README
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
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 the DeBERTa xlarge model(750M) fine-tuned with mnli task.
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.
- 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI. The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
- 2 To try the XXLarge model with HF transformers, you need to specify --sharded_ddp
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}
}
Contributors
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Used in code
README
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
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 the DeBERTa xlarge model(750M) fine-tuned with mnli task.
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.
- 1 Following RoBERTa, for RTE, MRPC, STS-B, we fine-tune the tasks based on DeBERTa-Large-MNLI, DeBERTa-XLarge-MNLI, DeBERTa-V2-XLarge-MNLI, DeBERTa-V2-XXLarge-MNLI. The results of SST-2/QQP/QNLI/SQuADv2 will also be slightly improved when start from MNLI fine-tuned models, however, we only report the numbers fine-tuned from pretrained base models for those 4 tasks.
- 2 To try the XXLarge model with HF transformers, you need to specify --sharded_ddp
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}
}