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| thumbnail: https://huggingface.co/front/thumbnails/microsoft.png |
| license: mit |
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| ## DeBERTa: Decoding-enhanced BERT with Disentangled Attention |
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| [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. |
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| Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates. |
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| #### Fine-tuning on NLU tasks |
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| We present the dev results on SQuAD 1.1/2.0 and MNLI tasks. |
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| | 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 | |
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| ### Citation |
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| If you find DeBERTa useful for your work, please cite the following paper: |
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| ``` 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} |
| } |
| ``` |
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