sonvx / PhoBERT

PhoBERT: Pre-trained language models for Vietnamese

Table of contents

1. Introduction
2. Using PhoBERT with transformers
   • Installation
   • Pre-trained models
   • Example usage
3. Using PhoBERT with fairseq
4. Using VnCoreNLP's word segmenter to pre-process input raw texts

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:

@article{phobert,
title     = {{PhoBERT: Pre-trained language models for Vietnamese}},
author    = {Dat Quoc Nguyen and Anh Tuan Nguyen},
journal   = {arXiv preprint},
volume    = {arXiv:2003.00744},
year      = {2020}
}

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

Using PhoBERT with transformers

Installation

• Python version >= 3.6
• PyTorch version >= 1.4.0
• Install transformers from our development branch:
  • git clone https://github.com/datquocnguyen/transformers.git
  • cd transformers
  • pip3 install .

We also created a pull request to integrate PhoBERT into the master branch of the transformers library. Please see the latest updates at: huggingface/transformers#6129

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 PhobertModel, PhobertTokenizer

phobert = PhobertModel.from_pretrained("vinai/phobert-base")
tokenizer = PhobertTokenizer.from_pretrained("vinai/phobert-base")

# PhoBERT can be also loaded in the `Auto` mode
# 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!
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

Using PhoBERT with fairseq

Please see details at HERE!

Using VnCoreNLP's word segmenter to pre-process input raw texts

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, it is recommended to also use the same word segmenter for PhoBERT-based downstream applications w.r.t. the input raw texts.

Installation

# Install the vncorenlp python wrapper
pip3 install vncorenlp

# Download VnCoreNLP-1.1.1.jar & its word segmentation component (i.e. RDRSegmenter) 
mkdir -p vncorenlp/models/wordsegmenter
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/VnCoreNLP-1.1.1.jar
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/models/wordsegmenter/vi-vocab
wget https://raw.githubusercontent.com/vncorenlp/VnCoreNLP/master/models/wordsegmenter/wordsegmenter.rdr
mv VnCoreNLP-1.1.1.jar vncorenlp/ 
mv vi-vocab vncorenlp/models/wordsegmenter/
mv wordsegmenter.rdr vncorenlp/models/wordsegmenter/

VnCoreNLP-1.1.1.jar (27MB) and folder models must be placed in the same working folder, here is vncorenlp!

Example usage

# See more details at: https://github.com/vncorenlp/VnCoreNLP

# Load rdrsegmenter from VnCoreNLP
from vncorenlp import VnCoreNLP
rdrsegmenter = VnCoreNLP("/Absolute-path-to/vncorenlp/VnCoreNLP-1.1.1.jar", annotators="wseg", max_heap_size='-Xmx500m') 

# Input 
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."

# To perform word (and sentence) segmentation
sentences = rdrsegmenter.tokenize(text) 
for sentence in sentences:
	print(" ".join(sentence))

License

MIT License

Copyright (c) 2020 VinAI Research

Permission is hereby granted, free of charge, to any person obtaining a copy
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The above copyright notice and this permission notice shall be included in all
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