DeBERTa 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 for more details and updates.

This model is the base DeBERTa model fine-tuned with MNLI task

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}
}

microsoft/deberta-base-mnli

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Fine-tuning on NLU tasks

Citation

microsoft/
deberta-base-mnli