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112 lines
3.8 KiB
112 lines
3.8 KiB
# Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. and the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
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from tokenizers.models import BPE
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from ...tokenization_utils_tokenizers import TokenizersBackend
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
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class HerbertTokenizer(TokenizersBackend):
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"""
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Construct a BPE tokenizer for HerBERT (backed by HuggingFace's tokenizers library).
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Peculiarities:
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- uses BERT's pre-tokenizer: BertPreTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of
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a punctuation character will be treated separately.
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This tokenizer inherits from [`TokenizersBackend`] which contains most of the methods. Users should refer to the
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superclass for more information regarding methods.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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merges_file (`str`):
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Path to the merges file.
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cls_token (`str`, *optional*, defaults to `"<s>"`):
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The classifier token.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The padding token.
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mask_token (`str`, *optional*, defaults to `"<mask>"`):
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The mask token.
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token.
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vocab (`str`, `dict` or `list`, *optional*):
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Custom vocabulary dictionary.
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merges (`str` or `list[str]`, *optional*):
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Custom merges list.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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model = BPE
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def __init__(
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self,
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vocab: str | dict[str, int] | None = None,
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merges: str | list[str] | None = None,
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cls_token: str = "<s>",
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unk_token: str = "<unk>",
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pad_token: str = "<pad>",
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mask_token: str = "<mask>",
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sep_token: str = "</s>",
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vocab_file: str | None = None,
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merges_file: str | None = None,
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**kwargs,
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):
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self._vocab = vocab if vocab is not None else {str(unk_token): 0}
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self._merges = merges or []
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self._tokenizer = Tokenizer(
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BPE(
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vocab=self._vocab,
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merges=self._merges,
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dropout=None,
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unk_token=str(unk_token),
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end_of_word_suffix="</w>",
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)
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)
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self._tokenizer.normalizer = normalizers.BertNormalizer(
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lowercase=False, strip_accents=False, clean_text=True, handle_chinese_chars=True
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)
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self._tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
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self._tokenizer.decoder = decoders.BPEDecoder(suffix="</w>")
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super().__init__(
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cls_token=cls_token,
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unk_token=unk_token,
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pad_token=pad_token,
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mask_token=mask_token,
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sep_token=sep_token,
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**kwargs,
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)
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self._tokenizer.post_processor = processors.BertProcessing(
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sep=(self.sep_token, 2),
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cls=(self.cls_token, 0),
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)
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__all__ = ["HerbertTokenizer"]
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