readme: ---
  language: et
  license: cc-by-4.0
  
  widget:
  - text: "Eesti President on Alar Karis."
  
  ---
  
  # EstBERT_NER
  
  ## Model description 
  
  EstBERT_NER is a fine-tuned EstBERT model that can be used for Named Entity Recognition. This model was trained on the Estonian NER dataset created by [Tkachenko et al](https://www.aclweb.org/anthology/W13-2412.pdf). It can recognize three types of entities: locations (LOC), organizations (ORG) and persons (PER). 
  
  ## How to use 
  
  You can use this model with Transformers pipeline for NER. Post-processing of results may be necessary as the model occasionally tags subword tokens as entities. 
  
  ```
  from transformers import BertTokenizer, BertForTokenClassification
  from transformers import pipeline
  
  tokenizer = BertTokenizer.from_pretrained('tartuNLP/EstBERT_NER')
  bertner = BertForTokenClassification.from_pretrained('tartuNLP/EstBERT_NER')
  
  nlp = pipeline("ner", model=bertner, tokenizer=tokenizer)
  sentence = 'Eesti Ekspressi teada on Eesti Pank uurinud Hansapanga tehinguid , mis toimusid kaks aastat tagasi suvel ja mille käigus voolas panka ligi miljardi krooni ulatuses kahtlast raha .'
  
  ner_results = nlp(sentence)
  print(ner_results)
  ```
  ```
  [{'word': 'Eesti', 'score': 0.9964128136634827, 'entity': 'B-ORG', 'index': 1}, {'word': 'Ekspressi', 'score': 0.9978809356689453, 'entity': 'I-ORG', 'index': 2}, {'word': 'Eesti', 'score': 0.9988121390342712, 'entity': 'B-ORG', 'index': 5}, {'word': 'Pank', 'score': 0.9985784292221069, 'entity': 'I-ORG', 'index': 6}, {'word': 'Hansapanga', 'score': 0.9979034662246704, 'entity': 'B-ORG', 'index': 8}]
  
  ```
  
  
  
  ## BibTeX entry and citation info
  
  ```
  @misc{tanvir2020estbert,
        title={EstBERT: A Pretrained Language-Specific BERT for Estonian}, 
        author={Hasan Tanvir and Claudia Kittask and Kairit Sirts},
        year={2020},
        eprint={2011.04784},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
  }
  ```