| 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. With those two improvements, DeBERTa out perform RoBERTa on a 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 several GLUE benchmark tasks. | |
| | Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m | SST-2 | QNLI | CoLA | RTE | MRPC | QQP |STS-B| | |
| |-------------------|-----------|-----------|--------|-------|------|------|------|------|------|-----| | |
| | BERT-Large | 90.9/84.1 | 81.8/79.0 | 86.6 | 93.2 | 92.3 | 60.6 | 70.4 | 88.0 | 91.3 |90.0 | | |
| | RoBERTa-Large | 94.6/88.9 | 89.4/86.5 | 90.2 | 96.4 | 93.9 | 68.0 | 86.6 | 90.9 | 92.2 |92.4 | | |
| | XLNet-Large | 95.1/89.7 | 90.6/87.9 | 90.8 | 97.0 | 94.9 | 69.0 | 85.9 | 90.8 | 92.3 |92.5 | | |
| | **DeBERTa-Large** | 95.5/90.1 | 90.7/88.0 | 91.1 | 96.5 | 95.3 | 69.5 | 88.1 | 92.5 | 92.3 |92.5 | | |
| ### Citation | |
| If you find DeBERTa useful for your work, please cite the following paper: | |
| ``` latex | |
| @misc{he2020deberta, | |
| title={DeBERTa: Decoding-enhanced BERT with Disentangled Attention}, | |
| author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen}, | |
| year={2020}, | |
| eprint={2006.03654}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` | |