opennyaiorg/en_legal_ner_trf

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[AUTHORS] "[PAPER NAME]". [PAPER DETAILS] [PAPER LINK]

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Indian Legal Named Entity Recognition(NER): Identifying relevant named entities in an Indian legal judgement using legal NER trained on spacy.

Scores

Type	Score
F1-Score	91.076
Precision	91.979
Recall	90.19

Feature	Description
Name	en_legal_ner_trf
Version	3.2.0
spaCy	>=3.2.2,<3.3.0
Default Pipeline	transformer, ner
Components	transformer, ner
Vectors	0 keys, 0 unique vectors (0 dimensions)
Sources	InLegalNER Train Data GitHub
License	MIT
Author	Aman Tiwari

Load Pretrained Model

Install the model using pip

pip install https://huggingface.co/opennyaiorg/en_legal_ner_trf/resolve/main/en_legal_ner_trf-any-py3-none-any.whl

Using pretrained NER model

# Using spacy.load().
import spacy
nlp = spacy.load("en_legal_ner_trf")
text = "Section 319 Cr.P.C. contemplates a situation where the evidence adduced by the prosecution for Respondent No.3-G. Sambiah on 20th June 1984"
doc = nlp(text)

# Print indentified entites
for ent in doc.ents:
     print(ent,ent.label_)

##OUTPUT     
#Section 319 PROVISION
#Cr.P.C. STATUTE
#G. Sambiah RESPONDENT
#20th June 1984 DATE

Label Scheme

View label scheme (14 labels for 1 components)

ENTITY	BELONGS TO
LAWYER	PREAMBLE
COURT	PREAMBLE, JUDGEMENT
JUDGE	PREAMBLE, JUDGEMENT
PETITIONER	PREAMBLE, JUDGEMENT
RESPONDENT	PREAMBLE, JUDGEMENT
CASE_NUMBER	JUDGEMENT
GPE	JUDGEMENT
DATE	JUDGEMENT
ORG	JUDGEMENT
STATUTE	JUDGEMENT
WITNESS	JUDGEMENT
PRECEDENT	JUDGEMENT
PROVISION	JUDGEMENT
OTHER_PERSON	JUDGEMENT

Author - Publication

[CITATION DETAILS]