readme: ---
  language: en
  tags:
  - glossbert
  license: mit
  datasets:
  - SemCor3.0
  ---
  
  ## GlossBERT
  
  A BERT-based model fine-tuned on SemCor 3.0 to perform word-sense-disambiguation by leveraging gloss information. This model is the research output of the paper titled: '[GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://arxiv.org/pdf/1908.07245.pdf)'
  
  Disclaimer: This model was built and trained by a group of researchers different than the repository's author. The original model code can be found on github: https://github.com/HSLCY/GlossBERT
  
  ## Usage
  
  The following code loads GlossBERT:
  
  ```py
  from transformers import AutoTokenizer, BertForSequenceClassification
  
  tokenizer = AutoTokenizer.from_pretrained('kanishka/GlossBERT')
  model = BertForSequenceClassification.from_pretrained('kanishka/GlossBERT')
  ```
  
  ## Citation
  
  If you use this model in any of your projects, please cite the original authors using the following bibtex:
  
  ```
  @inproceedings{huang-etal-2019-glossbert,
      title = "{G}loss{BERT}: {BERT} for Word Sense Disambiguation with Gloss Knowledge",
      author = "Huang, Luyao  and
        Sun, Chi  and
        Qiu, Xipeng  and
        Huang, Xuanjing",
      booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
      month = nov,
      year = "2019",
      address = "Hong Kong, China",
      publisher = "Association for Computational Linguistics",
      url = "https://www.aclweb.org/anthology/D19-1355",
      doi = "10.18653/v1/D19-1355",
      pages = "3507--3512"
  }
  ```