1. Introduction
2. Using PhoBERT with transformers
   • Installation
   • Pre-trained models
   • Example usage
3. Using PhoBERT with fairseq
4. Notes

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.

Users may also want to use BamiBERT, our new BERT-based pretrained language model for Vietnamese that addresses key limitations of PhoBERT. Trained from scratch on a 129 GB corpus of general-domain Vietnamese text for 20 epochs, BamiBERT supports an extended context length of up to 2,048 tokens and operates directly on raw input, eliminating the need for external word segmentation. Across eight Vietnamese benchmarks, it achieves the best performance on 11 of 15 metrics and the second-best performance on three others, setting a new state of the art among "base"-sized Vietnamese encoders and demonstrating strong cross-domain generalization.

• Install transformers with pip: pip install transformers, or install transformers from source.
  Note that we merged a slow tokenizer for PhoBERT into the main transformers branch. The process of merging a fast tokenizer for PhoBERT is in the discussion, as mentioned 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
pip3 install -e .

• Install tokenizers with pip: pip3 install tokenizers

import torch
from transformers import AutoModel, AutoTokenizer

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

# INPUT TEXT MUST BE ALREADY WORD-SEGMENTED!
sentence = 'Chúng_tôi là những nghiên_cứu_viên .'  

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

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")

Please see details at HERE!

In case the input texts are raw, i.e. without word segmentation, a word segmenter must be applied to produce word-segmented texts before feeding to PhoBERT. As PhoBERT employed the RDRSegmenter from VnCoreNLP to pre-process the pre-training data (including Vietnamese tone normalization and word and sentence segmentation), it is recommended to also use the same word segmenter for PhoBERT-based downstream applications w.r.t. the input raw texts.

pip install py_vncorenlp

import py_vncorenlp

# Automatically download VnCoreNLP components from the original repository
# and save them in some local machine folder
py_vncorenlp.download_model(save_dir='/absolute/path/to/vncorenlp')

# Load the word and sentence segmentation component
rdrsegmenter = py_vncorenlp.VnCoreNLP(annotators=["wseg"], save_dir='/absolute/path/to/vncorenlp')

text = "Ông Nguyễn Khắc Chúc  đang làm việc tại Đại học Quốc gia Hà Nội. Bà Lan, vợ ông Chúc, cũng làm việc tại đây."

output = rdrsegmenter.word_segment(text)

print(output)
# ['Ông Nguyễn_Khắc_Chúc đang làm_việc tại Đại_học Quốc_gia Hà_Nội .', 'Bà Lan , vợ ông Chúc , cũng làm_việc tại đây .']