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transformers/docs/source/en/model_doc/phobert.md

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+*This model was released on 2020-03-02 and added to Hugging Face Transformers on 2020-11-16.*
+
+# PhoBERT
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+The PhoBERT model was proposed in [PhoBERT: Pre-trained language models for Vietnamese](https://huggingface.co/papers/2003.00744) 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
+```
+
+<Tip>
+
+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.
+
+</Tip>
+
+## PhobertTokenizer
+
+[[autodoc]] PhobertTokenizer