PhoBERT: Pre-trained language models for Vietnamese

Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese (Pho, i.e. "Phở", is a popular food in Vietnam):

  • Two PhoBERT versions of "base" and "large" are the first public large-scale monolingual language models pre-trained for Vietnamese. PhoBERT pre-training approach is based on RoBERTa which optimizes the BERT pre-training procedure for more robust performance.
  • PhoBERT outperforms previous monolingual and multilingual approaches, obtaining new state-of-the-art performances on four downstream Vietnamese NLP tasks of Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference.

The general architecture and experimental results of PhoBERT can be found in our EMNLP-2020 Findings paper:

title     = {{PhoBERT: Pre-trained language models for Vietnamese}},
author    = {Dat Quoc Nguyen and Anh Tuan Nguyen},
journal   = {Findings of EMNLP},
year      = {2020}

Please CITE our paper when PhoBERT is used to help produce published results or is incorporated into other software.

For further information or requests, please go to PhoBERT's homepage!


  • Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+)
  • Install transformers:
     - `git clone`
     - `cd transformers`
     - `pip3 install --upgrade .`

Pre-trained models

Model #params Arch. Pre-training data
vinai/phobert-base 135M base 20GB of texts
vinai/phobert-large 370M large 20GB of texts

Example usage

import torch
from transformers import AutoModel, AutoTokenizer

phobert = AutoModel.from_pretrained("vinai/phobert-base")
tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")

line = "Tôi là sinh_viên trường đại_học Công_nghệ ."

input_ids = torch.tensor([tokenizer.encode(line)])

with torch.no_grad():
    features = phobert(input_ids)  # Models outputs are now tuples

## With TensorFlow 2.0+:
# from transformers import TFAutoModel
# phobert = TFAutoModel.from_pretrained("vinai/phobert-base")

Select AutoNLP in the “Train” menu to fine-tune this model automatically.

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