# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 Google AI, Google Brain 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.
"""Tokenization class for ALBERT model."""
import os
import unicodedata
from shutil import copyfile
import sentencepiece as spm
from .. import PretrainedTokenizer, BertTokenizer, AddedToken
__all__ = ["AlbertTokenizer"]
SPIECE_UNDERLINE = "▁"
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
"albert-chinese-tiny": 512,
"albert-chinese-small": 512,
"albert-chinese-base": 512,
"albert-chinese-large": 512,
"albert-chinese-xlarge": 512,
"albert-chinese-xxlarge": 512,
}
[docs]class AlbertTokenizer(PretrainedTokenizer):
"""
Constructs an Albert tokenizer based on SentencePiece or `BertTokenizer`.
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):
The vocabulary file path (ends with '.txt') required to instantiate
a `WordpieceTokenizer`.
sentence_model_file (str):
The vocabulary file (ends with '.spm') required to instantiate
a `SentencePiece <https://github.com/google/sentencepiece>`__ tokenizer.
do_lower_case (bool):
Whether or not to lowercase the input when tokenizing. Defaults to `True`.
remove_space (bool):
Whether or note to remove space when tokenizing. Defaults to `True`.
keep_accents (bool):
Whether or note to keep accents when tokenizing. Defaults to `False`.
unk_token (str):
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):
A special token separating two different sentences in the same input.
Defaults to "[SEP]".
pad_token (str):
A special token used to make arrays of tokens the same size for batching purposes.
Defaults to "[PAD]".
cls_token (str):
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):
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 AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
tokens = tokenizer('He was a puppeteer')
'''
{'input_ids': [2, 24, 23, 21, 10956, 7911, 3],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0]}
'''
"""
resource_files_names = {
"sentencepiece_model_file": "spiece.model",
"vocab_file": "vocab.txt",
}
pretrained_resource_files_map = {
"sentencepiece_model_file": {
"albert-base-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v1.spiece.model",
"albert-large-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v1.spiece.model",
"albert-xlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v1.spiece.model",
"albert-xxlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v1.spiece.model",
"albert-base-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v2.spiece.model",
"albert-large-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v2.spiece.model",
"albert-xlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v2.spiece.model",
"albert-xxlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v2.spiece.model",
"albert-chinese-tiny": None,
"albert-chinese-small": None,
"albert-chinese-base": None,
"albert-chinese-large": None,
"albert-chinese-xlarge": None,
"albert-chinese-xxlarge": None,
},
"vocab_file": {
"albert-base-v1": None,
"albert-large-v1": None,
"albert-xlarge-v1": None,
"albert-xxlarge-v1": None,
"albert-base-v2": None,
"albert-large-v2": None,
"albert-xlarge-v2": None,
"albert-xxlarge-v2": None,
"albert-chinese-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-tiny.vocab.txt",
"albert-chinese-small": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-small.vocab.txt",
"albert-chinese-base": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-base.vocab.txt",
"albert-chinese-large": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-large.vocab.txt",
"albert-chinese-xlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xlarge.vocab.txt",
"albert-chinese-xxlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xxlarge.vocab.txt",
},
}
pretrained_init_configuration = {
"albert-base-v1": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-large-v1": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-xlarge-v1": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-xxlarge-v1": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-base-v2": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-large-v2": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-xlarge-v2": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-xxlarge-v2": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-chinese-tiny": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
"albert-chinese-small": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
"albert-chinese-base": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
"albert-chinese-large": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
"albert-chinese-xlarge": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
"albert-chinese-xxlarge": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
}
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(
self,
vocab_file,
sentencepiece_model_file,
do_lower_case=True,
remove_space=True,
keep_accents=False,
bos_token="[CLS]",
eos_token="[SEP]",
unk_token="<unk>",
sep_token="[SEP]",
pad_token="<pad>",
cls_token="[CLS]",
mask_token="[MASK]",
**kwargs
):
mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
self._build_special_tokens_map_extended(mask_token=mask_token)
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.vocab_file = vocab_file
self.sentencepiece_model_file = sentencepiece_model_file
if vocab_file is not None:
self.tokenizer = AlbertChineseTokenizer(
vocab_file=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,
)
elif sentencepiece_model_file is not None:
self.tokenizer = AlbertEnglishTokenizer(
sentencepiece_model_file=sentencepiece_model_file,
do_lower_case=do_lower_case,
remove_space=remove_space,
keep_accents=keep_accents,
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
**kwargs,
)
else:
raise ValueError(
"You should only specify either one(not both) of 'vocal_file'"
"and 'sentencepiece_model_file' to construct an albert tokenizer."
"Specify 'vocal_file' for Chinese tokenizer and "
"'sentencepiece_model_file' for English tokenizer"
)
@property
def vocab_size(self):
"""
Return the size of vocabulary.
Returns:
int: The size of vocabulary.
"""
return self.tokenizer.vocab_size
def _tokenize(self, text):
return self.tokenizer._tokenize(text)
[docs] def tokenize(self, text):
"""
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.
Examples:
.. code-block::
from paddlenlp.transformers import RobertaTokenizer
tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext')
tokens = tokenizer.tokenize('He was a puppeteer')
"""
return self.tokenizer.tokenize(text)
def _convert_token_to_id(self, token):
"""
Converts a sequence of tokens (list of string) to a list of ids.
Args:
tokens (list): A list of string representing tokens to be converted.
Returns:
list: Converted ids from tokens.
Examples:
.. code-block::
from paddlenlp.transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
tokens = tokenizer.tokenize('He was a puppeteer')
#['▁he', '▁was', '▁a', '▁puppet', 'eer']
ids = tokenizer.convert_tokens_to_ids(tokens)
#[24, 23, 21, 10956, 7911]
"""
return self.tokenizer._convert_token_to_id(token)
def _convert_id_to_token(self, index):
"""
Converts a sequence of tokens (list of string) to a list of ids.
Args:
ids (list): A list of ids to be converted.
skip_special_tokens (bool, optional):
Whether or not to skip specical tokens. Defaults to `False`.
Returns:
list: A list of converted tokens.
Examples:
.. code-block::
from paddlenlp.transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
ids = [24, 23, 21, 10956, 7911]
tokens = tokenizer.convert_ids_to_tokens(ids)
#['▁he', '▁was', '▁a', '▁puppet', 'eer']
"""
return self.tokenizer._convert_id_to_token(index)
[docs] def convert_tokens_to_string(self, tokens):
"""
Converts a sequence of tokens (list of string) to a single string.
Args:
tokens (list): A list of string representing tokens to be converted.
Returns:
str: Converted string from tokens.
Examples:
.. code-block::
from paddlenlp.transformers import AlbertTokenizer
tokenizer = AlbertTokenizer.from_pretrained('bert-base-uncased')
tokens = tokenizer.tokenize('He was a puppeteer')
'''
['▁he', '▁was', '▁a', '▁puppet', 'eer']
'''
strings = tokenizer.convert_tokens_to_string(tokens)
'''
he was a puppeteer
'''
"""
return self.tokenizer.convert_tokens_to_string(tokens)
[docs] def num_special_tokens_to_add(self, pair=False):
"""
Returns the number of added tokens when encoding a sequence with special tokens.
Args:
pair(bool):
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.
"""
return self.tokenizer.num_special_tokens_to_add(pair=pair)
[docs] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
"""
Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.
A Albert offset_mapping has the following format:
- single sequence: ``(0,0) X (0,0)``
- pair of sequences: ``(0,0) A (0,0) B (0,0)``
Args:
offset_mapping_ids_0 (List[tuple]):
List of wordpiece 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[tuple]: A list of wordpiece offsets with the appropriate offsets of special tokens.
"""
return self.tokenizer.build_offset_mapping_with_special_tokens(
offset_mapping_0, offset_mapping_1=offset_mapping_1
)
[docs] def get_special_tokens_mask(self, 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.
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 IDs for sequence pairs. Defaults to None.
already_has_special_tokens (bool, optional): Whether or not the token list is already
formatted with special tokens for the model. Defaults to None.
Returns:
List[int]: The list of integers either be 0 or 1: 1 for a special token, 0 for a sequence token.
"""
return self.tokenizer.get_special_tokens_mask(
token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=already_has_special_tokens
)
[docs] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
return self.tokenizer.create_token_type_ids_from_sequences(token_ids_0, token_ids_1=token_ids_1)
[docs] def save_resources(self, save_directory):
return self.tokenizer.save_resources(save_directory)
class AlbertEnglishTokenizer(PretrainedTokenizer):
resource_files_names = {
"sentencepiece_model_file": "spiece.model",
}
pretrained_resource_files_map = {
"sentencepiece_model_file": {
"albert-base-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v1.spiece.model",
"albert-large-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v1.spiece.model",
"albert-xlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v1.spiece.model",
"albert-xxlarge-v1": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v1.spiece.model",
"albert-base-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-base-v2.spiece.model",
"albert-large-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-large-v2.spiece.model",
"albert-xlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xlarge-v2.spiece.model",
"albert-xxlarge-v2": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-xxlarge-v2.spiece.model",
},
}
pretrained_init_configuration = {
"albert-base-v1": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-large-v1": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-xlarge-v1": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-xxlarge-v1": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-base-v2": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-large-v2": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-xlarge-v2": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
"albert-xxlarge-v2": {
"do_lower_case": True,
"remove_space": True,
"keep_accents": False,
"unk_token": "<unk>",
"pad_token": "<pad>",
},
}
max_model_input_sizes = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
def __init__(
self,
sentencepiece_model_file,
do_lower_case=True,
remove_space=True,
keep_accents=False,
bos_token="[CLS]",
eos_token="[SEP]",
unk_token="<unk>",
sep_token="[SEP]",
pad_token="<pad>",
cls_token="[CLS]",
mask_token="[MASK]",
sp_model_kwargs=None,
**kwargs
):
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
self.do_lower_case = do_lower_case
self.remove_space = remove_space
self.keep_accents = keep_accents
self.sentencepiece_model_file = sentencepiece_model_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(sentencepiece_model_file)
@property
def vocab_size(self):
return len(self.sp_model)
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__(self):
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d):
self.__dict__ = d
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.sentencepiece_model_file)
def preprocess_text(self, inputs):
if self.remove_space:
outputs = " ".join(inputs.strip().split())
else:
outputs = inputs
outputs = outputs.replace("``", '"').replace("''", '"')
if not self.keep_accents:
outputs = unicodedata.normalize("NFKD", outputs)
outputs = "".join([c for c in outputs if not unicodedata.combining(c)])
if self.do_lower_case:
outputs = outputs.lower()
return outputs
def _tokenize(self, text):
"""Tokenize a string."""
text = self.preprocess_text(text)
pieces = self.sp_model.encode(text, out_type=str)
new_pieces = []
for piece in pieces:
if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, ""))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
cur_pieces = cur_pieces[1:]
else:
cur_pieces[0] = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(cur_pieces)
else:
new_pieces.append(piece)
return new_pieces
def _convert_token_to_id(self, token):
"""Converts a token (str) to an id using the vocab."""
return self.sp_model.PieceToId(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) to a token (str) using the vocab."""
return self.sp_model.IdToPiece(index)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
return out_string
def num_special_tokens_to_add(self, pair=False):
token_ids_0 = []
token_ids_1 = []
return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return cls + token_ids_0 + sep
return cls + token_ids_0 + sep + token_ids_1 + sep
def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
if offset_mapping_1 is None:
return [(0, 0)] + offset_mapping_0 + [(0, 0)]
return [(0, 0)] + offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)]
def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False):
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]
def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None):
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
def save_resources(self, save_directory):
for name, file_name in self.resource_files_names.items():
save_path = os.path.join(save_directory, file_name)
if os.path.abspath(self.sentencepiece_model_file) != os.path.abspath(save_path) and os.path.isfile(
self.sentencepiece_model_file
):
copyfile(self.sentencepiece_model_file, save_path)
elif not os.path.isfile(self.sentencepiece_model_file):
with open(save_path, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
class AlbertChineseTokenizer(BertTokenizer):
resource_files_names = {"vocab_file": "vocab.txt"}
pretrained_resource_files_map = {
"vocab_file": {
"albert-chinese-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-tiny.vocab.txt",
"albert-chinese-small": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-small.vocab.txt",
"albert-chinese-base": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-base.vocab.txt",
"albert-chinese-large": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-large.vocab.txt",
"albert-chinese-xlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xlarge.vocab.txt",
"albert-chinese-xxlarge": "https://bj.bcebos.com/paddlenlp/models/transformers/albert/albert-chinese-xxlarge.vocab.txt",
}
}
pretrained_init_configuration = {
"albert-chinese-tiny": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
"albert-chinese-small": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
"albert-chinese-base": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
"albert-chinese-large": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
"albert-chinese-xlarge": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
"albert-chinese-xxlarge": {
"do_lower_case": False,
"unk_token": "[UNK]",
"pad_token": "[PAD]",
},
}
max_model_input_sizes = {
"albert-chinese-tiny": 512,
"albert-chinese-small": 512,
"albert-chinese-base": 512,
"albert-chinese-large": 512,
"albert-chinese-xlarge": 512,
"albert-chinese-xxlarge": 512,
}
def __init__(
self,
vocab_file,
do_lower_case=True,
do_basic_tokenize=True,
never_split=None,
unk_token="[UNK]",
sep_token="[SEP]",
pad_token="[PAD]",
cls_token="[CLS]",
mask_token="[MASK]",
tokenize_chinese_chars=True,
strip_accents=None,
**kwargs
):
super(AlbertChineseTokenizer, self).__init__(
vocab_file,
do_lower_case=do_lower_case,
do_basic_tokenize=do_basic_tokenize,
never_split=never_split,
unk_token=unk_token,
sep_token=sep_token,
pad_token=pad_token,
cls_token=cls_token,
mask_token=mask_token,
tokenize_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
**kwargs,
)
