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177 lines
7.6 KiB
177 lines
7.6 KiB
# Copyright 2018 Google AI, Google Brain 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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"""Tokenization classes for ALBERT model."""
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from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
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from tokenizers.models import Unigram
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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": "spiece.model", "tokenizer_file": "tokenizer.json"}
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class AlbertTokenizer(TokenizersBackend):
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"""
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Construct a "fast" ALBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on
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[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This
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tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods
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Args:
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do_lower_case (`bool`, *optional*, defaults to `True`):
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Whether or not to lowercase the input when tokenizing.
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keep_accents (`bool`, *optional*, defaults to `False`):
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Whether or not to keep accents when tokenizing.
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bos_token (`str`, *optional*, defaults to `"[CLS]"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the beginning of
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sequence. The token used is the `cls_token`.
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</Tip>
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eos_token (`str`, *optional*, defaults to `"[SEP]"`):
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The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
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that is used for the end of sequence. The token used is the `sep_token`.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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sep_token (`str`, *optional*, defaults to `"[SEP]"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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cls_token (`str`, *optional*, defaults to `"[CLS]"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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mask_token (`str`, *optional*, defaults to `"[MASK]"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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add_prefix_space (`bool`, *optional*, defaults to `True`):
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Whether or not to add an initial space to the input. This allows to treat the leading word just as any
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other word.
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trim_offsets (`bool`, *optional*, defaults to `True`):
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Whether the post processing step should trim offsets to avoid including whitespaces.
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vocab (`str` or `list[tuple[str, float]]`, *optional*):
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Custom vocabulary with `(token, score)` tuples. If not provided, vocabulary is loaded from `vocab_file`.
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vocab_file (`str`, *optional*):
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[SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
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contains the vocabulary necessary to instantiate a tokenizer.
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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 = Unigram
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def __init__(
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self,
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vocab: str | list[tuple[str, float]] | None = None,
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do_lower_case: bool = True,
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keep_accents: bool = False,
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bos_token: str = "[CLS]",
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eos_token: str = "[SEP]",
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unk_token: str = "<unk>",
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sep_token: str = "[SEP]",
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pad_token: str = "<pad>",
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cls_token: str = "[CLS]",
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mask_token: str = "[MASK]",
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add_prefix_space: bool = True,
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trim_offsets: bool = True,
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**kwargs,
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):
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self.add_prefix_space = add_prefix_space
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self.trim_offsets = trim_offsets
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self.do_lower_case = do_lower_case
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self.keep_accents = keep_accents
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if vocab is not None:
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self._vocab_scores = vocab
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else:
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self._vocab_scores = [
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(str(pad_token), 0.0),
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(str(unk_token), 0.0),
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(str(cls_token), 0.0),
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(str(sep_token), 0.0),
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(str(mask_token), 0.0),
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]
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self._tokenizer = Tokenizer(
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Unigram(
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self._vocab_scores,
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unk_id=1,
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byte_fallback=False,
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)
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)
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list_normalizers = [
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normalizers.Replace("``", '"'),
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normalizers.Replace("''", '"'),
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normalizers.NFKD(),
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normalizers.StripAccents(),
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normalizers.Lowercase(),
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normalizers.Replace(Regex(" {2,}"), " "),
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]
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if not self.keep_accents:
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list_normalizers.append(normalizers.NFKD())
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list_normalizers.append(normalizers.StripAccents())
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if self.do_lower_case:
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list_normalizers.append(normalizers.Lowercase())
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list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " "))
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self._tokenizer.normalizer = normalizers.Sequence(list_normalizers)
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prepend_scheme = "always" if add_prefix_space else "never"
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self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
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[
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pre_tokenizers.WhitespaceSplit(),
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pre_tokenizers.Metaspace(replacement="▁", prepend_scheme=prepend_scheme),
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]
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)
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self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme=prepend_scheme)
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self._tokenizer.post_processor = processors.TemplateProcessing(
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single="[CLS]:0 $A:0 [SEP]:0",
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pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
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special_tokens=[
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("[CLS]", self._tokenizer.token_to_id(str(cls_token))),
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("[SEP]", self._tokenizer.token_to_id(str(sep_token))),
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],
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)
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super().__init__(
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do_lower_case=self.do_lower_case,
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keep_accents=self.keep_accents,
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bos_token=bos_token,
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eos_token=eos_token,
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sep_token=sep_token,
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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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add_prefix_space=add_prefix_space,
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trim_offsets=trim_offsets,
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**kwargs,
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)
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__all__ = ["AlbertTokenizer"]
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