#
BERTweet

##
개요

BERTweet 모델은 Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen에 의해 BERTweet: A pre-trained language model for English Tweets 에서 제안되었습니다.

해당 논문의 초록 :

영어 트윗을 위한 최초의 공개 대규모 사전 학습된 언어 모델인 BERTweet을 소개합니다. BERTweet은 BERT-base(Devlin et al., 2019)와 동일한 아키텍처를 가지고 있으며, RoBERTa 사전 학습 절차(Liu et al., 2019)를 사용하여 학습되었습니다. 실험 결과, BERTweet은 강력한 기준 모델인 RoBERTa-base 및 XLM-R-base(Conneau et al., 2020)의 성능을 능가하여 세 가지 트윗 NLP 작업(품사 태깅, 개체명 인식, 텍스트 분류)에서 이전 최신 모델보다 더 나은 성능을 보여주었습니다.

이 모델은 dqnguyen 께서 기여하셨습니다. 원본 코드는 여기.에서 확인할 수 있습니다.

##
사용 예시

    >>> import torch
    >>> from transformers import AutoModel, AutoTokenizer

    >>> bertweet = AutoModel.from_pretrained("vinai/bertweet-base")

    >>> # 트랜스포머 버전 4.x 이상 :
    >>> tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base", use_fast=False)

    >>> # 트랜스포머 버전 3.x 이상:
    >>> # tokenizer = AutoTokenizer.from_pretrained("vinai/bertweet-base")

    >>> # 입력된 트윗은 이미 정규화되었습니다!
    >>> line = "SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:"

    >>> input_ids = torch.tensor([tokenizer.encode(line)])

    >>> with torch.no_grad():
    ...     features = bertweet(input_ids)  # Models outputs are now tuples

    >>> # With TensorFlow 2.0+:
    >>> # from transformers import TFAutoModel
    >>> # bertweet = TFAutoModel.from_pretrained("vinai/bertweet-base")

이 구현은 토큰화 방법을 제외하고는 BERT와 동일합니다. API 참조 정보는 BERT 문서 를 참조하세요.

##
Bertweet 토큰화(BertweetTokenizer)

###
class transformers.BertweetTokenizer

< source >

( vocab_file merges_file normalization = False 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.
- normalization (`bool`, optional, defaults to `False`) — Whether or not to apply a normalization preprocess.
- bos_token (`str`, optional, defaults to `"<s>"`) — 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 `"</s>"`) — 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 `"</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.

Constructs a BERTweet tokenizer, using 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

< source >

( f )

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

####
build_inputs_with_special_tokens

< source >

( token_ids_0: List token_ids_1: Optional = 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 BERTweet sequence has the following format:

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

####
convert_tokens_to_string

< source >

( tokens )

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

####
create_token_type_ids_from_sequences

< source >

( token_ids_0: List token_ids_1: Optional = 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. BERTweet does not make use of token type ids, therefore a list of zeros is returned.

####
get_special_tokens_mask

< source >

( token_ids_0: List token_ids_1: Optional = 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.

####
normalizeToken

< source >

( token )

Normalize tokens in a Tweet

####
normalizeTweet

< source >

( tweet )

Normalize a raw Tweet

< > Update on GitHub