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ViDeBERTa: A powerful pre-trained language model for Vietnamese

ViDeBERTa, a new pre-trained monolingual language model for Vietnamese, with three versions - ViDeBERTa_xsmall, ViDeBERTa_base, and ViDeBERTa_large, which are pre-trained on 138GB of Vietnamese text of high-quality and diverse Vietnamese text using DeBERTaV3 architecture.

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 CC100 dataset, which consists of 138 GB of Vietnamese text.

Fine-tuning on NLU tasks

We present the dev results on VLSP POS, PhoNER, ViQuAD dataset.

Model #Params(M) POS NER MRC
XLM-R-base 125M 96.2 - 82.0
XLM-R-large 355M 96.3 93.8 87.0
PhoBERT-base 135M 96.7 80.1
PhoBERT-large 370M 96.8 83.5
ViT5-base 310M - 94.5 -
ViT5-large 866M - 93.8 -
ViDeBERTa-xsmall 22M 96.4 93.6 81.3
ViDeBERTa-base 86M 96.8 94.5 85.7
ViDeBERTa-large 304M 97.2 95.3 89.9

Citation

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

@article{dao2023videberta,
  title={ViDeBERTa: A powerful pre-trained language model for Vietnamese},
  author={Dao Tran, Cong and Pham, Nhut Huy and Nguyen, Anh and Son Hy, Truong and Vu, Tu},
  journal={arXiv e-prints},
  pages={arXiv--2301},
  year={2023}
}
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