Edit on GitHub
  • John Snow Labs
  • Apr 06, 2023

Legal NER for NDA (Preamble Clause)

  • en
  • licensed
  • legal
  • ner
  • nda
  • preamble
  • purpose

Description

This is a NER model, aimed to be run only after detecting the PREAMBLE clause with a proper classifier (use legmulticlf_mnda_sections_paragraph_other for that purpose). It will extract the following entities: PURPOSE, and PURPOSE_OBJECT.

Predicted Entities

PURPOSE, PURPOSE_OBJECT

Download Copy S3 URI

How to use

document_assembler = nlp.DocumentAssembler()\
        .setInputCol("text")\
        .setOutputCol("document")
        
sentence_detector = nlp.SentenceDetector()\
        .setInputCols(["document"])\
        .setOutputCol("sentence")

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

embeddings = nlp.RoBertaEmbeddings.pretrained("roberta_embeddings_legal_roberta_base","en") \
        .setInputCols(["sentence", "token"]) \
        .setOutputCol("embeddings")\
        .setMaxSentenceLength(512)\
        .setCaseSensitive(True)

ner_model = legal.NerModel.pretrained("legner_nda_preamble", "en", "legal/models")\
        .setInputCols(["sentence", "token", "embeddings"])\
        .setOutputCol("ner")

ner_converter = nlp.NerConverter()\
        .setInputCols(["sentence", "token", "ner"])\
        .setOutputCol("ner_chunk")

nlpPipeline = nlp.Pipeline(stages=[
        document_assembler,
        sentence_detector,
        tokenizer,
        embeddings,
        ner_model,
        ner_converter])

empty_data = spark.createDataFrame([[""]]).toDF("text")

model = nlpPipeline.fit(empty_data)

text = ["""In order to facilitate the consideration and negotiation of a possible transaction involving Chordiant and Pegasystems ( referred to collectively as the "Parties" and individually as a "Party"), each Party has requested access to certain non-public information regarding the other Party and the other Party’s subsidiaries."""]

result = model.transform(spark.createDataFrame([text]).toDF("text"))

Results

+-------------+--------------+
|chunk        |ner_label     |
+-------------+--------------+
|consideration|PURPOSE       |
|negotiation  |PURPOSE       |
|transaction  |PURPOSE_OBJECT|
+-------------+--------------+

Model Information

Model Name: legner_nda_preamble
Compatibility: Legal NLP 1.0.0+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en
Size: 16.3 MB

References

In-house annotations on the Non-disclosure Agreements

Benchmarking

label             precision  recall  f1-score  support 
B-PURPOSE         1.00       0.93    0.97      15      
B-PURPOSE_OBJECT  0.90       0.82    0.86      11      
I-PURPOSE_OBJECT  1.00       0.80    0.89      5       
micro-avg         0.96       0.87    0.92      31      
macro-avg         0.97       0.85    0.90      31      
weighted-avg      0.96       0.87    0.91      31