BamiBERT: A New BERT-based Language Model for Vietnamese

Dat Quoc Nguyen; Thinh Pham; Chi Tran; Linh The Nguyen

BamiBERT: A New BERT-based Language Model for Vietnamese

Computation and Language 2026-07-02 v1

Authors: Dat Quoc Nguyen , Thinh Pham , Chi Tran , Linh The Nguyen

Abstract

In this paper, we introduce BamiBERT, a new BERT-based pre-trained language model for Vietnamese that addresses key limitations of PhoBERT -- the current de facto Vietnamese text encoder. Trained from scratch on a 129GB corpus of general-domain Vietnamese text for 20 epochs, BamiBERT supports an extended context length of up to 2048 tokens and operates directly on raw input, eliminating the need for external word segmentation. Across 8 Vietnamese benchmarks, it achieves the best score on 11 of 15 metrics and the second-best on 3 others, setting a new state of the art among "base"-sized Vietnamese encoders and demonstrating strong cross-domain generalization. We release BamiBERT at: https://huggingface.co/Qualcomm-AI-Research/BamiBERT

Cite

@article{arxiv.2607.02259,
  title  = {BamiBERT: A New BERT-based Language Model for Vietnamese},
  author = {Dat Quoc Nguyen and Thinh Pham and Chi Tran and Linh The Nguyen},
  journal= {arXiv preprint arXiv:2607.02259},
  year   = {2026}
}