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:

latex

@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}
}

Fsoft-AIC/videberta-base

ViDeBERTa: A powerful pre-trained language model for Vietnamese

Fine-tuning on NLU tasks

Citation

Fsoft-AIC/
videberta-base