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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.""" | |
| from typing import List, Optional | |
| from ...tokenization_utils_base import AddedToken | |
| from ...utils import logging | |
| from ..gpt2.tokenization_gpt2_fast import GPT2TokenizerFast | |
| 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(GPT2TokenizerFast): | |
| """ | |
| Constructs a "fast" DeBERTa tokenizer, which runs end-to-end tokenization: punctuation splitting + wordpiece. It is | |
| backed by HuggingFace's `tokenizers` library. | |
| Args: | |
| vocab_file (:obj:`str`): | |
| File containing the vocabulary. | |
| do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Whether or not to lowercase the input when tokenizing. | |
| unk_token (:obj:`str`, `optional`, defaults to :obj:`"[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 (:obj:`str`, `optional`, defaults to :obj:`"[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 (:obj:`str`, `optional`, defaults to :obj:`"[PAD]"`): | |
| The token used for padding, for example when batching sequences of different lengths. | |
| cls_token (:obj:`str`, `optional`, defaults to :obj:`"[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 (:obj:`str`, `optional`, defaults to :obj:`"[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. | |
| """ | |
| 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, | |
| ) | |
| def mask_token(self) -> str: | |
| """ | |
| :obj:`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 and 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 (:obj:`List[int]`): | |
| List of IDs to which the special tokens will be added. | |
| token_ids_1 (:obj:`List[int]`, `optional`): | |
| Optional second list of IDs for sequence pairs. | |
| Returns: | |
| :obj:`List[int]`: List of `input IDs <../glossary.html#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 0 0 0 0 0 0 0 0 0 | |
| | first sequence | second sequence | | |
| If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s). | |
| Args: | |
| token_ids_0 (:obj:`List[int]`): | |
| List of IDs. | |
| token_ids_1 (:obj:`List[int]`, `optional`): | |
| Optional second list of IDs for sequence pairs. | |
| Returns: | |
| :obj:`List[int]`: List of `token type IDs <../glossary.html#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 + token_ids_1 + sep) * [0] | |