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# coding=utf-8 | |
# Copyright 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 ConvBERT.""" | |
import json | |
from typing import List, Optional, Tuple | |
from tokenizers import normalizers | |
from ...tokenization_utils_fast import PreTrainedTokenizerFast | |
from ...utils import logging | |
from .tokenization_convbert import ConvBertTokenizer | |
logger = logging.get_logger(__name__) | |
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"} | |
PRETRAINED_VOCAB_FILES_MAP = { | |
"vocab_file": { | |
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt", | |
"YituTech/conv-bert-medium-small": ( | |
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt" | |
), | |
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt", | |
} | |
} | |
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { | |
"YituTech/conv-bert-base": 512, | |
"YituTech/conv-bert-medium-small": 512, | |
"YituTech/conv-bert-small": 512, | |
} | |
PRETRAINED_INIT_CONFIGURATION = { | |
"YituTech/conv-bert-base": {"do_lower_case": True}, | |
"YituTech/conv-bert-medium-small": {"do_lower_case": True}, | |
"YituTech/conv-bert-small": {"do_lower_case": True}, | |
} | |
# Copied from transformers.models.bert.tokenization_bert_fast.BertTokenizerFast with bert-base-cased->YituTech/conv-bert-base, Bert->ConvBert, BERT->ConvBERT | |
class ConvBertTokenizerFast(PreTrainedTokenizerFast): | |
r""" | |
Construct a "fast" ConvBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece. | |
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should | |
refer to this superclass for more information regarding those methods. | |
Args: | |
vocab_file (`str`): | |
File containing the vocabulary. | |
do_lower_case (`bool`, *optional*, defaults to `True`): | |
Whether or not to lowercase the input when tokenizing. | |
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. | |
clean_text (`bool`, *optional*, defaults to `True`): | |
Whether or not to clean the text before tokenization by removing any control characters and replacing all | |
whitespaces by the classic one. | |
tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): | |
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see [this | |
issue](https://github.com/huggingface/transformers/issues/328)). | |
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 ConvBERT). | |
wordpieces_prefix (`str`, *optional*, defaults to `"##"`): | |
The prefix for subwords. | |
""" | |
vocab_files_names = VOCAB_FILES_NAMES | |
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP | |
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION | |
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES | |
slow_tokenizer_class = ConvBertTokenizer | |
def __init__( | |
self, | |
vocab_file=None, | |
tokenizer_file=None, | |
do_lower_case=True, | |
unk_token="[UNK]", | |
sep_token="[SEP]", | |
pad_token="[PAD]", | |
cls_token="[CLS]", | |
mask_token="[MASK]", | |
tokenize_chinese_chars=True, | |
strip_accents=None, | |
**kwargs, | |
): | |
super().__init__( | |
vocab_file, | |
tokenizer_file=tokenizer_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, | |
tokenize_chinese_chars=tokenize_chinese_chars, | |
strip_accents=strip_accents, | |
**kwargs, | |
) | |
normalizer_state = json.loads(self.backend_tokenizer.normalizer.__getstate__()) | |
if ( | |
normalizer_state.get("lowercase", do_lower_case) != do_lower_case | |
or normalizer_state.get("strip_accents", strip_accents) != strip_accents | |
or normalizer_state.get("handle_chinese_chars", tokenize_chinese_chars) != tokenize_chinese_chars | |
): | |
normalizer_class = getattr(normalizers, normalizer_state.pop("type")) | |
normalizer_state["lowercase"] = do_lower_case | |
normalizer_state["strip_accents"] = strip_accents | |
normalizer_state["handle_chinese_chars"] = tokenize_chinese_chars | |
self.backend_tokenizer.normalizer = normalizer_class(**normalizer_state) | |
self.do_lower_case = do_lower_case | |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): | |
""" | |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and | |
adding special tokens. A ConvBERT 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. | |
""" | |
output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id] | |
if token_ids_1 is not None: | |
output += token_ids_1 + [self.sep_token_id] | |
return output | |
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 ConvBERT | |
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]: | |
files = self._tokenizer.model.save(save_directory, name=filename_prefix) | |
return tuple(files) | |