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 several GLUE benchmark tasks.

Model SQuAD 1.1 SQuAD 2.0 MNLI-m/mm SST-2 QNLI CoLA RTE MRPC QQP STS-B
F1/EM F1/EM Acc Acc Acc MCC Acc Acc/F1 Acc/F1 P/S
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-Large1 95.5/90.1 90.7/88.0 91.3/91.1 96.5 95.3 69.5 91.0 92.6/94.6 92.3/- 92.8/92.5
DeBERTa-XLarge1 -/- -/- 91.5/91.2 97.0 - - 93.1 92.1/94.3 - 92.9/92.7
DeBERTa-V2-XLarge1 95.8/90.8 91.4/88.9 91.7/91.6 97.5 95.8 71.1 93.9 92.0/94.2 92.3/89.8 92.9/92.9
DeBERTa-V2-XXLarge1,2 96.1/91.4 92.2/89.7 91.7/91.9 97.2 96.0 72.0 93.5 93.1/94.9 92.7/90.3 93.2/93.1

Notes.

cd transformers/examples/text-classification/
export TASK_NAME=mrpc
python -m torch.distributed.launch --nproc_per_node=8 run_glue.py   --model_name_or_path microsoft/deberta-v2-xxlarge   \
--task_name $TASK_NAME   --do_train   --do_eval   --max_seq_length 128   --per_device_train_batch_size 4   \
--learning_rate 3e-6   --num_train_epochs 3   --output_dir /tmp/$TASK_NAME/ --overwrite_output_dir --sharded_ddp --fp16

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}
}
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