Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/VinAIResearch/PhoBERT

PhoBERT: Pre-trained language models for Vietnamese (EMNLP-2020 Findings)
https://github.com/VinAIResearch/PhoBERT

bert bert-embeddings deep-learning fairseq language-models named-entity-recognition natural-language-inference ner nli part-of-speech-tagging phobert pos-tagging python3 rdrsegmenter roberta transformers transformers-library vietnamese vietnamese-nlp vncorenlp

Last synced: 3 months ago
JSON representation

PhoBERT: Pre-trained language models for Vietnamese (EMNLP-2020 Findings)

Awesome Lists containing this project

README 

        

#### 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 | License

---|---|---|---|---|---

[`vinai/phobert-base-v2`](https://huggingface.co/vinai/phobert-base-v2) | 135M | base | 256 | 20GB  of Wikipedia and News texts + 120GB of texts from OSCAR-2301 | [GNU Affero GPL v3](https://github.com/VinAIResearch/PhoBERT/blob/master/LICENSE_for_PhoBERT_v2)

[`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base) | 135M | base | 256 | 20GB  of Wikipedia and News texts | [MIT License](https://github.com/VinAIResearch/PhoBERT/blob/master/LICENSE)

[`vinai/phobert-large`](https://huggingface.co/vinai/phobert-large) | 370M | large | 256 | 20GB  of Wikipedia and News texts | [MIT License](https://github.com/VinAIResearch/PhoBERT/blob/master/LICENSE)



### 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 .']

```