264 lines
10 KiB
Python
264 lines
10 KiB
Python
|
# coding=utf-8
|
||
|
|
# Copyright The HuggingFace Team and The HuggingFace Inc. team. 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.
|
||
|
|
"""Tokenization classes for RemBERT."""
|
||
|
|
|
||
|
|
|
||
|
|
import os
|
||
|
|
from shutil import copyfile
|
||
|
|
from typing import List, Optional, Tuple
|
||
|
|
|
||
|
|
import sentencepiece as spm
|
||
|
|
|
||
|
|
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
|
||
|
|
from ...utils import logging
|
||
|
|
|
||
|
|
|
||
|
|
logger = logging.get_logger(__name__)
|
||
|
|
|
||
|
|
VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.model"}
|
||
|
|
|
||
|
|
|
||
|
|
class RemBertTokenizer(PreTrainedTokenizer):
|
||
|
|
"""
|
||
|
|
Construct a RemBERT tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
|
||
|
|
|
||
|
|
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
|
||
|
|
this superclass for more information regarding those methods.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
vocab_file (`str`):
|
||
|
|
[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
|
||
|
|
contains the vocabulary necessary to instantiate a tokenizer.
|
||
|
|
bos_token (`str`, *optional*, defaults to `"[CLS]"`):
|
||
|
|
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
|
||
|
|
|
||
|
|
<Tip>
|
||
|
|
|
||
|
|
When building a sequence using special tokens, this is not the token that is used for the beginning of
|
||
|
|
sequence. The token used is the `cls_token`.
|
||
|
|
|
||
|
|
</Tip>
|
||
|
|
|
||
|
|
eos_token (`str`, *optional*, defaults to `"[SEP]"`):
|
||
|
|
The end of sequence token.
|
||
|
|
|
||
|
|
<Tip>
|
||
|
|
|
||
|
|
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
|
||
|
|
The token used is the `sep_token`.
|
||
|
|
|
||
|
|
</Tip>
|
||
|
|
|
||
|
|
unk_token (`str`, *optional*, defaults to `"<unk>"`):
|
||
|
|
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
|
||
|
|
token instead.
|
||
|
|
sep_token (`str`, *optional*, defaults to `"[SEP]"`):
|
||
|
|
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
|
||
|
|
sequence classification or for a text and a question for question answering. It is also used as the last
|
||
|
|
token of a sequence built with special tokens.
|
||
|
|
pad_token (`str`, *optional*, defaults to `"<pad>"`):
|
||
|
|
The token used for padding, for example when batching sequences of different lengths.
|
||
|
|
cls_token (`str`, *optional*, defaults to `"[CLS]"`):
|
||
|
|
The classifier token which is used when doing sequence classification (classification of the whole sequence
|
||
|
|
instead of per-token classification). It is the first token of the sequence when built with special tokens.
|
||
|
|
mask_token (`str`, *optional*, defaults to `"[MASK]"`):
|
||
|
|
The token used for masking values. This is the token used when training this model with masked language
|
||
|
|
modeling. This is the token which the model will try to predict.
|
||
|
|
|
||
|
|
Attributes:
|
||
|
|
sp_model (`SentencePieceProcessor`):
|
||
|
|
The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
|
||
|
|
"""
|
||
|
|
|
||
|
|
vocab_files_names = VOCAB_FILES_NAMES
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
vocab_file,
|
||
|
|
do_lower_case=False,
|
||
|
|
remove_space=True,
|
||
|
|
keep_accents=True,
|
||
|
|
bos_token="[CLS]",
|
||
|
|
eos_token="[SEP]",
|
||
|
|
unk_token="[UNK]",
|
||
|
|
sep_token="[SEP]",
|
||
|
|
pad_token="[PAD]",
|
||
|
|
cls_token="[CLS]",
|
||
|
|
mask_token="[MASK]",
|
||
|
|
**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.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.Load(vocab_file)
|
||
|
|
super().__init__(
|
||
|
|
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,
|
||
|
|
)
|
||
|
|
|
||
|
|
@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
|
||
|
|
self.sp_model = spm.SentencePieceProcessor()
|
||
|
|
self.sp_model.Load(self.vocab_file)
|
||
|
|
|
||
|
|
def _tokenize(self, text, sample=False):
|
||
|
|
"""Tokenize a string."""
|
||
|
|
pieces = self.sp_model.EncodeAsPieces(text)
|
||
|
|
return pieces
|
||
|
|
|
||
|
|
def _convert_token_to_id(self, token):
|
||
|
|
"""Converts a token (str) in an id using the vocab."""
|
||
|
|
return self.sp_model.PieceToId(token)
|
||
|
|
|
||
|
|
def _convert_id_to_token(self, index):
|
||
|
|
"""Converts an index (integer) in a token (str) using the vocab."""
|
||
|
|
return self.sp_model.IdToPiece(index)
|
||
|
|
|
||
|
|
def convert_tokens_to_string(self, tokens):
|
||
|
|
out_string = self.sp_model.decode_pieces(tokens)
|
||
|
|
return out_string
|
||
|
|
|
||
|
|
def build_inputs_with_special_tokens(
|
||
|
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||
|
|
) -> List[int]:
|
||
|
|
"""
|
||
|
|
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
|
||
|
|
adding special tokens. A REMBERT sequence has the following format:
|
||
|
|
|
||
|
|
- single sequence: `[CLS] X [SEP]`
|
||
|
|
- pair of sequences: `[CLS] A [SEP] B [SEP]`
|
||
|
|
|
||
|
|
Args:
|
||
|
|
token_ids_0 (`List[int]`):
|
||
|
|
List of IDs to which the special tokens will be added.
|
||
|
|
token_ids_1 (`List[int]`, *optional*):
|
||
|
|
Optional second list of IDs for sequence pairs.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
|
||
|
|
"""
|
||
|
|
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 get_special_tokens_mask(
|
||
|
|
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
||
|
|
) -> List[int]:
|
||
|
|
"""
|
||
|
|
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
||
|
|
special tokens using the tokenizer `prepare_for_model` method.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
token_ids_0 (`List[int]`):
|
||
|
|
List of IDs.
|
||
|
|
token_ids_1 (`List[int]`, *optional*):
|
||
|
|
Optional second list of IDs for sequence pairs.
|
||
|
|
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
||
|
|
Whether or not the token list is already formatted with special tokens for the model.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`List[int]`: A list of integers in the range [0, 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 [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in 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: List[int], token_ids_1: Optional[List[int]] = None
|
||
|
|
) -> List[int]:
|
||
|
|
"""
|
||
|
|
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RemBERT
|
||
|
|
sequence pair 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
|
||
|
|
| first sequence | second sequence |
|
||
|
|
```
|
||
|
|
|
||
|
|
If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
|
||
|
|
|
||
|
|
Args:
|
||
|
|
token_ids_0 (`List[int]`):
|
||
|
|
List of IDs.
|
||
|
|
token_ids_1 (`List[int]`, *optional*):
|
||
|
|
Optional second list of IDs for sequence pairs.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
`List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
|
||
|
|
"""
|
||
|
|
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_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||
|
|
if not os.path.isdir(save_directory):
|
||
|
|
logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
|
||
|
|
return
|
||
|
|
out_vocab_file = os.path.join(
|
||
|
|
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||
|
|
)
|
||
|
|
|
||
|
|
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
||
|
|
copyfile(self.vocab_file, out_vocab_file)
|
||
|
|
elif not os.path.isfile(self.vocab_file):
|
||
|
|
with open(out_vocab_file, "wb") as fi:
|
||
|
|
content_spiece_model = self.sp_model.serialized_model_proto()
|
||
|
|
fi.write(content_spiece_model)
|
||
|
|
|
||
|
|
return (out_vocab_file,)
|