU4KRQ5BMBLZ1p/vP+7fQOpzh8gYerTq4gUCIlkouBsEejuc5JS9uO9KyQ10JlwEKxCRczgWGYHL8iwF9eHchmBmgCljV5+OefbQYy1+fGy+sJOae3L209cb52eyuj8YxPU0tblEuOK+PsI31IKfHaJZ88vZyPnlCGroHfUZym7x4bfOwj5fz0oc5sW9hroT5io1jGmBjXvorl8o0mBTffuZWewYyJ8r3P9/Hpc6pZtaD4ItgUGRKG4Nu/acuaonf0Jfn6HVv5zg2NeIvPVi3vOCxw7DJ/TjTjQdXOormvppOU1EibEpcVkJKkqSFNcFiLq0hCPtE0wYb3R3IOrf74XB9LP1pdUpGLcw1NA5dDmxSxCGC1TB35LoSY0vR/W2+SpCHmVLSnQjHdKMFJsUuEJkgZYNVQG4a9pGdEcuudW7OLmVfeHcUw4e/OrVSRTnnCIiSfOaeah9YN8PKmEQ5udHHB6vCcNg2v8QtuvaaJ5zcOY0rJ0iYPtz+yQyzpH0nz1BvDXHi0f1o3Els641mxaYI/PN/LCSt8TBT00pAEx4WvYp1XTFOyqtlFeaCelzYNU1tm57D5brz2wvdXCEH3qOTt1iimlCyuLw5/htaeZFZsmuD2R7tY3tSIs/QCg/YJAw3TBJsui2rM948aORX4YkmTrqE03vLSXx5KU7L2UD+9wymefWsIh1Xj8rXl1IZK+/DCBN7tTPPzP3cyOJbmjKPCLJ/n5icPbCOWNDn/mAgrF7qx66V3DaQEmzVXgLDbBJoqLTiDCAZimWsddArycb85dMlVJ1fy3d9ty7aVB6w0lE19UCSlZFFd7inc2kMDOMajshUKRX4o/RWFYr8YigsefW2Q9e+OsGKeh9MODxJwqgfxnugcSOWcnG14f5TRuMRXJGbDpYDfLrnsuCAXrA5i01GLRKDaL7hkTQCLReMH93fR0Td5I/nOthhCCzBlPmKe0ERuyplFE8VVLW8vsWmwqMrCktoIplk8QkHXsMlXfr6FRGpHBZpbrm6k2l/YdD/DyL0+iaSJNIE5KzgJ3u9Jc+fj2xmOGpyzKsyRC1w49rIK2nTjsutYLSJbzWgC7xxSCL02yfWnRLjk2DAWTeCxF8+9Pl1sHzT5xp1bs69/+3QP0aRJMi3pG07z04c6sVurWbXAUXKPViklK5pc2K0aidQOReHiY8vITFaK6SaaEvzxxX7+sn4AgJMPD3L+0cFMpN0BICUsq7dz81WNbGwdI+KzsrjWuduDorqwzmfPreHnj3QSS5icdkSQNUs8JRvhp1AUCiU4KXJImhr/ce823muPA9De288bH4xy48frcJTgiVc+cU/h8RH0WrAd4INUkYs0JTZVKGoSpilJJg1WLvLx3MbhSf923DI/5jQbWM6rchDxWegd3lFC/rITy3HbZq+RbjGVEtY0wbMbh7JiE2Q8zf7y6gDXrY0UtK+1EVvOJu7c1RHc9rm7j+sYMrn5V63Zsf/fD20HUcVxi11FIWoEnHD9aZX8+IHt2bYL1kQo88ytiVVIid8Bc6Xa4wfbc32rnt84xMpFPh5elylA8eDL/Ry1oBZRgpHZ5V6Nb1zTwCvvjjEWN1i5yKsqC84QQsBbW2M8vG4g2/bn9QMcVO3Mi8CpC0ljWKMp4hufY3f/Cy0CjjrIwdKGRgwTPHbUAaZCMQ0owUmRQ+9IOis2TbCtN0n3UJr60Nw5+dwfakIWVh/s4/m/Zjb7moBPnVWF0zI3FrKK4mBRjZ2Ljo3wx+f7kFJyxlFhDpk3/V46VWE7N368nlfeH6OtO8FRzV4OqrIVxea6FBBCMDCazmkfHEkzRXDZjBJ2C265uoH7XuhnW0+Ckw8Pcvh8F3KOnhQLAZvaojnz/v0v9LGq2Y1VFP66SClZucBF43VNdA+lCHksVAZ0tCLoW6mQkoKBURO7TRBwFof46nXlruNCHivDYzvmlojfihAzV8p9JpFSUu4RnHm4F2BSNEssLegZTuO0aYQ8GvoBXIC4Idjen0YiqQpas8bycxld17Lr4515buMwxyxy5a2q7r6sOaSUOCd2w+orUiimBSU4KXKw6lPvXKy6OgHaE3Zdcs3JZZxyRJDRqEFlyEqZR1MbbsWM4rBIzl3p54TlPkBk0jlnaAwGnLB2mQdN845H3Ez9uSaCvlGTtCGJ+CxYRRHsxIocwzBZszTAU28MTWo/5YhQ3hbq+4uUkkqv4JOnlWGYmZPmmZj3EoagczCNEIIKv140vjNSTh3x6vfo6EUk6GhCUuUXVPknKnTtX98MKTAR2DRTHa6M0x8V/Nvvt9HWncBuFVx7aiVHLXAV/PufX2mnOmSjoz+Tdq1pcPIRQX72YCbSzaILLjgmMq3p18XAh9OmeqNw651b6R3OeNGdd0yYs44M7Jd59HBc8O17ttHanYkmq43Y+PIldfjneAEZ08z4Dr767uik9oMbXCqNTaEoYZTgpMgh7NFYe1iQR1/dEfK6+mAfZb65FWa/v9h1ybyIzg7jEnXNFDOPNCU+O8DMn1JLKaf09JkgloZ7nu3nkfE5ZmGtk8+eW42/CEy5ixmTTIWlq0+t5MnXBzFNyfHLA7jtBQ5v2glpmmjMjL65rSfOrXdvY3NnZlO3oMbJ58+rGh/3hae5xkHAY2FwPCpNiEyKaSlVwjKkoKUjyV1PdJM0JBcdW8aKBnvGW28/0TRBPC2w6hIxS9UrA8HPHu6kbVxwSKQkP35gOw3XNVLtL+zhndcu+cpldbR2J4glTerK7bhsgk+fU00ibTK/0kG5T8yppYuJxu2PdGbFJshUrjtknod5Zfs2mIUQrHt3NCs2QSZL4IVNI5xxmHdahJWxlKC1O8nAWJr6MjvVgeIStifIFOTw8sSGwazPZHXIxqpF03NdFApFcaAEJ0UOGpKL14Q4dL6bd9pjHFTlZEG1DUsRPrwUilLBBDqHJF0DSXxundqQBXuJZrC+35nKik2QMTR/bMMgFx4dKLkULE2D0aRAAG5b7qn6vpBIwxMbBjFMOHyhF03APU/3cOXJFTSEc6vtTDe6rhXMUF3TBM+8PpQVmwDebY/xyntjfGSppyiiSgNOuPnKet5pjxONmzTXOqn0l9bBTWtfmtvubsu+/sEf2/nSRbUsr7ft5qd2zVhK8PTGER57dYCaiJ1Ljy+jJjD7ooSjSXhzy1hOe9dgimp/4RVRr12ytG7yd3RY045+CQQpCVadkpuTpyKekmxszf2+eodS+yw46brgr1ujOe1vbh7j7CP9mKYxxU/tP7G04Af3beevrTs+83Pn1XDUQY6iFHH8DsnXPl5Hx0AaJFSFLLhUuqFiHxBCMJqEsYSJ36Vh348oRMXMogQnxZQ4LZJldTZWNNiL8oGlUJQSmiZY/16c7/+xPdt2wooAV5wYxlpiD1JNE7Rsy12Mr2sZ4ZyjAliKJ1jngImnBU+9NcI9z/Ri0eHjH6ngqIXOjNn9fuC0wqrFPh7fMMjzG3ek1dWE929zv7/E0tDSnmRdyzALap0cNt+Db4aj0zRN8Nr7Izntb22JsqzJjcsmcFsL750XcMBR8x3sqLheOvezrmu8+NeBnPYH1/VzSGM15r7WFReCB9cNcv+LfQB0D6bYuGWM/3t9EyFXPno8czisgrpyezbCaYKgu/DL7pQp2NafpmswRcRroS5inZSKOpQQ/OWVAda/M8qyJjdnHhUkOPN69ozitAqWNblzUr3KAvte8cUwTFYu8rKuZfL8dMwSH+l0fsUmgI7+1CSxCeB/Hu7k4BsaD7jy23ThtEjmlx14FsCOeVUxl9jUkeL797YzEjWoDNr4woU1VPpyF49JU2MoauC0afgcB3bgpzgwlCmPYreom1OhmH6G4/DTh7ZPanvy9UE6B/O/OC00pik5qDp397JingdrCT2RhIA3W2Pc8Xg3iZTJWNzkvx7czgdduabfe42UnL86zJKGzO7bahFcc0olNaGZ3MQK/rRuiO/+bhvPvDXM/zzcxbd+00YsPbNKoWGYrD7Yn9PeUOHgX/53C1/+WSvbh4rn+VWKmyIpJQFP7tgLeS1MtYmMpQXvdad5uyPFUFzkGN2PJeGh8SppEyTTkm29yXx2e68QAlJSI24ING3fx7ZVmHzqrCocO6nLpx8ZpHpG79UpEIJHXx/ha7e38qP7Orj5jq385tl+0jLzN6akxvf/2MH9L/azvT/JX14Z4La7txE3SugkYAoEJpefVE6ZP6PQCJGp2Fgb3PfvS0pYWu/g5MODCJH5XSeuCLCiyTUt80AsmSvsjiUMpkHbKhpiaXi7PcXDr42yaXuK+Aw/fxSFYyAG3/pNGyPRzADvHEjynXu2kfjQHNUXhVvubOOL/7WZL/7XZtZ/EC91W7qipvBHLQqFQjHHSaQk0UTuonE0blCK5wILqmysWuzjxbcz1WpqIzZOPTw469JmdofQNB55dTCn/cW3h1lSGxk3VN93/A7Jly6sZmDUwGoRBJxiRq/bYFzywEt9k9raepJ0DKR3OrGefqSEow/28eYHo9mqR0c2e4klzcy9lDD50f0dfPWy2pKLEiwWTFNyVLOX+17oy85fFl1w5spwzmHVaFLw77/v4N2OGAAuu8bNVzVQ7tmxSdC0TPtwdPJO2TYDYY+mhJ4RSfdQiqDHQsqQ/OcD24gmDM49OswxB3t2VLLaS+qCOt++PlMB0O3QKPfpBbcm6I9K7n6qe1LbI68MsPbQAFU+Qe9ImnfbY5P+fXt/kq7BNA3hEs3xHifigm9cXU/PkIHTrhF2C8R+Rt+4rHD5CSHOODIIQMi1/79rT1SHbFgtglR6x+8/Zokfn5NSCqjMYkjB3c/08fhrO56vZxwZ4pJjgyXlj6eYmp6hNOkPeYR2D6YYjJpUeDPPCgONn/ypPeujFkuafO/37Xz7hqbsexQzixKcFHkjloa23jSDo2mqQjaqg8VpWqhQFBt+l6C5zklL246Fvs0iqAoWaTz8AeKywidOLeO81SHShqTcb8FRJNXF8oaU1FfY2dQ2OdWhNnLgaco6JpHxjfpMi3RSTh2tUwifl4qgnU+cVsaFa8IMx0zue6GPP724Qwzb0pUglgJr4S1zSpYyD9x6TSPvtMdJGZLmWgcVXpEzRt7tiGfFJoBowuS3z/Tyt2eUZ43BXRa4+pTKSanFdWU26spsTOfOWQhY/36CH96743NPPSJE0Gthe3+S2x/txmHTOXbxvuX1SSnxO8DvKJ6a64mUyVSZjvGECei7rEZsLaVc593g0CV1oYlrcGDfl5CScHbI5P+7F0KQNqHMCzdd2cjtj3TS1pPg+OUBzjwqWJphlUDPiDlJbIJMGu9Jh/op88yNcTqX8btzhW+3Q8Nt3+GPOBqXbGqL5byvayBFhXdmLQgUGZTgpMgLSVPwv4908+LbO3LWP3VWFaubnaX6zFMo8oZFSP727Gpuf7SLV94ZpTZi42/OqiboKtk1IxZN7lStqfT+SNOUnHp4kGffHMpGfwQ9Fg5f4J7VkVwBl+CkQwI8utOCvyxgpSpkpRDfo4akzCNIpASvvTfZf6Wp0oGzNDXbokFKCLlg1QLHpLadEUKwfSA3LW7z9jgpg6ynmZSSQ5sc3HxlAy3bYkT8VhZWO3Bbp3dcDcbhvx/smNT25/X9XHFyZdYb508v97OqeZYZSU1B2KPTUG6fVEHN77ZQHsikQYbcgtOODPLwuslVisu9OqU4T89Wesfg3hd6ea8jxnHLAhy7xMuXL64mlRY4bbKkjd6T6amjg5NpCSjBqdSJeDQuP6mcXz2WidTUNPj0OTXjHk2Z9zhsgoqAla7B1KSfnSoFXDEzqCuvyAvbB9KTxCaA//1LF0sbm/DaSvfBp1Dki6BD8rmzKhlLSuwWgVUzS1ZsmiuUe+Cb1zbS1ptEE4KGMiveWR5tI6TkwjVh5lU5efqtIRbXuThhuX/aRYE9URnQuGJtOXc81o0pM5voT51VpdLpPoSBRt+oga6JaU3z2RkpJc21uWLNCSsC2PXJxu66kDRGdJrKvOPC7PT3L5qQJFK5n5PaaWNb5rdi2Yfs5p5RSXtfCpddoy5sxVng+2MCmyb5woW1/PqpHl59d5RFdU6uXFuBx5b5HjQkFxwd4pB5Ht7tiNFU6eCgSjsWdR8VDSNJwdd+uSXrYXPXE9209ya47pQIDouJ3L9s7VlDud9CVcjG9v4dInZDuZ0ynxJF5wK6kJy03MvyJjeDY2nK/FZCLjEpctyhm3z6nBpuubM1m2p6xsoQlYHSs6iYLSjBSZEXpjItjCfNzImDil4EIG0KuoYNBsfSlPutRDzajCz2FbMHgYnHBmrRVBpICUEnBOtKaxJ0WyXHLnZy3BI3SFkUxSUsAtYu93DYfDdjCZOI14LbVvgqdcXEcAJ++nAnG94fRYiM78m5q4I4ZqAkeX3YwrWnVXLHY90k0ybHLfNz/DLfLqP9ZjIKMOjWKA9Y6d7pNNy+UwUDiy645LiyvQ433dJrctOvtjBh07aozsXnz6squCg7QdAp+ZvTy4itLcNhyYhMO/9pDovk4BorS+ts4/d2cfRbkaGjL5kVmyZ45q0hLloTJlDi1QQhk/b45Uvr+OPzvby5eYxD5ns4Z1UImxJF5wy6kFT6BJW+iRDmyd+9lNBUpvGdG5roHkrjdWiU+wvvoTeXUYKTYo/0RzOGbD6XTplPzzkxNoVGTdhOZdBG505h80sb3QRcGlDixy17QVrCg68Mcc8zvUDGM+ILF9ZyaOO++bkILbMIlvtablqhUCjyiJQg99P4fLoQQNgtCLszJ93ToVkIITAk6JqYVfOwEPD0WyNseD+TdihlJk1sSaObpbXTn3do1SQnLnFxxEFNGKbE5xRZ76ZC49Al/3hJHf/vvg42d8YpD1j523NqiCfSfOacahor7JT7xF7pLmkp+Omft7PzrbGpLcrWniSLq4snv1NIiWsPO4BiEJIVuVim8Nmy6AJtDgVvhJySa9dGSBgR7Dql6z2g2G+yB37O4vHQm8sowUmxS4SAdzrT3HrX1uzi6dzVYc5dGciqxFv7Te56spO+oTSnHhkikTL53TM9HL3Yx8XHRtCV2ARAz7CZFZsgMxH++P4Ovn1DE3vjXycRbO0zuO/FHmIJk7NXhVhQZWWO+HgqFIr9xETQN5qp3Fbut+CcgWiWPSEEpEwNw5Q4rbNnYxtNCV55L8ojrw1QX2bn7FUhKn25BtnFiCG1bFXIndnYOsby+uCMfAdSgmcixb7ILlq5B/7lozWMJiQuu8ChS6Tc6eG8l91NGdDVn8ppH40ZQPEITorZS03IwkHVTt7byYT/0uPL8DtmtmJpwZGSnXyiixIhYCQh6BlO43ZohD0aejF3WKGYJpTgpNgl0ZTgh/d2TDqpu/f5PlY2e6kNanSPSL52+46w8V/8pZPLT6rgR5+Zj11TYdg7MxzNFd6iicwm0Gvbs2rUMWhw4+1bsmv0t7aM8c8frSvIiammCUaTE5uHma+SpZi9CCHQNIFRZJExpUrSgD+9MsQfns2I3SGvha98rJ4yd+H6ZAIt7Sluf6SLsYTJuUeHWb3Yvc8l52caIQSPvDbE78av5ZbOOOtaRrjtukaCsyCNRReSpY1utu5kFg0wv8o5awS/6caqyfHvcv+j45xWOPEQ/yTTbYCaSGml1SoKh8Mi+cIFVWzalqCtJ8GSBhcNZVa1FitCOgYlt9zZOi44w4XHRjjjcL/yFlTMOYoiALO5ufny5ubmN5qbm9PNzc2f+dC/uZqbm+9ubm5+r7m5eVNzc/NZhernXCOWlAyMpnPah8YybZu7Enx433j/i30zZvQ5mygPWLDqk4WlqpCNoHvPt6CmCV5uGc1ZAN/7fB8zHUOdMgXPvB3lCz/ZzOd+9AG/f3GIaEqFWSn2TF8UHt4wws8f6+XdrjRpU42b6aZ9wMiKTQD9I2n+989dGLJw135bv8k3f91Ge1+SwdE0v3iki/XvxhCiuMfDaBLue7FvUlssadLWk1t9rRiRUnLq4QEqgjsOKZY1uVlcm6kulzAF7YMmPaMSU1V62n+k5OyVIT5ySABNQNBr4cuX1lHpL4rldpakIRiOCwxZXP1S7B0eGxwxz86FR/tZWGnBrqs1d7GRMgU/fqAjKzYB/O6ZXrYPGrv5KYWiNCmWM8UNwEeBf5ri374EDLe0tBzU3Ny8AHimubn5oJaWltEp3qvII16HYF6Vgw+2x7NtmoDy8QWrw5a7UPE6NXRNoASnyQRd8M+X1fODe9sZGEnTUG7nc+fV7LXJod2auwFw2DRm+kpv6Unxkz9tz77+w3O9hLwWTlgyu0u9K6aXwTjceHtr1uj0kVcH+bsLaji8yV5smTUlRe9wbmrP21vHSKTBVYDsHiEEb7WO5bQ/uK6Poxe5ijpFWBNgs4hsxZsJLMXc6Q8RdMLNV9TTOZjGogkqAhZsmslgXPBv92yjdTz66YwjQ5x39MyYiZciXpvkqpPCXHRsGIsGTkvxmNcLkTE1/+G97XQNplhU5+STZ1YRzi0iqJgF7E10oqYJhADDKJJBOEeIp2FLVyKnvX8kRX1olperVSj2kaI42mhpaXmrpaXlr0ztLn0p8JPx970LrAdOn8HuzVmsmuQz51RTV56ZGD0OnS9eXEtkPCqnqcJGxDdZs7zy5AqsQqXL5CBhQYXObdc08O9/M48bP15HmWfvftQ0JYcv8GDdaWMjRMZPayZNazVNZA1nd+ax1wYLGjGhKH62dOVW1fnVY90kjKJ4BJUsZb5cVWlpoxtHgaxUB3ciAAAgAElEQVRkpJQE3LkfHvFZxw8qigMhMsUyXn4vznMtMXpGJR47XLG2YtL7ygNW6iOzy5fHaZE0RXTqQho2zQQhuP+l/qzYBPDgun62zJLIrWJFSInHKse9oArdmx0MROHmO1rpGq/It6ktxvf+0E7SVHNxqaEJiXt0K/b1d2B78ed4ht9HR0XXzBQum6C5LjffOuKfXc8MhSIfFEuE0+6oB1p3er0VqNvXXxIO7+XufpopK/MWugv7RFkZfOcTB9E7lMLt0KjYSZUvA269bj4bt4wxOJpmSaOb5joXNqtauOSbSETynU/O54WNw0STJmuW+FlU78Jq2f21zvd4qy2L5bQ1VDgIh1xY9OLZMCoKx5Rjbmt/TlPakDhdNkJetfiaLhzuNB89oZzfPNWNKaHMb+X6M6upKCtcOMOh0krIZ6F/OJOarWvwsZMqKY/sn7HUdDxTP9ge4ys/f4+xeEbQt+qC226Yx9rDwlRHHKx/Z4SasI1DF3ipK3Pk/fNnkoHRFK++O5J9HfJa+NgRkga5lZC1Ej1QWcDeFSezbR23Mx+8PZQTpdfalSCWFtTUzN6/q5TZ3/GWat/E6N3/DGZGZEpteBjvx76OtfGQfHZPsRs+fW4tX//VFroHU1h0wVWnVNJc78Fl1wvdtd0ym+c4RXEyI4JTc3Pzq2SEo6moaGlpmXbJva9vtODGmGVlXnp6Rvb8xiLEawUM6PnQqadDwOFNNoSwIaVkaDA3XUKRH4J2OOuIzEPANCWDA7u/1tMx3hbXOagIWukayJyOuuwaZ60MMdCvMlwVux5zdRErdqsgkdoxB190bASZTNDTE895vyJ/nH6Yl5XNHqJJkzKfjkM3Cvoccgq46fIGNnfFSaQkTRV2yn1yv/o0HXOcpgmef2skKzYBpAzJH57t4W9OK6MxJJi32o+UEilT9PTkpi3OKoRgWZObJ18fosyvc9Ox3QRe+H+QjDHi8GA/84uMhQ/OW8q0ITUGYyZ2i8DnmD0VCieYzes4AMcUq36XXUPHnNV/V6myv+NN0wT2jU9nxaYMkthLf2TUN5+0CnSaEQI2+PpV9fSNGLjsGkGXYGw4SjHvlGb7HKcoDJomdhvcMyOCU0tLy2EH8ONbgQagZ/x1PfDEAXdKkVeKKWS8lCn04jzggK99vJ623iRpQ1JXZifoLK6UgWIilhb0DKVx2DQiHg1NzM0LFXELbrmqkQfXDdDZn+S0I4IsqbcXfDzPBQSSsBvC7umtH500BR39aQbH0pQHbFT6NbRdfJ7fITmkYScPiyIaBkJA30iuiNQ7lEaOu+YdyLhNS0HSELissjgenFJy3uoILW0xLjs0ReC570M6c7Ak46PEH/g2jo/9GzFL8IA/ajAu+MmfOtjYGsVl17jutCoOn+eYs/NiISj365yxMsSDL+2IOv3EGVXj4l8BO7YPCAG6rmOapnqG7AIhACO36A9GWpUEmGEcuqQmMJ6NUAxzvkJRAGZDSt1vgU8C68dNw48ELitslxSKuYvHJllcPZEGpcSmXdE7BrfetTVr3HzWyhDnrgrOyWoyUkoqfYLrTwmDFJizZWczTWiaIGUKLBoz6sM2XaSl4J7n+ieVgv/0OdWsWuCYdfODYUhWLfLllLU/46jQAX1XQkBrn8nPHu6koy/BsUv9nLc6jM9e+AsUckpuurwOd/+mrNiUJRFFRAfAd2CCk0Rwx+PdbGyNAhBNmPzg3na+eW3jjs2YYtqxCMmFRwc5erGXoVGD8oCVcq+YNcJNNCV4fXOMF94eYkmDm1WLPPhnd1brtGAYEr35GFKvPcjOir71sLMYVdFNCoVihikKwam5ufky4NtAEDi3ubn5n4BTxo3Evw38vLm5+T3AAD7R0tKiYv0UigNE14WqWjJNSDTueLxzUpWwB17q5/AFXuaXF3fu/nRiGpKiCmfJI4NxGBgxCHp0gq5dH2SOpQQvbBrl8Q2DNFY4OG91mArv7D747BoycgSanz7UyeK6RnxFVIxnwgy8tTuBlNBQbifszr32DREL/3hJHXc+3k0ybXLRcWUcXGvnQMZu3xjc9MtWUuNz7qOvDRJNGHzytHJEEdwTDovE7g+S1Cxg7hQZYbWDw3/Avz+agpenWLp1DqSoCRTRIJkDWDVJQ0iH0Ox6FkkEv322j8deGwRgw/tjPP3mIP9yWR1OVVExh5i3EeclN5N+7U/IdALroWcRDy0sdLcUCsUcpCgEp5aWlruAu3bxb2PAxTPbI4WidBlNCjZujfFeR4xljW4WVNtwFsVMUDrE0pK3tkRz2nuGkswvz61aopjFCHizLcn3ftdOypBYdcHnL6hheZ1tivcK7ntpgIdezqSzbOtJ8Oq7I3zrusa9PqWPG4K+EQOHVSPkFkUhVuzsdzRBImUST0p89uJJ4OgZha/+Yku2vy67xi1XNeZUDNWFZFmdlZuvqEUCNo0D9jDq6E9mxaYJXnh7hI+dWF40olzCEcFx6qdJ/PmHGe8X3YL9tM8Rs4cPWCe26VBfZp9UDQ/A755dooeicPRHJY9vGJzU1taTpHMgTVOZGkcfxkBj1L8Ay0l/j0CqyCaFQlEw1DZToZhDJE3BD+/fzl/H0xr+vH6AM44Kcema4LRsXDVNIITAMGZ/2tC+4LDA8nlu1n3oRL88MIUIoZjVDMUE//H79qyYkDIk//GHdv7tE00EPiQijSTgz+snV+yLJky29abw1+65Wl9/TPCtX7exfSCJJuCCNRFOO8yHrcB7rYqABbtVI5HacZ83VtgJuHWgOO59TRM889bQJHEsmjB54vVBLjsumBPtKWUm/Wji/w8Upy03bczj1LEW0T7ZkBrxupU4Lp8HYwNId5iYPUI+sq2smuQTZ1Zx0y9bSY5XSTt2mY/akJVSjXrcX3RdQ8oD8worRXYpXRePpl2UpFUku0KhKDBKcFIo5hCdg0ZWbJrgoXX9nHJYgFAeK6ULkYkmeLllmN7hFKsP9tEYsWCZI1YdGpKPn1jOtu4E2weSCAHnHxOhNmRBba5Ki6GxdHYDPUEqLRkaMwg4JqsJmgC7RSOWnCzCWC173jGZIuOBs30g47FjSrjnmV6WNrqZV+DT/YBTcOPl9fz4/g629SZZ2ujm+tMqsWnFITZBRnDa3p/Mae/oSyJExgx8OqkJW1ne5OaNzTvqE11/ehUua3GlUxpSY8xeAfaKvP/u+pDGd25oYvtACrdDo9JvwTYHPe12hSkFW/rSPPl6Pz6nzpplPqp8oqjGRyEJugUnHRLg0dd2RDnVldmoCqjnqkKhUBQzSnBSKOYQxhQnplJOtOfvmLBvDP7l51uIJjIbzkdfHeQfLq5leb1tziyeQy7JzVfW0TOe/hQukvSn6UBogrQp8lY6fTYR8Og50T12qyDgzt0EeWySj59Uzk8f6sy21ZXZqI3sOcojnoI3PhjNae8ZShVccJJSUhfUuOnyOuJpcFkzaWl5QUD3sGRLVwKHTaOxwrZfKWjptMkJK/y8+PbwpPa1hwZIp6dfGHPokr89q5KtvUmGowbVYRvVAX1O3TNSQsAJAeeOohMzgaYJxlIgTXDbQRZp5NC7XSm+cefW7OuH1vfzzWubKN91pek5hZCSi9aEaK5z8cLbwyxtcHNksxuH8m9SKBSKokYJTgrFHKIyYKEyZKNzp5P+IxZ6CHnyWzL9ve3xrNg0wZ1PdLPoinqsoniiHqYbuy6pzVZgKs1FcV8UHlzXz19boxy3zM+ag714C1h5S4iMabsQM1MBzu+AL11cy3fv2UYsaeK0afz9hTUEnLkVHKWEo5vdVIXqeWtLlOqQjcX1Dlx7sWFyWASL691seH+y6BTx7TkVb6awahJrnrNG2/pNbvzFFiayciM+K1+7vG6/KlMdVGnlb8+u5q4nu5ESLj2+jIU1B2YGvi+4rJJFVVZgZgWXvUEIga5rGIZZMBFMCID8CtcpE17aFOWOx7tIpSXnHxPhIyt8RWcyLYXGPU/3TGpLpSVvbB7j5OWeOSVM7g6XFVYtcHDMIheGIZFSYiCIp8BhFehFksKrUCgUih0owUlRkiQNwfbBNCMxg6qQjXAe08VmM06L5J8vreOxDYO8uXmM1Qf7WL3Yi57njc9UkVSpdGZxqPwWSoexlODmO1oZGMlUtbrriW7e64jx6TPL0QqwmU4Ygr+2xfnTS/0EvRbOXR2hNiimdV8vJSyutvKdG5oYHDPwuzX8jl2nSVk1yYIKC81V/n3yaNEwuXJtOVu74/SPX++zVoaoDZduOokUGr9+ooudLeB6h1Ns2hZn5UH7rjjZNDh6oZND5zUgAaflwM3A84mmCWKpzH9nUpgfSQpeeW+U9e+McsRCL4fPd8+4aNwfhY2tMUZiaZY1uakJauQjA/uD7jT/9eD27Ou7n+oh7LNy9EJH0UXbTmW1M9WzdK4jJdmoxJ4x+J+Ht7OxNUpzrZMbzqia9VU/FQqFotRQgpOi5EgYgl8+0cvTbwwBoGvwLx9r4KAKXS1CgKBTcvExAS5cHUQX02NMelCVE6tFkNrJ2+bi48qw62ohWEps709lxaYJ1rWM0H9ChIh7ZpVFIeD1zTF+eF9Htm39OyPcdl0TFd7p7YtpSrx28NozW+S9GeP7c99F3PDNaxroHkrjsGlEPFr+UteKkLSZEZg+zOBYetx3ad+RUmLXJ/7/QHqXX+JpwfObRrn3+T7cDo0r1lbQXG3Ni+iyO1Km4McPdPLWloy31BsfjLGuyc3fnVeJdYbG1kAMvnp7KyPRTBmtXz/Zw42XN3BQ+YGliuq6llO4AeCRVwc4urkmk2NXJAhpcuGaCN+6uy3bpmuwosk946KoEIKhGHQNZby2yn161kC/mIilBd/89VZ6hzJzRMu2GLfcsZXbrm3AbS2+/ioUCsVcZY5Y+CrmEh0D6azYBGCY8KP7O4gbKrRmAmlKhDSnrQpOuRe+fnUjJ6wIsKTBxRcvquXQJmdRRRMopsZi0dC0vbtXpjK71jSw7OXP55OkqfG753ontRkmtGyLs5/aRFHitEgawjoVXlHSYhOATZOcfmQop31xnWu/5hJTiMxzoMgGhBCCV96P8vO/dDEwmmZbb5Jv/rqN9v7pF0R6Roys2DTBm5vH6B2eOTGmZVs8KzZNcOfj3RgHuEQ1TUlNODfHs6Hcvks/vaG44M22JG+0JRmMz+xQaa628i8fq+eIhR4+ckiAb1zTRKV/5sdq24DJl366mVvu2Mo//WwL9zzbT9IsrnsGoHfEyIpNEwyNpekdTu/iJxQKhUJRCFSEk6LkGI7mLjZ6hlIkUmDfD7NZxb4jJVT5BNefEgYEpmFSqmk/pULCELzTkeC5jcM0VjpY2ewh6Nz9z1QGLBzc4JpU+fC81RECLjHj4SOaAIc1d4Nqs6gqT7MFISCaFmhC4NAzXkIrm90k0+Xc90IfHqfOVSdXUBvadyGidwzueqKLt7fGOGyhh4vWhAnshw/UdJA24U8v9+e0v7lljPqwr6SFeiEgnswVt8biRua+PQCdQ0rJofPd3O+zZEUIl13jtCNDUx629Efha7/cwtBYRvxyOzRuuaqRiHv/+7AvWAQsrLSw+NxKAAzDnPHHZkpq/PiBtknfyYPrBli12EdjpLDFCT6My6ahiUzFzgmEyHzHCoVCoSgelOCkKDkqg7aMYfBOi5BD5nvwKLFpxjENiRKaih9Ng6ffGOGXj3YD8Pxfh3l4XT+3XNmwWy8Xuy75zNlVtLTH2dIVZ/k8D3WhwuRN6phcdmI5t961o8qT26GxsKZIVIX9ZDAOrd1JDFPSUG4n4t43U2WLJbP5molKbAdCPC145q8j/OHZPuw2wRVrK1jeYMdpkZx2qJfjlnrRNYFNM/d5eI2lBLfc0crAaEZ0ePqNIbZ2xfnqZbVYtcLPT7qWMUPf1pOY1B7wTL8/V5lPZ/k8N298sCPKacV8N2Xe/BaS2BVSQnOdM0c4OO+YMFYt13h/Xwk44aYrGtjam8Q0JbURGyFX7hSlaYIXNw1nxSaAsbjJ4xsGuey4IMZUBkvThGEU7l5NpCRt3Ymc9v7RdNEJTiE3XPaRcu54rDvbdsGaCOE8F0FRKBQKxYGhBCdFyVHu1fiHi+v4zwc6GI4aLG1wc+2pFQUxMVYoZgMjCcFvnppcIal/JE1bX5KDq3dfBc1jkxzeZOfI+Q7CYQ89PbmeKTPFwiort1zVwLp3Rgm4LRw6303YXVxePftCf3Syt43dqvH1qxup8O75Z02ZqfD28PpehIDTjghRF9J3mUpUSISA1z6IZgXP0Th87/ftfO2KBuaX6ZimxKED7J8A0T2YzopNE2zpStA7alDlK4JoCCm5+LgIb24ezRqkBz0WDq7fv9TBfcEqJH9zRgWvvR/l1fdHOWy+h0Pnu7DMoBBX5RfcdFUjv326h8HRNGevCnNIkyNvf7vXLllSs2Mem+rXapqgrSdXaGntSYz7hRXffTMdOG2wtNGdk2ZZGSyeapgTCOAjy7wcXOeiZyhF2GelOqirtZ5CoVAUGUpwUpQcAsmyOhvfvr6ReEric4q8V2FTKEoJydSbMLkPHl/T5Qe2L2hk/I2ajgkgZSalZraKTZomePmdkUneNomUycPr+7l2bXiPERdt/SZf/cWW7OvnNg7z9asbqQsWgcDyIUw0Hl4/kNP+6rsjLKgIHvDYctim9hqzW4rnWtSFNG67ronNXQnsFsG8Sgd+x8wMXo8NjjvYxYnLPBjGvkeQHTASGkIaXzy/ClOCRcx8H9JpkzVL/Ty3cXhS+0krAkUfHZhPdCTXnVbJd3+/jbbuBDaL4JrTKqnw6RSj6GbVJHUhjbrQRAh78fVRoVAo5jpKcFKUJFJKnJZM2Wu1AFEodo/XDucfE+HunaKcfC6dujI7s/H+KQbx60ARQtA1kMxp396XRLL7iAuLRePP6ycbqEsJj28Y5Nq1kYKm7EyFJqAqZGNzZ3xSe3nAlpcolzKfzomHBHhiw2C27cI1ZYRcRRS5IqHCK6jwOnY0zOTHy31Pu0yYGv0jmYqJwTz4tglpolO4iMSDKm1cf3oldz3RjWnCxceXsaTewV59F0KQMgQ2vQCCXZ4JuyRf+1gtA2MmDpsg4Ny3wweFQqFQKHZGCU4KhUIxzmzxu8k30pSctMJHRcjGExsGmVfl4ITlfny78W9STC+GYXL0wT4ee21wUvupRwTHTfj3wBRmywUoHrhXSNPkvNVh1r8zQjKdGXNBj4XlTa68bN4tQnLZcWFWH+yleyBNdcRGfSi//kgWi45pTl/lz2KjLyr49m+30tGXRNfg4x+p4PilnqLwxNpf7LrkhCVujlzYhJQCt9Xco+AphGD7kMndT3fT1p3gxEMCHL/Uh8c2e68DZCpEVngzE4acW49DxSzHlNA9IukaSOJ3W6gOWrDps/t+VChmO0pwUigUcx4TaO01eGhdLxYdTj8yRG2wOP1upguHRXJEk52VC6qQpizajbOmCdImWLTSiGTaHU1lVj5/Xg13PN5Nysj4/Bxct+eIi3Ta5LQjQjy3cTgr2GgCTlwRKLropgmq/JmUsq09Cay6oKHMji+PKWUOi6S50kpz5YQXTX5+dzwteHtbnCdeH6Sp0slxy3yEXXn51XlHioygLg5QQTAR/OKRTjr6MhF4hgm3P9rFwlon9ftRQbCYME2Jcx/8wvqjkq/+opVEKnNN736yh4HRNJcfH5q95nEKxSxF0wSvbU7w77/blm1be1iAy44Lz2oxXKGY7SjBSaFQzHm29Bj86y9bs6+f2zjMrdc0URMo0pCQacQo4uiu/ig8/MoAm9qiHLfUz6pFnlkfSbA7rJrkiPkOljY2IKXEZd17ka0upPP1qxt5fMNgVmyqCRanDwtk0qAjboi4Z48Xi9AET745wp1PZMzON7w/xmOvDfCNqxvwF1FxxJQp2NiW4I/P9eKwa1x8bBlN5Tr7Kw3FU4LXPxjLae8eSlEfmlvlYNv7klmxaYJHXx3g3FUhvLYCdUpREgzF4c3X+oklDJoqHJR7lYa5J4bj8JMHOia1PfrqICeuCBSlf6FCMVdQgpNCoZjT6LrGn16aXKFNSnj6rSEuP35my2Erds1YSnDTHa0MjGSqjW3pjPNBZ5zrT4mUdFUiKSX28ZNZcx+0QIGkLqhx7doIMFFqvXSvUyEYTcDvnp08dwxHDbb1JvHXFo/asKk9wXd3OvG/qbWVb1yz/wbydgssrHHSsi02qT3kmXtLStsUxvNOm4au9raKA2AgBjfevpWhsczzzqoLbr6qcU4egu0L8ZQkmsh9UI7GDFCCk0JRMNTdp1Ao5jzaFDOh2jAUFx39qazYNMGzbw0xGC0eEUUIGIxBa5/BUCLzel/QNIGe54FnGGbRptGVBrlfstjXL34aEZrGfS/25bSvaxlB209TL12YXHd6FV6Xnm07a2WI2tDcE5xqI1aaKidHdV15ciWe4tEbFbMMIeCNLdGs2ASQMiS/f663eI34ioSAS3BQzeTwUqsuqAxad/ET+48QmQqrhtpKKxR7ZO6tDhQKhWInDMPkzJVhXto0km3TNFizxJ/X6CYhBGkp0AXKhXU/sEyx0Na1qcXCgiDgtS1JfnBvO6m0xGHT+NJFtTRXWffCeBh6x+ClTcN0DqRYs8THvHIrFuU5UdR47ZJLjo/wy0e7s20Bj4XaSP43NweC15m71HM79QNKz6nywbeubaR7KI3LrlHm1dDF3BuvLovkSxfV8G57gu6hFM21TupClpL3l5sLSAT9Y5KhqEHEa8Hv3DtfrwNFCEH/hw5XAHoGU5imKOmI3gPFIiSfPaean/25izc+GKMqaONT51QTcuU3HTFlCt7eluCeZ3rRNLjkuDKaq21zcg5UKPYGJTgpFIo5T31Y4+tXN/Lk64NYLBrHL/dT7c/fSWI0JVj37hiPvDpAbcTO+ceEqfRpeSn5PleoDFo4qNrJex070njOOyZCwHng5djzQf8YfP+P/5+9+w6P7Kzv/v++z/Sm0aj3sk32uq3XveACNrbBgI0L1WAMJiThCQmkPA/BIXQMfpKQ/CAEeEJzINgQMBibZgy2wQX3LuP1du1Kqy6NpGnn/v0xK+1qZ21vkTRFn9d1+fKlW2XPzJwzc87nfO/vvZ3s7pByJu3yTz/Yxuff003sZdraDE/Bdd/YzORMDoDfPDbKX1zSykkrA6Xw0MrWYBK2D6YJBx3aan2EvAv7ZLounHVUlMaEn7ufGKOzMcjpa2NUlVAbo9kVAB/+4wSzGUjQ77B+ZeSw3n+shajfEq2frXJavjtqzA/ruwMYE9z9nC7f56JSWAu/f26Kr96+A9eFgM/h797UzurGwwtqD4TrWo5fEeGH9wzOG3/NyTV4jKvPhJeRCMFfvaGJZBoCPgg4Cx8UbuhPc8P390xT/uz3tvLRt3eyssHzEr8lsnwpcBKRZc8BOmoc3jWv383CMMbwy0fG+MHuk8ctAyke+uMk17+ni5rQgv0zFS/ktfzVpS08tWWajTtnOHZFhFVN/pIImwBGJnNzYdOsqZTL2HSOWOCly7A29afmwqZZ3/n1AMd2deJ3VA13KDYO5vj4jZuZPZSP7g7z/oubCfsWdn8JeODYdj/ruxtxS3R1x45aD5++ppvHXkgS9BuO6YpQHy2ZQ6di6AZC5RhMWr5y2465YySVcfnCD7dx/TVdC/4esj/ttV7+5sp2bvxVP9NplzeeUce67pD2sQPkMXbRgn+v1+FnD44UjP/m8VHWXFCvKewi+6HASURkt8U4UUimKeihksq4bBtMU9OuRh8HIxawnLYmyOk9od0X9qVz8l0T8+D1mHmhUzjgEA+//Mpw+5u5mXNtRV5cGJMPYRczmMlah6/evp29D+cnN06xZTDNEc2LM90tW8KrO2ItLXFD6/oYkL/bX4G7lsiCGZ7MFRwjY8kcEzMuYd/i91HyGMux7T7++c9WMzExQ+ggViiVxWVtfur0vhJRb0V+ZosshFLpfiEiUpGMyZfj72t/PYnk5VlbmifeiTB84NJW/N786xryO3zo8rYDusva3Rgo2EeuOKueYAXdEjImP3XwzqeS/PD+MbYMuyzWy5jOWfqH0wXjk9MlHAotAWuXpgeNSLmrq/IW9OeuiXmpCi3dZZO1EI94CXhKs3JyucrlXC44IYHPs2cH8XsNZx5Vtd/XyQWGkrBhV46xGXPQi4mIVIIKOp0VESk9ET+847xGvvSTvrmxxoSPjvrSaiwsh8nCcZ1+bnhvN+PJHNVRD/HggU2zqYsYPnl1F794aJgdIxlevb6atW3BirpbOpSEj3xzc355auAHdw/ykbd2sKZp4U9Dwj4469g4dzwyOm+8pbbMKgqNYWzakrNQHVKzYJGlUhM2fOCNbXzpx9tJZSxVYQ8fvLyNsE+hrUBL3PDpa7p4evMUxjEc1RGmIWYKPrNdDHc/k+TrP9uJa/P98z50eTtrmj2ov7gsJwqcRGTBmNLo31xSXNdywsoQH72qg8deSNKU8HNUR5ioX09UxbFQHYTqYL5x6IEeC9ZaGmPwzlfWYozZPbWzsvaP57bPzIVNs75z5wDXvbkNj1ngyiNrufT0WjJZyz1PjhGPennva5ppWsCFABZb2jXc+fgEN/12F5mc5fS1Md72ygZiet8QWXQGy/Fdfm64dgUT0zkSUQ9Rv8ImybMWGmOGpmMic1/v7wbRrgmX/3f7zrmvZ9IuX/rxdj781g4ao0u2uSJFp8BJRA5bJmfYMpRh484UzbV+VjT6D2tFKGNgaCrfYNsxhs4GP/HgAm7wEvM5lpX1XlY3Vu+e1qKzVim0EH2pjAGLk/+/WxpTyIzJ9y3b13TKXbRYrSpgefer63jT2XX4PPmm8+V02G0ayPBfvx6Y+/r3T0/QVh/kdSfGyupxLEcWw1QmP83G9zJh6nTWsG0ow3TKpbXWT52auZcOC/GgJR50mO19JrK3l9snhkDf6aYAACAASURBVCdyBWMjk1kGRjM0xfw6F5RlQ4GTiBweY/jlYxP892/2XByd3BPjvRc14HcO7cO0fwKu+8YmptP5k/WqsIePv6OTmvCCbHHRqA+DLKZUzvDklhl+ev8wtVVeLjm9ltaEU/RiKWuhpy2E48DeGdilZ9TiW4Qlq2cZa4n592xDuXAcwzNbpwrG73lyjAvXV+HVXIySNTpj+M6vB7i/d4LWugB/8tpmumqd/e5/yYzhX2/ZwTNb8q+1z2P46Ds66UiovapIJair8hZU/tfFfQR8hqJ/MIssIX2qiZQIj8fB6y2/Q3JkynLzXQPzxh7onaB/tPDOzoFwHMPPHxyeC5sAxqdy/OG5SRw12pYllLWGZMbgUvr7nTGGh1+Y4gs/3M7zfdPc/+wE131zMwPjpXFS21Rl+MQ7uzhhdZTupiD/65IWjl8R1h3e/XBdS1tdYbf5Va0hLTZQwnIYvnrbTu57dgJrYduuFB+/cTPDhdkhAFt2pefCJoBMzvKNX+wkp1NzkYpQG4U/e13L3GIi8YiXK86qpyHuLaubICKHSxVOIkVmDOwYs/zy4UFGJrO8+oQEKxv9+A6xOmipZXJ23vLjs1JZl0PKtI2hb6hwham+oTTG6K6QLL7ZY/Irt/exoW+G41ZEeOf5TdSGS3ffS+Xyjbj3ls1ZnuuboaEnVKStmq894fCB1zdiMTi4CpteQk9rkJ62ML3b8oFEVdjDJafVgi2NaZJSaHzK8sSm5LyxTNayYyRDIlS4SMTYVOFNmb7BNOmsJaSzc5Gy5wCnrgnS2djNwGiGaMihLuqhqoxbRIgcCn2kiRRZ/4Tl77+xiUw2f/H14HOTfOjyNtZ1+sviDkhNxOGozjBPbd5zpzYe8dBc7eNQwiE353L++sS8vwdw+tqq3c2URRbXeMrw8Rs3MzmTvyB8dEOSXaNb+dhV7Yc8TXSxOQaCvsKA1+8psYoYazFYxcYvI+q3fOiyZnYMZ0nnLC01vmXZMNxiGJ6yDE9kqYl5qQkbTInuPX6fIRJ0SM7M/5wKB/Z/46WtrnDVxHOOqybsL69cMWsNrmsI+qymjYvsw7rQFIPmKl9ZnNOLLAbV7YoUkTHw9ObpubBp1vfv2kXWlsfh6TWW913czEUn1ZCIeTn1yBgffXsnkcO4OFrbHuCaC5qIhjxUR738+etbWNFYeIdYZDEMjGXmwqZZ24fSDO2nAWip8BrLW1/ZMG8sGvSwulW3UpeScfLTMKeyzmFPAQ56LN31HnqavMsybDIGHtk0w4e+8gKf+K8tfOgrL/DIphlMiWWos2IBuPY1zfPGTl9bRUti//d2W6odPnR5G/GIB8fAOcfFee3JCWyZhDbWQu/OLP944zb+7uub+PUTSWayJfriiBSZwiZZzlThJFJk+zt5dowpg64xe8QDlrecleCNpyfwe/PNeg9H0AuvPCbCKUdEMEDIu/8lZ0UWQyTgKRjzegwhf2mHwEe0+PnEOzt5oHeCRMzH8Ssj1IR1ortUprNw5+MT/M89u/B6DG99ZQOnrYng8+gFOBQj0/DFW/rmGs27Lnzxx33832u7qS6NWaLzuK5lXVeAz767mx3DaaojXlprvS9aFekA6zoDfO7dXWTdfGBVTgfr9lGXT31ny9zX//nznfi8zZx5hHqziYjIHgqcRIrIWljbEcbvNaT3qnK64px6HMqoph7AWgIeFqzFkutaQp65Py2HyBgYmYKtg2m8HkN7nZ/oMqyWOBj1MYfXnlLDT+8fnhu76lWNVIdNSe+MjrF01nroPjOBtfmQtoQ3t6IYA49vmplbrTOdtXzt9p3UVbWztlXVmS/GYhhOWrKupTbqzFuBbzyZI5ObvwNnspaxqRzVocJQuBQ4QEvc0BKfbfr+0gegtXv1ayqjY9UYw5ObC7uh33r/MKf0hHVxISIic/SZIFJkDTHDp97Vxd1PjjMymeXc4+J01x9a/yORffWPw3Xf3DS36l9jwsdH3tJBPKj968V4jOXSUxOc3BNjaCJLY9xHc8Ip6bBpb8ulj8pMOsfodL76LBYobrhmHIc7HhktGL+/d4JjOurUf24/ZrKGW+4b4bY/DGMtHN0Z4X0XN1EVyL+QiZiXkN+Zt2JpyO9QE/Oiz8fistZSFS4M/WqrvHj2XQdeRESWtdKeHyCyDFhraYwZrjyjmj+9qJ5VDV48RidrcuiMgcm0YcNAlk0DKY5dEZ37Xv9Ihic2TZVsH5RS4fdYuus8nNgdoL3GwVuuz5cxTKTy+4M5zJ5CpWQibfj8TVv4i39/gQ9+ZSN3PT1Fxi3i47OWrsZAwXB7fWDZBIAHa0N/mp8+MDyXTTy5Ocmdj4/h7D4zrQpY/vbKdmK7g41Y2MPfXtlOVeHTLEWwtiNMdXTPfWvHgSvPrse8SMdzYwzGOfzeZiIiUl5U4SRSIlxXK7zIwtg5Dh+7cROT0/km1yf1xDjnuGp+81i+AmPHcApj1Gej0k1l4Kd/GOW2B4bwegxXnFXPOUfH8Jd5TyFjDL98eJTfPzUOwEza5au376StvpPuuuJMtXJdy/nrE9z95NjcKmW1MS8nrIqUxHFmjYNrwWvckig+cRzDs1sLp2Td98wEF59UjYf8dq5q9PK5d3cxPpWjKuwh6i+tfn7GGJLpfMgf8S+f6kKA6qDl4+/oYMOOFDNpl5UtQZpi+w+TJtOG+5+b5N6nxzmmO8LZx1RRrfUMRESWBQVOIiIVxMXwrV/unAubAP7QO8E7zm+a+3rdyuiyujBajoyBhzdM85P7hgDIuZYb7xigrS5Q9j2FZrJw1xNjBeMv7JhhRX2kaIFKQww+e00XW3alcRxDR52fWKD4x9nWEZebftvP8ESW155Sw/oVYYLe4m6X61pWNBUmDsd0h/E6ltkiGWstER9E4g5QWj3JpjKGXz06xi33DhHwOlx1fiMnrQrN60NV6aqDcEL3S5ecuRi+dccA9z0zAcBz26a59+lxPvq2dkJF3g9l8WStk1/Z1VDQn01ElhdNqRMRqSCpLPyxb7pwPO0SDTm897XNdNWXd+AgB8A43PlYYU+hB5+bwOMp749+nxc6GwvDipoqH49tTvHCYI5Ubumn7VgL8SAc0+7nqFZfSYRNOycs131zE4+9kGTrrhRfvnUHD/wxWRLTmta0Bjl+VWTu68aEj4tOTGDLIAw3xvDQ81N8/+5BMlnL5EyOf/9JH5t2ZYq9aSVnOOnOhU2z+obS7BzNFmmLZLFNpg1fvHUnf/O1jfzNVzfy77f1M5ku/nuOiBSHKpxERCpIyAcnrYlx95PzK0CO7Axx7nHdRJfZtI/ly7KyJcgft88PHzsbg7hueTewdqzlrec28OyWqbmG0ke2h3m+b5qf3Juv6Drz6CquPq/+RZekL1VmtufWjEs87BD2HV5Vz/N9M+z7cv/od0Oc2hPBX6Tc0RiTn4Lms7z/4ib6x/Kr0TVVe8um4sXF8MuHRwrGH3l+kjVNCb3H7sXZ/Xrvux8fSObpOIacNTjYkppKWemMAb9NYTGk8R/k7xoe3pDkoT9Ozo39oXeS9atinHlEqKSqFEVkaShwEhGpJNZyxSvq2DGS4vntM/g8hrec20BHjRevYwsuPl9M1hqGky5+j6E6rFWHyo11La9en+B3T40zMZWfXtmU8LFuRbgiXsqmKvjC+1ezsW8Kn9fhkQ2Tc2ETwD1PjvPqExJ01Ranp9OheqYvzRd+uJ3kjEsi5uWvL2+jo8Y55Ncs6CtMlcIh54Au9heaMYb+CcujGybI5CzHr4zSUg1tidltLJ8d0zHQVh9gU//MvPHmmoCCkX0kIobz1yf4xUN7Aro1rSGaql96tcHBJPzsoWE29M1w7rFxTlgdIeLTc7vYfDaFv+8xMvfdDI6HyGlvItV4NFkOrDLa43F4oHeiYPzBP05w9lERstnyvuEhIgdPgZOIVByLYTKVn3oT8pZW34+lUB2y/J8rWhlJuvh9hurQwQVGYynDl2/dwVObp/A4cMXZDZx/bAxfmTebXm7qIvCZd3WxbSiN1zG01voq5oLNWmivDxIkw+gM88KmWZNTOSijwGlkGj5/0zYyufxrNDKR5fM3b+P6azoPufJnVUuAeMTLWHLP9KWrXtVYlH4qO8dd/v7rm0hn8//2zb/dxSeu7qI9UX5TPK3r8obTanjwuQlmdlfZNVT7OKZLFRz7MtZy2Rk1HNUZ5pHnJ+lpD3Nsd5jAS3yejKcMH/32prmwfEPfNDtHarjyzIRufiwy/65nSf30n+a+Tv34eoKX/QOTNWsP6PdzOZfjV0V5/IXkvPF1K6LkcnrtRJYjBU4iUlHGU4Ybf51vUFod9fLe1zRzVJsfU0Z3zxeCz7E0zK4YdDAn6MZw6/3DPLU5v4JUzoX/vnOANa0hVjWUz8W75FUFLGtbZu9MV+YxEAsaju2O8PjGPRc4jgNNNQc3FaTYhsazc2HTrNHJLKPJHKH4oYUy1UH4+Ds6eHrLNGPJLMd0R2irXvqAx3EMDzw7Phc2AbgWfnLfEO+/uBE3V35VD01Vhuvf3cXWwXyg217vJ+bPP76MC32jOcaSORriPhpipihVZaUi5LUc3xXgxJUhXNd92Sqw7UPpubBp1u1/GObCExNUvXSPcjkMXq8h+9jPCsazT/8Wz9lHkzuA49Ray0mrI9z3dJjebfnziLUdYdaXyIqdIrL0FDiJSOUwhpvuGpxrUDo6meXzN2/ls9d00xxfxmf7ByGVgweeLSyH39w/w+rG4q0AtlRcYGDcMjieIRH10hh38JbYruM4BmuXX+Xei/Fgec9FjXz9FwM88vwkdXEff/a6FuoiL/+7paQ66i3odRMOOESDHg4nLEyE4MwjQoAp2gWfMZBMFV6sTk6XX9A0y9r8c5tonw02889t1jXccv8oP96r6u4vL23lhBWBZX/MHkhgAeDZTzrn9Szv0G4pWGsw0UTBuIkmDuq9oyoAf3N5MwNjOYyB+iovfqd8j3UROTwKnESkYiTThnv2aZZtLfQNp2mO67bogfB7YE1biPv3CZ2aavwVf7FkjOH+56b495/smBt7yysbuGBdDKcEqoOyrmHzUIZHNyRprPZzTFeIeOFibctSdRA+8PpGJlONBLwQLMOptDVheM9FTXzt9p1YCx4H3v+GVuIhDrj32ovJPxfFe0JyOcvpa6u47YHheeMXn1pTltVNL2VgIjcvbAL4j5/u4IZru0ti5cJy0Fbro6XGT99wem7syrPriQXBVtbuUlJyOZfQsReQfeYuyO2ehusL4Kw586Ab4fsdu1d/Nr1oIsuZAicRqRh+r6Wpxk/fUHreeFVIU8EOlLGWN51dz7Nbp+f6vpx8RIzuBj+VOiVr1si05Wu375w39t93DnDCqggN0eLeWjfG8MjGGf7tlu1zY/VxH//49o6XvIi1GIaTlqm0S33MS9BbuSf+DnZuuk25hU2Qb0R9Rk+YnrZuRiez1FX5qIlUzqqS7QmHj769k+/fs4tM1nLpGXWsbqq895WJ/VRtTaddptMusYBKdA5E2Gf58JvbeWzjFFt2zXD8yiirmvzYCjkWStlUtJPQm6+HvqfBONByJFPh1vJ8UxWRkqDASUQqhs9Y3vvaZj5x42Zmb5ofvypCe52PSruoWUx1EfjsNZ3sGMkQ8Dk0xj1lt7z8oUjOuGSy8x+ntTAx5dIQLW5oOZ01fOtX88OwXWMZtuxKc1Tb/lcPyriGnz8yzs137cJaqIv7+PCb28tuqtly4hhoiBoaortf0wo67IyBlQ0e/u6yFixgrEtFPcDdGqu9+L1mXr+q9oYA1RGHSny8i6UqaDlrbQjHCe9uNq3nbim4FpLhVszqVmB3zqSwSUQOgwInEakoK+o8fO7abnYMZYgEHVprfQS1utpBi/gsqxpmPyKWx/NXG/VSV+VjcDwzNxYOODTEi18hZ62dWw1rb/s2md7b9uEsN/1219zXg2MZvvGLfv7qkiY8RVilTAQA61LJdT6JEHzkbZ188Zbt9I9m6GkL8SevbV4Wof1CsxatbFYkyphEZKEocBKRJZd2oW84x/BElrq4j5ZqD94FPBmvjxjqI/MbuYq8nKDX5e/e1M6/3bKdLQMpmhJ+3n9JC1XB4jVbnhX2wxtOq+Omu/YESAGfQ0f9i6/ENjieLRh7clOSmSxE9l8UJSKHyVrornP45Ds7mc64RAMGrwJeWWLGwGTaMDCaJRJyqI06eHQ+JCJFoMBJRJZUzhp+8sAot/x+T1PVd57fyKuOjeqWmhRdYwz+4a3tJFMuYb9DwPPyS3gvBetaXrWuinjEyy8eHqG11s8lp9dSG3nxMKy2qvAjfm1HhOAChU3WOORci8+hJJ4jkVJhLQQ8LgEP6KaHFMOOMcsnv7OZ8akcAJedWcdrTqzC57zML4qILDAFTiKypAYn3XlhE8CNd/SzflWERKhIGyWyF7/j4g9Bqa2sE/Jazlob5owjI3gcsO5Lh2GtNR4uO7OO//ndINZCTczLuy5oXJC73FuGXW76bT+D4xlee3ItJ64OEdIZhSwwx+OABfdwl+kTWUay1vCV2/rmwiaAH9wzyLqVUTprlTiJyNLS6aGILKnkTOGFQ86F6ZRLIqQTIZGXYq3FwR7Q0uB+B153cpzT18aYSrk0xL2EvIcfNvVPWD76rU1zjfm/evsOsrkmXnVspGJWVJPiylrYsDPLTx8YIhzw8NpTamivcVQsJHIAZjLwfN9MwfjwRIbO2kARtkhEljNd3YnIkmqIe4mF5zdhbk74qY0VvzGzSKVxsNRHDZ21ngUJmwBe2DkzFzbN+tG9g0wXtowSOSR/3JHhU9/dwqMbkvz+6XGu+8Ym+kaVNokciJDfsLYjXDBeF1fzPhFZegqcRGRJxQKW697WwerW/Py5o7si/M2VbQS0kpxIWQjupwlINOjBU8lLj5URYwDjYJzyfEGM4/Cj382fdu1aeOiPEzhl+phEFpIxMDoDT21L07sjSzIz/7jw4PLui5qo3x0wOQ6847xGWqp1Y09Elp6m1InIkrIWmmKG/3NlC6ksBL1oifbFYgypLPi9YNTUWRbIiqYgiZiXkYk9JU1XndeolbhKwEzW8OjGaX7x0AgtdX5ed0oNTVVOeTV1txavtzBY8nocrSshC8Iaw8iUxXUtNREHp8zmavZPwD98cxNTqXypaVudn//9pjaq9potVx+BT13dweBEjnDAoSZstDCLiBSFAicRKQqvsXhV3b1oxlKGnz4wzP3PTLC6LcSbzq6nPlLsrZJKEA9aPvb2Dp7ZNsNoMsvRHWFaEyqYLjZj4K6nJrjxjgEAnu+b5v5nJrj+3V3UFM6uKVnWWt54Zh1PbEzOjfm8hvWrIuUVnC2xjDXsGs9hgPqYB6+j52p/ZrKGWx8Y5db7h3AtnHxEjKvPayDqL4/nyzgOt9y7ay5sAtg2mObpLTOctiY4L1MKeixt1bvfm3XsiEiRKHASEakwORy+fGsfT22eAuCBZyfo3TrFZ67pIurTSaccvuoQnLY6iDGm8kIAY5jOQMBLWVU+TGUN/3PP4LyxVMZly64UNZ3l1Si4u87DJ6/u4p6nxokEHU49IkZTldE184uYSBn+47adPL47pFu3MsK1FzURK5MQZSk9vzPNj+/bM2XzgWcnWNkc4rUnxMpi0YOcC5v7CxuC9w2nMCZUee/HIlL2dEtSRKTCDE/m5sKmWWPJHDuHM0XaosozlTU8P5Bl02COmdzy7StTaRc3I9PwpdsG+It/38g//WgnuyaLvUUHzgF83sLTOm8Z9j1yDHTUOFx1Tg2XnhKnMaaw6cUYY3hoQ3IubAJ4dEOSR1+Yyvfzkjkej5lXOTfr3qfHyR7Ayp+lwOtYXrmuumD8mC6tEioipUmBk4hIhfF6DJ79vLsH9tPsWQ7e8DRc980tfPzGLfzDtzbzuZv7mEjpyq7cpV3DDT/Yzn3PTJDKuDz+QpKP/9eWgoa8pSrohavOa5g3loh66WjwF2mLDl8u5+oi+mV4PA4PPjdRMP7I8xN49vdBsIzlcpaVzcGC8aO6wvv9zCxFrms5pSfKxafU4HEgHHC49qImuurVo0BESpOm1ImIVJhE2HDl2Q18986BubGTe2I0VnugjKYIlSLHMdzxyAi7xvZUiz3fN83jm6c4sydUklUY1hjS2fwUsZLcwBIxNJFj60Bq3thYMsvAaJbu+tJf3clay/HdIa57WwcPPjdJU42PdSsi8xoJS+XJ5VxOXB3l8RfmV+4cvzJKLlcmZTtLaG1HiLUdYZ7ekq8Cboj7OH99NbaMgs2Iz3LlmQlec1ICYyAWQMGsvKygncJJ7gJfmFSwjpwtj5spUv4UOImIVBprOe/YGKtbQ2zun6G5xk9Xgx9/iTeRNQaSacP4tEss5BD125LLRyyGp7dMF4xv2D7NWUdGSu4Cb2gKvvfbAZ7ZMsXxq6K88fQ6qkMl9qSWiIDfwXHA3eclDAbKpPQB8DmW1Y1ejmhJ4LqVN+VRCllrOWF1lAd6J3hyUz5EOXZFhONXhkvu/bMURP2Wv7q0mf7RLJmcpbnGR9hbhk+UtUR3Fy/u+54lsq/ozA5SP/kc2dEd4HgJnHUVmVXnkjHlWwEr5UOBk8giMFp9VorM57GsavCwujGye18s7R3SGMOGgSz/9INtjE/liEc8fPCyNlbUe0vrotlazjyqig1980OndatKr5pgKmv45Hc2MzSeBeA3j42xpT/F37+lFZ8poee0RCRChjftUxn46hMSNMQcSv342VcuV17bK4cn5rf81SXN7BrPgTHUVzn4HJiYsTiOUQXMPgIeS0ftbNWinpdDZgxZF3weyqpCbLnxkyHzm/+HHd2RH3CzpH/zdQKNq8lUrSjuxsmyoMBJZAFlXNg8mGNT/wxNCT8rGn2ENa1eiqiUspqXMj4D139vK9PpfGgzlsxx/fe2csO13XN3cUuBtfn+GRt2zHDPk2M4Dlx8Si1rmgOU2oXLwGh2Lmya9cLOGQbHczTHy6dqZ6kYLOcdF+WI9hD9oxnqYl7aar14FM5JGfA5lpbq/HE9mYab7xvmFw+PEPI7vOP8Rk5aGcZb4lWuUj6Gp+HW+4Z4assUpxwR47x11VQFynf/yvc7s+RylkzOsG0ky+B4hoa4j5aEF18ZHzvebJL01icLvzE+AAqcZAkocBJZIMYYfvPkBN/+1Z6748evivDnFzeV/FQmkWIbnszOhU2zplIuw5NZojWl1T8n6re854I6LjuzFsfke2aVYrIX8BeGSo4B/zJqHp9xYdtwjl2jGWrjPtprvPg9L/5a+RzorvPQXVda+5zIgTIG7n5qgp89OAJAcsbl33+yg8arOllRBr3IpPRNZw2f+s6WuV6GP/rdEM9vm+GDb2zCW2YBfcaF3u1pbn9whNoqHxeemOCxF8b57p2Dcz/z5nPquWh9jHLteJTzhnAaVuIObJj/jWhtcTZIlp3lc9YpsshGp+G/79w1b+yR55PsHMm+yG+IyKyqsAevZ/7pnM9jqAqV5gWSYy21YUiEKMmwCaA+5nDOcfF5Y5ecUUdNuFxPmw+OBX7+yAQf/dZm/r8f9/Gxb2/mR/eNkCvbywaRl5d2HX796GjBeO+2aYzRvi+Hb+dIdt7CGQBPbk4yNFla08pfjjHw2KYUn7t5G09sTPKbx0a57hubCPjn12Pc9NtdDCdL83P+QKQI4H/VezHB6NyY74TXk4m3FXGrZDlRhZPIAsnkXDL76ZsxkynfDymRpZIIG953cQtf+vF2XJuvxPnT17WQCJdsnlMSXAxDky7jUy71VV6qgnueLK+xvPXsOs5YW8WO4TStdQE663yYEpv6t1iGk/D9u+bfBLj1/mHOPjZOY0wX3lKZfI6loyFA/0h63nhDta+0+uFJ2fJ5C98/HZO/SVRO0q7DzXfP/4zI5CwTUzkCPkNq9/m7a2E6bSFSXo9vb8lYJ8G3fB5nvB8CEdLhRjUMlyWjwElkgSQiDutWRnh0w56liWNhD80JH6XW20Wk5FjLSSuD3PDeFQxPZKmJeamLGF0gvYScNdz5xATfvmMAayHod/j7t3TQVefMhXRBr6Wn2UdP82wzueXzfM5kXPbXx3Y65UKsNCvnRA6btVzxijqe2JhkZvc05c7GAGtaAkXesNJkjCGVA78H3d04QI1xDyesjvLQHyfnxi4+pTY/vbyMPmMMFp9TGCL5PGbertBQ7aOuykM5PbZ9WQvT3gTUJIq9KbIMKXASWSAeLO+5sInbHxzh90+Ps7o1xJvOqqcqWHpLu4uUIoOlLgJ1kdmPJh04L2VgPMe39uoZN5N2+cIPt/OpqzsIleMy3wusLuahOeFnx16VHomol4a4F+1bUsma4w7Xv7uL7UNp/F6HtjofYb0nFBhPGX7x8Cj3Pj1OT3uYN55RS12k2FtV+nyO5T0XNnLOcdVs6p9hTWuI7gZ/2VXP+hzLm89t4HM3bZ0bC/kdjl8V5cmNSZ7emuSYrgjvPL+R4Ev0/hORl2aWwd3jLmDj0NBk0ZeEra+PsWvXRFG3QRafcQwzGUPAa4t6t0z7myw17XNL66ntaa7/3raC8X9+3wpqw0XYoCV2IPvb8JThv37dz6MvJFnbEeYd5zdSrwtKOUR6j6scOQz/8qOdPPbCnqr0eMTLZ9/VScRf/GsjxzFkHR/JqQyxACVbfeU4pujXV4cjZw2bdmW5+6kxaqNeTj2yioYYZK0hlYWgF5wyC9IOh97j5FA4jqG2NgrQDWza9/uqcBJZYNa1BDxWN9BFZFHVx30FYy21fqLB8prWsJhqwpY/f10jM5mlu3CwOGRcS9BL2V2I5axh14RLKuPSEPeqUk4q1kjSnRc2AYwls+wYybCqsbiXRxnXcN+zSW781QDprMsFJ9bwulMSRHyldzyW23vcvjzGsrLBw5rmOqy1uG5+VoIHS1hXySILQoeSiIhIGaqLGP7XG1r5MBp/fQAAHetJREFUj5/2kc5aamJePnBpKwGntC8ArDFMZ/I9U5ZiCW3HLtGFg4Ftwy7fvbOf/tE0rz4hwZlrY4RL8CJxf2ayhpt/N8wvHxoB8oHm37+lnZplUC0ny4/XY/A4kNtnYTW/r/gLeG8ezPDV23bOfX3bA8PUx32cf1y07AOeUpXbd0cQkQWjwElERKQMOQZOXhVgzXu7mZxxqYl6CPtKu2fceMrwg3uGuOepMdrqArz7oiY6a5yS3uYDtWsC/uFbm8nuXq30xjsGmE67XHJKHFsGF4mbB9NzYRPArrEMN901yJ9cVI8p8xdodAbGJnNUR73EQ6pAFqgOGS5/RT3f++2eVcqOXxWhqbq4zaEdx/DEpqmC8TseHeWcY2J4tPOKSJlR4CQiUi4MjEzBrrEsVWEP9TEHzxJUiEjpshbiQYgHHaC0wyaLw7d+1c8Dvfn+EBt3zvCxb2/m89d2UxMq8sYtgG2Dqbmwadat9w1x/vHVZdGwecdQumDsyU1JUtl6gmW8qN/jW9N84YfbyWQtAZ/hQ5e3cWSLr6SPFVkC1nL+uirWtIV4vm+Gtjo/K5sC+ItcIeq6lpaawuXquxqDeDRbWkTKkAInEZEyYAw8P5Dj09/dQiabP+O84qx6LlpfhbfEp1BVsmTasGUwTSpjaavzUR81upB9EeMz7lzYNCuTtfQNZahpK+xHVW783sKpOLGQl/0Ml6TWukDB2LqVUQJlvKjfyDR84X+2k9kdBKYyln/6wXZuuLaLqsKHuyisMYxN5//96pAp2ebPy5HfY1nd6GVNU4z8Ikql8doc2R6kvd7P1l35EDgccHjDabVgNe1LRMqPAicRkTIwlTH864+2z4VNADfftYv1qyK0VpfJFW2FmUgbrr9pG1sGUgD4PIaPvbOTNr0e++XzGKIhD5PTuXnj4UBlPF+dDX5aavz0De+pFLr6gkYCHrcsMoaOOh9vOK2WH983hLXQVh/gsjPryno63ehkbi5smjWTdhlL5qgKLH7Z1lTG8OP7R/jZH4bBwGtOruF1JyfUjL3ElNqK3VUB+PCb29k5lmN6JktbnZ9ESFmliJQnBU4iImVgKm0ZmcgWjI8ms7RWF5bfy+LbsCM1FzYBZHKWG+8Y4G8va15WyygfqGgA3nNRE//yP9vnxo5fFaG1poxLaPYS9Vs+/JY2nutLMTqRZU1bkLaEt+QuZl9MwGN542nVnHNsnJmMS32VF79TPhUVxkAwM4pJDkEwRipYRyLqwec184L6kN+hOrL4+5wx8MSmaW57YDg/YOHW+4ZZ0RTixBVLVF4lZSvis5x6ZHxuifoyeRsRESmgwElEpEgyrqF/LMdYMkt9tY/6qIN5kYugWNChvSHA1r0CDmOgIV7+U5HKkTEwMpkpGN85nCabM/g9ujrYl+ta1nUF+cw1XfQNp6kKeemo9xGooOeqKgAndgcwJrD7ArG8HpvBUhsBcIDyCpsiYxtI3fIZ7MwkOF6Cr3wPdd1n8pdvbONff7iNVMYS9Dt88PI24iFwF/nheTwe7n5qrGD83mfGOXVNI9ls+Ty/IiIih0qBk4hIEeSs4faHxvj+3YNA/oLpL9/Yxgndgf0ue+x3XD5wSSv/9+Zt7BhJEw44vO/iFuqi+WbRsrSshVUthZ2uz1ufIOAtjylUxeBgaa12aK0O7h6pzCdKr//SyGGYmLaEPVl8d34tHzYBuFlSv/oygbet4pj2Nm64dgVjU1mqIx7iQZZkaXnXdTmyI8zjLyTnjfe0hbS0vYiILBsKnEREimBgwp0LmyB/gfrln/Rxw3u7ib3IDLmGKHz8nR2MJnOEA86SXTgVm3EMEykwQCxgF70y4UC1Vnv40OVt/OfPdjIxnePCkxKce0xV2UyhKoacNQxOuqQzloa4p6Kqm2Rpjc4YvnrbTp7YlKQq7OF9Z/0Zx7pfwhncuOeHJocg3EY8aInvXmpvqQ5P17WcsTbGbx4bpX8kXw3ZXOPnlJ7YsnjfFhERAQVOIiJFMTGVKxibTrtMpVxifvOivxdwXBpj+bWRSyV4WUwzWcOdT0zwg7t3YYzhirPqOPvoKEvQ7/dlOcayrtPP597TSc6FiB+sLiRf1HQWbrp7iDseGQWgrc7P31zRRqKwUEzkJbk4fOMX+bAJYHwqx+d/nuPzF76Zlns+s+cHozVF2sK86iB87O0dbB/OYAy01vjUMFxERJaVylgaRkSkzNTHvfi884Ol5ho/iXAJJCkl5OmtM3z3zgHSWUsq43LjHQM8uz398r+4RKyFgGMJe63CppexaSAzFzYBbBtMc8u9w2SsTkXKgTGQch3GUwaXFw/Fl0IybXn4+cl5Y9bCjkw8/4VxCLzqWlLh5iJs3Xxhn2V1o5dVDV6FTSIisuzoLE9EpAgSYfjwWzqorcoXmnY2Bvjry9vwe5ZB2dIB8vk83Pn4aMH4PU+O4fXq46ucGGPY2D9TMP7ExiTPbk/h6OUsec8P5Pjw1zfz/i9u4F9u2cnoTPFCp4DH0FBduGBCvKkZ/xWfwP/2f2Zm5blkK6yQ3xjDTM5hMm2wprihn4iIyIGorE9iEZFyYWFVg4fPXN3JVNpSFXLwGoVNe8vlXFY0hXhsw/ymu91NQfVAKTPWWrobgwXjPe1hfnzvICubWgir+qNkDU3BJ/9rM7ndb1GPbkjylZ/u5EOXNeEpQuP3gNflT1/XMm+bXnFMFc21QaY8q/MDFbY7ucCTW1J87fadjE9lOfe4ai47o5aov8IeqIiIVBQFTiIiRWItBL2WoBfKaQnypeK6lrOOqeLXj44wlsz3vEpEvZx6xPJqumsxjExZRiez1MS8VJdpz6PuBj+vXFfNrx/NV6211gVY1RrioecmKi4cqDQ7hzNzwc6sJzcnGZ+2RenBZS2srPdww3tXsHM4QzTk0Jzw4ncqd0fqG3G54eZtc1/f8cgooYDDm85IaKECEREpWQqcRESkZNWG4VNXd7FtMN+3qb3eT2wZ3dG3GB7cMMMXf7Id1wWfx/DBy9s4us23ZKttLZSg13L5K2rpaAySzliGxjN8+5c7ueq8RiJ+u+SPx3EM1i79v1uOoqHCOY9VYQ8BX34Bg2KpDUNteHZqXWW/kFsHUwVjdz46yutOThDS2byIiJQodU0QEZGSVhWwrG31sbbVt6zCJoDhKTsXNgFkcpZ//eF2xorYP+dwRH2Wdd1hsjmX0ckMH7ysjTOOjC5p6JOzhk1DOX5w7xi/eWqKkeml+7fLVXO1l7OOjc99bQz8ycXNRArbKMkiqQoXpkrNtX783vJ8LxARkeVB90RERERK1FgyNxc2zZpOu0xM56gKHP49IxfD6LTF5xiqgktT7VMThtefVIXjxMlmXZayMsUYeGZbms/dtHVuLB7x8ol3dlBd2GJKdvN7LFedW8er1lUznszSVOOnIeZoKtcS6mrws6Y1xHPb8wmp12O4+vwmPJqOLSIiJUyBk4iISImqiXrweQyZ3J4L+2jQQ3XEw+EGNeMpw9d/2c9Dz00S8Dm84/wGTlsTwbsEfXBc1xalD1fadfj2r/rnjY0ls2zcmeL4rsCSb085CXgs3XUeqPPsHlHYtJQiPssHL2th+2CG6bRLS62fuih6GUREpKRpSp2IiEiJqg7Bh65oI+TPf1xHQx7++so2ov7D/MPGcOsDwzz03CQAqYzLV2/bybaR7GH+4dLmWstMurAiJJ3VVbuUvrDXsrrJy7EdfuoiKGwSEZGSpwonERGREnZ0m58bru1mfDpHdcRD1M9hT2WaycDvnhovGN8ykKartkyXwTsAIS9cekYd//nznXNjPo+hu0nVTSIiIiILTYGTiIhICXNdSywAsYADLEyfJb8XuhuDPL4xOW+8rqqyTwtc13JqT4RQoIXbHhimvtrHpafX0RgzWq1OREREZIFV9pmliCyaybRh80CaqVSO9voAjVUORvX9ImXBwfL2VzXw0W9tZnr3FLPjVkboavBT6fN0gl7LqauDnLy6Lf+eZZemWbqIiIjIcqPASUQO2mTacP3N29jcnwLyKz995K0drG7UW4pIuWiOGz73nm52DKcJ+B1aEl4CnuWRvFgLRqt7iYiIiCwqNQ0XkYO2ZVd6LmyC/MXb13++k4zVW4pIubAW4kHLES0+uus8RQ+bjGOYzhky1mBMUTdFRERERBaAyhFE5KAlZ3IFY0PjWbI5i0/vKiJykJIZw68eGeP2PwwTD3t514VNrGn26q6YyGIz0D9ueW7bDD6vYU1rkNoImmYqIiILQpeGInLQ2usDOAbcvU5IX31igrBPJ6kicnCMgd8+McEP7hkEYCqV5tPf3cJnrumitVqRk8hiSbuGvpEcH79xM9lc/sM7HHD45NVd1EWKvHEiIlIRdCYnIgetscrhI2/rpK0+QDjgcMnpdVywvvqwl2oXkeVnOmv42YPDBeMbd6b289MishCms3D7Q2P8+N6hubAJYCrl8vDzkxjNaxURkQWgCicROWgGy6oGDx97ezvZnN1d2aSwSUQOns8x1MW9jE5m543Hwp4ibZFI5dsymGFTf4qJqWzB90YmshijimURETl8qnASkUPmMy4hr1XYJCKHzGNc3nFeE85eZySttX5WNAWKt1EiFW54PMtTm5Kc2FNV8L1TjojhuvpcFxGRw6cKJxERESmqrjqH66/pZutgmpDfobPBT9S/fC94HccwlclPaQr7rC7+ZcE11/iZSbts6Jvmzec08NvHR/F5DW99ZSMdtV5A+5yIiBw+BU4iIiJSXBYaqwyNVYE9A8tU2jU88OwU//2bAQDecm4DJ60O43eW73MiC6+1xss1FzTx7Tv6eWpTktefWsuZR1cR9bks5+NPREQWlgInERERkRLRuz3FV27bMff1f/x0B/Er2zi6zV/ErZJDMZU1DIxmCfod6mMOHlM6QY7PsZx7dITjV3WTyVoSEQ8e3GJvloiIVBgFTiIiIhXMGEi7DgaLz7FqBFzCvF6HOx8bLRi/87Ex1nU1ks0qECgXg0n45Hc2MzyRb8p94YkJ3nh6gmAJnXlba4kHgIABhU0iIrII1DRcRESkQqVdeGBDio98cwvXfXsbj2/JoMyidLmupbW2sFl6W11AfZzKiGsM37lzYC5sAvjZgyNsGy5cEU5ERKSSKXASERGpUL3b0/zbj7azYzjN9sEUn795K5sHc8XeLHkRrms5+9g4keCe07NI0OEVR1cpcCojqQw8vXmqYLx/NFOErRERESmeEirsFRERkYXi8Tj87MGRgvG7nxxjzfl15HIqdSpF9VH49Lu62DyQwhhDZ72fRBhNhSwjQR+sWxXl90+NzxtvSqgPl4iILC8KnERERCqSpa7KVzBaW+XFKr0oWdZCIgSJzsC8MSkfxlredFYdm/tn2D6YxjFw6Zl1tNd40QpwB8YYo/cpEZEKoMBJRESkAuVylgtPSnDPU2NksvkLt5Df4dQjND1LZLElQvCxt7WzayJH0GeoieQb98tLy7iweVeWZ7dN01LjZ01rkKhfz5uISLlS4CQiIlKhWuKGz17TzXPbp/F6DKtagtRFVDEjshT8Hktr9Ww/Lh10L8c4hrufnOQbv+ifG1vTGuJDl7UQ8ur5ExEpRwqcREREKpS1+Z5A9T2heWMiIqVmfBq+8+uBeWPPbZ9mx0iWFfWeIm2ViIgcjpIInHp6er4IvApIAZPAB3p7ex/c/b1G4NtAFzANvLe3t/f+Im2qiIiIiIgssJxryeQKE/F0xgUUOImIlCPn5X9kSdwOHNPb23sc8Bnge3t97zPAXb29vWuAPwdu7OnpMUXYRhGRZcPoXVZElpAx4PU6eu9ZxuIhwxlHxeeNRYMeWmq1up+ISLkqiQqn3t7eW/f68l6graenx+nt7XWBK8lXN9Hb23tPT09PCjgR+MOSb6iISAUzBoaS8Oy2aWbSLkd2hGmOm4NuPZJMG7YMpplOubTV+2mMmYJpXI5jmMrkp3eFfVbTvESWscm04bGNUzz0xwmOWxFl/coIscDSvynkrGFgwiU5k6Mh7iMeRCulLSGD5a3n1NFc4+OuJ8ZZ2RLkjWfUEQ/qM0JEpFyVROC0j/cDP+3t7XV7enpqAdPb2zu41/e3AO0cZOBUWxtdwE08dPX1sWJvgiwj2t/kYLywY5qPfGMDkzM5ABwHPn3NCo5beeD7Uc7j5/rvb2LTzpm5v/HJq1dw/Oo9f2NiKsuvHxnhxl/141rLm85p4NUn1lAd9S3sA5KKp/e48jc5nePfvreZP/ROAPDgc5Pc3x3murd1UxVZutPUiaks/3XHTm75/RCQX9Hx41d3c3T3/H1M+9ziqge6WiJcflYjoYCDz1sqkzGKQ/ubLDXtc7LQluSTvKen52Gg40W+3djb25vb/XNvBt4KnLXQ2zA0NFn0ZaDr62Ps2jVR1G2Q5UP7mxwMY+Ch3uRc2ATguvDfdw7QEjcH1Gm6vj7GM5sm58Km2b/xtdv7+Ei8Fa/J/41n+jJ8+da+uZ/5+s93Eg97WN8dWMBHJJVO73GVoX/CzoVNs57cOMULfcm9VnhbfJuGcnNhE8B02uVff7SNf3xbOz7jAtrnllpqqthbUFza32SpaZ+TQ+E45iWLe5YkcOrt7V3/cj/T09NzKfAp4FW9vb39u39vqKenh56enrq9qpw6gK2Lt7UiIsuPMYbxqVzB+Ggyi8VgDnBe3cR04d8YGM2QdQ1ej8Xjcfjd0+MFP/OrR0Y5aVUzuZx78BsvImWrVKZKjU5mC8a2DqSYyVh8aiEkIiJySEqiTrWnp+di4J+AC3p7ezft8+2bgfft/rkzgRDw0JJuoIhIhXNdy7qVhXcnXn9qDQ4HHgJ1NBRWKZ2/PkHIl7+qtNbutwFs+35+T0QqX13M4fhVkXljR7SHqa9a2lXJGqoLp/Qe1RkmrLBJRETkkJVE4AR8HfAD3+/p6Xl093+1u7/3v4Fzenp6/gh8CbhqdzNxERFZQG0Jh394eyerW0O01vl5/+tbOLYzdFAVCM3VDv/nLe00VPvweQ2vO7WW84+PY3dPaXZdy6k9MeKRPReT4YDDeeuqVd0ksgx5jeXaCxu55sImjumO8M7zG3n/65vwO0tb+tRY5eFPXtuM35tfJq+tzs+7L2rCc7CrJoiIiMgcswxW3+gCNqqHkyw32t/kULkYXAs+5+BWBprd54yBVM6QdSHiZy5s2tvYjGHLrhTW5quiEiGtQiQHR+9xlcUYg9frkM26RVsZzjiG0al8/6aaqFMQemmfk6Wk/U2WmvY5ORR79XDqBjbt+/1SXKVORESKyMHiHFif8P2yFvyOxe+AfZGipXjQckz77FwVhU0iy521lkymsAfckm6Da4kHIR40oMomERGRw1YqU+pERERERERERKRCKHASEREREREREZEFpcBJREREREREREQWlAInERERERERERFZUAqcRERERERERERkQSlwEhGRZcUYSOUME2mDa0yxN0cWmYthMGkZngKLXm8RERGRpeIt9gaIiIgsFYuhd0eGL9/ax9B4llOOiPH2VzYQD2oJ9Eo0mTZ8644B7ntmAmPgwhNruOS0BCGvXm8RERGRxaYKJxERWTZ2Tbp85rtbGBrPAnD/sxN8584BVb5UIMcx3N87yX3PTABgLdz+h2F6t6eKvGUiIiIiy4MCJxERWTb6htK4+xS33PfsBJPp4myPLB6L4fdPjxeMP7phEo9Hpz8iIiIii01nXCIismxEQ56CsdoqH/7CYSlzBpejusIF42vaQriuW4QtEhEREVleFDiJiMiy0V7j46Se2NzXjoH3XdxMUD19Ko7rwrnHxmmq8c+NrWkNcUxnGKuXW0RERGTRqWm4iIgsGwGv5doLGnjNSQkmp11aav3UxwxWCURFSoTgY29vZ8dIBo9jaKr2EvDotRYRERFZCgqcRERkWQl6LSsb9vr4U9hU0UJey4r62ddbr7WIiIjIUtGUOhERERERERERWVAKnEREREREREREZEEpcBIRERERERERkQWlwElERERERERERBaUAicREREREREREVlQCpxERERERERERGRBKXASEREREREREZEFpcBJREREREREREQWlAInERERERERERFZUAqcRERERERERERkQSlwEhERERERERGRBaXASUREREREREREFpQCJxERERERERERWVAKnEREREREREREZEEpcBIRERERERERkQWlwElERERERERERBaUAicREREREREREVlQCpxERERERERERGRBKXASEREREREREZEFpcBJREREREREREQWlAInERERERERERFZUAqcRERERERERERkQSlwEhERERERERGRBaXASUREREREREREFpQCJxERERERERERWVAKnEREREREREREZEEpcBIRERERERERkQWlwElERERERERERBaUAicREREREREREVlQ3mJvwBLwADiOKfZ2AKWzHbI8aH+TpaZ9TpaS9jdZatrnZClpf5Olpn1ODtZe+4xnf9831tql25riOBO4u9gbISIiIiIiIiJSgV4B3LPv4HIInALAScAOIFfkbRERERERERERqQQeoBn4A5Da95vLIXASEREREREREZElpKbhIiIiIiIiIiKyoBQ4/f/t3U+opXUZB/DvMIKggkmYMaaVjj1azsJJl25CMhcRpKGD4qqINmEyqeBaEBTyP0qLLAoXIpkkKAgGuhAU/zPw0MYaVPBPgSRoOHNdnDNwlZm5cu/v3Hc89/OBwz3nd3jhu/jx3Hu+933PCwAAAMBQCicAAAAAhlI4AQAAADCUwgkAAACAoRROAAAAAAylcAIAAABgKIUTAAAAAEMdN3WAZVdV1yS5Icl3k1zX3feseu/BJJckeW++9HB337LpIVkqa+y5E5L8Icn3k3ySZG93/32SoCwlc43NUFXfSfLHJF9N8n6Sa7v7n9OmYllV1RtJPpo/kuTG7n5yskAsnaq6PcnlSb6VZFd3vz5fN+sY7ij77Y2YdQymcFq8l5NcleSmI7x/6+pCAAY42p7bm+SD7t5ZVeckeaaqdnb3/zY1IcvOXGPR7k9yb3f/eV6yP5DkBxNnYrldcehDGSzAo0nuTPLM59bNOhbhSPstMesYzCV1C9bdr3f3viQHp87C1rDGnrsysz9WMv8P2QtJLtvEeAAbUlVfS7I7yUPzpYeS7K6qU6dLBbB+3f1sd+9fvWbWsSiH22+wKAqn6V1fVa9V1aNVdd7UYVh6Zyb516rX/05yxkRZWF7mGot0RpI3u/tAksx/vhWzjMX6S1W9WlX3VdVXpg7DlmDWMQWzjqFcUrdBVfViZh/iD+e0Q78kjuDmJG9398GqujbJE1V11hrHsMVtcM/Bhqy1/2KuAcvn4u7eX1XHJ7kjyT1Jrpk4E8BoZh3DKZw2qLt3b+DYN1c9/1NV/S7JN/LZM1DgMzay5zI7o+mbSd6dvz4zydMbDsWW8QX2n7nGou1PcnpVbe/uA1W1PcmO+ToMd+jSk+7+uKruS/LYxJHYGsw6NpVZxyK4pG5CVXX6queXJjmQVR/WYAEeTvLLJJl/afhFSZ6YNBFLxVxj0br7ncxujrBnvrQnyUvd/e6Rj4L1qaoTq+rk+fNtmd2U4+VpU7EVmHVsJrOORdm2srIydYalVlV7ktyW5JQk/0/yYZIfdve+qnoqs0tQDib5IMlvu/u5ycKyFNbYcycmeTDJBZkVATd099+mysryMdfYDFV1bma3Cj8lyX8zu1V4T5uKZVRVZyV5JMn2+WNfkl9399uTBmOpVNVdSX6a5OtJ3kvyfnd/z6xjEQ6335L8OGYdC6BwAgAAAGAol9QBAAAAMJTCCQAAAIChFE4AAAAADKVwAgAAAGAohRMAAAAAQymcAAAAABhK4QQAAADAUAonAAAAAIY6buoAAABbXVWdneT5JJd094tVtSPJK0l+1t3/mDQcAMA6bFtZWZk6AwDAlldVv0jymyQXJvlrkte6e++0qQAA1kfhBABwjKiqx5J8O8lKkou6++OJIwEArIvvcAIAOHb8Psn5Se5WNgEAX2bOcAIAOAZU1UmZfW/T00kuS7Kru/8zbSoAgPVxhhMAwLHhziQvdPfPkzye5P6J8wAArJvCCQBgYlX1kyQ/SvKr+dL1SXZX1dXTpQIAWD+X1AEAAAAwlDOcAAAAABhK4QQAAADAUAonAAAAAIZSOAEAAAAwlMIJAAAAgKEUTgAAAAAMpXACAAAAYCiFEwAAAABDfQqll2sJ0tDC3wAAAABJRU5ErkJggg==\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"l3sRcFW9muEZ"},"source":["# 6.1 Plot low dimensional T-SNE USE embeddings with hue for POS \n","Because we will have a list of pos labels for each sentence, we need to explode on the pos column and then do the data peperation for T-SNE again before we can visualize with hue for POS\n"]},{"cell_type":"code","metadata":{"id":"OZ_2DTk9bC-O","colab":{"base_uri":"https://localhost:8080/","height":406},"executionInfo":{"status":"ok","timestamp":1619907823814,"user_tz":-120,"elapsed":334,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"bd7358c4-c440-4804-ea37-68a67776b4d8"},"source":["predictions_exploded_on_pos = predictions.explode('pos')\n","predictions_exploded_on_pos"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
pos
\n","
spell
\n","
sentiment
\n","
sentiment_confidence
\n","
sentence_embedding_bert
\n","
\n"," \n"," \n","
\n","
0
\n","
NC and NH.
\n","
NNP
\n","
[NC, and, NH, .]
\n","
negative
\n","
0.5229
\n","
[-1.059532642364502, 0.9238302707672119, -1.06...
\n","
\n","
\n","
0
\n","
NC and NH.
\n","
CC
\n","
[NC, and, NH, .]
\n","
negative
\n","
0.5229
\n","
[-1.059532642364502, 0.9238302707672119, -1.06...
\n","
\n","
\n","
0
\n","
NC and NH.
\n","
NNP
\n","
[NC, and, NH, .]
\n","
negative
\n","
0.5229
\n","
[-1.059532642364502, 0.9238302707672119, -1.06...
\n","
\n","
\n","
0
\n","
NC and NH.
\n","
.
\n","
[NC, and, NH, .]
\n","
negative
\n","
0.5229
\n","
[-1.059532642364502, 0.9238302707672119, -1.06...
\n","
\n","
\n","
1
\n","
You do know west teams play against west teams...
\n","
PRP
\n","
[You, do, know, west, teams, play, against, we...
\n","
negative
\n","
0.4733
\n","
[-0.9636414647102356, -0.046410106122493744, -...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
499
\n","
Hard drive requirements tend to include extra ...
\n","
IN
\n","
[Hard, drive, requirements, tend, to, include,...
\n","
positive
\n","
0.5396
\n","
[-0.8011002540588379, 1.111019253730774, -0.85...
\n","
\n","
\n","
499
\n","
Hard drive requirements tend to include extra ...
\n","
DT
\n","
[Hard, drive, requirements, tend, to, include,...
\n","
positive
\n","
0.5396
\n","
[-0.8011002540588379, 1.111019253730774, -0.85...
\n","
\n","
\n","
499
\n","
Hard drive requirements tend to include extra ...
\n","
NN
\n","
[Hard, drive, requirements, tend, to, include,...
\n","
positive
\n","
0.5396
\n","
[-0.8011002540588379, 1.111019253730774, -0.85...
\n","
\n","
\n","
499
\n","
Hard drive requirements tend to include extra ...
\n","
VBG
\n","
[Hard, drive, requirements, tend, to, include,...
\n","
positive
\n","
0.5396
\n","
[-0.8011002540588379, 1.111019253730774, -0.85...
\n","
\n","
\n","
499
\n","
Hard drive requirements tend to include extra ...
\n","
.
\n","
[Hard, drive, requirements, tend, to, include,...
\n","
positive
\n","
0.5396
\n","
[-0.8011002540588379, 1.111019253730774, -0.85...
\n","
\n"," \n","
\n","
7916 rows × 6 columns
\n","
"],"text/plain":[" sentence ... sentence_embedding_bert\n","0 NC and NH. ... [-1.059532642364502, 0.9238302707672119, -1.06...\n","0 NC and NH. ... [-1.059532642364502, 0.9238302707672119, -1.06...\n","0 NC and NH. ... [-1.059532642364502, 0.9238302707672119, -1.06...\n","0 NC and NH. ... [-1.059532642364502, 0.9238302707672119, -1.06...\n","1 You do know west teams play against west teams... ... [-0.9636414647102356, -0.046410106122493744, -...\n",".. ... ... ...\n","499 Hard drive requirements tend to include extra ... ... [-0.8011002540588379, 1.111019253730774, -0.85...\n","499 Hard drive requirements tend to include extra ... ... [-0.8011002540588379, 1.111019253730774, -0.85...\n","499 Hard drive requirements tend to include extra ... ... [-0.8011002540588379, 1.111019253730774, -0.85...\n","499 Hard drive requirements tend to include extra ... ... [-0.8011002540588379, 1.111019253730774, -0.85...\n","499 Hard drive requirements tend to include extra ... ... [-0.8011002540588379, 1.111019253730774, -0.85...\n","\n","[7916 rows x 6 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"markdown","metadata":{"id":"k1M_a4pmfMGA"},"source":["## 6.2 Preprocess data for TSNE again"]},{"cell_type":"code","metadata":{"id":"K0rpmiy6a2UK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619907897657,"user_tz":-120,"elapsed":66145,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"9260eb2c-7e12-4ed5-d243-e6190d872cee"},"source":["\n","# Make a matrix from the vectors in the np_array column via list comprehension\n","mat = np.matrix([x for x in predictions_exploded_on_pos.sentence_embedding_bert])\n","\n","\n","from sklearn.manifold import TSNE\n","model = TSNE(n_components=2) #n_components means the lower dimension\n","low_dim_data = model.fit_transform(mat)\n","print('Lower dim data has shape',low_dim_data.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Lower dim data has shape (7916, 2)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6ze0HWqqfQDh"},"source":["# 6.3 Plot low dimensional T-SNE BERT Sentence embeddings with hue for POS \n"]},{"cell_type":"code","metadata":{"id":"RB1qdDP3fJHN","colab":{"base_uri":"https://localhost:8080/","height":844},"executionInfo":{"status":"ok","timestamp":1619907908491,"user_tz":-120,"elapsed":3280,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b1c42a80-a517-45d3-ac8b-1b2acbad24cd"},"source":["tsne_df = pd.DataFrame(low_dim_data, predictions_exploded_on_pos.pos)\n","tsne_df.columns = ['x','y']\n","ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)\n","ax.set_title('T-SNE BERT Sentence Embeddings, colored by Part of Speech Tag')\n","plt1.savefig(\"bert_pos\")\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"uXb-FMA6mX13"},"source":["# 7. NLU has many more sentence embedding models! \n","Make sure to try them all out! \n","You can change 'embed_sentence.bert' in nlu.load('embed_sentence.bert') to bert, xlnet, albert or any other of the **20+ sentence embeddings** offerd by NLU"]},{"cell_type":"code","metadata":{"id":"9qUF7jPlme-R","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619907911454,"user_tz":-120,"elapsed":433,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"ae8ad993-5113-49ff-97f8-b4a606e6bf38"},"source":["nlu.print_all_model_kinds_for_action('embed_sentence')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MvSC3rl5-adJ"},"source":[""],"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/sentence_embeddings/NLU_ELECTRA_sentence_embeddings_and_t-SNE_visualization_example.ipynb b/examples/colab/component_examples/sentence_embeddings/NLU_ELECTRA_sentence_embeddings_and_t-SNE_visualization_example.ipynb
deleted file mode 100644
index 5036229e..00000000
--- a/examples/colab/component_examples/sentence_embeddings/NLU_ELECTRA_sentence_embeddings_and_t-SNE_visualization_example.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_ELECTRA_sentence_embeddings_and_t-SNE_visualization_example.ipynb","provenance":[{"file_id":"1pgqoRJ6yGWbTLWdLnRvwG5DLSU3rxuMq","timestamp":1599401652794},{"file_id":"1JrlfuV2jNGTdOXvaWIoHTSf6BscDMkN7","timestamp":1599401257319},{"file_id":"1svpqtC3cY6JnRGeJngIPl2raqxdowpyi","timestamp":1599400881246},{"file_id":"1tW833T3HS8F5Lvn6LgeDd5LW5226syKN","timestamp":1599398724652},{"file_id":"1CYzHfQyFCdvIOVO2Z5aggVI9c0hDEOrw","timestamp":1599354735581}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"rBXrqlGEYA8G"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sentence_embeddings/NLU_ELECTRA_sentence_embeddings_and_t-SNE_visualization_example.ipynb)\n","\n","# ELECTRA Sentence Embeddings with NLU \n","\n","A text encoder trained to distinguish real input tokens from plausible fakes efficiently learns effective language representations.\n","\n","### Sources :\n","- https://arxiv.org/abs/2003.10555\n","\n","### Paper abstract :\n","\n","Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sample-efficient pre-training task called replaced token detection. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute.\n","\n","\n","# 1. Install Java and NLU"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620180622987,"user_tz":-300,"elapsed":113422,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"72c280d7-bc52-486b-e062-2992ca01cf19"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":1,"outputs":[{"output_type":"stream","text":["--2021-05-05 02:08:30-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.109.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \r- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-05 02:08:30 (40.7 MB/s) - written to stdout [1671/1671]\n","\n","Installing NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","\u001b[K |████████████████████████████████| 204.8MB 74kB/s \n","\u001b[K |████████████████████████████████| 153kB 51.9MB/s \n","\u001b[K |████████████████████████████████| 204kB 24.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 38.2MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"N_CL8HZ8Ydry"},"source":["## 2. Load Model and embed sample sentence with ELECTRA Sentence Embedder"]},{"cell_type":"code","metadata":{"id":"j2ZZZvr1uGpx","colab":{"base_uri":"https://localhost:8080/","height":182},"executionInfo":{"status":"ok","timestamp":1620180675651,"user_tz":-300,"elapsed":166069,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"d2a8c8e4-eddc-4c82-be89-f7ed8bcdd0bd"},"source":["import nlu\n","pipe = nlu.load('embed_sentence.electra')\n","pipe.predict('He was suprised by the diversity of NLU')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["sent_electra_small_uncased download started this may take some time.\n","Approximate size to download 48.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
origin_index
\n","
sentence_embedding_electra
\n","
text
\n","
sentence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
8589934592
\n","
[[0.005376073066145182, 0.1803598701953888, -0...
\n","
He was suprised by the diversity of NLU
\n","
[He was suprised by the diversity of NLU]
\n","
He was suprised by the diversity of NLU
\n","
\n"," \n","
\n","
"],"text/plain":[" origin_index ... document\n","0 8589934592 ... He was suprised by the diversity of NLU\n","\n","[1 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"BAUFklCqLr3V"},"source":["# 3. Download Sample dataset"]},{"cell_type":"code","metadata":{"id":"wAFAOUSuLqvn","colab":{"base_uri":"https://localhost:8080/","height":606},"executionInfo":{"status":"ok","timestamp":1620180686526,"user_tz":-300,"elapsed":176930,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"14d2629c-d8b1-4298-9a8d-1376877d7955"},"source":["import pandas as pd\n","# Download the dataset \n","! wget -N https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv -P /tmp\n","# Load dataset to Pandas\n","df = pd.read_csv('/tmp/train-balanced-sarcasm.csv')\n","df"],"execution_count":3,"outputs":[{"output_type":"stream","text":["--2021-05-05 02:11:14-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.161.181\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.161.181|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 255268960 (243M) [text/csv]\n","Saving to: ‘/tmp/train-balanced-sarcasm.csv’\n","\n","train-balanced-sarc 100%[===================>] 243.44M 43.7MB/s in 5.8s \n","\n","2021-05-05 02:11:20 (41.8 MB/s) - ‘/tmp/train-balanced-sarcasm.csv’ saved [255268960/255268960]\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
label
\n","
comment
\n","
author
\n","
subreddit
\n","
score
\n","
ups
\n","
downs
\n","
date
\n","
created_utc
\n","
parent_comment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
NC and NH.
\n","
Trumpbart
\n","
politics
\n","
2
\n","
-1
\n","
-1
\n","
2016-10
\n","
2016-10-16 23:55:23
\n","
Yeah, I get that argument. At this point, I'd ...
\n","
\n","
\n","
1
\n","
0
\n","
You do know west teams play against west teams...
\n","
Shbshb906
\n","
nba
\n","
-4
\n","
-1
\n","
-1
\n","
2016-11
\n","
2016-11-01 00:24:10
\n","
The blazers and Mavericks (The wests 5 and 6 s...
\n","
\n","
\n","
2
\n","
0
\n","
They were underdogs earlier today, but since G...
\n","
Creepeth
\n","
nfl
\n","
3
\n","
3
\n","
0
\n","
2016-09
\n","
2016-09-22 21:45:37
\n","
They're favored to win.
\n","
\n","
\n","
3
\n","
0
\n","
This meme isn't funny none of the \"new york ni...
\n","
icebrotha
\n","
BlackPeopleTwitter
\n","
-8
\n","
-1
\n","
-1
\n","
2016-10
\n","
2016-10-18 21:03:47
\n","
deadass don't kill my buzz
\n","
\n","
\n","
4
\n","
0
\n","
I could use one of those tools.
\n","
cush2push
\n","
MaddenUltimateTeam
\n","
6
\n","
-1
\n","
-1
\n","
2016-12
\n","
2016-12-30 17:00:13
\n","
Yep can confirm I saw the tool they use for th...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1010821
\n","
1
\n","
I'm sure that Iran and N. Korea have the techn...
\n","
TwarkMain
\n","
reddit.com
\n","
2
\n","
2
\n","
0
\n","
2009-04
\n","
2009-04-25 00:47:52
\n","
No one is calling this an engineered pathogen,...
\n","
\n","
\n","
1010822
\n","
1
\n","
whatever you do, don't vote green!
\n","
BCHarvey
\n","
climate
\n","
1
\n","
1
\n","
0
\n","
2009-05
\n","
2009-05-14 22:27:40
\n","
In a move typical of their recent do-nothing a...
\n","
\n","
\n","
1010823
\n","
1
\n","
Perhaps this is an atheist conspiracy to make ...
\n","
rebelcommander
\n","
atheism
\n","
1
\n","
1
\n","
0
\n","
2009-01
\n","
2009-01-11 00:22:57
\n","
Screw the Disabled--I've got to get to Church ...
\n","
\n","
\n","
1010824
\n","
1
\n","
The Slavs got their own country - it is called...
\n","
catsi
\n","
worldnews
\n","
1
\n","
1
\n","
0
\n","
2009-01
\n","
2009-01-23 21:12:49
\n","
I've always been unsettled by that. I hear a l...
\n","
\n","
\n","
1010825
\n","
1
\n","
values, as in capitalism .. there is good mone...
\n","
frogking
\n","
politics
\n","
2
\n","
2
\n","
0
\n","
2009-01
\n","
2009-01-24 06:20:14
\n","
Why do the people who make our laws seem unabl...
\n","
\n"," \n","
\n","
1010826 rows × 10 columns
\n","
"],"text/plain":[" label ... parent_comment\n","0 0 ... Yeah, I get that argument. At this point, I'd ...\n","1 0 ... The blazers and Mavericks (The wests 5 and 6 s...\n","2 0 ... They're favored to win.\n","3 0 ... deadass don't kill my buzz\n","4 0 ... Yep can confirm I saw the tool they use for th...\n","... ... ... ...\n","1010821 1 ... No one is calling this an engineered pathogen,...\n","1010822 1 ... In a move typical of their recent do-nothing a...\n","1010823 1 ... Screw the Disabled--I've got to get to Church ...\n","1010824 1 ... I've always been unsettled by that. I hear a l...\n","1010825 1 ... Why do the people who make our laws seem unabl...\n","\n","[1010826 rows x 10 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"OPdBQnV46or5"},"source":["# 4. Visualize Embeddings with T-SNE\n","\n","\n","\n","\n","Lets add Sentiment and Part Of Speech to our pipeline because its so easy and so we can hue our T-SNE plots by POS and Sentiment \n"]},{"cell_type":"code","metadata":{"id":"9bujAZtOCfRW","colab":{"base_uri":"https://localhost:8080/","height":827},"executionInfo":{"status":"ok","timestamp":1620181010117,"user_tz":-300,"elapsed":500506,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"b8115009-b98f-4662-b930-8b4ef7e965be"},"source":["pipe = nlu.load('pos sentiment embed_sentence.electra') # emotion\n","df['text'] = df['comment']\n","\n","# We must set output level to sentence since NLU will infer a different output level for this pipeline composition\n","predictions = pipe.predict(df[['text','label']].iloc[0:10000], output_level='sentence')\n","predictions"],"execution_count":4,"outputs":[{"output_type":"stream","text":["pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","analyze_sentiment download started this may take some time.\n","Approx size to download 4.9 MB\n","[OK!]\n","sent_electra_small_uncased download started this may take some time.\n","Approximate size to download 48.7 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
This meme isn't funny none of the \"new york ni...
\n","
This meme isn't funny none of the \"new york ni...
\n","
\n","
\n","
4
\n","
[I, could, use, one, of, those, tools, .]
\n","
[0.00914192758500576, 0.6086333394050598, -0.3...
\n","
[PRP, MD, VB, CD, IN, DT, NNS, .]
\n","
0
\n","
I could use one of those tools.
\n","
4
\n","
0.4745
\n","
[I, could, use, one, of, those, tools, .]
\n","
negative
\n","
I could use one of those tools.
\n","
I could use one of those tools.
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
9995
\n","
[probably, a, young, latino, boy]
\n","
[-0.12988166511058807, 0.009930764324963093, 0...
\n","
[RB, DT, JJ, NN, NN]
\n","
1
\n","
probably a young latino boy
\n","
8589939467
\n","
0.5137
\n","
[probably, a, young, litany, boy]
\n","
positive
\n","
probably a young latino boy
\n","
probably a young latino boy
\n","
\n","
\n","
9996
\n","
[Dog, filter=giving, up, ?]
\n","
[-0.03425365686416626, -0.045875661075115204, ...
\n","
[NNP, VBG, RP, .]
\n","
1
\n","
Dog filter=giving up?
\n","
8589939468
\n","
0.9366
\n","
[Dog, filter=giving, up, ?]
\n","
negative
\n","
Dog filter=giving up?
\n","
Dog filter=giving up?
\n","
\n","
\n","
9997
\n","
[Saturday, Night, dead, amirite, ?]
\n","
[-0.05446026101708412, 0.3432471752166748, -0....
\n","
[NNP, NNP, JJ, NN, .]
\n","
1
\n","
Saturday Night dead amirite?
\n","
8589939469
\n","
0.9189
\n","
[Saturday, Night, dead, amirate, ?]
\n","
negative
\n","
Saturday Night dead amirite?
\n","
Saturday Night dead amirite?
\n","
\n","
\n","
9998
\n","
[Moderators, ,, not, fact, checkers, .]
\n","
[-0.2055562287569046, -0.03593125194311142, 0....
\n","
[NNP, ,, RB, NN, NNS, .]
\n","
1
\n","
Moderators, not fact checkers.
\n","
8589939470
\n","
0.5611
\n","
[moderators, ,, not, fact, checkers, .]
\n","
negative
\n","
Moderators, not fact checkers.
\n","
Moderators, not fact checkers.
\n","
\n","
\n","
9999
\n","
[She, hacked, the, online, votes]
\n","
[0.09554111957550049, 0.11254823207855225, 0.1...
\n","
[PRP, VBD, DT, NN, NNS]
\n","
1
\n","
She hacked the online votes
\n","
8589939471
\n","
0.3832
\n","
[She, hacked, the, online, votes]
\n","
negative
\n","
She hacked the online votes
\n","
She hacked the online votes
\n","
\n"," \n","
\n","
12013 rows × 11 columns
\n","
"],"text/plain":[" token ... document\n","0 [NC, and, NH, .] ... NC and NH.\n","1 [You, do, know, west, teams, play, against, we... ... You do know west teams play against west teams...\n","2 [They, were, underdogs, earlier, today, ,, but... ... They were underdogs earlier today, but since G...\n","3 [This, meme, isn't, funny, none, of, the, \", n... ... This meme isn't funny none of the \"new york ni...\n","4 [I, could, use, one, of, those, tools, .] ... I could use one of those tools.\n","... ... ... ...\n","9995 [probably, a, young, latino, boy] ... probably a young latino boy\n","9996 [Dog, filter=giving, up, ?] ... Dog filter=giving up?\n","9997 [Saturday, Night, dead, amirite, ?] ... Saturday Night dead amirite?\n","9998 [Moderators, ,, not, fact, checkers, .] ... Moderators, not fact checkers.\n","9999 [She, hacked, the, online, votes] ... She hacked the online votes\n","\n","[12013 rows x 11 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"_OypFES-8EwY"},"source":["## 4.1 Checkout sentiment distribution"]},{"cell_type":"code","metadata":{"id":"ggbC0PxHgc2t","colab":{"base_uri":"https://localhost:8080/","height":332},"executionInfo":{"status":"ok","timestamp":1620181010933,"user_tz":-300,"elapsed":501307,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"f94d31e7-698e-4052-9f37-f6d30d6a3ad3"},"source":["# Some Tokens are None which we must drop first\n","predictions.dropna(how='any', inplace=True)\n","# Some sentiment are 'na' which we must drop first\n","predictions = predictions[predictions.sentiment!= 'na']\n","predictions.sentiment.value_counts().plot.bar(title='Dataset sentiment distribution')"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":5},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"ZUYHpsHTINsF"},"source":["# 5.Prepare data for T-SNE algorithm.\n","We create a Matrix with one row per Embedding vector for T-SNE algorithm"]},{"cell_type":"code","metadata":{"id":"L_0jefTB6i52","executionInfo":{"status":"ok","timestamp":1620181010935,"user_tz":-300,"elapsed":501302,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}}},"source":["import numpy as np\n","\n","\n","# Make a matrix from the vectors in the np_array column via list comprehension\n","mat = np.matrix([x for x in predictions.sentence_embedding_electra])"],"execution_count":6,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pbdi4CY2Iqc0"},"source":["## 5.1 Fit and transform T-SNE algorithm\n"]},{"cell_type":"code","metadata":{"id":"fAFGB6iYIqmO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620181184236,"user_tz":-300,"elapsed":674594,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a86c1080-efca-4e86-ffe9-957fbaccd9ae"},"source":["\n","from sklearn.manifold import TSNE\n","model = TSNE(n_components=2) #n_components means the lower dimension\n","low_dim_data = model.fit_transform(mat)\n","print('Lower dim data has shape',low_dim_data.shape)"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Lower dim data has shape (11605, 2)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gsi0b0XhImaz"},"source":["### Set plotting styles"]},{"cell_type":"code","metadata":{"id":"CsPVw7NHfEgt","executionInfo":{"status":"ok","timestamp":1620181184237,"user_tz":-300,"elapsed":674590,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}}},"source":["# set some styles for for Plotting\n","import seaborn as sns\n","# Style Plots a bit\n","sns.set_style('darkgrid')\n","sns.set_palette('muted')\n","sns.set_context(\"notebook\", font_scale=1,rc={\"lines.linewidth\": 2.5})\n","\n","%matplotlib inline\n","import matplotlib as plt\n","plt.rcParams['figure.figsize'] = (20, 14)\n","import matplotlib.pyplot as plt1\n"],"execution_count":8,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8tuoCxNPmzbo"},"source":["##5.2 Plot low dimensional T-SNE ELECTRA Sentence embeddings with hue for Sarcasm\n"]},{"cell_type":"code","metadata":{"id":"Fbq5MAv0jkft","colab":{"base_uri":"https://localhost:8080/","height":844},"executionInfo":{"status":"ok","timestamp":1620181187282,"user_tz":-300,"elapsed":677630,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"fc8fecfd-4d78-4922-d8c2-ded24c437554"},"source":["tsne_df = pd.DataFrame(low_dim_data, df.iloc[:low_dim_data.shape[0]].label.replace({1:'sarcasm',0:'normal'}))\n","tsne_df.columns = ['x','y']\n","ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)\n","ax.set_title('T-SNE ELECTRA Sentence Embeddings, colored by Sarcasm label')\n","plt1.savefig(\"electra_sarcasm\")\n"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Snb1gtqrnIJi"},"source":["## 5.3 Plot low dimensional T-SNE ELECTRA Sentence embeddings with hue for Sentiment\n"]},{"cell_type":"code","metadata":{"id":"QET-Y6PdnIJt","colab":{"base_uri":"https://localhost:8080/","height":844},"executionInfo":{"status":"ok","timestamp":1620181189137,"user_tz":-300,"elapsed":679466,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"a1207683-b983-4528-a783-57116cfe465e"},"source":["tsne_df = pd.DataFrame(low_dim_data, predictions.sentiment)\n","tsne_df.columns = ['x','y']\n","ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)\n","ax.set_title('T-SNE ELECTRA Sentence Embeddings, colored by Sentiment')\n","plt1.savefig(\"electra_entiment\")\n"],"execution_count":10,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"l3sRcFW9muEZ"},"source":["# 6.1 Plot low dimensional T-SNE USE embeddings with hue for POS \n","Because we will have a list of pos labels for each sentence, we need to explode on the pos column and then do the data peperation for T-SNE again before we can visualize with hue for POS\n"]},{"cell_type":"code","metadata":{"id":"OZ_2DTk9bC-O","colab":{"base_uri":"https://localhost:8080/","height":606},"executionInfo":{"status":"ok","timestamp":1620181189712,"user_tz":-300,"elapsed":680029,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"db594d96-57fd-4931-b6d1-bce3ed2c5d0b"},"source":["predictions_exploded_on_pos = predictions.explode('pos')\n","predictions_exploded_on_pos"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
token
\n","
sentence_embedding_electra
\n","
pos
\n","
label
\n","
text
\n","
origin_index
\n","
sentiment_confidence
\n","
spell
\n","
sentiment
\n","
sentence
\n","
document
\n","
\n"," \n"," \n","
\n","
0
\n","
[NC, and, NH, .]
\n","
[0.23152296245098114, -0.09077221900224686, -0...
\n","
NNP
\n","
0
\n","
NC and NH.
\n","
0
\n","
0.5229
\n","
[NC, and, NH, .]
\n","
negative
\n","
NC and NH.
\n","
NC and NH.
\n","
\n","
\n","
0
\n","
[NC, and, NH, .]
\n","
[0.23152296245098114, -0.09077221900224686, -0...
\n","
CC
\n","
0
\n","
NC and NH.
\n","
0
\n","
0.5229
\n","
[NC, and, NH, .]
\n","
negative
\n","
NC and NH.
\n","
NC and NH.
\n","
\n","
\n","
0
\n","
[NC, and, NH, .]
\n","
[0.23152296245098114, -0.09077221900224686, -0...
\n","
NNP
\n","
0
\n","
NC and NH.
\n","
0
\n","
0.5229
\n","
[NC, and, NH, .]
\n","
negative
\n","
NC and NH.
\n","
NC and NH.
\n","
\n","
\n","
0
\n","
[NC, and, NH, .]
\n","
[0.23152296245098114, -0.09077221900224686, -0...
\n","
.
\n","
0
\n","
NC and NH.
\n","
0
\n","
0.5229
\n","
[NC, and, NH, .]
\n","
negative
\n","
NC and NH.
\n","
NC and NH.
\n","
\n","
\n","
1
\n","
[You, do, know, west, teams, play, against, we...
\n","
[-0.057340413331985474, -0.08123737573623657, ...
\n","
PRP
\n","
0
\n","
You do know west teams play against west teams...
\n","
1
\n","
0.4733
\n","
[You, do, know, west, teams, play, against, we...
\n","
negative
\n","
You do know west teams play against west teams...
\n","
You do know west teams play against west teams...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
9999
\n","
[She, hacked, the, online, votes]
\n","
[0.09554111957550049, 0.11254823207855225, 0.1...
\n","
PRP
\n","
1
\n","
She hacked the online votes
\n","
8589939471
\n","
0.3832
\n","
[She, hacked, the, online, votes]
\n","
negative
\n","
She hacked the online votes
\n","
She hacked the online votes
\n","
\n","
\n","
9999
\n","
[She, hacked, the, online, votes]
\n","
[0.09554111957550049, 0.11254823207855225, 0.1...
\n","
VBD
\n","
1
\n","
She hacked the online votes
\n","
8589939471
\n","
0.3832
\n","
[She, hacked, the, online, votes]
\n","
negative
\n","
She hacked the online votes
\n","
She hacked the online votes
\n","
\n","
\n","
9999
\n","
[She, hacked, the, online, votes]
\n","
[0.09554111957550049, 0.11254823207855225, 0.1...
\n","
DT
\n","
1
\n","
She hacked the online votes
\n","
8589939471
\n","
0.3832
\n","
[She, hacked, the, online, votes]
\n","
negative
\n","
She hacked the online votes
\n","
She hacked the online votes
\n","
\n","
\n","
9999
\n","
[She, hacked, the, online, votes]
\n","
[0.09554111957550049, 0.11254823207855225, 0.1...
\n","
NN
\n","
1
\n","
She hacked the online votes
\n","
8589939471
\n","
0.3832
\n","
[She, hacked, the, online, votes]
\n","
negative
\n","
She hacked the online votes
\n","
She hacked the online votes
\n","
\n","
\n","
9999
\n","
[She, hacked, the, online, votes]
\n","
[0.09554111957550049, 0.11254823207855225, 0.1...
\n","
NNS
\n","
1
\n","
She hacked the online votes
\n","
8589939471
\n","
0.3832
\n","
[She, hacked, the, online, votes]
\n","
negative
\n","
She hacked the online votes
\n","
She hacked the online votes
\n","
\n"," \n","
\n","
154707 rows × 11 columns
\n","
"],"text/plain":[" token ... document\n","0 [NC, and, NH, .] ... NC and NH.\n","0 [NC, and, NH, .] ... NC and NH.\n","0 [NC, and, NH, .] ... NC and NH.\n","0 [NC, and, NH, .] ... NC and NH.\n","1 [You, do, know, west, teams, play, against, we... ... You do know west teams play against west teams...\n","... ... ... ...\n","9999 [She, hacked, the, online, votes] ... She hacked the online votes\n","9999 [She, hacked, the, online, votes] ... She hacked the online votes\n","9999 [She, hacked, the, online, votes] ... She hacked the online votes\n","9999 [She, hacked, the, online, votes] ... She hacked the online votes\n","9999 [She, hacked, the, online, votes] ... She hacked the online votes\n","\n","[154707 rows x 11 columns]"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"k1M_a4pmfMGA"},"source":["## 6.2 Preprocess data for TSNE again"]},{"cell_type":"code","metadata":{"id":"K0rpmiy6a2UK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191221370,"user_tz":-300,"elapsed":10711681,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"3dba7cd2-bda4-4e92-80ce-f077786932c1"},"source":["# Make a matrix from the vectors in the np_array column via list comprehension\n","mat = np.matrix([x for x in predictions_exploded_on_pos.sentence_embedding_electra])\n","\n","\n","from sklearn.manifold import TSNE\n","model = TSNE(n_components=2) #n_components means the lower dimension\n","low_dim_data = model.fit_transform(mat)\n","print('Lower dim data has shape',low_dim_data.shape)"],"execution_count":12,"outputs":[{"output_type":"stream","text":["Lower dim data has shape (154707, 2)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6ze0HWqqfQDh"},"source":["# 6.3 Plot low dimensional T-SNE ELECTRA Sentence embeddings with hue for POS \n"]},{"cell_type":"code","metadata":{"id":"RB1qdDP3fJHN","colab":{"base_uri":"https://localhost:8080/","height":844},"executionInfo":{"status":"ok","timestamp":1620191242365,"user_tz":-300,"elapsed":10732673,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"efc25d46-9091-40cb-a9c3-e622fac9003c"},"source":["tsne_df = pd.DataFrame(low_dim_data, predictions_exploded_on_pos.pos)\n","tsne_df.columns = ['x','y']\n","ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)\n","ax.set_title('T-SNE ELECTRA Sentence Embeddings, colored by Part of Speech Tag')\n","plt1.savefig(\"electra_pos\")\n"],"execution_count":13,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"uXb-FMA6mX13"},"source":["# 7. NLU has many more sentence embedding models! \n","Make sure to try them all out! \n","You can change 'embed_sentence.electra' in nlu.load('embed_sentence.electra') to bert, xlnet, albert or any other of the **20+ sentence embeddings** offerd by NLU"]},{"cell_type":"code","metadata":{"id":"9qUF7jPlme-R","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1620191242369,"user_tz":-300,"elapsed":10732674,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}},"outputId":"8ac2e5f8-2c0d-4966-a943-032fde313905"},"source":["nlu.print_all_model_kinds_for_action('embed_sentence')"],"execution_count":14,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed_sentence') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.tfhub_use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed_sentence.use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.tfhub_use.lg') returns Spark NLP model tfhub_use_lg\n","nlu.load('en.embed_sentence.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed_sentence.electra') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_small_uncased') returns Spark NLP model sent_electra_small_uncased\n","nlu.load('en.embed_sentence.electra_base_uncased') returns Spark NLP model sent_electra_base_uncased\n","nlu.load('en.embed_sentence.electra_large_uncased') returns Spark NLP model sent_electra_large_uncased\n","nlu.load('en.embed_sentence.bert') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_uncased') returns Spark NLP model sent_bert_base_uncased\n","nlu.load('en.embed_sentence.bert_base_cased') returns Spark NLP model sent_bert_base_cased\n","nlu.load('en.embed_sentence.bert_large_uncased') returns Spark NLP model sent_bert_large_uncased\n","nlu.load('en.embed_sentence.bert_large_cased') returns Spark NLP model sent_bert_large_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_base_cased') returns Spark NLP model sent_biobert_pubmed_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_large_cased') returns Spark NLP model sent_biobert_pubmed_large_cased\n","nlu.load('en.embed_sentence.biobert.pmc_base_cased') returns Spark NLP model sent_biobert_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.pubmed_pmc_base_cased') returns Spark NLP model sent_biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed_sentence.biobert.clinical_base_cased') returns Spark NLP model sent_biobert_clinical_base_cased\n","nlu.load('en.embed_sentence.biobert.discharge_base_cased') returns Spark NLP model sent_biobert_discharge_base_cased\n","nlu.load('en.embed_sentence.covidbert.large_uncased') returns Spark NLP model sent_covidbert_large_uncased\n","nlu.load('en.embed_sentence.small_bert_L2_128') returns Spark NLP model sent_small_bert_L2_128\n","nlu.load('en.embed_sentence.small_bert_L4_128') returns Spark NLP model sent_small_bert_L4_128\n","nlu.load('en.embed_sentence.small_bert_L6_128') returns Spark NLP model sent_small_bert_L6_128\n","nlu.load('en.embed_sentence.small_bert_L8_128') returns Spark NLP model sent_small_bert_L8_128\n","nlu.load('en.embed_sentence.small_bert_L10_128') returns Spark NLP model sent_small_bert_L10_128\n","nlu.load('en.embed_sentence.small_bert_L12_128') returns Spark NLP model sent_small_bert_L12_128\n","nlu.load('en.embed_sentence.small_bert_L2_256') returns Spark NLP model sent_small_bert_L2_256\n","nlu.load('en.embed_sentence.small_bert_L4_256') returns Spark NLP model sent_small_bert_L4_256\n","nlu.load('en.embed_sentence.small_bert_L6_256') returns Spark NLP model sent_small_bert_L6_256\n","nlu.load('en.embed_sentence.small_bert_L8_256') returns Spark NLP model sent_small_bert_L8_256\n","nlu.load('en.embed_sentence.small_bert_L10_256') returns Spark NLP model sent_small_bert_L10_256\n","nlu.load('en.embed_sentence.small_bert_L12_256') returns Spark NLP model sent_small_bert_L12_256\n","nlu.load('en.embed_sentence.small_bert_L2_512') returns Spark NLP model sent_small_bert_L2_512\n","nlu.load('en.embed_sentence.small_bert_L4_512') returns Spark NLP model sent_small_bert_L4_512\n","nlu.load('en.embed_sentence.small_bert_L6_512') returns Spark NLP model sent_small_bert_L6_512\n","nlu.load('en.embed_sentence.small_bert_L8_512') returns Spark NLP model sent_small_bert_L8_512\n","nlu.load('en.embed_sentence.small_bert_L10_512') returns Spark NLP model sent_small_bert_L10_512\n","nlu.load('en.embed_sentence.small_bert_L12_512') returns Spark NLP model sent_small_bert_L12_512\n","nlu.load('en.embed_sentence.small_bert_L2_768') returns Spark NLP model sent_small_bert_L2_768\n","nlu.load('en.embed_sentence.small_bert_L4_768') returns Spark NLP model sent_small_bert_L4_768\n","nlu.load('en.embed_sentence.small_bert_L6_768') returns Spark NLP model sent_small_bert_L6_768\n","nlu.load('en.embed_sentence.small_bert_L8_768') returns Spark NLP model sent_small_bert_L8_768\n","nlu.load('en.embed_sentence.small_bert_L10_768') returns Spark NLP model sent_small_bert_L10_768\n","nlu.load('en.embed_sentence.small_bert_L12_768') returns Spark NLP model sent_small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('fi.embed_sentence') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.cased') returns Spark NLP model sent_bert_finnish_cased\n","nlu.load('fi.embed_sentence.bert.uncased') returns Spark NLP model sent_bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('xx.embed_sentence') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.bert.cased') returns Spark NLP model sent_bert_multi_cased\n","nlu.load('xx.embed_sentence.labse') returns Spark NLP model labse\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"MvSC3rl5-adJ","executionInfo":{"status":"ok","timestamp":1620191242375,"user_tz":-300,"elapsed":10732678,"user":{"displayName":"ahmed lone","photoUrl":"","userId":"02458088882398909889"}}},"source":[""],"execution_count":14,"outputs":[]}]}
\ No newline at end of file
diff --git a/examples/colab/component_examples/sentence_embeddings/NLU_USE_sentence_embeddings_and_t-SNE_visualization_example.ipynb b/examples/colab/component_examples/sentence_embeddings/NLU_USE_sentence_embeddings_and_t-SNE_visualization_example.ipynb
deleted file mode 100644
index d1e92592..00000000
--- a/examples/colab/component_examples/sentence_embeddings/NLU_USE_sentence_embeddings_and_t-SNE_visualization_example.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"NLU_USE_sentence_embeddings_and_t-SNE_visualization_example.ipynb","provenance":[{"file_id":"1pgqoRJ6yGWbTLWdLnRvwG5DLSU3rxuMq","timestamp":1599401652794},{"file_id":"1JrlfuV2jNGTdOXvaWIoHTSf6BscDMkN7","timestamp":1599401257319},{"file_id":"1svpqtC3cY6JnRGeJngIPl2raqxdowpyi","timestamp":1599400881246},{"file_id":"1tW833T3HS8F5Lvn6LgeDd5LW5226syKN","timestamp":1599398724652},{"file_id":"1CYzHfQyFCdvIOVO2Z5aggVI9c0hDEOrw","timestamp":1599354735581}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"rBXrqlGEYA8G"},"source":["\n","\n","[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sentence_embeddings/NLU_USE_sentence_embeddings_and_t-SNE_visualization_example.ipynb)\n","\n","# USE Sentence Embeddings with NLU \n","The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.\n","\n","## Sources :\n","- https://arxiv.org/abs/1803.11175\n","- https://tfhub.dev/google/universal-sentence-encoder/2\n","\n","## Paper Abstract : \n","\n","We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. Comparisons are made with baselines that use word level transfer learning via pretrained word embeddings as well as baselines do not use any transfer learning. We find that transfer learning using sentence embeddings tends to outperform word level transfer. With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task. We obtain encouraging results on Word Embedding Association Tests (WEAT) targeted at detecting model bias. Our pre-trained sentence encoding models are made freely available for download and on TF Hub.\n","\n","\n","\n","# 1. Install Java and NLU"]},{"cell_type":"code","metadata":{"id":"M2-GiYL6xurJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619907421318,"user_tz":-120,"elapsed":121310,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"0c76afd2-f360-4303-8190-5ce0cecdb09a"},"source":["!wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash\n"," \n","\n","import nlu"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 22:15:00-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1671 (1.6K) [text/plain]\n","Saving to: ‘STDOUT’\n","\n","\r- 0%[ ] 0 --.-KB/s \rInstalling NLU 3.0.0 with PySpark 3.0.2 and Spark NLP 3.0.1 for Google Colab ...\n","- 100%[===================>] 1.63K --.-KB/s in 0s \n","\n","2021-05-01 22:15:00 (52.9 MB/s) - written to stdout [1671/1671]\n","\n","\u001b[K |████████████████████████████████| 204.8MB 57kB/s \n","\u001b[K |████████████████████████████████| 153kB 13.1MB/s \n","\u001b[K |████████████████████████████████| 204kB 21.7MB/s \n","\u001b[K |████████████████████████████████| 204kB 47.5MB/s \n","\u001b[?25h Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"N_CL8HZ8Ydry"},"source":["## 2. Load Model and embed sample string with USE"]},{"cell_type":"code","metadata":{"id":"j2ZZZvr1uGpx","colab":{"base_uri":"https://localhost:8080/","height":182},"executionInfo":{"status":"ok","timestamp":1619907551128,"user_tz":-120,"elapsed":251097,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"396a0a68-7ec4-4e56-909f-b0511d1c4d03"},"source":["import nlu\n","pipe = nlu.load('use')\n","pipe.predict('He was suprised by the diversity of NLU')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","sentence_detector_dl download started this may take some time.\n","Approximate size to download 354.6 KB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
document
\n","
sentence
\n","
sentence_embedding_use
\n","
\n"," \n"," \n","
\n","
0
\n","
He was suprised by the diversity of NLU
\n","
[He was suprised by the diversity of NLU]
\n","
[[0.08481953293085098, -0.06140690669417381, 0...
\n","
\n"," \n","
\n","
"],"text/plain":[" document ... sentence_embedding_use\n","0 He was suprised by the diversity of NLU ... [[0.08481953293085098, -0.06140690669417381, 0...\n","\n","[1 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"BAUFklCqLr3V"},"source":["# 3. Download Sample dataset"]},{"cell_type":"code","metadata":{"id":"wAFAOUSuLqvn","colab":{"base_uri":"https://localhost:8080/","height":606},"executionInfo":{"status":"ok","timestamp":1619907561180,"user_tz":-120,"elapsed":261139,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"3feecc13-24c5-46ce-b56e-8829fd42939a"},"source":["import pandas as pd\n","# Download the dataset \n","! wget -N https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv -P /tmp\n","# Load dataset to Pandas\n","df = pd.read_csv('/tmp/train-balanced-sarcasm.csv')\n","df"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-05-01 22:19:10-- https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/resources/en/sarcasm/train-balanced-sarcasm.csv\n","Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.8.101\n","Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.8.101|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 255268960 (243M) [text/csv]\n","Saving to: ‘/tmp/train-balanced-sarcasm.csv’\n","\n","train-balanced-sarc 100%[===================>] 243.44M 86.5MB/s in 2.8s \n","\n","2021-05-01 22:19:13 (86.5 MB/s) - ‘/tmp/train-balanced-sarcasm.csv’ saved [255268960/255268960]\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
label
\n","
comment
\n","
author
\n","
subreddit
\n","
score
\n","
ups
\n","
downs
\n","
date
\n","
created_utc
\n","
parent_comment
\n","
\n"," \n"," \n","
\n","
0
\n","
0
\n","
NC and NH.
\n","
Trumpbart
\n","
politics
\n","
2
\n","
-1
\n","
-1
\n","
2016-10
\n","
2016-10-16 23:55:23
\n","
Yeah, I get that argument. At this point, I'd ...
\n","
\n","
\n","
1
\n","
0
\n","
You do know west teams play against west teams...
\n","
Shbshb906
\n","
nba
\n","
-4
\n","
-1
\n","
-1
\n","
2016-11
\n","
2016-11-01 00:24:10
\n","
The blazers and Mavericks (The wests 5 and 6 s...
\n","
\n","
\n","
2
\n","
0
\n","
They were underdogs earlier today, but since G...
\n","
Creepeth
\n","
nfl
\n","
3
\n","
3
\n","
0
\n","
2016-09
\n","
2016-09-22 21:45:37
\n","
They're favored to win.
\n","
\n","
\n","
3
\n","
0
\n","
This meme isn't funny none of the \"new york ni...
\n","
icebrotha
\n","
BlackPeopleTwitter
\n","
-8
\n","
-1
\n","
-1
\n","
2016-10
\n","
2016-10-18 21:03:47
\n","
deadass don't kill my buzz
\n","
\n","
\n","
4
\n","
0
\n","
I could use one of those tools.
\n","
cush2push
\n","
MaddenUltimateTeam
\n","
6
\n","
-1
\n","
-1
\n","
2016-12
\n","
2016-12-30 17:00:13
\n","
Yep can confirm I saw the tool they use for th...
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
1010821
\n","
1
\n","
I'm sure that Iran and N. Korea have the techn...
\n","
TwarkMain
\n","
reddit.com
\n","
2
\n","
2
\n","
0
\n","
2009-04
\n","
2009-04-25 00:47:52
\n","
No one is calling this an engineered pathogen,...
\n","
\n","
\n","
1010822
\n","
1
\n","
whatever you do, don't vote green!
\n","
BCHarvey
\n","
climate
\n","
1
\n","
1
\n","
0
\n","
2009-05
\n","
2009-05-14 22:27:40
\n","
In a move typical of their recent do-nothing a...
\n","
\n","
\n","
1010823
\n","
1
\n","
Perhaps this is an atheist conspiracy to make ...
\n","
rebelcommander
\n","
atheism
\n","
1
\n","
1
\n","
0
\n","
2009-01
\n","
2009-01-11 00:22:57
\n","
Screw the Disabled--I've got to get to Church ...
\n","
\n","
\n","
1010824
\n","
1
\n","
The Slavs got their own country - it is called...
\n","
catsi
\n","
worldnews
\n","
1
\n","
1
\n","
0
\n","
2009-01
\n","
2009-01-23 21:12:49
\n","
I've always been unsettled by that. I hear a l...
\n","
\n","
\n","
1010825
\n","
1
\n","
values, as in capitalism .. there is good mone...
\n","
frogking
\n","
politics
\n","
2
\n","
2
\n","
0
\n","
2009-01
\n","
2009-01-24 06:20:14
\n","
Why do the people who make our laws seem unabl...
\n","
\n"," \n","
\n","
1010826 rows × 10 columns
\n","
"],"text/plain":[" label ... parent_comment\n","0 0 ... Yeah, I get that argument. At this point, I'd ...\n","1 0 ... The blazers and Mavericks (The wests 5 and 6 s...\n","2 0 ... They're favored to win.\n","3 0 ... deadass don't kill my buzz\n","4 0 ... Yep can confirm I saw the tool they use for th...\n","... ... ... ...\n","1010821 1 ... No one is calling this an engineered pathogen,...\n","1010822 1 ... In a move typical of their recent do-nothing a...\n","1010823 1 ... Screw the Disabled--I've got to get to Church ...\n","1010824 1 ... I've always been unsettled by that. I hear a l...\n","1010825 1 ... Why do the people who make our laws seem unabl...\n","\n","[1010826 rows x 10 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"OPdBQnV46or5"},"source":["# 4.1 Visualize Embeddings with T-SNE\n","\n","\n","\n","\n","Lets add Sentiment Part Of Speech to our pipeline because its so easy and so we can hue our T-SNE plots by POS and Sentiment \n","We predict the first 5k comments "]},{"cell_type":"code","metadata":{"id":"9bujAZtOCfRW","colab":{"base_uri":"https://localhost:8080/","height":793},"executionInfo":{"status":"ok","timestamp":1619907613727,"user_tz":-120,"elapsed":313680,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"dea3e962-b088-47b7-a6ae-c33ac8af9b13"},"source":["pipe = nlu.load('pos sentiment use emotion') # emotion\n","df['text'] = df['comment']\n","\n","# We must set output level to sentence since NLU will infer a different output level for this pipeline composition\n","predictions = pipe.predict(df[['text','label']].iloc[0:500], output_level='sentence')\n","predictions"],"execution_count":null,"outputs":[{"output_type":"stream","text":["pos_anc download started this may take some time.\n","Approximate size to download 3.9 MB\n","[OK!]\n","analyze_sentiment download started this may take some time.\n","Approx size to download 4.9 MB\n","[OK!]\n","tfhub_use download started this may take some time.\n","Approximate size to download 923.7 MB\n","[OK!]\n","classifierdl_use_emotion download started this may take some time.\n","Approximate size to download 21.3 MB\n","[OK!]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
pos
\n","
spell
\n","
sentiment_sentiment
\n","
sentiment_sentiment_confidence
\n","
sentence_embedding_use
\n","
emotion
\n","
emotion_confidence_confidence
\n","
\n"," \n"," \n","
\n","
0
\n","
NC and NH.
\n","
[NNP, CC, NNP, .]
\n","
[NC, and, NH, .]
\n","
negative
\n","
0.5229
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
surprise
\n","
0.99959
\n","
\n","
\n","
1
\n","
You do know west teams play against west teams...
\n","
[PRP, VBP, VB, NN, NNS, VBP, IN, NN, NNS, JJR,...
\n","
[You, do, know, west, teams, play, against, we...
\n","
negative
\n","
0.4733
\n","
[-0.0254225991666317, 0.05448468029499054, -0....
\n","
fear
\n","
0.975006
\n","
\n","
\n","
2
\n","
They were underdogs earlier today, but since G...
\n","
[PRP, VBD, NNS, RBR, NN, ,, CC, IN, NNP, NN, D...
\n","
[They, were, underdogs, earlier, today, ,, but...
\n","
negative
\n","
0.5118
\n","
[-0.0035701016895473003, -0.030124755576252937...
\n","
surprise
\n","
0.999544
\n","
\n","
\n","
3
\n","
This meme isn't funny none of the \"new york ni...
"],"text/plain":[" sentence ... emotion_confidence_confidence\n","0 NC and NH. ... 0.99959\n","1 You do know west teams play against west teams... ... 0.975006\n","2 They were underdogs earlier today, but since G... ... 0.999544\n","3 This meme isn't funny none of the \"new york ni... ... 0.991544\n","4 I could use one of those tools. ... 0.998017\n",".. ... ... ...\n","495 CS 1.6, Source and GO Cities skylines Getting ... ... 0.973829\n","496 Or a \"Your Welcome\" ... 0.999995\n","497 But I want it to charge Super fast! ... 0.990641\n","498 Right, but I don't think it makes sense to com... ... 0.99998\n","499 Hard drive requirements tend to include extra ... ... 0.977163\n","\n","[600 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"_OypFES-8EwY"},"source":["## 4.2 Checkout sentiment distribution"]},{"cell_type":"code","metadata":{"id":"ggbC0PxHgc2t","colab":{"base_uri":"https://localhost:8080/","height":332},"executionInfo":{"status":"ok","timestamp":1619909086953,"user_tz":-120,"elapsed":820,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"e60da44e-a8a2-48f6-eb8a-079dd5a221c9"},"source":["# Some Tokens are None which we must drop first\n","predictions.dropna(how='any', inplace=True)\n","# Some sentiment are 'na' which we must drop first\n","predictions.sentiment_sentiment.value_counts().plot.bar(title='Dataset sentiment distribution')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":7},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"ZUYHpsHTINsF"},"source":["# 5.Prepare data for T-SNE algorithm.\n","We create a Matrix with one row per Embedding vector for T-SNE algorithm"]},{"cell_type":"code","metadata":{"id":"L_0jefTB6i52"},"source":["import numpy as np\n","\n","# We first create a column of type np array\n","predictions['np_array'] = predictions.sentence_embedding_use.apply(lambda x: np.array(x))\n","# Make a matrix from the vectors in the np_array column via list comprehension\n","mat = np.matrix([x for x in predictions.np_array])"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pbdi4CY2Iqc0"},"source":["## 5.1 Fit and transform T-SNE algorithm\n"]},{"cell_type":"code","metadata":{"id":"fAFGB6iYIqmO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619909119915,"user_tz":-120,"elapsed":6844,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"bb2163c6-989c-4635-9f5c-d7eda087c86f"},"source":["\n","from sklearn.manifold import TSNE\n","model = TSNE(n_components=2) #n_components means the lower dimension\n","low_dim_data = model.fit_transform(mat)\n","print('Lower dim data has shape',low_dim_data.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Lower dim data has shape (600, 2)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gsi0b0XhImaz"},"source":["### Set plotting styles"]},{"cell_type":"code","metadata":{"id":"CsPVw7NHfEgt"},"source":["# set some styles for for Plotting\n","import seaborn as sns\n","# Style Plots a bit\n","sns.set_style('darkgrid')\n","sns.set_palette('muted')\n","sns.set_context(\"notebook\", font_scale=1,rc={\"lines.linewidth\": 2.5})\n","\n","%matplotlib inline\n","import matplotlib as plt\n","plt.rcParams['figure.figsize'] = (20, 14)\n","import matplotlib.pyplot as plt1\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8tuoCxNPmzbo"},"source":["##5.2 Plot low dimensional T-SNE USE embeddings with hue for Sarcasm\n"]},{"cell_type":"code","metadata":{"id":"Fbq5MAv0jkft","colab":{"base_uri":"https://localhost:8080/","height":844},"executionInfo":{"status":"ok","timestamp":1619909190838,"user_tz":-120,"elapsed":1756,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"39c80911-b4fd-4b32-ba40-cd8fcc921994"},"source":["tsne_df = pd.DataFrame(low_dim_data, df[:low_dim_data.shape[0]].label.replace({1:'sarcasm',0:'normal'}))\n","tsne_df.columns = ['x','y']\n","ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)\n","ax.set_title('T-SNE USE Embeddings, colored by Sarcasm label')\n","plt1.savefig(\"use_sarcasam\")"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Snb1gtqrnIJi"},"source":["## 5.3 Plot low dimensional T-SNE USE embeddings with hue for Sentiment\n"]},{"cell_type":"code","metadata":{"id":"QET-Y6PdnIJt","colab":{"base_uri":"https://localhost:8080/","height":844},"executionInfo":{"status":"ok","timestamp":1619909214794,"user_tz":-120,"elapsed":1618,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"227556e6-4640-410a-f537-98be05306d43"},"source":["tsne_df = pd.DataFrame(low_dim_data, predictions.sentiment_sentiment)\n","tsne_df.columns = ['x','y']\n","ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)\n","ax.set_title('T-SNE USE Embeddings, colored by Sentiment')\n","plt1.savefig(\"use_sentiment\")\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"7QNgruV-6eV1","colab":{"base_uri":"https://localhost:8080/","height":844},"executionInfo":{"status":"ok","timestamp":1619909230696,"user_tz":-120,"elapsed":1736,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"55297bbd-9222-4470-ff4e-194ed95b51cc"},"source":["tsne_df = pd.DataFrame(low_dim_data, predictions.emotion)\n","tsne_df.columns = ['x','y']\n","ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)\n","ax.set_title('T-SNE use Embeddings, colored by Emotion')\n","plt1.savefig(\"use_emotion\")"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"fv3FIQ7j6eVv"},"source":["# 5.4 Plot low dimensional T-SNE USE embeddings with hue for Emotions\n"]},{"cell_type":"markdown","metadata":{"id":"l3sRcFW9muEZ"},"source":["# 6.1 Plot low dimensional T-SNE USE embeddings with hue for POS \n","Because we will have a list of pos labels for each sentence, we need to explode on the pos column and then do the data peperation for T-SNE again before we can visualize with hue for POS\n"]},{"cell_type":"code","metadata":{"id":"OZ_2DTk9bC-O","colab":{"base_uri":"https://localhost:8080/","height":759},"executionInfo":{"status":"ok","timestamp":1619909233187,"user_tz":-120,"elapsed":372,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"a9cabd11-6b38-4835-e3ae-b9cf99190ca5"},"source":["predictions_exploded_on_pos = predictions.explode('pos')\n","predictions_exploded_on_pos"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","
\n"," \n","
\n","
\n","
sentence
\n","
pos
\n","
spell
\n","
sentiment_sentiment
\n","
sentiment_sentiment_confidence
\n","
sentence_embedding_use
\n","
emotion
\n","
emotion_confidence_confidence
\n","
np_array
\n","
\n"," \n"," \n","
\n","
0
\n","
NC and NH.
\n","
NNP
\n","
[NC, and, NH, .]
\n","
negative
\n","
0.5229
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
surprise
\n","
0.99959
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
\n","
\n","
0
\n","
NC and NH.
\n","
CC
\n","
[NC, and, NH, .]
\n","
negative
\n","
0.5229
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
surprise
\n","
0.99959
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
\n","
\n","
0
\n","
NC and NH.
\n","
NNP
\n","
[NC, and, NH, .]
\n","
negative
\n","
0.5229
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
surprise
\n","
0.99959
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
\n","
\n","
0
\n","
NC and NH.
\n","
.
\n","
[NC, and, NH, .]
\n","
negative
\n","
0.5229
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
surprise
\n","
0.99959
\n","
[-0.06570463627576828, -0.03522052243351936, -...
\n","
\n","
\n","
1
\n","
You do know west teams play against west teams...
\n","
PRP
\n","
[You, do, know, west, teams, play, against, we...
\n","
negative
\n","
0.4733
\n","
[-0.0254225991666317, 0.05448468029499054, -0....
\n","
fear
\n","
0.975006
\n","
[-0.0254225991666317, 0.05448468029499054, -0....
\n","
\n","
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
...
\n","
\n","
\n","
499
\n","
Hard drive requirements tend to include extra ...
\n","
IN
\n","
[Hard, drive, requirements, tend, to, include,...
\n","
positive
\n","
0.5396
\n","
[0.029393689706921577, -0.02757555991411209, -...
\n","
sadness
\n","
0.977163
\n","
[0.029393689706921577, -0.02757555991411209, -...
\n","
\n","
\n","
499
\n","
Hard drive requirements tend to include extra ...
\n","
DT
\n","
[Hard, drive, requirements, tend, to, include,...
\n","
positive
\n","
0.5396
\n","
[0.029393689706921577, -0.02757555991411209, -...
\n","
sadness
\n","
0.977163
\n","
[0.029393689706921577, -0.02757555991411209, -...
\n","
\n","
\n","
499
\n","
Hard drive requirements tend to include extra ...
\n","
NN
\n","
[Hard, drive, requirements, tend, to, include,...
\n","
positive
\n","
0.5396
\n","
[0.029393689706921577, -0.02757555991411209, -...
\n","
sadness
\n","
0.977163
\n","
[0.029393689706921577, -0.02757555991411209, -...
\n","
\n","
\n","
499
\n","
Hard drive requirements tend to include extra ...
\n","
VBG
\n","
[Hard, drive, requirements, tend, to, include,...
\n","
positive
\n","
0.5396
\n","
[0.029393689706921577, -0.02757555991411209, -...
\n","
sadness
\n","
0.977163
\n","
[0.029393689706921577, -0.02757555991411209, -...
\n","
\n","
\n","
499
\n","
Hard drive requirements tend to include extra ...
\n","
.
\n","
[Hard, drive, requirements, tend, to, include,...
\n","
positive
\n","
0.5396
\n","
[0.029393689706921577, -0.02757555991411209, -...
\n","
sadness
\n","
0.977163
\n","
[0.029393689706921577, -0.02757555991411209, -...
\n","
\n"," \n","
\n","
7957 rows × 9 columns
\n","
"],"text/plain":[" sentence ... np_array\n","0 NC and NH. ... [-0.06570463627576828, -0.03522052243351936, -...\n","0 NC and NH. ... [-0.06570463627576828, -0.03522052243351936, -...\n","0 NC and NH. ... [-0.06570463627576828, -0.03522052243351936, -...\n","0 NC and NH. ... [-0.06570463627576828, -0.03522052243351936, -...\n","1 You do know west teams play against west teams... ... [-0.0254225991666317, 0.05448468029499054, -0....\n",".. ... ... ...\n","499 Hard drive requirements tend to include extra ... ... [0.029393689706921577, -0.02757555991411209, -...\n","499 Hard drive requirements tend to include extra ... ... [0.029393689706921577, -0.02757555991411209, -...\n","499 Hard drive requirements tend to include extra ... ... [0.029393689706921577, -0.02757555991411209, -...\n","499 Hard drive requirements tend to include extra ... ... [0.029393689706921577, -0.02757555991411209, -...\n","499 Hard drive requirements tend to include extra ... ... [0.029393689706921577, -0.02757555991411209, -...\n","\n","[7957 rows x 9 columns]"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"k1M_a4pmfMGA"},"source":["## 6.2 Preprocess data for TSNE again"]},{"cell_type":"code","metadata":{"id":"K0rpmiy6a2UK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619909375922,"user_tz":-120,"elapsed":119780,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"5ab5525e-54ff-48ea-ed56-0f9cce4284b4"},"source":["\n","# We first create a column of type np array\n","predictions_exploded_on_pos['np_array'] = predictions_exploded_on_pos.sentence_embedding_use.apply(lambda x: np.array(x))\n","# Make a matrix from the vectors in the np_array column via list comprehension\n","mat = np.matrix([x for x in predictions_exploded_on_pos.np_array])\n","\n","\n","from sklearn.manifold import TSNE\n","model = TSNE(n_components=2) #n_components means the lower dimension\n","low_dim_data = model.fit_transform(mat)\n","print('Lower dim data has shape',low_dim_data.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Lower dim data has shape (7957, 2)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6ze0HWqqfQDh"},"source":["# 6.3 Plot low dimensional T-SNE USE embeddings with hue for POS \n"]},{"cell_type":"code","metadata":{"id":"RB1qdDP3fJHN","colab":{"base_uri":"https://localhost:8080/","height":844},"executionInfo":{"status":"ok","timestamp":1619909390922,"user_tz":-120,"elapsed":3073,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"4795a107-4163-4d6b-a894-be5c3dea3ba5"},"source":["tsne_df = pd.DataFrame(low_dim_data, predictions_exploded_on_pos.pos)\n","tsne_df.columns = ['x','y']\n","ax = sns.scatterplot(data=tsne_df, x='x', y='y', hue=tsne_df.index)\n","ax.set_title('T-SNE USE Embeddings, colored by Part of Speech Tag')\n","plt1.savefig(\"use_pos\")"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":[""]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"uXb-FMA6mX13"},"source":["# 7. NLU has many more embedding models! \n","Make sure to try them all out! \n","You can change 'use' in nlu.load('use') to bert, xlnet, albert or any other of the **100+ word embeddings** offerd by NLU"]},{"cell_type":"code","metadata":{"id":"9qUF7jPlme-R","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1619909393091,"user_tz":-120,"elapsed":597,"user":{"displayName":"Christian Kasim Loan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjqAD-ircKP-s5Eh6JSdkDggDczfqQbJGU_IRb4Hw=s64","userId":"14469489166467359317"}},"outputId":"b9d405dc-3425-4400-c0ac-f8c76b6ec043"},"source":["nlu.print_all_model_kinds_for_action('embed')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["For language NLU provides the following Models : \n","nlu.load('en.embed') returns Spark NLP model glove_100d\n","nlu.load('en.embed.glove') returns Spark NLP model glove_100d\n","nlu.load('en.embed.glove.100d') returns Spark NLP model glove_100d\n","nlu.load('en.embed.bert') returns Spark NLP model bert_base_uncased\n","nlu.load('en.embed.bert.base_uncased') returns Spark NLP model bert_base_uncased\n","nlu.load('en.embed.bert.base_cased') returns Spark NLP model bert_base_cased\n","nlu.load('en.embed.bert.large_uncased') returns Spark NLP model bert_large_uncased\n","nlu.load('en.embed.bert.large_cased') returns Spark NLP model bert_large_cased\n","nlu.load('en.embed.biobert') returns Spark NLP model biobert_pubmed_base_cased\n","nlu.load('en.embed.biobert.pubmed_base_cased') returns Spark NLP model biobert_pubmed_base_cased\n","nlu.load('en.embed.biobert.pubmed_large_cased') returns Spark NLP model biobert_pubmed_large_cased\n","nlu.load('en.embed.biobert.pmc_base_cased') returns Spark NLP model biobert_pmc_base_cased\n","nlu.load('en.embed.biobert.pubmed_pmc_base_cased') returns Spark NLP model biobert_pubmed_pmc_base_cased\n","nlu.load('en.embed.biobert.clinical_base_cased') returns Spark NLP model biobert_clinical_base_cased\n","nlu.load('en.embed.biobert.discharge_base_cased') returns Spark NLP model biobert_discharge_base_cased\n","nlu.load('en.embed.elmo') returns Spark NLP model elmo\n","nlu.load('en.embed.use') returns Spark NLP model tfhub_use\n","nlu.load('en.embed.albert') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed.albert.base_uncased') returns Spark NLP model albert_base_uncased\n","nlu.load('en.embed.albert.large_uncased') returns Spark NLP model albert_large_uncased\n","nlu.load('en.embed.albert.xlarge_uncased') returns Spark NLP model albert_xlarge_uncased\n","nlu.load('en.embed.albert.xxlarge_uncased') returns Spark NLP model albert_xxlarge_uncased\n","nlu.load('en.embed.xlnet') returns Spark NLP model xlnet_base_cased\n","nlu.load('en.embed.xlnet_base_cased') returns Spark NLP model xlnet_base_cased\n","nlu.load('en.embed.xlnet_large_cased') returns Spark NLP model xlnet_large_cased\n","nlu.load('en.embed.electra') returns Spark NLP model electra_small_uncased\n","nlu.load('en.embed.electra.small_uncased') returns Spark NLP model electra_small_uncased\n","nlu.load('en.embed.electra.base_uncased') returns Spark NLP model electra_base_uncased\n","nlu.load('en.embed.electra.large_uncased') returns Spark NLP model electra_large_uncased\n","nlu.load('en.embed.covidbert') returns Spark NLP model covidbert_large_uncased\n","nlu.load('en.embed.covidbert.large_uncased') returns Spark NLP model covidbert_large_uncased\n","nlu.load('en.embed.bert.small_L2_128') returns Spark NLP model small_bert_L2_128\n","nlu.load('en.embed.bert.small_L4_128') returns Spark NLP model small_bert_L4_128\n","nlu.load('en.embed.bert.small_L6_128') returns Spark NLP model small_bert_L6_128\n","nlu.load('en.embed.bert.small_L8_128') returns Spark NLP model small_bert_L8_128\n","nlu.load('en.embed.bert.small_L10_128') returns Spark NLP model small_bert_L10_128\n","nlu.load('en.embed.bert.small_L12_128') returns Spark NLP model small_bert_L12_128\n","nlu.load('en.embed.bert.small_L2_256') returns Spark NLP model small_bert_L2_256\n","nlu.load('en.embed.bert.small_L4_256') returns Spark NLP model small_bert_L4_256\n","nlu.load('en.embed.bert.small_L6_256') returns Spark NLP model small_bert_L6_256\n","nlu.load('en.embed.bert.small_L8_256') returns Spark NLP model small_bert_L8_256\n","nlu.load('en.embed.bert.small_L10_256') returns Spark NLP model small_bert_L10_256\n","nlu.load('en.embed.bert.small_L12_256') returns Spark NLP model small_bert_L12_256\n","nlu.load('en.embed.bert.small_L2_512') returns Spark NLP model small_bert_L2_512\n","nlu.load('en.embed.bert.small_L4_512') returns Spark NLP model small_bert_L4_512\n","nlu.load('en.embed.bert.small_L6_512') returns Spark NLP model small_bert_L6_512\n","nlu.load('en.embed.bert.small_L8_512') returns Spark NLP model small_bert_L8_512\n","nlu.load('en.embed.bert.small_L10_512') returns Spark NLP model small_bert_L10_512\n","nlu.load('en.embed.bert.small_L12_512') returns Spark NLP model small_bert_L12_512\n","nlu.load('en.embed.bert.small_L2_768') returns Spark NLP model small_bert_L2_768\n","nlu.load('en.embed.bert.small_L4_768') returns Spark NLP model small_bert_L4_768\n","nlu.load('en.embed.bert.small_L6_768') returns Spark NLP model small_bert_L6_768\n","nlu.load('en.embed.bert.small_L8_768') returns Spark NLP model small_bert_L8_768\n","nlu.load('en.embed.bert.small_L10_768') returns Spark NLP model small_bert_L10_768\n","nlu.load('en.embed.bert.small_L12_768') returns Spark NLP model small_bert_L12_768\n","For language NLU provides the following Models : \n","nlu.load('ar.embed') returns Spark NLP model arabic_w2v_cc_300d\n","nlu.load('ar.embed.cbow') returns Spark NLP model arabic_w2v_cc_300d\n","nlu.load('ar.embed.cbow.300d') returns Spark NLP model arabic_w2v_cc_300d\n","nlu.load('ar.embed.aner') returns Spark NLP model arabic_w2v_cc_300d\n","nlu.load('ar.embed.aner.300d') returns Spark NLP model arabic_w2v_cc_300d\n","nlu.load('ar.embed.glove') returns Spark NLP model arabic_w2v_cc_300d\n","For language NLU provides the following Models : \n","nlu.load('bn.embed.glove') returns Spark NLP model bengaliner_cc_300d\n","nlu.load('bn.embed') returns Spark NLP model bengaliner_cc_300d\n","For language NLU provides the following Models : \n","nlu.load('fi.embed.bert.') returns Spark NLP model bert_finnish_cased\n","nlu.load('fi.embed.bert.cased.') returns Spark NLP model bert_finnish_cased\n","nlu.load('fi.embed.bert.uncased.') returns Spark NLP model bert_finnish_uncased\n","For language NLU provides the following Models : \n","nlu.load('he.embed') returns Spark NLP model hebrew_cc_300d\n","nlu.load('he.embed.glove') returns Spark NLP model hebrew_cc_300d\n","nlu.load('he.embed.cbow_300d') returns Spark NLP model hebrew_cc_300d\n","For language NLU provides the following Models : \n","nlu.load('hi.embed') returns Spark NLP model hindi_cc_300d\n","For language NLU provides the following Models : \n","nlu.load('fa.embed') returns Spark NLP model persian_w2v_cc_300d\n","nlu.load('fa.embed.word2vec') returns Spark NLP model persian_w2v_cc_300d\n","nlu.load('fa.embed.word2vec.300d') returns Spark NLP model persian_w2v_cc_300d\n","For language NLU provides the following Models : \n","nlu.load('zh.embed') returns Spark NLP model bert_base_chinese\n","nlu.load('zh.embed.bert') returns Spark NLP model bert_base_chinese\n","For language