---
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---

## DeBERTa: Decoding-enhanced BERT with Disentangled Attention

[DeBERTa](https://arxiv.org/abs/2006.03654) 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](https://github.com/microsoft/DeBERTa) for more details and updates.

This the DeBERTa V2 xxlarge model(60%) with 48 layers, 1536 hidden size. Total parameters 1.5B.


#### 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            | -/-       | -/-       | 91.5/91.0   | -     | -    | -    | 89.5   | 92.1/94.3    | -    |-    |
| DeBERTa-XLarge-V2         | -         | -         | 91.7/91.6   | -     | -    | -    | -      | -            | -    |-    |
|**DeBERTa-XXLarge-V2(60%)**| 96.1/91.4 | 92.2/89.7 |**91.7/91.8**| -     | -    | -    | -      | -            | -    |-    |
| DeBERTa-XLarge-V2-mnli    | -         | -         | 91.7/91.6   | -     | -    | -    | 93.9   | -            | -    |-    |
|**DeBERTa-XXLarge-V2-mnli**| -         | -         |**91.7/91.8**| -     | -    | -    | 93.5   | -            | -    |-    |


## Note

To try the **XXLarge** model with **HF transformers**, you need to specify **--sharded_ddp**

```bash

cd transformers/examples/text-classification/

python -m torch.distributed.launch --nproc_per_node=8 run_glue.py   --model_name_or_path microsoft/deberta-xxlarge-v2   \
--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
```

### Citation

If you find DeBERTa useful for your work, please cite the following paper:

``` latex
@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}
		}
```