# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .. import BPETokenizer
from ..ernie.tokenizer import ErnieTokenizer
__all__ = ["ErnieDocTokenizer", "ErnieDocBPETokenizer"]
[文档]class ErnieDocTokenizer(ErnieTokenizer):
r"""
Constructs an ERNIE-Doc 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 :class:`~paddlenlp.transformers.ernie.tokenizer.ErnieTokenizer`.
For more information regarding those methods, please refer to this superclass.
Args:
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:
.. code-block::
from paddlenlp.transformers import ErnieDocTokenizer
tokenizer = ErnieDocTokenizer.from_pretrained('ernie-doc-base-zh')
encoded_inputs = tokenizer('He was a puppeteer')
"""
resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
"ernie-doc-base-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-doc-base-zh/vocab.txt",
}
}
pretrained_init_configuration = {
"ernie-doc-base-zh": {"do_lower_case": True},
}
max_model_input_sizes = {
"ernie-doc-base-zh": 512,
}
def __init__(
self,
vocab_file,
do_lower_case=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
**kwargs
):
super(ErnieDocTokenizer, self).__init__(
vocab_file,
do_lower_case=do_lower_case,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
**kwargs,
)
[文档]class ErnieDocBPETokenizer(BPETokenizer):
r"""
Constructs an ERNIE-Doc BPE tokenizer. It uses a bpe tokenizer to do punctuation
splitting, lower casing and so on, then tokenize words as subwords.
This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.BPETokenizer`.
For more information regarding those methods, please refer to this superclass.
Args:
vocab_file (str):
File path of the vocabulary.
encoder_json_path (str, optional):
File path of the id to vocab.
vocab_bpe_path (str, optional):
File path of word merge text.
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:
.. code-block::
from paddlenlp.transformers import ErnieDocBPETokenizer
tokenizer = ErnieDocBPETokenizer.from_pretrained('ernie-doc-base-en')
encoded_inputs = tokenizer('He was a puppeteer')
"""
resource_files_names = {
"vocab_file": "vocab.txt",
"encoder_json_path": "encoder.json",
"vocab_bpe_path": "vocab.bpe",
} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
"ernie-doc-base-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-doc-base-en/vocab.txt"
},
"encoder_json_path": {
"ernie-doc-base-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-doc-base-en/encoder.json"
},
"vocab_bpe_path": {
"ernie-doc-base-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie-doc-base-en/vocab.bpe"
},
}
pretrained_init_configuration = {
"ernie-doc-base-en": {"unk_token": "[UNK]"},
}
def __init__(
self,
vocab_file,
encoder_json_path="./configs/encoder.json",
vocab_bpe_path="./configs/vocab.bpe",
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
**kwargs
):
super(ErnieDocBPETokenizer, self).__init__(
vocab_file,
encoder_json_path=encoder_json_path,
vocab_bpe_path=vocab_bpe_path,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
**kwargs,
)
@property
def vocab_size(self):
"""
Return the size of vocabulary.
Returns:
int: The size of vocabulary.
"""
return len(self.vocab)
