2022-12-06-legclf_agreement_and_plan_of_reorganization_bert_en

docs/_posts/bunyamin-polat/2022-12-06-legclf_agreement_and_plan_of_reorganization_bert_en.md

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+---
+layout: model
+title: Legal Agreement and Plan of Reorganization Document Classifier (Bert Sentence Embeddings)
+author: John Snow Labs
+name: legclf_agreement_and_plan_of_reorganization_bert
+date: 2022-12-06
+tags: [en, legal, classification, agreement, plan, reorganizationlicensed, bert, licensed, tensorflow]
+task: Text Classification
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The `legclf_agreement_and_plan_of_reorganization_bert` model is a Bert Sentence Embeddings Document Classifier used to classify if the document belongs to the class `agreement-and-plan-of-reorganization` (check [Lawinsider](https://www.lawinsider.com/tags) for similar document type classification) or not (Binary Classification).
+
+Unlike the Longformer model, this model is lighter in terms of inference time.
+
+## Predicted Entities
+
+`agreement-and-plan-of-reorganization`, `other`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/legal/models/legclf_agreement_and_plan_of_reorganization_bert_en_1.0.0_3.0_1670349241846.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+document_assembler = nlp.DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+embeddings = nlp.BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "en")\
+    .setInputCols("document")\
+    .setOutputCol("sentence_embeddings")
+
+doc_classifier = legal.ClassifierDLModel.pretrained("legclf_agreement_and_plan_of_reorganization_bert", "en", "legal/models")\
+    .setInputCols(["sentence_embeddings"])\
+    .setOutputCol("category")
+
+nlpPipeline = nlp.Pipeline(stages=[
+    document_assembler, 
+    embeddings,
+    doc_classifier])
+
+df = spark.createDataFrame([["YOUR TEXT HERE"]]).toDF("text")
+
+model = nlpPipeline.fit(df)
+
+result = model.transform(df)
+
+```
+
+</div>
+
+## Results
+
+```bash
+
++-------+
+|result|
++-------+
+|[agreement-and-plan-of-reorganization]|
+|[other]|
+|[other]|
+|[agreement-and-plan-of-reorganization]|
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legclf_agreement_and_plan_of_reorganization_bert|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|22.8 MB|
+
+## References
+
+Legal documents, scrapped from the Internet, and classified in-house + SEC documents + Lawinsider categorization
+
+## Benchmarking
+
+```bash
+
+                                      precision    recall  f1-score   support
+
+agreement-and-plan-of-reorganization       1.00      1.00      1.00        31
+                               other       1.00      1.00      1.00        35
+
+                            accuracy                           1.00        66
+                           macro-avg       1.00      1.00      1.00        66
+                        weighted-avg       1.00      1.00      1.00        66
+
+```

docs/_posts/bunyamin-polat/2022-12-06-legclf_deposit_agreement_bert_en.md

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+---
+layout: model
+title: Legal Deposit Agreement Document Classifier (Bert Sentence Embeddings)
+author: John Snow Labs
+name: legclf_deposit_agreement_bert
+date: 2022-12-06
+tags: [en, legal, classification, agreement, deposit, licensed, bert, tensorflow]
+task: Text Classification
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The `legclf_deposit_agreement_bert` model is a Bert Sentence Embeddings Document Classifier used to classify if the document belongs to the class `deposit-agreement` (check [Lawinsider](https://www.lawinsider.com/tags) for similar document type classification) or not (Binary Classification).
+
+Unlike the Longformer model, this model is lighter in terms of inference time.
+
+## Predicted Entities
+
+`deposit-agreement`, `other`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/legal/models/legclf_deposit_agreement_bert_en_1.0.0_3.0_1670349380582.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+document_assembler = nlp.DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+embeddings = nlp.BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "en")\
+    .setInputCols("document")\
+    .setOutputCol("sentence_embeddings")
+
+doc_classifier = legal.ClassifierDLModel.pretrained("legclf_deposit_agreement_bert", "en", "legal/models")\
+    .setInputCols(["sentence_embeddings"])\
+    .setOutputCol("category")
+
+nlpPipeline = nlp.Pipeline(stages=[
+    document_assembler, 
+    embeddings,
+    doc_classifier])
+
+df = spark.createDataFrame([["YOUR TEXT HERE"]]).toDF("text")
+
+model = nlpPipeline.fit(df)
+
+result = model.transform(df)
+
+```
+
+</div>
+
+## Results
+
+```bash
+
++-------+
+|result|
++-------+
+|[deposit-agreement]|
+|[other]|
+|[other]|
+|[deposit-agreement]|
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legclf_deposit_agreement_bert|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|22.7 MB|
+
+## References
+
+Legal documents, scrapped from the Internet, and classified in-house + SEC documents + Lawinsider categorization
+
+## Benchmarking
+
+```bash
+
+                   precision    recall  f1-score   support
+
+deposit-agreement       0.97      0.97      0.97        36
+            other       0.98      0.98      0.98        65
+
+         accuracy                           0.98       101
+        macro-avg       0.98      0.98      0.98       101
+     weighted-avg       0.98      0.98      0.98       101
+
+```

docs/_posts/bunyamin-polat/2022-12-06-legclf_indemnity_agreement_bert_en.md

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+---
+layout: model
+title: Legal Indemnity Agreement Document Classifier (Bert Sentence Embeddings)
+author: John Snow Labs
+name: legclf_indemnity_agreement_bert
+date: 2022-12-06
+tags: [en, legal, classification, agreement, indemnity, licensed, bert, tensorflow]
+task: Text Classification
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The `legclf_indemnity_agreement_bert` model is a Bert Sentence Embeddings Document Classifier used to classify if the document belongs to the class `indemnity-agreement` (check [Lawinsider](https://www.lawinsider.com/tags) for similar document type classification) or not (Binary Classification).
+
+Unlike the Longformer model, this model is lighter in terms of inference time.
+
+## Predicted Entities
+
+`indemnity-agreement`, `other`
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/legal/models/legclf_indemnity_agreement_bert_en_1.0.0_3.0_1670349475264.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+document_assembler = nlp.DocumentAssembler()\
+    .setInputCol("text")\
+    .setOutputCol("document")
+
+embeddings = nlp.BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "en")\
+    .setInputCols("document")\
+    .setOutputCol("sentence_embeddings")
+
+doc_classifier = legal.ClassifierDLModel.pretrained("legclf_indemnity_agreement_bert", "en", "legal/models")\
+    .setInputCols(["sentence_embeddings"])\
+    .setOutputCol("category")
+
+nlpPipeline = nlp.Pipeline(stages=[
+    document_assembler, 
+    embeddings,
+    doc_classifier])
+
+df = spark.createDataFrame([["YOUR TEXT HERE"]]).toDF("text")
+
+model = nlpPipeline.fit(df)
+
+result = model.transform(df)
+
+```
+
+</div>
+
+## Results
+
+```bash
+
++-------+
+|result|
++-------+
+|[indemnity-agreement]|
+|[other]|
+|[other]|
+|[indemnity-agreement]|
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legclf_indemnity_agreement_bert|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|22.7 MB|
+
+## References
+
+Legal documents, scrapped from the Internet, and classified in-house + SEC documents + Lawinsider categorization
+
+## Benchmarking
+
+```bash
+
+                     precision    recall  f1-score   support
+
+indemnity-agreement       0.88      0.82      0.85        28
+              other       0.86      0.91      0.89        35
+
+           accuracy                           0.87        63
+          macro-avg       0.87      0.87      0.87        63
+       weighted-avg       0.87      0.87      0.87        63
+
+```