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167 lines
7.1 KiB
167 lines
7.1 KiB
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors 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 Camembert 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_python import AddedToken
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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": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
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SPIECE_UNDERLINE = "▁"
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class CamembertTokenizer(TokenizersBackend):
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"""
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Construct a "fast" CamemBERT tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
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[`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
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[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).
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This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
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refer to this superclass for more information regarding those methods.
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Args:
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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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 `"</s>"`):
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The end of sequence 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 end of sequence.
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The token used is the `sep_token`.
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</Tip>
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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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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cls_token (`str`, *optional*, defaults to `"<s>"`):
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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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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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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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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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additional_special_tokens (`list[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
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Additional special tokens used by the tokenizer.
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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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vocab_file (`str`, *optional*):
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[SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
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contains the vocabulary necessary to instantiate a tokenizer.
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vocab (`str`, `dict` or `list`, *optional*):
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Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file.
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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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slow_tokenizer_class = None
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def __init__(
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self,
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bos_token="<s>",
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eos_token="</s>",
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sep_token="</s>",
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cls_token="<s>",
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unk_token="<unk>",
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pad_token="<pad>",
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mask_token="<mask>",
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additional_special_tokens=None,
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add_prefix_space=True,
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vocab_file=None,
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vocab: str | dict | list | None = None,
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**kwargs,
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):
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self.vocab_file = vocab_file
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self.add_prefix_space = add_prefix_space
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mask_token = AddedToken(mask_token, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token
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if additional_special_tokens is None:
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additional_special_tokens = ["<s>NOTUSED", "</s>NOTUSED", "<unk>NOTUSED"]
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if vocab is not None:
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self._vocab = vocab
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unk_index = next((i for i, (tok, _) in enumerate(self._vocab) if tok == str(unk_token)), 0)
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self._tokenizer = Tokenizer(Unigram(self._vocab, unk_id=unk_index, byte_fallback=False))
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else:
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self._vocab = [
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("<s>NOTUSED", 0.0),
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(str(pad_token), 0.0),
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("</s>NOTUSED", 0.0),
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(str(unk_token), 0.0),
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("<unk>NOTUSED", -100),
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(str(mask_token), 0.0),
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]
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self._tokenizer = Tokenizer(Unigram(self._vocab, unk_id=3, byte_fallback=False))
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self._tokenizer.normalizer = normalizers.Sequence(
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[
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normalizers.Replace(Regex(r"\s{2,}|[\n\r\t]"), " "),
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normalizers.Strip(left=False, right=True),
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]
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)
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prepend_scheme = "always" if add_prefix_space else "never"
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self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement="▁", prepend_scheme=prepend_scheme)
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self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme=prepend_scheme)
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super().__init__(
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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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additional_special_tokens=additional_special_tokens,
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add_prefix_space=add_prefix_space,
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**kwargs,
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)
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# always adds BOS/EOS with "</s> </s>" separator for pairs
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self._tokenizer.post_processor = processors.TemplateProcessing(
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single=f"{self.bos_token} $A {self.eos_token}",
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pair=f"{self.bos_token} $A {self.eos_token} {self.eos_token} $B {self.eos_token}",
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special_tokens=[
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(self.bos_token, self.bos_token_id),
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(self.eos_token, self.eos_token_id),
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],
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)
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__all__ = ["CamembertTokenizer"]
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