readme: ---
  language: de
  datasets:
  - Legal-Entity-Recognition
  ---
  
  ### German BERT for Legal NER
  
  #### Use:
  
  ```python
  from transformers import pipeline
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
  tokenizer = AutoTokenizer.from_pretrained("harshildarji/gbert-legal-ner", use_auth_token="AUTH_TOKEN")
  model = AutoModelForTokenClassification.from_pretrained("harshildarji/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|