模型:
opennyaiorg/en_legal_ner_trf
[作者] "[论文名称]"。[论文细节] [论文链接]
印度法律命名实体识别(NER):使用训练于 spacy 的法律NER在印度法律判决中识别相关命名实体。
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 | 1234321 1235321 |
License | MIT |
Author | 1236321 |
使用pip安装模型
pip install https://huggingface.co/opennyaiorg/en_legal_ner_trf/resolve/main/en_legal_ner_trf-any-py3-none-any.whl
使用预训练NER模型
# 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
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 |
[CITATION DETAILS]