Obligations Pipeline

Description

IMPORTANT: Don’t run this model on the whole legal agreement. Instead:

• Split by paragraphs. You can use notebook 1 in Finance or Legal as inspiration;
• Use the legclf_cuad_obligations_clause Text Classifier to select only these paragraphs;

This is a Pretrained Pipeline to process agreements, more specifically the sentences where all the obligations of the parties are expressed (what they agreed upon in the contract).

This pipeline returns:

• NER entities for the subject, the action/verb, the object and the indirect object of the clause;
• Syntactic dependencies of the chunks, so that you can disambiguate in case different clauses/agreements are present in the same sentence.

This model does not include a Sentence Detector, it executes everything at document-level. If you want to split by sentences, do it before and call this pipeline with the text of the sentences.

Predicted Entities

OBLIGATION_SUBJECT, OBLIGATION_ACTION, OBLIGATION, OBLIGATION_INDIRECT_OBJECT

Live Demo Open in Colab Download Copy S3 URI

How to use

from johnsnowlabs import *

deid_pipeline = PretrainedPipeline("legpipe_obligations", "en", "legal/models")

deid_pipeline.annotate('The Supplier agrees to provide the Buyer with all the necessary documents to fulfill the agreement')

# Return NER chunkcs
pipeline_result['ner_chunk']

# Visualize the Dependencies

dependency_vis = viz.DependencyParserVisualizer()

dependency_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe.
                       pos_col = 'pos', #specify the pos column
                       dependency_col = 'dependencies', #specify the dependency column
                       dependency_type_col = 'dependency_type' #specify the dependency type column
                       )

Results

# NER

['Supplier',
 'agrees to provide',
 'Buyer',
 'with all the necessary documents to fulfill the agreement']

# DEP
# Use Spark NLP Display to see the dependency tree

Model Information

References

In-house annotations on CUAD dataset

Included Models

• nlp.DocumentAssembler
• nlp.Tokenizer
• nlp.PerceptronModel
• nlp.DependencyParserModel
• nlp.TypedDependencyParserModel
• legal.BertForTokenClassification
• nlp.NerConverter