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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. | |
| """ Tokenization class for model DeBERTa.""" | |
| from typing import List, Optional | |
| from ...tokenization_utils import AddedToken | |
| from ...utils import logging | |
| from ..gpt2.tokenization_gpt2 import GPT2Tokenizer | |
| logger = logging.get_logger(__name__) | |
| VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"} | |
| 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 DebertaTokenizer(GPT2Tokenizer): | |
| r""" | |
| Constructs a DeBERTa tokenizer, which runs end-to-end tokenization: punctuation splitting + wordpiece | |
| 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"] | |
| def __init__( | |
| self, | |
| vocab_file, | |
| merges_file, | |
| 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 | |
| ): | |
| bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token | |
| eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token | |
| sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token | |
| cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token | |
| unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token | |
| pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token | |
| # 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 | |
| super().__init__( | |
| vocab_file=vocab_file, | |
| merges_file=merges_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 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 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]: | |
| """ | |
| Retrieves 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`` or ``encode_plus`` methods. | |
| 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. | |
| already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| Whether or not the token list is already formatted with special tokens for the model. | |
| Returns: | |
| :obj:`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: | |
| return super().get_special_tokens_mask( | |
| token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True | |
| ) | |
| if token_ids_1 is None: | |
| return [1] + ([0] * len(token_ids_0)) + [1] | |
| return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [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 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 :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] | |
| def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs): | |
| add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space) | |
| if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()): | |
| text = " " + text | |
| return (text, kwargs) | |