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 is the DeBERTa V2 xxlarge model with 48 layers, 1536 hidden size. Total parameters 1.5B. It's trained with 160GB data.
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 | 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
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-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 --fp16
Citation
If you find DeBERTa useful for your work, please cite the following paper:
@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}
}