gbert-legal-ner / README.md
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added a default example
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metadata
language: de
datasets:
  - Legal-Entity-Recognition
widget:
  - text: >-
      1. Das Bundesarbeitsgericht ist gemäß § 9 Abs. 2 Satz 2 ArbGG iVm. § 201
      Abs. 1 Satz 2 GVG für die beabsichtigte Klage gegen den Bund zuständig .

German BERT for Legal NER

Use:

from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("PaDaS-Lab/gbert-legal-ner", use_auth_token="AUTH_TOKEN")
model = AutoModelForTokenClassification.from_pretrained("PaDaS-Lab/gbert-legal-ner", use_auth_token="AUTH_TOKEN")

ner = pipeline("ner", model=model, tokenizer=tokenizer)
example = "1. Das Bundesarbeitsgericht ist gemäß § 9 Abs. 2 Satz 2 ArbGG iVm. § 201 Abs. 1 Satz 2 GVG für die beabsichtigte Klage gegen den Bund zuständig ."

results = ner(example)
print(results)

Classes:

Abbreviation Class
PER Person
RR Judge
AN Lawyer
LD Country
ST City
STR Street
LDS Landscape
ORG Organization
UN Company
INN Institution
GRT Court
MRK Brand
GS Law
VO Ordinance
EUN European legal norm
VS Regulation
VT Contract
RS Court decision
LIT Legal literature

Please reference our work when using the model.

@conference{icaart23,
  author={Harshil Darji. and Jelena Mitrović. and Michael Granitzer.},
  title={German BERT Model for Legal Named Entity Recognition},
  booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
  year={2023},
  pages={723-728},
  publisher={SciTePress},
  organization={INSTICC},
  doi={10.5220/0011749400003393},
  isbn={978-989-758-623-1},
  issn={2184-433X},
}