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+---
+layout: model
+title: Legal Agreement Of Purchase And Sale Document Classifier (Bert Sentence Embeddings)
+author: John Snow Labs
+name: legclf_agreement_of_purchase_and_sale_bert
+date: 2023-01-26
+tags: [en, legal, classification, agreement, purchase, sale, licensed, bert, tensorflow]
+task: Text Classification
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: LegalClassifierDLModel
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The `legclf_agreement_of_purchase_and_sale_bert` model is a Bert Sentence Embeddings Document Classifier used to classify if the document belongs to the class `agreement-of-purchase-and-sale` (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-of-purchase-and-sale`, `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_of_purchase_and_sale_bert_en_1.0.0_3.0_1674731506904.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legclf_agreement_of_purchase_and_sale_bert_en_1.0.0_3.0_1674731506904.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+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_of_purchase_and_sale_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-of-purchase-and-sale]|
+|[other]|
+|[other]|
+|[agreement-of-purchase-and-sale]|
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legclf_agreement_of_purchase_and_sale_bert|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|22.5 MB|
+
+## References
+
+Legal documents, scrapped from the Internet, and classified in-house + SEC documents + Lawinsider categorization
+
+## Benchmarking
+
+```bash
+
+
+                         label  precision    recall  f1-score   support
+agreement-of-purchase-and-sale       0.96      0.98      0.97        51
+                         other       0.99      0.98      0.99       116
+                      accuracy          -         -      0.98       167
+                     macro-avg       0.98      0.98      0.98       167
+                  weighted-avg       0.98      0.98      0.98       167
+                  
+```
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+---
+layout: model
+title: Legal Confidential Treatment Document Classifier (Bert Sentence Embeddings)
+author: John Snow Labs
+name: legclf_confidential_treatment_bert
+date: 2023-01-26
+tags: [en, legal, classification, confidential, treatment, licensed, bert, tensorflow]
+task: Text Classification
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: LegalClassifierDLModel
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The `legclf_confidential_treatment_bert` model is a Bert Sentence Embeddings Document Classifier used to classify if the document belongs to the class `confidential-treatment` (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
+
+`confidential-treatment`, `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_confidential_treatment_bert_en_1.0.0_3.0_1674732030348.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legclf_confidential_treatment_bert_en_1.0.0_3.0_1674732030348.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+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_confidential_treatment_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|
++-------+
+|[confidential-treatment]|
+|[other]|
+|[other]|
+|[confidential-treatment]|
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legclf_confidential_treatment_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
+
+
+                 label  precision    recall  f1-score   support
+confidential-treatment       0.98      0.98      0.98        55
+                 other       0.99      0.99      0.99       116
+              accuracy          -         -      0.99       171
+             macro-avg       0.99      0.99      0.99       171
+          weighted-avg       0.99      0.99      0.99       171
+                  
+```
@@ -0,0 +1,116 @@
+---
+layout: model
+title: Legal Form Of Indemnification Agreement Document Classifier (Bert Sentence Embeddings)
+author: John Snow Labs
+name: legclf_form_of_indemnification_agreement_bert
+date: 2023-01-26
+tags: [en, legal, classification, indemnification, agreement, licensed, bert, tensorflow]
+task: Text Classification
+language: en
+edition: Legal NLP 1.0.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: LegalClassifierDLModel
+article_header:
+type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+The `legclf_form_of_indemnification_agreement_bert` model is a Bert Sentence Embeddings Document Classifier used to classify if the document belongs to the class `form-of-indemnification-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
+
+`form-of-indemnification-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_form_of_indemnification_agreement_bert_en_1.0.0_3.0_1674732486525.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/legal/models/legclf_form_of_indemnification_agreement_bert_en_1.0.0_3.0_1674732486525.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+
+```python
+
+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_form_of_indemnification_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|
++-------+
+|[form-of-indemnification-agreement]|
+|[other]|
+|[other]|
+|[form-of-indemnification-agreement]|
+
+```
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|legclf_form_of_indemnification_agreement_bert|
+|Compatibility:|Legal NLP 1.0.0+|
+|License:|Licensed|
+|Edition:|Official|
+|Input Labels:|[sentence_embeddings]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|22.4 MB|
+
+## References
+
+Legal documents, scrapped from the Internet, and classified in-house + SEC documents + Lawinsider categorization
+
+## Benchmarking
+
+```bash
+
+
+                            label  precision    recall  f1-score   support
+form-of-indemnification-agreement       1.00      0.97      0.99        36
+                            other       0.98      1.00      0.99        65
+                         accuracy          -         -      0.99       101
+                        macro-avg       0.99      0.99      0.99       101
+                     weighted-avg       0.99      0.99      0.99       101
+                  
+```