metadata
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: 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.
This the DeBERTa V2 xlarge model fine-tuned with MNLI task, 24 layers, 1536 hidden size. Total parameters 900M.
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/mm | SST-2 | QNLI | CoLA | RTE | MRPC(acc/f1) | 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 | 91.3/91.1 | 96.5 | 95.3 | 69.5 | 86.6 | 92.6/94.6 | 92.3 | 92.5 |
| DeBERTa-XLarge | -/- | -/- | 91.5/91.2 | - | - | - | 89.5 | 92.1/94.3 | - | - |
| DeBERTa-XLarge-V2 | - | - | 91.7/91.6 | - | - | - | - | - | - | - |
| DeBERTa-XXLarge-V2 | 96.1/91.4 | 92.2/89.7 | 91.7/91.9 | - | - | - | - | - | - | - |
| DeBERTa-XLarge-V2-MNLI | - | - | 91.7/91.6 | - | - | - | 93.9 | - | - | - |
| DeBERTa-XXLarge-V2-MNLI | - | - | 91.7/91.9 | - | - | - | 93.5 | - | - | - |
Citation
If you find DeBERTa useful for your work, please cite the following paper:
@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}
}