ViDeBERTa: A powerful pre-trained language model for Vietnamese
Cong Dao Tran, Nhut Huy Pham, Anh Tuan Nguyen, Truong Son Hy, Tu Vu · 2023
This paper presents 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 a large-scale corpus of high-quality and diverse Vietnamese texts using DeBERTa architecture.Although many successful pre-trained language models based on Transformer have been widely proposed for the English language, there are still few pre-trained models for Vietnamese, a low-resource language, that perform good results on downstream tasks, especially Question answering.We fine-tune and evaluate our model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering.The empirical results demonstrate that ViDeBERTa with far fewer parameters surpasses the previous state-of-the-art models on multiple Vietnamese-specific natural language understanding tasks.Notably, ViDeBERTa base with 86M parameters, which is only about 23% of PhoBERT large with 370M parameters, still performs the same or better results than the previous state-of-the-art model.Our ViDeBERTa models are available at: https://github.com/HySonLab/ViDeBERTa.