#### Table of contents 1. [Introduction](#introduction) 2. [Using PhoBERT with `transformers`](#transformers) - [Installation](#install2) - [Pre-trained models](#models2) - [Example usage](#usage2) 3. [Using PhoBERT with `fairseq`](#fairseq) 4. [Notes](#vncorenlp) # PhoBERT: Pre-trained language models for Vietnamese Pre-trained PhoBERT models are the state-of-the-art language models for Vietnamese ([Pho](https://en.wikipedia.org/wiki/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](https://github.com/pytorch/fairseq/blob/master/examples/roberta/README.md) which optimizes the [BERT](https://github.com/google-research/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](https://www.aclweb.org/anthology/2020.findings-emnlp.92/): @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 with `transformers` ### Installation - Install `transformers` with pip: `pip install transformers`, or [install `transformers` from source](https://huggingface.co/docs/transformers/installation#installing-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](https://github.com/huggingface/transformers/pull/17254#issuecomment-1133932067). 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` ### Pre-trained models Model | #params | Arch. | Max length | Pre-training data ---|---|---|---|--- `vinai/phobert-base` | 135M | base | 256 | 20GB of Wikipedia and News texts `vinai/phobert-large` | 370M | large | 256 | 20GB of Wikipedia and News texts `vinai/phobert-base-v2` | 135M | base | 256 | 20GB of Wikipedia and News texts + 120GB of texts from OSCAR-2301 ### Example usage ```python 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") ``` ## Using PhoBERT with `fairseq` Please see details at [HERE](https://github.com/VinAIResearch/PhoBERT/blob/master/README_fairseq.md)! ## Notes 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](https://github.com/datquocnguyen/RDRsegmenter) from [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) to pre-process the pre-training data (including [Vietnamese tone normalization](https://github.com/VinAIResearch/BARTpho/blob/main/VietnameseToneNormalization.md) 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. #### Installation pip install py_vncorenlp #### Example usage ```python 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 .'] ``` ## 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 .