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# coding=utf-8 | |
# Copyright 2020 Microsoft 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. | |
""" Fast Tokenization class for model DeBERTa.""" | |
import json | |
from typing import List, Optional, Tuple | |
from tokenizers import pre_tokenizers | |
from ...tokenization_utils_base import AddedToken, BatchEncoding | |
from ...tokenization_utils_fast import PreTrainedTokenizerFast | |
from ...utils import logging | |
from .tokenization_deberta import DebertaTokenizer | |
logger = logging.get_logger(__name__) | |
VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} | |
PRETRAINED_VOCAB_FILES_MAP = { | |
"vocab_file": { | |
"microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json", | |
"microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json", | |
"microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json", | |
"microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json", | |
"microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json", | |
"microsoft/deberta-xlarge-mnli": ( | |
"https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json" | |
), | |
}, | |
"merges_file": { | |
"microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt", | |
"microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt", | |
"microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt", | |
"microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt", | |
"microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt", | |
"microsoft/deberta-xlarge-mnli": ( | |
"https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt" | |
), | |
}, | |
} | |
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { | |
"microsoft/deberta-base": 512, | |
"microsoft/deberta-large": 512, | |
"microsoft/deberta-xlarge": 512, | |
"microsoft/deberta-base-mnli": 512, | |
"microsoft/deberta-large-mnli": 512, | |
"microsoft/deberta-xlarge-mnli": 512, | |
} | |
PRETRAINED_INIT_CONFIGURATION = { | |
"microsoft/deberta-base": {"do_lower_case": False}, | |
"microsoft/deberta-large": {"do_lower_case": False}, | |
} | |
class DebertaTokenizerFast(PreTrainedTokenizerFast): | |
""" | |
Construct a "fast" DeBERTa tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level | |
Byte-Pair-Encoding. | |
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will | |
be encoded differently whether it is at the beginning of the sentence (without space) or not: | |
```python | |
>>> from transformers import DebertaTokenizerFast | |
>>> tokenizer = DebertaTokenizerFast.from_pretrained("microsoft/deberta-base") | |
>>> tokenizer("Hello world")["input_ids"] | |
[1, 31414, 232, 2] | |
>>> tokenizer(" Hello world")["input_ids"] | |
[1, 20920, 232, 2] | |
``` | |
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since | |
the model was not pretrained this way, it might yield a decrease in performance. | |
<Tip> | |
When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. | |
</Tip> | |
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`, *optional*): | |
Path to the vocabulary file. | |
merges_file (`str`, *optional*): | |
Path to the merges file. | |
tokenizer_file (`str`, *optional*): | |
The path to a tokenizer file to use instead of the vocab file. | |
errors (`str`, *optional*, defaults to `"replace"`): | |
Paradigm to follow when decoding bytes to UTF-8. See | |
[bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. | |
bos_token (`str`, *optional*, defaults to `"[CLS]"`): | |
The beginning of sequence token. | |
eos_token (`str`, *optional*, defaults to `"[SEP]"`): | |
The end of sequence token. | |
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. | |
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. | |
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. | |
pad_token (`str`, *optional*, defaults to `"[PAD]"`): | |
The token used for padding, for example when batching sequences of different lengths. | |
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. | |
add_prefix_space (`bool`, *optional*, defaults to `False`): | |
Whether or not to add an initial space to the input. This allows to treat the leading word just as any | |
other word. (Deberta tokenizer detect beginning of words by the preceding space). | |
""" | |
vocab_files_names = VOCAB_FILES_NAMES | |
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP | |
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES | |
model_input_names = ["input_ids", "attention_mask", "token_type_ids"] | |
slow_tokenizer_class = DebertaTokenizer | |
def __init__( | |
self, | |
vocab_file=None, | |
merges_file=None, | |
tokenizer_file=None, | |
errors="replace", | |
bos_token="[CLS]", | |
eos_token="[SEP]", | |
sep_token="[SEP]", | |
cls_token="[CLS]", | |
unk_token="[UNK]", | |
pad_token="[PAD]", | |
mask_token="[MASK]", | |
add_prefix_space=False, | |
**kwargs, | |
): | |
super().__init__( | |
vocab_file, | |
merges_file, | |
tokenizer_file=tokenizer_file, | |
errors=errors, | |
bos_token=bos_token, | |
eos_token=eos_token, | |
unk_token=unk_token, | |
sep_token=sep_token, | |
cls_token=cls_token, | |
pad_token=pad_token, | |
mask_token=mask_token, | |
add_prefix_space=add_prefix_space, | |
**kwargs, | |
) | |
self.add_bos_token = kwargs.pop("add_bos_token", False) | |
pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) | |
if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: | |
pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) | |
pre_tok_state["add_prefix_space"] = add_prefix_space | |
self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) | |
self.add_prefix_space = add_prefix_space | |
def mask_token(self) -> str: | |
""" | |
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not | |
having been set. | |
Deberta tokenizer has a special mask token to be used in the fill-mask pipeline. The mask token will greedily | |
comprise the space before the *[MASK]*. | |
""" | |
if self._mask_token is None: | |
if self.verbose: | |
logger.error("Using mask_token, but it is not set yet.") | |
return None | |
return str(self._mask_token) | |
def mask_token(self, value): | |
""" | |
Overriding the default behavior of the mask token to have it eat the space before it. | |
""" | |
# Mask token behave like a normal word, i.e. include the space before it | |
# So we set lstrip to True | |
value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value | |
self._mask_token = value | |
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 DeBERTa 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. | |
""" | |
if token_ids_1 is None: | |
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] | |
cls = [self.cls_token_id] | |
sep = [self.sep_token_id] | |
return cls + token_ids_0 + sep + token_ids_1 + sep | |
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 DeBERTa | |
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] | |
# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast._batch_encode_plus | |
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: | |
is_split_into_words = kwargs.get("is_split_into_words", False) | |
assert self.add_prefix_space or not is_split_into_words, ( | |
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " | |
"to use it with pretokenized inputs." | |
) | |
return super()._batch_encode_plus(*args, **kwargs) | |
# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast._encode_plus | |
def _encode_plus(self, *args, **kwargs) -> BatchEncoding: | |
is_split_into_words = kwargs.get("is_split_into_words", False) | |
assert self.add_prefix_space or not is_split_into_words, ( | |
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " | |
"to use it with pretokenized inputs." | |
) | |
return super()._encode_plus(*args, **kwargs) | |
# Copied from transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast.save_vocabulary | |
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) | |