# Source: https://huggingface.co/docs/transformers/v5.0.0/model_doc/phobert.md

# PhoBERT

## 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
```

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[[transformers.PhobertTokenizer]]

#### transformers.PhobertTokenizer[[transformers.PhobertTokenizer]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/models/phobert/tokenization_phobert.py#L49)

Construct a PhoBERT tokenizer. Based on Byte-Pair-Encoding.

This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/v5.0.0/en/main_classes/tokenizer#transformers.PythonBackend) which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.

add_from_filetransformers.PhobertTokenizer.add_from_filehttps://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/models/phobert/tokenization_phobert.py#L325[{"name": "f", "val": ""}]

Loads a pre-existing dictionary from a text file and adds its symbols to this instance.

**Parameters:**

vocab_file (`str`) : Path to the vocabulary file.

merges_file (`str`) : Path to the merges file.

bos_token (`st`, *optional*, defaults to `""`) : The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.    When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the `cls_token`.   

eos_token (`str`, *optional*, defaults to `""`) : The end of sequence token.    When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the `sep_token`.   

sep_token (`str`, *optional*, defaults to `""`) : The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens.

cls_token (`str`, *optional*, defaults to `""`) : The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.

unk_token (`str`, *optional*, defaults to `""`) : The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

pad_token (`str`, *optional*, defaults to `""`) : The token used for padding, for example when batching sequences of different lengths.

mask_token (`str`, *optional*, defaults to `""`) : The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.
#### build_inputs_with_special_tokens[[transformers.PhobertTokenizer.build_inputs_with_special_tokens]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/models/phobert/tokenization_phobert.py#L144)

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A PhoBERT sequence has the following format:

- single sequence: ` X `
- pair of sequences: ` A  B `

**Parameters:**

token_ids_0 (`list[int]`) : List of IDs to which the special tokens will be added.

token_ids_1 (`list[int]`, *optional*) : Optional second list of IDs for sequence pairs.

**Returns:**

``list[int]``

List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
#### convert_tokens_to_string[[transformers.PhobertTokenizer.convert_tokens_to_string]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/models/phobert/tokenization_phobert.py#L291)

Converts a sequence of tokens (string) in a single string.
#### create_token_type_ids_from_sequences[[transformers.PhobertTokenizer.create_token_type_ids_from_sequences]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/models/phobert/tokenization_phobert.py#L198)

Create a mask from the two sequences passed to be used in a sequence-pair classification task. PhoBERT does not
make use of token type ids, therefore a list of zeros is returned.

**Parameters:**

token_ids_0 (`list[int]`) : List of IDs.

token_ids_1 (`list[int]`, *optional*) : Optional second list of IDs for sequence pairs.

**Returns:**

``list[int]``

List of zeros.
#### get_special_tokens_mask[[transformers.PhobertTokenizer.get_special_tokens_mask]]

[Source](https://github.com/huggingface/transformers/blob/v5.0.0/src/transformers/models/phobert/tokenization_phobert.py#L170)

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.

**Parameters:**

token_ids_0 (`list[int]`) : List of IDs.

token_ids_1 (`list[int]`, *optional*) : Optional second list of IDs for sequence pairs.

already_has_special_tokens (`bool`, *optional*, defaults to `False`) : Whether or not the token list is already formatted with special tokens for the model.

**Returns:**

``list[int]``

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.