Phobert Base V2

🚀 PhoBERT: Pre-trained language models for Vietnamese

PhoBERT offers state-of-the-art pre-trained language models tailored for Vietnamese, achieving new benchmarks on multiple NLP tasks.

🚀 Quick Start

PhoBERT provides two pre - trained models, "base" and "large", which are the first public large - scale monolingual language models for Vietnamese. It is based on the RoBERTa approach, optimizing the BERT pre - training procedure. PhoBERT outperforms previous methods on four downstream Vietnamese NLP tasks.

✨ Features

• State - of - the - art: PhoBERT sets new performance records on four downstream Vietnamese NLP tasks: Part - of - speech tagging, Dependency parsing, Named - entity recognition, and Natural language inference.
• Monolingual focus: The "base" and "large" versions are the first public large - scale monolingual pre - trained models for Vietnamese.
• Optimized pre - training: Based on RoBERTa, it optimizes the BERT pre - training process for better performance.

📦 Installation

Using with transformers

• Install transformers with pip: pip install transformers, or install transformers from source. Note that a slow tokenizer for PhoBERT has been merged into the main transformers branch. The process of merging a fast tokenizer is under discussion, as mentioned in this pull request. If you want to use the fast tokenizer, 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

For word segmentation (if input texts are raw)

pip install py_vncorenlp

💻 Usage Examples

Using PhoBERT with transformers

Basic Usage

import torch
from transformers import AutoModel, AutoTokenizer

phobert = AutoModel.from_pretrained("vinai/phobert-base-v2")
tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base-v2")

# 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")

Word segmentation example (using 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 .']

📚 Documentation

Pre - trained models

Using PhoBERT with fairseq

Please see details at HERE!

🔧 Technical Details

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.

📄 License

Copyright (c) 2023 VinAI Research

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.

⚠️ Important Note

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.