tokenizer#

class PPMiniLMTokenizer(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)[source]#

Bases: PretrainedTokenizer

Constructs an PPMiniLM 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 (str, optional) – Whether or not to lowercase the input when tokenizing. Defaults to`True`.
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 PPMiniLMTokenizer
tokenizer = PPMiniLMTokenizer.from_pretrained('ppminilm-6l-768h')

encoded_inputs = tokenizer('He was a puppeteer')
# encoded_inputs:
# { 'input_ids': [1, 4444, 4385, 1545, 6712, 10062, 9568, 9756, 9500, 2],
#  'token_type_ids': [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

get_vocab()[source]#

Returns the vocabulary as a dictionary of token to index.

tokenizer.get_vocab()[token] is equivalent to tokenizer.convert_tokens_to_ids(token) when token 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) in a single string. Since the usage of WordPiece introducing ## to concat subwords, also remove ## when converting.

Parameters:: tokens (List[str]) – A list of string representing tokens to be converted.
Returns:: Converted string from tokens.
Return type:: str

Examples

from paddlenlp.transformers import PPMiniLMTokenizer
tokenizer = PPMiniLMTokenizer.from_pretrained('ppminilm-6l-768h')

tokens = tokenizer.tokenize('He was a puppeteer')
strings = tokenizer.convert_tokens_to_string(tokens)
#he was a puppeteer

num_special_tokens_to_add(pair=False)[source]#

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

Note

This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this inside your training loop.

Parameters:: pair (bool, optional) – 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 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.

An 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 char 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 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).

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 None.

Returns:

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

Return type:

List[int]

tokenizer

Contents

tokenizer#