readme: ---
  language:
    - en
  tags:
    - part-of-speech
    - finetuned
  license: cc-by-nc-3.0
  ---
  
  # BERT-base-multilingual-cased finetuned for Part-of-Speech tagging
  
  This is a multilingual BERT model fine tuned for part-of-speech tagging for English. It is trained using the Penn TreeBank (Marcus et al., 1993) and achieves an F1-score of 96.69.
  
  ## Usage
  A *transformers* pipeline can be used to run the model:
  
  ```python
  from transformers import AutoTokenizer, AutoModelForTokenClassification, TokenClassificationPipeline
  
  model_name = "QCRI/bert-base-multilingual-cased-pos-english"
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  model = AutoModelForTokenClassification.from_pretrained(model_name)
  
  pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer)
  outputs = pipeline("A test example")
  print(outputs)
  ```
  
  
  ## Citation
  This model was used for all the part-of-speech tagging based results in *Analyzing Encoded Concepts in Transformer Language Models*, published at NAACL'22. If you find this model useful for your own work, please use the following citation:
  
  ```bib
  @inproceedings{sajjad-NAACL,
    title={Analyzing Encoded Concepts in Transformer Language Models},
    author={Hassan Sajjad, Nadir Durrani, Fahim Dalvi, Firoj Alam, Abdul Rafae Khan and Jia Xu},
    booktitle={North American Chapter of the Association of Computational Linguistics: Human Language Technologies (NAACL)},
    series={NAACL~'22},
    year={2022},
    address={Seattle}
  }
  ```