tokenizer¶

class PPMiniLMTokenizer(vocab_file, do_lower_case=True, unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', **kwargs)[source]¶

Bases: paddlenlp.transformers.tokenizer_utils.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

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]