class SqueezeBertTokenizer(vocab_file, do_lower_case=True, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', **kwargs)[source]

Bases: paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer

Constructs a SqueezeBert tokenizer. It uses a basic tokenizer to do punctuation splitting, lower casing and so on, and follows a WordPiece tokenizer to tokenize as subwords.

  • vocab_file (str) – file path of the vocabulary

  • do_lower_case (bool) – Whether the text strips accents and convert to lower case. Default: True. Default: True.

  • unk_token (str) – The special token for unkown words. Default: “[UNK]”.

  • sep_token (str) – The special token for separator token . Default: “[SEP]”.

  • pad_token (str) – The special token for padding. Default: “[PAD]”.

  • cls_token (str) – The special token for cls. Default: “[CLS]”.

  • mask_token (str) – The special token for mask. Default: “[MASK]”.


property vocab_size

return the size of vocabulary. :returns: the size of vocabulary. :rtype: int


Converts a sequence of tokens (list of string) in a single string. Since the usage of WordPiece introducing ## to concat subwords, also remove ## when converting. :param tokens: A list of string representing tokens to be converted. :type tokens: list


Converted string from tokens.

Return type



Returns the number of added tokens when encoding a sequence with special tokens. .. note:

This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this
inside your training loop.

pair – Returns the number of added tokens in the case of a sequence pair if set to True, returns the number of added tokens in the case of a single sequence if set to False.


Number of tokens added to sequences

build_inputs_with_special_tokens(token_ids_0, token_ids_1=None)[source]

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

- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
  • 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.


List of input_id with the appropriate special tokens.

Return type


build_offset_mapping_with_special_tokens(offset_mapping_0, offset_mapping_1=None)[source]

Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. A SqueezeBert offset_mapping has the following format:

- single sequence: ``(0,0) X (0,0)``
- pair of sequences: `(0,0) A (0,0) B (0,0)``
  • offset_mapping_ids_0 (List[tuple]) – List of char offsets to which the special tokens will be added.

  • offset_mapping_ids_1 (List[tuple], optional) – Optional second list of char offsets for offset mapping pairs.


List of char offsets with the appropriate offsets of special tokens.

Return type


create_token_type_ids_from_sequences(token_ids_0, token_ids_1=None)[source]

Create a mask from the two sequences passed to be used in a sequence-pair classification task. A SqueezeBert 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). :param token_ids_0: List of IDs. :type token_ids_0: List[int] :param token_ids_1: Optional second list of IDs for sequence pairs. :type token_ids_1: List[int], optional


List of token_type_id according to the given sequence(s).

Return type


get_special_tokens_mask(token_ids_0, token_ids_1=None, already_has_special_tokens=False)[source]

Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer encode methods. :param token_ids_0: List of ids of the first sequence. :type token_ids_0: List[int] :param token_ids_1: List of ids of the second sequence. :type token_ids_1: List[int], optional :param already_has_special_tokens: Whether or not the token list is already

formatted with special tokens for the model. Defaults to None.


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

Return type

results (List[int])