# PhoBERT

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## 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[[transformers.PhobertTokenizer]]

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<docstring><name>class transformers.PhobertTokenizer</name><anchor>transformers.PhobertTokenizer</anchor><source>https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/phobert/tokenization_phobert.py#L51</source><parameters>[{"name": "vocab_file", "val": ""}, {"name": "merges_file", "val": ""}, {"name": "bos_token", "val": " = '<s>'"}, {"name": "eos_token", "val": " = '</s>'"}, {"name": "sep_token", "val": " = '</s>'"}, {"name": "cls_token", "val": " = '<s>'"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "mask_token", "val": " = '<mask>'"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_file** (`str`) --
  Path to the vocabulary file.
- **merges_file** (`str`) --
  Path to the merges file.
- **bos_token** (`st`, *optional*, defaults to `"<s>"`) --
  The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

  <Tip>

  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`.

  </Tip>

- **eos_token** (`str`, *optional*, defaults to `"</s>"`) --
  The end of sequence token.

  <Tip>

  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`.

  </Tip>

- **sep_token** (`str`, *optional*, defaults to `"</s>"`) --
  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 `"<s>"`) --
  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 `"<unk>"`) --
  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 `"<pad>"`) --
  The token used for padding, for example when batching sequences of different lengths.
- **mask_token** (`str`, *optional*, defaults to `"<mask>"`) --
  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.</paramsdesc><paramgroups>0</paramgroups></docstring>

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

This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/v4.57.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.





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<docstring><name>add_from_file</name><anchor>transformers.PhobertTokenizer.add_from_file</anchor><source>https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/phobert/tokenization_phobert.py#L327</source><parameters>[{"name": "f", "val": ""}]</parameters></docstring>

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


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<docstring><name>build_inputs_with_special_tokens</name><anchor>transformers.PhobertTokenizer.build_inputs_with_special_tokens</anchor><source>https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/phobert/tokenization_phobert.py#L146</source><parameters>[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]</parameters><paramsdesc>- **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.</paramsdesc><paramgroups>0</paramgroups><rettype>`list[int]`</rettype><retdesc>List of [input IDs](../glossary#input-ids) with the appropriate special tokens.</retdesc></docstring>

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: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`








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<docstring><name>convert_tokens_to_string</name><anchor>transformers.PhobertTokenizer.convert_tokens_to_string</anchor><source>https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/phobert/tokenization_phobert.py#L293</source><parameters>[{"name": "tokens", "val": ""}]</parameters></docstring>
Converts a sequence of tokens (string) in a single string.

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<docstring><name>create_token_type_ids_from_sequences</name><anchor>transformers.PhobertTokenizer.create_token_type_ids_from_sequences</anchor><source>https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/phobert/tokenization_phobert.py#L200</source><parameters>[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}]</parameters><paramsdesc>- **token_ids_0** (`list[int]`) --
  List of IDs.
- **token_ids_1** (`list[int]`, *optional*) --
  Optional second list of IDs for sequence pairs.</paramsdesc><paramgroups>0</paramgroups><rettype>`list[int]`</rettype><retdesc>List of zeros.</retdesc></docstring>

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.








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<docstring><name>get_special_tokens_mask</name><anchor>transformers.PhobertTokenizer.get_special_tokens_mask</anchor><source>https://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/phobert/tokenization_phobert.py#L172</source><parameters>[{"name": "token_ids_0", "val": ": list"}, {"name": "token_ids_1", "val": ": typing.Optional[list[int]] = None"}, {"name": "already_has_special_tokens", "val": ": bool = False"}]</parameters><paramsdesc>- **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.</paramsdesc><paramgroups>0</paramgroups><rettype>`list[int]`</rettype><retdesc>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</retdesc></docstring>

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.








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