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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 paper:

@inproceedings{phobert,
title     = {{PhoBERT: Pre-trained language models for Vietnamese}},
author    = {Dat Quoc Nguyen and Anh Tuan Nguyen},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
year      = {2020},
pages     = {1037--1042}
}

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

Using PhoBERT in fairseq

Installation

  • Python version >= 3.6
  • PyTorch version >= 1.4.0
  • fairseq
  • fastBPE: pip3 install fastBPE

Pre-trained models

Model #params size Download
PhoBERT-base 135M 1.2GB PhoBERT_base_fairseq.tar.gz
PhoBERT-large 370M 3.2GB PhoBERT_large_fairseq.tar.gz

PhoBERT-base:

  • wget https://public.vinai.io/PhoBERT_base_fairseq.tar.gz
  • tar -xzvf PhoBERT_base_fairseq.tar.gz

PhoBERT-large:

  • wget https://public.vinai.io/PhoBERT_large_fairseq.tar.gz
  • tar -xzvf PhoBERT_large_fairseq.tar.gz

Example usage

Assume that the input texts are already word-segmented!

import torch

# Load PhoBERT-base in fairseq
from fairseq.models.roberta import RobertaModel
bpe_codes_file = '/Absolute-path-to/PhoBERT_base_fairseq/bpe.codes'
phobert = RobertaModel.from_pretrained('/Absolute-path-to/PhoBERT_base_fairseq', checkpoint_file='model.pt', bpe='fastbpe', bpe_codes=bpe_codes_file).eval()

# INPUT TEXT IS WORD-SEGMENTED!
line = "Tôi là sinh_viên trường đại_học Công_nghệ ."  

# Extract the last layer's features  
subwords = phobert.encode(line)  
last_layer_features = phobert.extract_features(subwords)  
assert last_layer_features.size() == torch.Size([1, 9, 768])  
  
# Extract all layer's features (layer 0 is the embedding layer)  
all_layers = phobert.extract_features(subwords, return_all_hiddens=True)  
assert len(all_layers) == 13  
assert torch.all(all_layers[-1] == last_layer_features)  

# Filling marks  
masked_line = 'Tôi là  <mask> trường đại_học Công_nghệ .'  
topk_filled_outputs = phobert.fill_mask(masked_line, topk=5)  
print(topk_filled_outputs)

License

MIT License

Copyright (c) 2020 VinAI Research

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.