| # <a name="introduction"></a> 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 EMNLP-2020 Findings [paper](https://arxiv.org/abs/2003.00744): | |
| @article{phobert, | |
| title = {{PhoBERT: Pre-trained language models for Vietnamese}}, | |
| author = {Dat Quoc Nguyen and Anh Tuan Nguyen}, | |
| journal = {Findings of EMNLP}, | |
| year = {2020} | |
| } | |
| **Please CITE** our paper when PhoBERT is used to help produce published results or is incorporated into other software. | |
| For further information or requests, please go to [PhoBERT's homepage](https://github.com/VinAIResearch/PhoBERT)! | |
| ### Installation <a name="install2"></a> | |
| - Python 3.6+, and PyTorch 1.1.0+ (or TensorFlow 2.0+) | |
| - Install `transformers`: | |
| - `git clone https://github.com/huggingface/transformers.git` | |
| - `cd transformers` | |
| - `pip3 install --upgrade .` | |
| ### Pre-trained models <a name="models2"></a> | |
| Model | #params | Arch. | Pre-training data | |
| ---|---|---|--- | |
| `vinai/phobert-base` | 135M | base | 20GB of texts | |
| `vinai/phobert-large` | 370M | large | 20GB of texts | |
| ### Example usage <a name="usage2"></a> | |
| ```python | |
| 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! | |
| 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 | |
| ## With TensorFlow 2.0+: | |
| # from transformers import TFAutoModel | |
| # phobert = TFAutoModel.from_pretrained("vinai/phobert-base") | |
| ``` | |