tokenizer#

class MobileBertTokenizer(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, **kwargs)[source]#

Bases: BertTokenizer

Construct a MobileBERT tokenizer. BertTokenizer and runs end-to-end tokenization: punctuation splitting and wordpiece. Refer to superclass BertTokenizer for usage examples and documentation concerning parameters.

batch_encode(batch_text_or_text_pairs, max_length: int = 512, padding: bool | str | PaddingStrategy = False, truncation: bool | str | TruncationStrategy = False, stride=0, is_split_into_words=False, return_position_ids=False, return_token_type_ids=True, return_attention_mask=False, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False, return_dict=True, pad_to_multiple_of: int | None = None, return_tensors: str | TensorType | None = None, verbose: bool = True, **kwargs)[source]#

Performs tokenization and uses the tokenized tokens to prepare model inputs. It supports batch inputs of sequence or sequence pair.

Parameters:

  • batch_text_or_text_pairs (list) – The element of list can be sequence or sequence pair, and the sequence is a string or a list of strings depending on whether it has been pretokenized. If each sequence is provided as a list of strings (pretokenized), you must set is_split_into_words as True to disambiguate with a sequence pair.

  • max_length (int, optional) – If set to a number, will limit the total sequence returned so that it has a maximum length. If there are overflowing tokens, those overflowing tokens will be added to the returned dictionary when return_overflowing_tokens is True. Defaults to None.

  • stride (int, optional) – Only available for batch input of sequence pair and mainly for question answering usage. When for QA, text represents questions and text_pair represents contexts. If stride is set to a positive number, the context will be split into multiple spans where stride defines the number of (tokenized) tokens to skip from the start of one span to get the next span, thus will produce a bigger batch than inputs to include all spans. Moreover, ‘overflow_to_sample’ and ‘offset_mapping’ preserving the original example and position information will be added to the returned dictionary. Defaults to 0.

  • padding (bool, optional) – If set to True, the returned sequences would be padded up to max_length specified length according to padding side (self.padding_side) and padding token id. Defaults to False.

  • truncation_strategy (str, optional) – String selected in the following options: - ‘longest_first’ (default) Iteratively reduce the inputs sequence until the input is under max_length starting from the longest one at each token (when there is a pair of input sequences). - ‘only_first’: Only truncate the first sequence. - ‘only_second’: Only truncate the second sequence. - ‘do_not_truncate’: Do not truncate (raise an error if the input sequence is longer than max_length). Defaults to ‘longest_first’.

  • return_position_ids (bool, optional) – Whether to include tokens position ids in the returned dictionary. Defaults to False.

  • return_token_type_ids (bool, optional) – Whether to include token type ids in the returned dictionary. Defaults to True.

  • return_attention_mask (bool, optional) – Whether to include the attention mask in the returned dictionary. Defaults to False.

  • return_length (bool, optional) – Whether to include the length of each encoded inputs in the returned dictionary. Defaults to False.

  • return_overflowing_tokens (bool, optional) – Whether to include overflowing token information in the returned dictionary. Defaults to False.

  • return_special_tokens_mask (bool, optional) – Whether to include special tokens mask information in the returned dictionary. Defaults to False.

Returns:

The dict has the following optional items: - input_ids (list[int]): List of token ids to be fed to a model. - position_ids (list[int], optional): List of token position ids to be

fed to a model. Included when return_position_ids is True

  • token_type_ids (list[int], optional): List of token type ids to be fed to a model. Included when return_token_type_ids is True.

  • attention_mask (list[int], optional): List of integers valued 0 or 1, where 0 specifies paddings and should not be attended to by the model. Included when return_attention_mask is True.

  • seq_len (int, optional): The input_ids length. Included when return_length is True.

  • overflowing_tokens (list[int], optional): List of overflowing tokens. Included when if max_length is specified and return_overflowing_tokens is True.

  • num_truncated_tokens (int, optional): The number of overflowing tokens. Included when if max_length is specified and return_overflowing_tokens is True.

  • special_tokens_mask (list[int], optional): List of integers valued 0 or 1, with 0 specifying special added tokens and 1 specifying sequence tokens. Included when return_special_tokens_mask is True.

  • offset_mapping (list[int], optional): list of pair preserving the index of start and end char in original input for each token. For a sqecial token, the index pair is (0, 0). Included when stride works.

  • overflow_to_sample (int, optional): Index of example from which this feature is generated. Included when stride works.

Return type:

dict