tokenizer#
- class NeZhaTokenizer(vocab_file, do_lower_case=True, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', **kwargs)[source]#
Bases:
PretrainedTokenizer
Constructs a NeZha tokenizer. It uses a basic tokenizer to do punctuation splitting, lower casing and so on, and follows a WordPiece tokenizer to tokenize as subwords.
This tokenizer inherits from
PretrainedTokenizer
which contains most of the main methods. 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) – Whether or not to lowercase the input when tokenizing. Defaults to`True`.
unk_token (str) – 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) – A special token separating two different sentences in the same input. Defaults to “[SEP]”.
pad_token (str) – A special token used to make arrays of tokens the same size for batching purposes. Defaults to “[PAD]”.
cls_token (str) – 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) – 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 NeZhaTokenizer tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese') inputs = tokenizer('欢迎使用百度飞桨!') print(inputs) ''' {'input_ids': [101, 3614, 6816, 886, 4500, 4636, 2428, 7607, 3444, 8013, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]} '''
- property vocab_size#
Return the size of vocabulary.
- Returns:
The size of vocabulary.
- Return type:
int
- convert_tokens_to_string(tokens)[source]#
Converts a sequence of tokens (list of string) to a single string. Since the usage of WordPiece introducing
##
to concat subwords, also removes##
when converting.- Parameters:
tokens (list) – A list of string representing tokens to be converted.
- Returns:
Converted string from tokens.
- Return type:
str
Examples
from paddlenlp.transformers import NeZhaTokenizer tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese') tokens = tokenizer.tokenize('欢迎使用百度飞桨!') ''' ['欢', '迎', '使', '用', '百', '度', '飞', '桨', '!'] ''' strings = tokenizer.convert_tokens_to_string(tokens) ''' 欢 迎 使 用 百 度 飞 桨 ! '''
- num_special_tokens_to_add(pair=False)[source]#
Returns the number of added tokens when encoding a sequence with special tokens.
- Parameters:
pair (bool) – Whether the input is a sequence pair or a single sequence. Defaults to
False
and the input is a single sequence.- Returns:
Number of tokens added to sequences.
- Return type:
int
- 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 NeZha 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. Defaults to
None
.
- Returns:
List of input_id with the appropriate special tokens.
- Return type:
List[int]
- 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 NeZha offset_mapping has the following format:
single sequence:
(0,0) X (0,0)
pair of sequences:
(0,0) A (0,0) B (0,0)
- Parameters:
offset_mapping_ids_0 (List[tuple]) – List of wordpiece offsets to which the special tokens will be added.
offset_mapping_ids_1 (List[tuple], optional) – Optional second list of wordpiece offsets for offset mapping pairs. Defaults to
None
.
- Returns:
A list of wordpiece offsets with the appropriate offsets of special tokens.
- Return type:
List[tuple]
- 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 NeZha 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]) – A list of
inputs_ids
for the first sequence.token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs. Defaults to None.
- Returns:
List of token_type_id according to the given sequence(s).
- Return type:
List[int]
- 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.- Parameters:
token_ids_0 (List[int]) – A list of
inputs_ids
for the first sequence.token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs. Defaults to
None
.already_has_special_tokens (bool, optional) – Whether or not the token list is already formatted with special tokens for the model. Defaults to
False
.
- Returns:
The list of integers either be 0 or 1: 1 for a special token, 0 for a sequence token.
- Return type:
List[int]