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.

Example of use:

>>> 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")

This model was contributed by dqnguyen. The original code can be found here.

PhobertTokenizer

class transformers.PhobertTokenizer < >

( vocab_file merges_file bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' **kwargs )

Parameters

  • 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.

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

This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

add_from_file < >

( f )

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

build_inputs_with_special_tokens < >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

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 with the appropriate special tokens.

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:

convert_tokens_to_string < >

( tokens )

Converts a sequence of tokens (string) in a single string.

create_token_type_ids_from_sequences < >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) → List[int]

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.

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.

get_special_tokens_mask < >

( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False ) → List[int]

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.

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.