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_introduction_clauseText Classifier to select only these paragraphs; - This is a Legal Relation Extraction model, which can be used after the NER Model for extracting Parties, Document Types, Effective Dates and Aliases, called legner_contract_doc_parties.
As an output, you will get the relations linking the different concepts together, if such relation exists. The list of relations is:
- dated_as: A Document has an Effective Date
- has_alias: The Alias of a Party all along the document
- has_collective_alias: An Alias hold by several parties at the same time
- signed_by: Between a Party and the document they signed
This is a md model with Unidirectional Relations, meaning that the model retrieves in chunk1 the left side of the relation (source), and in chunk2 the right side (target).
Predicted Entities
dated_as, has_alias, has_collective_alias, signed_by
How to use
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = nlp.Tokenizer()\
.setInputCols("document")\
.setOutputCol("token")
embeddings = nlp.RoBertaEmbeddings.pretrained("roberta_embeddings_legal_roberta_base", "en") \
.setInputCols("document", "token")\
.setOutputCol("embeddings")\
.setMaxSentenceLength(512)
ner_model = legal.NerModel.pretrained('legner_contract_doc_parties', 'en', 'legal/models')\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = nlp.NerConverter()\
.setInputCols(["document","token","ner"])\
.setOutputCol("ner_chunk")
re_model = legal.RelationExtractionDLModel().pretrained('legre_contract_doc_parties_md', 'en', 'legal/models')\
.setPredictionThreshold(0.5)\
.setInputCols(["ner_chunk", "document"])\
.setOutputCol("relations")
nlpPipeline = nlp.Pipeline(stages=[
document_assembler,
tokenizer,
embeddings,
ner_model,
ner_converter,
re_model
])
empty_df = spark.createDataFrame([[""]]).toDF("text")
model = nlpPipeline.fit(empty_df)
text="""This INTELLECTUAL PROPERTY AGREEMENT (this "Agreement"), dated as of December 31, 2018 (the "Effective Date") is entered into by and between Armstrong Flooring, Inc., a Delaware corporation ("Seller") and AFI Licensing LLC, a Delaware limited liability company ("Licensing" and together with Seller, "Arizona") and AHF Holding, Inc. (formerly known as Tarzan HoldCo, Inc.), a Delaware corporation ("Buyer") and Armstrong Hardwood Flooring Company, a Tennessee corporation (the "Company" and together with Buyer the "Buyer Entities") (each of Arizona on the one hand and the Buyer Entities on the other hand, a "Party" and collectively, the "Parties")."""
data = spark.createDataFrame([[text]]).toDF("text")
result = model.transform(data)
Results
| relation | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_begin | entity2_end | chunk2 | confidence |
|----------------------|---------|---------------|-------------|---------------------------------|---------|---------------|-------------|-------------------------------------|------------|
| dated_as | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | EFFDATE | 70 | 86 | December 31, 2018 | 0.99994016 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | PARTY | 142 | 164 | Armstrong Flooring, Inc | 0.9995191 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | ALIAS | 193 | 198 | Seller | 0.9823355 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | PARTY | 206 | 222 | AFI Licensing LLC | 0.9989542 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | ALIAS | 264 | 272 | Licensing | 0.92109 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | ALIAS | 293 | 298 | Seller | 0.9938019 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | PARTY | 316 | 331 | AHF Holding, Inc | 0.9989403 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | ALIAS | 400 | 404 | Buyer | 0.89959186 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | PARTY | 412 | 446 | Armstrong Hardwood Flooring Company | 0.9974464 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | ALIAS | 479 | 485 | Company | 0.95839113 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | ALIAS | 506 | 510 | Buyer | 0.95839113 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | ALIAS | 517 | 530 | Buyer Entities | 0.95839113 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | ALIAS | 612 | 616 | Party | 0.95839113 |
| signed_by | DOC | 6 | 36 | INTELLECTUAL PROPERTY AGREEMENT | ALIAS | 642 | 648 | Parties | 0.95839113 |
| dated_as | EFFDATE | 70 | 86 | December 31, 2018 | PARTY | 142 | 164 | Armstrong Flooring, Inc | 0.69556713 |
| dated_as | ALIAS | 193 | 198 | Seller | EFFDATE | 70 | 86 | December 31, 2018 | 0.7183331 |
| dated_as | EFFDATE | 70 | 86 | December 31, 2018 | ALIAS | 264 | 272 | Licensing | 0.7500907 |
| dated_as | EFFDATE | 70 | 86 | December 31, 2018 | ALIAS | 293 | 298 | Seller | 0.6601122 |
| dated_as | EFFDATE | 70 | 86 | December 31, 2018 | PARTY | 316 | 331 | AHF Holding, Inc | 0.52062315 |
| dated_as | EFFDATE | 70 | 86 | December 31, 2018 | ALIAS | 400 | 404 | Buyer | 0.7104727 |
| dated_as | EFFDATE | 70 | 86 | December 31, 2018 | PARTY | 412 | 446 | Armstrong Hardwood Flooring Company | 0.70473474 |
| dated_as | ALIAS | 479 | 485 | Company | EFFDATE | 70 | 86 | December 31, 2018 | 0.98484945 |
| dated_as | ALIAS | 506 | 510 | Buyer | EFFDATE | 70 | 86 | December 31, 2018 | 0.98484945 |
| dated_as | ALIAS | 517 | 530 | Buyer Entities | EFFDATE | 70 | 86 | December 31, 2018 | 0.98484945 |
| dated_as | ALIAS | 612 | 616 | Party | EFFDATE | 70 | 86 | December 31, 2018 | 0.98484945 |
| dated_as | ALIAS | 642 | 648 | Parties | EFFDATE | 70 | 86 | December 31, 2018 | 0.98484945 |
| has_alias | PARTY | 142 | 164 | Armstrong Flooring, Inc | ALIAS | 264 | 272 | Licensing | 0.686296 |
| has_alias | PARTY | 206 | 222 | AFI Licensing LLC | ALIAS | 264 | 272 | Licensing | 0.8194909 |
| has_collective_alias | ALIAS | 264 | 272 | Licensing | PARTY | 316 | 331 | AHF Holding, Inc | 0.5534526 |
| has_alias | PARTY | 316 | 331 | AHF Holding, Inc | ALIAS | 479 | 485 | Company | 0.52909577 |
| has_alias | PARTY | 316 | 331 | AHF Holding, Inc | ALIAS | 506 | 510 | Buyer | 0.52909607 |
| has_alias | PARTY | 316 | 331 | AHF Holding, Inc | ALIAS | 517 | 530 | Buyer Entities | 0.52909607 |
| has_alias | PARTY | 316 | 331 | AHF Holding, Inc | ALIAS | 612 | 616 | Party | 0.52909607 |
| has_alias | PARTY | 316 | 331 | AHF Holding, Inc | ALIAS | 642 | 648 | Parties | 0.52909607 |
Model Information
| Model Name: | legre_contract_doc_parties_md |
| Compatibility: | Spark NLP for Legal 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 402.3 MB |
References
Manual annotations on CUAD dataset
Benchmarking
label Recall Precision F1 Support
dated_as 1.000 0.957 0.978 44
has_alias 0.950 0.974 0.962 40
has_collective_alias 0.667 1.000 0.800 3
signed_by 0.957 0.989 0.972 92
Avg. 0.913 0.977 0.938 -
Weighted-Avg. 0.973 0.974 0.973 -