Legal Investment Advisory Agreement Document Classifier (Bert Sentence Embeddings)

Description

The legclf_investment_advisory_agreement_bert model is a Bert Sentence Embeddings Document Classifier used to classify if the document belongs to the class investment-advisory-agreement or not (Binary Classification).

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

Predicted Entities

investment-advisory-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_investment_advisory_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|
+-------+
|[investment-advisory-agreement]|
|[other]|
|[other]|
|[investment-advisory-agreement]|

Model Information

Model Name:	legclf_investment_advisory_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

Benchmarking

                        label   precision    recall  f1-score   support
investment-advisory-agreement       0.98      0.98      0.98        42
                        other       0.97      0.97      0.97        35
                     accuracy         -         -       0.97        77
                    macro-avg       0.97      0.97      0.97        77
                 weighted-avg       0.97      0.97      0.97        77

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