Legal Termination Clause Binary Classifier (CUAD dataset, USE version)

Description

This model is a Binary Classifier (True, False) for the termination clause type. To use this model, make sure you provide enough context as an input. Adding Sentence Splitters to the pipeline will make the model see only sentences, not the whole text, so it’s better to skip it, unless you want to do Binary Classification as sentence level.

This version was trained with Universal Sentence Encoder. There is another version using Sentence Bert, called legclf_sbert_cuad_termination_clause

If you have big legal documents, and you want to look for clauses, we recommend you to split the documents using any of the techniques available in our Spark NLP for Legal Workshop Tokenization & Splitting Tutorial (link here), namely:

• Paragraph splitting (by multiline);
• Splitting by headers / subheaders;
• etc.

Take into consideration the embeddings of this model allows up to 512 tokens. If you have more than that, consider splitting in smaller pieces (you can also check the same tutorial link provided above).

This model can be combined with any of the other “hundreds” of Legal Clauses Classifiers you will find in Models Hub, getting as an output a series of True/False values for each of the legal clause model you have added.

There are other models in this dataset with similar title, but the difference is the dataset it was trained on. This one was trained with cuad dataset.

Predicted Entities

termination, other

Live Demo Open in Colab Download Copy S3 URI

How to use

documentAssembler = nlp.DocumentAssembler() \
     .setInputCol("clause_text") \
     .setOutputCol("document")
  
embeddings = UniversalSentenceEncoder.pretrained("tfhub_use", "en") \
    .setInputCols("document") \
    .setOutputCol("sentence_embeddings")

docClassifier = nlp.ClassifierDLModel.pretrained("legclf_cuad_termination_clause", "en", "legal/models")\
    .setInputCols(["sentence_embeddings"])\
    .setOutputCol("category")
    
nlpPipeline = nlp.Pipeline(stages=[
    documentAssembler, 
    embeddings,
    docClassifier])
 
df = spark.createDataFrame([["              ---------------------\n\n     This Agreement may be terminated immediately by Developer..."]]).toDF("clause_text")
model = nlpPipeline.fit(df)
result = model.transform(df)

Results

+-------+
| result|
+-------+
|[termination]|

Model Information

References

In-house annotations on CUAD dataset

Benchmarking

label              precision    recall  f1-score   support
       other       1.00      0.97      0.99        35
 termination       0.98      1.00      0.99        44
    accuracy          -         -      0.99        79
   macro-avg       0.99      0.99      0.99        79
weighted-avg       0.99      0.99      0.99        79