Legal Private Placement Warrants Purchase Agreement Document Classifier (Bert Sentence Embeddings)

Description

The legclf_private_placement_warrants_purchase_agreement_bert model is a Bert Sentence Embeddings Document Classifier used to classify if the document belongs to the class private-placement-warrants-purchase-agreement or not (Binary Classification).

Unlike the Longformer model, this model is lighter in terms of inference time.

Predicted Entities

private-placement-warrants-purchase-agreement, other

Download Copy S3 URI

How to use

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_private_placement_warrants_purchase_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)

Results

+-------+
|result|
+-------+
|[private-placement-warrants-purchase-agreement]|
|[other]|
|[other]|
|[private-placement-warrants-purchase-agreement]|

Model Information

Model Name:	legclf_private_placement_warrants_purchase_agreement_bert
Compatibility:	Legal NLP 1.0.0+
License:	Licensed
Edition:	Official
Input Labels:	[sentence_embeddings]
Output Labels:	[class]
Language:	en
Size:	22.1 MB

References

Legal documents, scrapped from the Internet, and classified in-house + SEC documents

Benchmarking

                                        label  precision    recall  f1-score   support
                                        other       1.00      1.00      1.00        99
private-placement-warrants-purchase-agreement       1.00      1.00      1.00        42
                                     accuracy          -         -      1.00       141
                                    macro-avg       1.00      1.00      1.00       141
                                 weighted-avg       1.00      1.00      1.00       141

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