7a0cf0ec93
* Add DeBERTa model * Remove dependency of deberta * Address comments * Patch DeBERTa Documentation Style * Add final tests * Style * Enable tests + nitpicks * position IDs * BERT -> DeBERTa * Quality * Style * Tokenization * Last updates. * @patrickvonplaten's comments * Not everything can be a copy * Apply most of @sgugger's review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Last reviews * DeBERTa -> Deberta Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
thumbnail, license
| thumbnail | license |
|---|---|
| https://huggingface.co/front/thumbnails/microsoft.png | mit |
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
DeBERTa 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 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:
@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}
}