DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

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.

In DeBERTa V3, we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our paper.

Please check the official repository for more implementation details and updates.

The DeBERTa V3 xsmall model comes with 12 layers and a hidden size of 384. It has only 22M backbone parameters with a vocabulary containing 128K tokens which introduces 48M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2.

Fine-tuning on NLU tasks

We present the dev results on SQuAD 2.0 and MNLI tasks.

Model Vocabulary(K) Backbone #Params(M) SQuAD 2.0(F1/EM) MNLI-m/mm(ACC)
RoBERTa-base 50 86 83.7/80.5 87.6/-
XLNet-base 32 92 -/80.2 86.8/-
ELECTRA-base 30 86 -/80.5 88.8/
DeBERTa-base 50 100 86.2/83.1 88.8/88.5
DeBERTa-v3-large 128 304 91.5/89.0 91.8/91.9
DeBERTa-v3-base 128 86 88.4/85.4 90.6/90.7
DeBERTa-v3-small 128 44 82.8/80.4 88.3/87.7
DeBERTa-v3-xsmall 128 22 84.8/82.0 88.1/88.3
DeBERTa-v3-xsmall+SiFT 128 22 -/- 88.4/88.5

Fine-tuning with HF transformers

#!/bin/bash

cd transformers/examples/pytorch/text-classification/

pip install datasets
export TASK_NAME=mnli

output_dir="ds_results"

num_gpus=8

batch_size=8

python -m torch.distributed.launch --nproc_per_node=${num_gpus} \
  run_glue.py \
  --model_name_or_path microsoft/deberta-v3-xsmall \
  --task_name $TASK_NAME \
  --do_train \
  --do_eval \
  --evaluation_strategy steps \
  --max_seq_length 256 \
  --warmup_steps 1000 \
  --per_device_train_batch_size ${batch_size} \
  --learning_rate 4.5e-5 \
  --num_train_epochs 3 \
  --output_dir $output_dir \
  --overwrite_output_dir \
  --logging_steps 1000 \
  --logging_dir $output_dir

Citation

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

@misc{he2021debertav3,
      title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing}, 
      author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
      year={2021},
      eprint={2111.09543},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@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}
}
Downloads last month
83,019
Inference API
Examples
Mask token: [MASK]

Model tree for microsoft/deberta-v3-xsmall

Adapters
12 models
Finetunes
44 models
Quantizations
2 models

Space using microsoft/deberta-v3-xsmall 1