# PhoBERT
## Overview
The 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 monolingual
language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent
best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple
Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and
Natural 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")
```
PhoBERT implementation is the same as BERT, except for tokenization. Refer to [BERT documentation](bert) for information on
configuration classes and their parameters. PhoBERT-specific tokenizer is documented below.
## PhobertTokenizer
[[autodoc]] PhobertTokenizer
