mindformers.models.bert.BertTokenizer

class mindformers.models.bert.BertTokenizer(vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', tokenize_chinese_chars=True, strip_accents=None, is_tokenize_char=False, **kwargs)

Construct a BERT tokenizer. 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.

Args:

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.

never_split (Iterable, optional):

Collection of tokens which will never be split during tokenization. Only has an effect when do_basic_tokenize=True

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.

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.

tokenize_chinese_chars (bool, optional, defaults to True):

Whether or not to tokenize Chinese characters.

is_tokenize_char (bool, optional, defaults to False):

Whether or not to tokenize characters.

This should likely be deactivated for Japanese (see this [issue](https://github.com/huggingface/transformers/issues/328)).

strip_accents (bool, optional):

Whether or not to strip all accents. If this option is not specified, then it will be determined by the value for lowercase (as in the original BERT).

build_inputs_with_special_tokens(token_ids_0: List[int], token_ids_1: Optional[List[int]] = 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]

Args:

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](../glossary#input-ids) with the appropriate special tokens.

convert_tokens_to_string(tokens)

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

create_token_type_ids_from_sequences(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int]

Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT 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).

Args:

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

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

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.

Args:

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.