Table of contents

1. Introduction
2. Using BARTpho with transformers
3. Using BARTpho with fairseq
4. Notes

BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese

We present BARTpho with two versions, BARTpho-syllable and BARTpho-word, which are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and the pre-training scheme of the sequence-to-sequence denoising autoencoder BART, thus it is especially suitable for generative NLP tasks. We conduct experiments to compare our BARTpho with its competitor mBART on a downstream task of Vietnamese text summarization and show that: in both automatic and human evaluations, BARTpho outperforms the strong baseline mBART and improves the state-of-the-art. We further evaluate and compare BARTpho and mBART on the Vietnamese capitalization and punctuation restoration tasks and also find that BARTpho is more effective than mBART on these two tasks.

The general architecture and experimental results of BARTpho can be found in our paper:

@inproceedings{bartpho,
    title     = {{BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese}},
    author    = {Nguyen Luong Tran and Duong Minh Le and Dat Quoc Nguyen},
    booktitle = {Proceedings of the 23rd Annual Conference of the International Speech Communication Association},
    year      = {2022}
}

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

Using BARTpho in transformers

Installation

• Install transformers with pip: pip install transformers, or install transformers from source.
  Note that we merged a slow tokenizer for BARTpho into the main transformers branch. The process of merging a fast tokenizer for BARTpho is in the discussion, as detailed in this pull request. If users would like to utilize the fast tokenizer, the users might install transformers as follows:

git clone --single-branch --branch fast_tokenizers_BARTpho_PhoBERT_BERTweet https://github.com/datquocnguyen/transformers.git
cd transformers
pip install -e .

• Install sentencepiece and tokenizers with pip: pip install sentencepiece tokenizers

Pre-trained models

Model	#params	Arch.	Max length	Input text
vinai/bartpho-syllable-base	132M	base	1024	Syllable level
vinai/bartpho-syllable	396M	large	1024	Syllable level
vinai/bartpho-word-base	150M	base	1024	Word level
vinai/bartpho-word	420M	large	1024	Word level

Example usage

import torch
from transformers import AutoModel, AutoTokenizer

#BARTpho-syllable
syllable_tokenizer = AutoTokenizer.from_pretrained("vinai/bartpho-syllable")
bartpho_syllable = AutoModel.from_pretrained("vinai/bartpho-syllable")
TXT = 'Chúng tôi là những nghiên cứu viên.'  
input_ids = syllable_tokenizer(TXT, return_tensors='pt')['input_ids']
features = bartpho_syllable(input_ids)

#BARTpho-word
word_tokenizer = AutoTokenizer.from_pretrained("vinai/bartpho-word")
bartpho_word = AutoModel.from_pretrained("vinai/bartpho-word")
TXT = 'Chúng_tôi là những nghiên_cứu_viên .'  
input_ids = word_tokenizer(TXT, return_tensors='pt')['input_ids']
features = bartpho_word(input_ids)

Using BARTpho in fairseq

Installation

There is an issue w.r.t. the encode function in the BART hub_interface, as discussed in this pull request facebookresearch/fairseq#3905. While waiting for this pull request's approval, please install fairseq as follows:

git clone https://github.com/datquocnguyen/fairseq.git
cd fairseq
pip install --editable ./

Pre-trained models

Model	#params	Download	Input text
BARTpho-syllable	396M	fairseq-bartpho-syllable.zip	Syllable level
BARTpho-word	420M	fairseq-bartpho-word.zip	Word level

• unzip fairseq-bartpho-syllable.zip
• unzip fairseq-bartpho-word.zip

Example usage

from fairseq.models.bart import BARTModel  

#Load BARTpho-syllable model:  
model_folder_path = '/PATH-TO-FOLDER/fairseq-bartpho-syllable/'  
spm_model_path = '/PATH-TO-FOLDER/fairseq-bartpho-syllable/sentence.bpe.model'  
bartpho_syllable = BARTModel.from_pretrained(model_folder_path, checkpoint_file='model.pt', bpe='sentencepiece', sentencepiece_model=spm_model_path).eval()
#Input syllable-level/raw text:  
sentence = 'Chúng tôi là những nghiên cứu viên.'  
#Apply SentencePiece to the input text
tokenIDs = bartpho_syllable.encode(sentence, add_if_not_exist=False)
#Extract features from BARTpho-syllable
last_layer_features = bartpho_syllable.extract_features(tokenIDs)

##Load BARTpho-word model:  
model_folder_path = '/PATH-TO-FOLDER/fairseq-bartpho-word/'  
bpe_codes_path = '/PATH-TO-FOLDER/fairseq-bartpho-word/bpe.codes'  
bartpho_word = BARTModel.from_pretrained(model_folder_path, checkpoint_file='model.pt', bpe='fastbpe', bpe_codes=bpe_codes_path).eval()
#Input word-level text:  
sentence = 'Chúng_tôi là những nghiên_cứu_viên .'  
#Apply BPE to the input text
tokenIDs = bartpho_word.encode(sentence, add_if_not_exist=False)
#Extract features from BARTpho-word
last_layer_features = bartpho_word.extract_features(tokenIDs)

Notes

• Before fine-tuning BARTpho on a downstream task, users should perform Vietnamese tone normalization on the downstream task's data as this pre-process was also applied to the pre-training corpus. A Python script for Vietnamese tone normalization is available at HERE.
• For BARTpho-word, users should use VnCoreNLP to segment input raw texts as it was used to perform both Vietnamese tone normalization and word segmentation on the pre-training corpus.

License

MIT License

Copyright (c) 2021 VinAI

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