PhoBERT
Source: transformers

# PhoBERT## OverviewThe PhoBERT model was proposed in [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92.pdf) by Dat Quoc Nguyen, Anh Tuan Nguyen.The abstract from the paper is the following:*We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolinguallanguage models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recentbest pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multipleVietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition andNatural language inference.*This model was contributed by [dqnguyen](https://huggingface.co/dqnguyen). The original code can be found [here](https://github.com/VinAIResearch/PhoBERT).## Usage example```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")```<Tip> PhoBERT implementation is the same as BERT, except for tokenization. Refer to [EART documentation](bert) for information on configuration classes and their parameters. PhoBERT-specific tokenizer is documented below.  </Tip>## PhobertTokenizer[[autodoc]] PhobertTokenizer