tokenizer¶
-
class
ElectraTokenizer
(vocab_file, do_lower_case=True, do_basic_tokenize=True, never_split=None, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', tokenize_chinese_chars=True, strip_accents=None, **kwargs)[源代码]¶ 基类:
paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer
Constructs an Electra 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 ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') tokens = tokenizer('He was a puppeteer') ''' {'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102], '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
-
get_vocab
()[源代码]¶ 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.- 返回
The vocabulary.
- 返回类型
Dict[str, int]
-
convert_tokens_to_string
(tokens)[源代码]¶ 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.- 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 ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') tokens = tokenizer.tokenize('He was a puppeteer') string = tokenizer.convert_tokens_to_string(tokens)
-
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 -- 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.
- 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 ELECTRA 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.
- 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 ELECTRA 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 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.
- 返回
List of char 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 ELECTRA 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).- 参数
token_ids_0 (
List[int]
) -- List of IDs.token_ids_1 (
List[int]
,optional
) -- Optional second list of IDs for sequence pairs.
- 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]) -- List of ids of the first sequence.
token_ids_1 (List[int], optional) -- List of ids of the second sequence.
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 in the range [0, 1]: 1 for a special token, 0 for a sequence token.
- 返回类型
List[int]