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},
}