tokenizer

class BartTokenizer(vocab_file, merges_file, errors='replace', max_len=None, bos_token='<s>', eos_token='</s>', cls_token='<s>', sep_token='</s>', unk_token='<unk>', pad_token='<pad>', mask_token='<mask>', **kwargs)

基类：paddlenlp.transformers.gpt.tokenizer.GPTTokenizer

Construct a BART tokenizer based on byte-level Byte-Pair-Encoding.

This tokenizer inherits from GPTTokenizer. For more information regarding those methods, please refer to this superclass.

参数

• vocab_file (str) -- Path to the vocabulary file. The vocab file contains a mapping from vocabulary strings to indices.

• merges_file (str) -- Path to the merge file. The merge file is used to split the input sentence into "subword" units. The vocab file is then used to encode those units as intices.

• errors (str) -- Paradigm to follow when decoding bytes to UTF-8. Defaults to 'replace'.

• max_len (int, optional) -- The maximum value of the input sequence length. Defaults to None.

• bos_token (str, optional) -- The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. Defaults to "<s>".

• eos_token (str, optional) -- A special token representing the end of a sequence that was used during pretraining. Defaults to "</s>".

• 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 "<s>".

• sep_token (str, optional) -- A special token separating two different sentences in the same input. Defaults to "</s>".

• 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>".

• pad_token (str, optional) -- A special token used to make arrays of tokens the same size for batching purposes. Defaults to "<pad>".

• 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>".

实际案例

from paddlenlp.transformers import BartTokenizer

tokenizer = BartTokenizer.from_pretrained('bart-base')
print(tokenizer('He was a puppeteer'))

'''
{'input_ids': [0, 894, 21, 10, 32986, 9306, 254, 2],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}
'''

build_inputs_with_special_tokens(token_ids_0, token_ids_1=None)

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens.

get_special_tokens_mask(token_ids_0, token_ids_1=None, already_has_special_tokens=False)

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.

create_token_type_ids_from_sequences(token_ids_0, token_ids_1=None)

Create a mask from the two sequences passed to be used in a sequence-pair classification task.