readme: ---
  language: en
  tags: deberta-v1
  thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
  license: mit
  ---
  
  ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
  
  [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on  majority of NLU tasks with 80GB training data. 
  
  Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
  
  
  #### Fine-tuning on NLU tasks
  
  We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.
  
  | Model             | SQuAD 1.1 | SQuAD 2.0 | MNLI-m |
  |-------------------|-----------|-----------|--------|
  | RoBERTa-base      | 91.5/84.6 | 83.7/80.5 | 87.6   |
  | XLNet-Large       | -/-       | -/80.2    | 86.8   |
  | **DeBERTa-base**  | 93.1/87.2 | 86.2/83.1 | 88.8   |
  
  ### Citation
  
  If you find DeBERTa useful for your work, please cite the following paper:
  
  ``` latex
  @inproceedings{
  he2021deberta,
  title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
  author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
  booktitle={International Conference on Learning Representations},
  year={2021},
  url={https://openreview.net/forum?id=XPZIaotutsD}
  }
  ```
  