#
BARTpho

##
개요

BARTpho 모델은 Nguyen Luong Tran, Duong Minh Le, Dat Quoc Nguyen에 의해 BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese에서 제안되었습니다.

이 논문의 초록은 다음과 같습니다:

우리는 BARTpho_word와 BARTpho_syllable의 두 가지 버전으로 BARTpho를 제시합니다. 이는 베트남어를 위해 사전훈련된 최초의 대규모 단일 언어 시퀀스-투-시퀀스 모델입니다. 우리의 BARTpho는 시퀀스-투-시퀀스 디노이징 모델인 BART의 “large” 아키텍처와 사전훈련 방식을 사용하여, 생성형 NLP 작업에 특히 적합합니다. 베트남어 텍스트 요약의 다운스트림 작업 실험에서, 자동 및 인간 평가 모두에서 BARTpho가 강력한 기준인 mBART를 능가하고 최신 성능을 개선했음을 보여줍니다. 우리는 향후 연구 및 베트남어 생성형 NLP 작업의 응용을 촉진하기 위해 BARTpho를 공개합니다.

이 모델은 dqnguyen이 기여했습니다. 원본 코드는 여기에서 찾을 수 있습니다.

##
사용 예시

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

    >>> bartpho = AutoModel.from_pretrained("vinai/bartpho-syllable")

    >>> tokenizer = AutoTokenizer.from_pretrained("vinai/bartpho-syllable")

    >>> line = "Chúng tôi là những nghiên cứu viên."

    >>> input_ids = tokenizer(line, return_tensors="pt")

    >>> with torch.no_grad():
    ...     features = bartpho(**input_ids)  # 이제 모델 출력은 튜플입니다

    >>> # With TensorFlow 2.0+:
    >>> from transformers import TFAutoModel

    >>> bartpho = TFAutoModel.from_pretrained("vinai/bartpho-syllable")
    >>> input_ids = tokenizer(line, return_tensors="tf")
    >>> features = bartpho(**input_ids)

##
사용 팁

- mBART를 따르며, BARTpho는 BART의 “large” 아키텍처에 인코더와 디코더의 상단에 추가적인 레이어 정규화 레이어를 사용합니다. 따라서 BART 문서에 있는 사용 예시를 BARTpho에 맞게 적용하려면 BART 전용 클래스를 mBART 전용 클래스로 대체하여 조정해야 합니다. 예를 들어:

    >>> from transformers import MBartForConditionalGeneration

    >>> bartpho = MBartForConditionalGeneration.from_pretrained("vinai/bartpho-syllable")
    >>> TXT = "Chúng tôi là <mask> nghiên cứu viên."
    >>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
    >>> logits = bartpho(input_ids).logits
    >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
    >>> probs = logits[0, masked_index].softmax(dim=0)
    >>> values, predictions = probs.topk(5)
    >>> print(tokenizer.decode(predictions).split())

- 이 구현은 토큰화만을 위한 것입니다: “monolingual_vocab_file”은 다국어 XLM-RoBERTa에서 제공되는 사전훈련된 SentencePiece 모델 “vocab_file”에서 추출된 베트남어 전용 유형으로 구성됩니다. 다른 언어들도 이 사전훈련된 다국어 SentencePiece 모델 “vocab_file”을 하위 단어 분할에 사용하면, 자신의 언어 전용 “monolingual_vocab_file”과 함께 BartphoTokenizer를 재사용할 수 있습니다.

##
BartphoTokenizer

###
class transformers.BartphoTokenizer

< source >

( vocab_file monolingual_vocab_file bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' sp_model_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None **kwargs )

Parameters

- vocab_file (`str`) — Path to the vocabulary file. This vocabulary is the pre-trained SentencePiece model available from the multilingual XLM-RoBERTa, also used in mBART, consisting of 250K types.
- monolingual_vocab_file (`str`) — Path to the monolingual vocabulary file. This monolingual vocabulary consists of Vietnamese-specialized types extracted from the multilingual vocabulary vocab_file of 250K types.
- 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.
- sp_model_kwargs (`dict`, optional) — Will be passed to the `SentencePieceProcessor.__init__()` method. The Python wrapper for SentencePiece can be used, among other things, to set:

  - `enable_sampling`: Enable subword regularization.

  - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.

    - `nbest_size = {0,1}`: No sampling is performed.
    - `nbest_size > 1`: samples from the nbest_size results.
    - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm.

  - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.

- sp_model (`SentencePieceProcessor`) — The SentencePiece processor that is used for every conversion (string, tokens and IDs).

Adapted from `XLMRobertaTokenizer`. Based on SentencePiece.

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

####
build_inputs_with_special_tokens

< source >

( 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. An BARTPho 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 (strings for sub-words) in a single string.

####
create_token_type_ids_from_sequences

< source >

( 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. BARTPho does not make use of token type ids, therefore a list of zeros is returned.

####
get_special_tokens_mask

< source >

( 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.

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