# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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.
"""Tokenization class for XLNet model."""
import os
import unicodedata
from shutil import copyfile
import sentencepiece as spm
from .. import AddedToken, PretrainedTokenizer
__all__ = ["XLNetTokenizer"]
SENTENCEPIECE_UNDERLINE = "▁"
SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
# Segments (not really needed)
SEG_ID_A = 0
SEG_ID_B = 1
SEG_ID_CLS = 2
SEG_ID_SEP = 3
SEG_ID_PAD = 4
[文档]class XLNetTokenizer(PretrainedTokenizer):
"""
Constructs an XLNet tokenizer based on `SentencePiece <https://github.com/google/sentencepiece>`__.
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 (ends with '.spm') required to instantiate
a `SentencePiece <https://github.com/google/sentencepiece>`__ tokenizer.
do_lower_case (bool, optional):
Whether or not to lowercase the input when tokenizing. Defaults to `False` and
**does not** lowercase the input.
remove_space (bool, optional):
Whether or not to strip the text when tokenizing. Defaults to `True` and
removes excess spaces before and after the string.
keep_accents (bool, optional):
Whether or not to keep accents when tokenizing. Defaults to `False` and **does not** keep accents.
bos_token (str, optional):
A special token representing the beginning of a sequence that was used during pretraining.
Defaults to `"<s>"`.
eos_token (str, optional):
A special token representing the end of a sequence that was used during pretraining.
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>"`.
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>"`.
additional_special_tokens (List[str], optional):
A list of additional special tokens to be used by the tokenizer.
Defaults to `["<eop>", "<eod>"]`.
Attributes:
sp_model (SentencePieceProcessor):
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
"""
resource_files_names = {"vocab_file": "spiece.model"}
pretrained_resource_files_map = {
"vocab_file": {
"xlnet-base-cased": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/xlnet-base-cased-spiece.model",
"xlnet-large-cased": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/xlnet-large-cased-spiece.model",
"chinese-xlnet-base": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/chinese-xlnet-base-spiece.model",
"chinese-xlnet-mid": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/chinese-xlnet-mid-spiece.model",
"chinese-xlnet-large": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/chinese-xlnet-large-spiece.model",
}
}
pretrained_init_configuration = {
"xlnet-base-cased": {"do_lower_case": False},
"xlnet-large-cased": {"do_lower_case": False},
"chinese-xlnet-base": {"do_lower_case": False},
"chinese-xlnet-mid": {"do_lower_case": False},
"chinese-xlnet-large": {"do_lower_case": False},
}
pretrained_positional_embedding_sizes = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
"chinese-xlnet-base": None,
"chinese-xlnet-mid": None,
"chinese-xlnet-large": None,
}
max_model_input_sizes = pretrained_positional_embedding_sizes
padding_side = "left"
pad_token_type_id = 3
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=True,
keep_accents=False,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
sep_token="<sep>",
pad_token="<pad>",
cls_token="<cls>",
mask_token="<mask>",
additional_special_tokens=["<eop>", "<eod>"],
sp_model_kwargs=None,
**kwargs
):
# Mask token behave like a normal word, i.e. include the space before it
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._pad_token_type_id = 3
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.vocab_file = vocab_file
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(vocab_file)
@property
def vocab_size(self):
return len(self.sp_model)
[文档] 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
# for backward compatibility
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.vocab_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)
[文档] 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
[文档] 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, 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.
"""
token_ids_0 = []
token_ids_1 = []
return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None))
[文档] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None):
"""
Builds offset map from a pair of offset map by concatenating
and adding offsets of special tokens.
An XLNet offset_mapping has the following format:
- single sequence: ``X (0,0) (0,0)``
- pair of sequences: ``A (0,0) B (0,0) (0,0)``
Args:
offset_mapping_0 (List[tuple]):
List of char offsets to which the special tokens will be added.
offset_mapping_1 (List[tuple], optional):
Optional second list of char offsets for offset mapping pairs.
Defaults to `None`.
Returns:
List[tuple]: A list of char offsets with the appropriate offsets of special tokens.
"""
if offset_mapping_1 is None:
return offset_mapping_0 + [(0, 0)] + [(0, 0)]
return offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)] + [(0, 0)]
[文档] 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 ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1]
return ([0] * len(token_ids_0)) + [1, 1]
[文档] 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]
cls_segment_id = [2]
if token_ids_1 is None:
return len(token_ids_0 + sep) * [0] + cls_segment_id
return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id
[文档] 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.vocab_file) != os.path.abspath(save_path) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file, save_path)
elif not os.path.isfile(self.vocab_file):
with open(save_path, "wb") as fi:
content_spiece_model = self.sp_model.serialized_model_proto()
fi.write(content_spiece_model)
