tokenizer

class NeZhaTokenizer(vocab_file, do_lower_case=True, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]')[源代码]

基类:paddlenlp.transformers.tokenizer_utils.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.

参数
  • 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]".

实际案例

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.

返回

The size of vocabulary.

返回类型

int

tokenize(text)[源代码]

Converts a string to a list of tokens.

参数

text (str) -- The text to be tokenized.

返回

A list of string representing converted tokens.

返回类型

List(str)

实际案例

from paddlenlp.transformers import NeZhaokenizer

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', '!']
'''
convert_tokens_to_string(tokens)[源代码]

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.

参数

tokens (list) -- A list of string representing tokens to be converted.

返回

Converted string from tokens.

返回类型

str

实际案例

from paddlenlp.transformers import NeZhaTokenizer

tokenizer = NeZhaTokenizer.from_pretrained('bert-base-uncased')
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)[源代码]

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

参数

pair (bool) -- Whether the input is a sequence pair or a single sequence. Defaults to False and the input is a single sequence.

返回

Number of tokens added to sequences.

返回类型

int

build_inputs_with_special_tokens(token_ids_0, token_ids_1=None)[源代码]

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]

参数
  • 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.

返回

List of input_id with the appropriate special tokens.

返回类型

List[int]

build_offset_mapping_with_special_tokens(offset_mapping_0, offset_mapping_1=None)[源代码]

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)

参数
  • 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.

返回

A list of wordpiece offsets with the appropriate offsets of special tokens.

返回类型

List[tuple]

create_token_type_ids_from_sequences(token_ids_0, token_ids_1=None)[源代码]

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 is None, this method only returns the first portion of the mask (0s).

参数
  • 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.

返回

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

返回类型

List[int]

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

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.

参数
  • token_ids_0 (List[int]) -- A list of inputs_ids for the first sequence.

  • token_ids_1 (List[int], optinal) -- 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.

返回

The list of integers either be 0 or 1: 1 for a special token, 0 for a sequence token.

返回类型

List[int]