PhoBERT
Overview
The PhoBERT model was proposed in PhoBERT: Pre-trained language models for Vietnamese 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. The original code can be found here.
Usage example
thon

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 EART documentation for information on 
configuration classes and their parameters. PhoBERT-specific tokenizer is documented below.  

PhobertTokenizer
[[autodoc]] PhobertTokenizer