tokenizer#

Tokenization class for ALBERT model.

class AlbertTokenizer(vocab_file, sentencepiece_model_file, do_lower_case=True, remove_space=True, keep_accents=False, bos_token='[CLS]', eos_token='[SEP]', unk_token='<unk>', sep_token='[SEP]', pad_token='<pad>', cls_token='[CLS]', mask_token='[MASK]', **kwargs)[源代码]#

基类：PretrainedTokenizer

Constructs an Albert tokenizer based on SentencePiece or BertTokenizer.

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.
sentence_model_file (str) -- The vocabulary file (ends with '.spm') required to instantiate a SentencePiece tokenizer.
do_lower_case (bool) -- Whether or not to lowercase the input when tokenizing. Defaults to True.
remove_space (bool) -- Whether or note to remove space when tokenizing. Defaults to True.
keep_accents (bool) -- Whether or note to keep accents when tokenizing. Defaults to False.
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]".

示例

from paddlenlp.transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
tokens = tokenizer('He was a puppeteer')
'''
{'input_ids': [2, 24, 23, 21, 10956, 7911, 3],
 'token_type_ids': [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)

示例

from paddlenlp.transformers import RobertaTokenizer

tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext')
tokens = tokenizer.tokenize('He was a puppeteer')

convert_tokens_to_string(tokens)[源代码]#

Converts a sequence of tokens (list of string) to a single string.

参数:: tokens (list) -- A list of string representing tokens to be converted.
返回:: Converted string from tokens.
返回类型:: str

示例

from paddlenlp.transformers import AlbertTokenizer

tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
tokens = tokenizer.tokenize('He was a puppeteer')
'''
['▁he', '▁was', '▁a', '▁puppet', 'eer']
'''
strings = tokenizer.convert_tokens_to_string(tokens)
'''
he was a puppeteer
'''

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.

An Albert 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 Albert 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]

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], 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 None.

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]

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

Create the token type IDs corresponding to the sequences passed. [What are token type IDs?](../glossary#token-type-ids)

Should be overridden in a subclass if the model has a special way of building those.

参数:

token_ids_0 (List[int]) -- The first tokenized sequence.
token_ids_1 (List[int], optional) -- The second tokenized sequence.

返回:

The token type ids.

返回类型:

List[int]

save_resources(save_directory)[源代码]#

Save tokenizer related resources to resource_files_names indicating files under save_directory by copying directly. Override it if necessary.

参数:: save_directory (str) -- Directory to save files into.

tokenizer

目录

tokenizer#