Legal Asset Purchase Agreement Document Classifier (Longformer)

Description

The legclf_asset_purchase_agreement model is a Legal Longformer Document Classifier to classify if the document belongs to the class asset-purchase-agreement or not (Binary Classification).

Longformers have a restriction on 4096 tokens, so only the first 4096 tokens will be taken into account. We have realised that for the big majority of the documents in legal corpora, if they are clean and only contain the legal document without any extra information before, 4096 is enough to perform Document Classification.

If not, let us know and we can carry out another approach for you: getting chunks of 4096 tokens and average the embeddings, training with the averaged version, what means all document will be taken into account. But this theoretically should not be required.

Predicted Entities

asset-purchase-agreement, other

Live Demo Open in Colab Download Copy S3 URI

How to use

document_assembler = nlp.DocumentAssembler()\
     .setInputCol("text")\
     .setOutputCol("document")

tokenizer = nlp.Tokenizer()\
     .setInputCols(["document"])\
     .setOutputCol("token")

embeddings = nlp.LongformerEmbeddings.pretrained("legal_longformer_base", "en")\
    .setInputCols("document", "token")\
    .setOutputCol("embeddings")

sentence_embeddings = nlp.SentenceEmbeddings()\
    .setInputCols(["document", "embeddings"])\
    .setOutputCol("sentence_embeddings")\
    .setPoolingStrategy("AVERAGE")

doc_classifier = nlp.ClassifierDLModel.pretrained("legclf_asset_purchase_agreement", "en", "legal/models")\
    .setInputCols(["sentence_embeddings"])\
    .setOutputCol("category")
    
nlpPipeline = nlp.Pipeline(stages=[
    document_assembler, 
    tokenizer,
    embeddings,
    sentence_embeddings,
    doc_classifier])
 
df = spark.createDataFrame([["YOUR TEXT HERE"]]).toDF("text")

model = nlpPipeline.fit(df)

result = model.transform(df)

Results

+-------+
| result|
+-------+
|[asset-purchase-agreement]|
|[other]|
|[other]|
|[asset-purchase-agreement]|

Model Information

References

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

Benchmarking

label                     precision  recall  f1-score  support 
asset-purchase-agreement  0.96       0.96    0.96      27      
other                     0.99       0.99    0.99      85      
accuracy                  -          -       0.98      112     
macro-avg                 0.98       0.98    0.98      112     
weighted-avg              0.98       0.98    0.98      112