| language: en | |
| tags: | |
| - deberta-v1 | |
| - fill-mask | |
| 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} | |
| } | |
| ``` | |