tokenizer

class ErnieTokenizer(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 ERNIE 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 ErnieTokenizer
tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')

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

extend_chinese_char()[source]

For, char level model such as ERNIE, we need add ## chinese token to demonstrate the segment information.

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 ErnieTokenizer
tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')

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.

An Ernie 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 ERNIE 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 ERNIE 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. :param token_ids_0: List of ids of the first sequence. :type token_ids_0: List[int] :param token_ids_1: Optional second list of IDs for sequence pairs.

Defaults to None.

Parameters

already_has_special_tokens (str, optional) – Whether or not the token list is already formatted with special tokens for the model. Defaults to False.

Returns

The list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Return type

List[int]

class ErnieTinyTokenizer(vocab_file, sentencepiece_model_file, word_dict, do_lower_case=True, encoding='utf8', unk_token='[UNK]', sep_token='[SEP]', pad_token='[PAD]', cls_token='[CLS]', mask_token='[MASK]', **kwargs)[source]

Bases: paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer

Constructs a ErnieTiny tokenizer. It uses the dict.wordseg.pickle cut the text to words, and use the sentencepiece tools to cut the words to sub-words.

Examples

from paddlenlp.transformers import ErnieTokenizer
tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')

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]}
# }
Parameters

  • vocab_file (str) – The file path of the vocabulary.

  • sentencepiece_model_file (str) – The file path of sentencepiece model.

  • word_dict (str) – The file path of word vocabulary, which is used to do chinese word segmentation.

  • 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 ErnieTinyTokenizer
tokenizer = ErnieTinyTokenizer.from_pretrained('ernie-tiny')
inputs = tokenizer('He was a puppeteer')
'''
{'input_ids': [3, 941, 977, 16690, 269, 11346, 11364, 1337, 13742, 1684, 5],
'token_type_ids': [0, 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) to a single string. Since the usage of WordPiece introducing ## to concat subwords, also removes ## when converting.

Parameters

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

Returns

Converted string from tokens.

Return type

str

Examples: .. code-block:

from paddlenlp.transformers import ErnieTinyTokenizer
tokenizer = ErnieTinyTokenizer.from_pretrained('ernie-tiny')
inputs = tokenizer.tokenize('He was a puppeteer')
#['▁h', '▁e', '▁was', '▁a', '▁pu', 'pp', 'e', '▁te', 'er']
strings = tokenizer.convert_tokens_to_string(tokens)
save_resources(save_directory)[source]

Save tokenizer related resources to files under save_directory.

Parameters

save_directory (str) – Directory to save files into.

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.

An ERNIE 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 ERNIE 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

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 ERNIE 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]) – List of ids of the first sequence.

  • token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs. Defaults to None.

  • already_has_special_tokens (str, optional) – Whether or not the token list is already formatted with special tokens for the model. Defaults to False.

Returns

The list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Return type

List[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]