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_whereas_clauseText Classifier to select only these paragraphs;
This is a Legal NER Model, able to process WHEREAS clauses, to detect the SUBJECT (Who?), the ACTION, the OBJECT (what?) and, in some cases, the INDIRECT OBJECT (to whom?) of the clause.
Predicted Entities
WHEREAS_SUBJECT, WHEREAS_OBJECT, WHEREAS_ACTION
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_whereas', '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])
empty_data = spark.createDataFrame([[""]]).toDF("text")
model = nlpPipeline.fit(empty_data)
text = ["""WHEREAS, Seller and Buyer have entered into that certain Stock Purchase Agreement, dated November 14, 2018 (the "Stock Purchase Agreement"); WHEREAS, pursuant to the Stock Purchase Agreement, Seller has agreed to sell and transfer, and Buyer has agreed to purchase and acquire, all of Seller's right, title and interest in and to Armstrong Wood Products, Inc., a Delaware corporation ("AWP") and its Subsidiaries, the Company and HomerWood Hardwood Flooring Company, a Delaware corporation ("HHFC," and together with the Company, the "Company Subsidiaries" and together with AWP, the "Company Entities" and each a "Company Entity") by way of a purchase by Buyer and sale by Seller of the Shares, all upon the terms and condition set forth therein;"""]
res = model.transform(spark.createDataFrame([text]).toDF("text"))
Results
+------------+-----------------+
| token| ner_label|
+------------+-----------------+
| WHEREAS| O|
| ,| O|
| Seller|B-WHEREAS_SUBJECT|
| and| O|
| Buyer|B-WHEREAS_SUBJECT|
| have| B-WHEREAS_ACTION|
| entered| I-WHEREAS_ACTION|
| into| I-WHEREAS_ACTION|
| that| B-WHEREAS_OBJECT|
| certain| I-WHEREAS_OBJECT|
| Stock| I-WHEREAS_OBJECT|
| Purchase| I-WHEREAS_OBJECT|
| Agreement| I-WHEREAS_OBJECT|
| ,| O|
| dated| O|
| November| O|
| 14| O|
| ,| O|
| 2018| O|
| (| O|
| the| O|
| "| O|
| Stock| O|
| Purchase| O|
| Agreement| O|
| ");| O|
| WHEREAS| O|
| ,| O|
| pursuant| O|
| to| O|
| the| O|
| Stock| O|
| Purchase| O|
| Agreement| O|
| ,| O|
| Seller|B-WHEREAS_SUBJECT|
| has| B-WHEREAS_ACTION|
| agreed| I-WHEREAS_ACTION|
| to| I-WHEREAS_ACTION|
| sell| I-WHEREAS_ACTION|
| and| O|
| transfer| O|
| ,| O|
| and| O|
| Buyer|B-WHEREAS_SUBJECT|
| has| B-WHEREAS_ACTION|
| agreed| I-WHEREAS_ACTION|
| to| I-WHEREAS_ACTION|
| purchase| I-WHEREAS_ACTION|
| and| O|
| acquire| O|
| ,| O|
| all| O|
| of| O|
| Seller's| O|
| right| O|
| ,| O|
| title| O|
| and| O|
| interest| O|
| in| O|
| and| O|
| to| O|
| Armstrong| O|
| Wood| O|
| Products| O|
| ,| O|
| Inc| O|
| .,| O|
| a| O|
| Delaware| O|
| corporation| O|
| ("| O|
| AWP| O|
| ")| O|
| and| O|
| its| O|
|Subsidiaries| O|
| ,| O|
| the| O|
| Company| O|
| and| O|
| HomerWood| O|
| Hardwood| O|
| Flooring| O|
| Company| O|
| ,| O|
| a| O|
| Delaware| O|
| corporation| O|
| ("| O|
| HHFC| O|
| ,"| O|
+------------+-----------------+
Model Information
| Model Name: | legner_whereas |
| Type: | legal |
| Compatibility: | Legal NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 16.5 MB |
References
Manual annotations on CUAD dataset
Benchmarking
label tp fp fn prec rec f1
B-WHEREAS_SUBJECT 191 14 15 0.9317073 0.92718446 0.9294404
I-WHEREAS_ACTION 202 38 59 0.84166664 0.77394634 0.8063872
I-WHEREAS_SUBJECT 52 8 16 0.8666667 0.7647059 0.8125
B-WHEREAS_OBJECT 101 63 68 0.61585367 0.5976331 0.6066066
B-WHEREAS_ACTION 152 19 16 0.8888889 0.9047619 0.89675516
I-WHEREAS_OBJECT 361 194 194 0.65045047 0.65045047 0.65045047
Macro-average 1059 336 368 0.7992056 0.76978034 0.784217
Micro-average 1059 336 368 0.7591398 0.74211633 0.75053155