tokenizer¶
-
class
RemBertTokenizer
(vocab_file, do_lower_case=False, remove_space=True, keep_accents=True, cls_token='[CLS]', unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', mask_token='[MASK]', **kwargs)[source]¶ Bases:
paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer
Construct a RemBertTokenizer. For more information regarding those methods, please refer to this superclass.
- Parameters
vocab_file (str) – The vocabulary file path (ends with ‘.txt’) required to instantiate a
WordpieceTokenizer
.do_lower_case (bool, optional) – Whether or not to lowercase the input when tokenizing. Defaults to
False
.unk_token (str, optional) – A special token representing the unknown (out-of-vocabulary) token. An unknown token is set to be
unk_token
inorder to be converted to an ID. Defaults to “[UNK]”.sep_token (str, optional) – A special token separating two different sentences in the same input. Defaults to “[SEP]”.
pad_token (str, optional) – A special token used to make arrays of tokens the same size for batching purposes. Defaults to “[PAD]”.
cls_token (str, optional) – A special token used for sequence classification. It is the last token of the sequence when built with special tokens. Defaults to “[CLS]”.
mask_token (str, optional) – A special token representing a masked token. This is the token used in the masked language modeling task which the model tries to predict the original unmasked ones. Defaults to “[MASK]”.
Examples
from paddlenlp.transformers import RemBertTokenizer tokenizer = RemBertTokenizer.from_pretrained('rembert') inputs = tokenizer('欢迎使用飞桨!') print(inputs) ''' {'input_ids': [312, 573, 36203, 3916, 9744, 242391, 646, 313], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0]} '''
-
property
vocab_size
¶ Size of the base vocabulary (without the added tokens).
- Type
int
-
get_vocab
()[source]¶ Returns the vocabulary as a dictionary of token to index.
tokenizer.get_vocab()[token]
is equivalent totokenizer.convert_tokens_to_ids(token)
whentoken
is in the vocab.- Returns
The vocabulary.
- Return type
Dict[str, int]
-
convert_tokens_to_string
(tokens)[source]¶ Converts a sequence of tokens (list of string) to a single string by using
' '.join(tokens)
.- Parameters
tokens (list[str]) – A sequence of tokens.
- Returns
Converted string.
- Return type
str
-
build_inputs_with_special_tokens
(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]¶ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A REMBERT sequence has the following format:
single sequence:
[CLS] X [SEP]
pair of sequences:
[CLS] A [SEP] B [SEP]
- 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 of input IDs with the appropriate special tokens.
- Return type
List[int]
-
get_special_tokens_mask
(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False) → List[int][source]¶ 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.- 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 toFalse
) – Whether or not the token list is already formatted with special tokens for the model.
- Returns
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
- Return type
List[int]
-
create_token_type_ids_from_sequences
(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int][source]¶ Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RemBERT 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
isNone
, this method only returns the first portion of the mask (0s).- 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 of token type IDs according to the given sequence(s).
- Return type
List[int]
-
save_vocabulary
(save_directory: str, filename_prefix: Optional[str] = None)[source]¶ 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.
- Parameters
filepath (str) – File path to be saved to.
vocab (Vocab|dict) – The
Vocab
ordict
instance to be saved.