Lexpredict Lexnlp Versions Save

Scikit-Learn Pipeline

Name	Class	State
transformerpreprocessor	TransformerPreprocessor	head_character_n=2000, normalizer=<lexnlp.ml.normalizers.Normalizer object>
transformervectorizer	TransformerVectorizer	vectorizers=(<lexnlp.ml.vectorizers.VectorizerDoc2Vec object>, <lexnlp.ml.vectorizers.VectorizerKeywordSearch object>)
minmaxscaler	MinMaxScaler	feature_range=(-1.0, 1.0)
gaussiannb	GaussianNB

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Training data

Dataset	Description	Hyperlink
corpus/uspto-sample/0.1	A sample of Patent grant backgrounds from the United States Patent and Trademark Office	https://github.com/EleutherAI/pile-uspto
corpus/govinfo-fr-2021/0.1	United States Federal Register, 2021	https://www.govinfo.gov/bulkdata/FR/2021
corpus/contract-types/0.1	A sample of labeled contract types obtained from SEC EDGAR	https://www.sec.gov/edgar.shtml
corpus/bonds/0.1	A sample of municipal bonds	?
corpus/caselaw-access-project-ark-ill-nc-nm-subset-144million-characters/0.1	Caselaw Access Project; official, book-published state case law from from Arkansas, Illinois, North Carolina, New Mexico	https://case.law/download/bulk_exports/latest/by_jurisdiction/case_text_open/
corpus/atticus-cuad-v1-plaintext/0.1	Atticus CUAD v1 contracts	https://www.atticusprojectai.org/cuad
corpus/eurlex-sample-10000/0.1	EUR-Lex documents downloaded via api.epdb.eu	https://eur-lex.europa.eu/ http://api.epdb.eu/
corpus/arxiv-abstracts-with-agreement/0.1	ArXiv abstracts containing "agreement"	https://www.kaggle.com/datasets/Cornell-University/arxiv
corpus/sec-edgar-forms-3-4-5-8k-10k-sample/0.1	assorted SEC EDGAR filings	https://www.sec.gov/edgar.shtml

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Metrics

              precision    recall  f1-score   support

       False       1.00      1.00      1.00     20652
        True       0.85      0.91      0.88       580

    accuracy                           0.99     21232
   macro avg       0.93      0.95      0.94     21232
weighted avg       0.99      0.99      0.99     21232

Confusion matrix: true (vertical) vs. predicted (horizontal)

	0	1
0	20562	90
1	50	530

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Usage

with open('pipeline_is_contract_classifier.cloudpickle', 'rb') as f:
    pipeline_is_contract_classifier: Pipeline = cloudpickle.load(f)

probability_predictor_is_contract: ProbabilityPredictorIsContract = \
    ProbabilityPredictorIsContract(pipeline=pipeline_is_contract_classifier)

probability_predictor_is_contract.is_contract(
    text='...',
    min_probability=0.5,
    return_probability=True,
)