# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# 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.
import os
from .. import PretrainedTokenizer
__all__ = ["ErnieCtmTokenizer"]
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"ernie-ctm": 512, "wordtag": 512, "nptag": 512}
[docs]class ErnieCtmTokenizer(PretrainedTokenizer):
r"""
Construct an ERNIE-CTM tokenizer.
This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer`
which contains most of the main methods. For more information regarding those methods,
please refer to this superclass.
Args:
vocab_file (str):
File path of the vocabulary.
do_lower_case (bool, optional):
Whether or not to lowercase the input when tokenizing. Defaults to `True`
do_basic_tokenize (bool, optional):
Whether or not to do basic tokenization before WordPiece. 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_template (str, optional)
The template of summary token for multiple summary placeholders. Defaults to `"[CLS{}]"`
cls_num (int, optional):
Summary placeholder used in ernie-ctm model. For catching a sentence global feature from multiple aware.
Defaults to `1`.
mask_token (str, optional):
A special token representing a masked token. This is the token used in the masked
language modeling task. This is the token which the model will try to predict the original unmasked ones.
Defaults to `"[MASK]"`.
strip_accents: (bool, optional):
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for `lowercase` (as in the original BERT).
Examples:
.. code-block::
from paddlenlp.transformers import ErnieCtmTokenizer
tokenizer = ErnieCtmTokenizer.from_pretrained('ernie-ctm')
encoded_inputs = tokenizer('He was a puppeteer')
# encoded_inputs:
# {'input_ids': [101, 98, 153, 150, 99, 168, 146, 164, 99, 146, 99, 161, 166, 161,
# 161, 150, 165, 150, 150, 163, 102],
# 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
"""
resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained
pretrained_resource_files_map = {
"vocab_file": {
"ernie-ctm": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_ctm/vocab.txt",
"wordtag": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_ctm/vocab.txt",
"nptag": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_ctm/vocab.txt",
}
}
pretrained_init_configuration = {
"ernie-ctm": {"do_lower_case": True, "cls_num": 2},
"wordtag": {"do_lower_case": True, "cls_num": 2},
"nptag": {"do_lower_case": True, "cls_num": 2},
}
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token_template="[CLS{}]",
cls_num=1,
mask_token="[MASK]",
**kwargs
):
if not os.path.isfile(vocab_file):
raise ValueError(
"Can't find a vocabulary file at path '{}'. To load the "
"vocabulary from a pretrained model please use "
"`tokenizer = ErnieTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)
)
self.do_lower_case = do_lower_case
self.cls_token_template = cls_token_template
self.cls_num = cls_num
self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
@property
def vocab_size(self):
"""
Return the size of vocabulary.
Returns:
int: The size of vocabulary.
"""
return len(self.vocab)
[docs] def convert_tokens_to_string(self, tokens):
r"""
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.
Args:
tokens (List[str]): A list of string representing tokens to be converted.
Returns:
str: Converted string from tokens.
Examples:
.. code-block::
from paddlenlp.transformers import ErnieCtmTokenizer
tokenizer = ErnieCtmTokenizer.from_pretrained('ernie-ctm')
tokens = tokenizer.tokenize('He was a puppeteer')
strings = tokenizer.convert_tokens_to_string(tokens)
#he was a puppeteer
"""
out_string = " ".join(tokens).replace(" ##", "").strip()
return out_string
[docs] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
"""
Creates a special tokens mask from the input sequences.
This method is called when adding special tokens using the tokenizer `encode` method.
Args:
token_ids_0 (List[int]):
A list of `inputs_ids` for the first sequence.
token_ids_1 (List[int], optional):
Optional second list of `inputs_ids` for the second sequence.
Defaults to `None`.
already_has_special_tokens (bool, optional):
Whether or not the token list already contains special tokens for the model.
Defaults to `False`.
Returns:
List[int]: A list of integers which is either 0 or 1: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
if token_ids_1 is not None:
return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
return [1] + ([0] * len(token_ids_0)) + [1]
[docs] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
"""
Creates a token_type mask from the input sequences.
If `token_ids_1` is not `None`, then a sequence pair
token_type 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 2
| first sequence | second sequence |
Else if `token_ids_1` is `None`, then a single sequence
token_type mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
| first sequence |
- 0 stands for the segment id of **first segment tokens**,
- 1 stands for the segment id of **second segment tokens**,
- 2 stands for the segment id of **cls_token**.
Args:
token_ids_0 (List[int]):
A list of `inputs_ids` for the first sequence.
token_ids_1 (List[int], optional):
Optional second list of `inputs_ids` for the second sequence.
Defaults to `None`.
Returns:
List[int]: List of token type IDs according to the given sequence(s).
"""
sep = [self.sep_token_id]
if token_ids_1 is None:
return (self.cls_num + len(token_ids_0 + sep)) * [0]
return (self.cls_num + len(token_ids_0 + sep)) * [0] + len(token_ids_1 + sep) * [1]
[docs] def num_special_tokens_to_add(self, pair=False):
"""
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.
Args:
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:
int: Number of tokens added to sequences.
"""
if pair is True:
return self.cls_num + 2
else:
return self.cls_num + 1
def _tokenize(self, text, **kwargs):
r"""
Converts a string to a list of tokens.
Args:
text (str): The text to be tokenized.
Returns:
List[str]: A list of string representing converted tokens.
"""
orig_tokens = list(text)
output_tokens = []
for token in orig_tokens:
if self.do_lower_case is True:
token = token.lower()
output_tokens.append(token)
return output_tokens
