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_warranty_clauseText Classifier to select only these paragraphs;
This is a Legal Named Entity Recognition Model to identify the Subject (who), Action (what), Object(the indemnification) and Indirect Object (to whom) from Warranty clauses.
Predicted Entities
WARRANTY, WARRANTY_ACTION, WARRANTY_SUBJECT, WARRANTY_INDIRECT_OBJECT
How to use
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl","xx")\
.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")
ner_model = legal.NerModel.pretrained('legner_warranty', 'en', 'legal/models')\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = nlp.NerConverter()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = nlp.Pipeline(stages=[documentAssembler,sentenceDetector,tokenizer,embeddings,ner_model,ner_converter])
data = spark.createDataFrame([["""8 . Representations and Warranties SONY hereby makes the following representations and warranties to PURCHASER , each of which shall be true and correct as of the date hereof and as of the Closing Date , and shall be unaffected by any investigation heretofore or hereafter made : 8.1 Power and Authority SONY has the right and power to enter into this IP Agreement and to transfer the Transferred Patents and to grant the license set forth in Section 3.1 ."""]]).toDF("text")
result = nlpPipeline.fit(data).transform(data)
Results
+--------------------------------------------------------------------------+------------------------+
|chunk |entity |
+--------------------------------------------------------------------------+------------------------+
|SONY |WARRANTY_SUBJECT |
|makes the following representations and warranties |WARRANTY_ACTION |
|PURCHASER |WARRANTY_INDIRECT_OBJECT|
|shall be true and correct as of the date hereof and as of the Closing Date|WARRANTY |
|shall be unaffected by any investigation |WARRANTY |
|SONY |WARRANTY_SUBJECT |
|has the right and power to enter into this IP Agreement |WARRANTY |
+--------------------------------------------------------------------------+------------------------+
Model Information
| Model Name: | legner_warranty |
| 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 annotated examples from CUAD legal dataset
Benchmarking
label precision recall f1-score support
B-WARRANTY 0.8993 0.9178 0.9085 146
B-WARRANTY_ACTION 1.0000 0.9318 0.9647 44
B-WARRANTY_INDIRECT_OBJECT 1.0000 0.9474 0.9730 19
B-WARRANTY_SUBJECT 0.8554 0.9726 0.9103 73
I-WARRANTY 0.9695 0.9618 0.9656 1885
I-WARRANTY_ACTION 0.9515 0.9800 0.9655 100
I-WARRANTY_INDIRECT_OBJECT 0.8333 0.8333 0.8333 6
I-WARRANTY_SUBJECT 1.0000 0.9444 0.9714 36
O 0.9758 0.9772 0.9765 3381
accuracy - - 0.9698 5690
macro-avg 0.9428 0.9407 0.9410 5690
weighted-avg 0.9700 0.9698 0.9698 5690