tokenizer

load_vocab(vocab_file)

Loads a vocabulary file into a dictionary.

class ProphetNetTokenizer(vocab_file, do_lower_case=True, do_basic_tokenize=True, unk_token='[UNK]', sep_token='[SEP]', bos_token='[SEP]', eos_token='[SEP]', cls_token='[CLS]', x_sep_token='[X_SEP]', pad_token='[PAD]', mask_token='[MASK]', **kwargs)

基类：PretrainedTokenizer

Construct a ProphetNetTokenizer. Based on WordPiece.

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

参数:

• vocab_file (str) -- File containing the vocabulary.

• do_lower_case (bool, optional, defaults to True) -- Whether or not to lowercase the input when tokenizing.

• do_basic_tokenize (bool, optional, defaults to True) -- Whether or not to do basic tokenization before WordPiece.

• 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.

• sep_token (str, optional, defaults to "[SEP]") -- 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.

• x_sep_token (str, optional, defaults to "[X_SEP]") -- Special second separator token, which can be generated by [ProphetNetForConditionalGeneration]. It is used to separate bullet-point like sentences in summarization, e.g..

• pad_token (str, optional, defaults to "[PAD]") -- The token used for padding, for example when batching sequences of different lengths.

• cls_token (str, optional, defaults to "[CLS]") -- 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.

• 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.

property vocab_size

Size of the base vocabulary (without the added tokens).

Type:

int

get_vocab()

Returns the vocabulary as a dictionary of token to index.

tokenizer.get_vocab()[token] is equivalent to tokenizer.convert_tokens_to_ids(token) when token is in the vocab.

返回:

The vocabulary.

返回类型:

Dict[str, int]

tokenize(text)

Converts a string in a sequence of tokens, using the tokenizer.

Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces). Takes care of added tokens.

参数:

• text (str) -- The sequence to be encoded.

• **kwargs (additional keyword arguments) -- Passed along to the model-specific prepare_for_tokenization preprocessing method.

返回:

The list of tokens.

返回类型:

List[str]

convert_tokens_to_ids(tokens)

Converts a sequence of tokens into ids using the vocab attribute (an instance of Vocab). Override it if needed.

Args：

tokens (list[int]): List of token ids.

返回:

Converted id list.

返回类型:

list

convert_ids_to_tokens(ids, skip_special_tokens=False)

Converts a single index or a sequence of indices to a token or a sequence of tokens, using the vocabulary and added tokens.

参数:

• ids (int or List[int]) -- The token id (or token ids) to be converted to token(s).

• skip_special_tokens (bool, optional) -- Whether or not to remove special tokens in the decoding. Defaults to False and we do not remove special tokens.

返回:

The decoded token(s).

返回类型:

str or List[str]

convert_tokens_to_string(tokens)

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

get_special_tokens_mask(token_ids_0, token_ids_1=None, already_has_special_tokens=False)

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.

参数:

• 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.

返回:

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

返回类型:

List[int]

create_token_type_ids_from_sequences(token_ids_0, token_ids_1=None)

Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ProphetNet sequence pair mask has the following format:

` 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence    | second sequence | `

If token_ids_1 is None, this method only returns the first portion of the mask (0s).

参数:

• token_ids_0 (List[int]) -- List of IDs.

• token_ids_1 (List[int], optional) -- Optional second list of IDs for sequence pairs.

返回:

List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).

返回类型:

List[int]

build_inputs_with_special_tokens(token_ids_0, token_ids_1=None) → List[int]

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

• single sequence: [CLS] X [SEP]

• pair of sequences: [CLS] A [SEP] B [SEP]

参数:

• 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.

返回:

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

返回类型:

List[int]

save_vocabulary(save_directory)

Save all tokens to a vocabulary file. The file contains a token per line, and the line number would be the index of corresponding token.

参数:

• filepath (str) -- File path to be saved to.

• vocab (Vocab|dict) -- The Vocab or dict instance to be saved.