Papers › ViDeBERTa: A powerful pre-trained language model for Vietnamese

ViDeBERTa: A powerful pre-trained language model for Vietnamese

25 Jan 2023arXiv:2301.10439archive 2025-07-28

Cong Dao Tran, Nhut Huy Pham, Anh Nguyen, Truong Son Hy, Tu Vu

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.

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strip_answer_string HySonLab/ViDeBERTa/fine-tuning/QA/utils/preprocess.py official repository unverified MIT (permissive) · 28b31200887eaa4a · report
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Tasks

Language ModelingLanguage ModellingNamed Entity RecognitionNamed Entity Recognition (NER)Natural Language UnderstandingPart-Of-Speech TaggingQuestion Answeringnamed-entity-recognition

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Methods

Absolute Position EncodingsAdamAttentionBPEDeBERTaDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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