mirror of
https://github.com/mii443/tokenizers.git
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95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
from tokenizers import Tokenizer, decoders, trainers
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from tokenizers.models import WordPiece
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from tokenizers.normalizers import BertNormalizer
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from tokenizers.pre_tokenizers import BertPreTokenizer
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from tokenizers.processors import BertProcessing
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from .base_tokenizer import BaseTokenizer
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from typing import Optional, List, Union
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class BertWordPieceTokenizer(BaseTokenizer):
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""" Bert WordPiece Tokenizer """
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def __init__(
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self,
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vocab_file: Optional[str] = None,
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add_special_tokens: bool = True,
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unk_token: str = "[UNK]",
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sep_token: str = "[SEP]",
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cls_token: str = "[CLS]",
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clean_text: bool = True,
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handle_chinese_chars: bool = True,
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strip_accents: bool = True,
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lowercase: bool = True,
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wordpieces_prefix: str = "##",
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):
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if vocab_file is not None:
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tokenizer = Tokenizer(WordPiece.from_files(vocab_file, unk_token=unk_token))
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else:
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tokenizer = Tokenizer(WordPiece.empty())
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tokenizer.add_special_tokens([unk_token, sep_token, cls_token])
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tokenizer.normalizer = BertNormalizer(
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clean_text=clean_text,
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handle_chinese_chars=handle_chinese_chars,
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strip_accents=strip_accents,
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lowercase=lowercase,
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)
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tokenizer.pre_tokenizer = BertPreTokenizer()
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if add_special_tokens and vocab_file is not None:
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sep_token_id = tokenizer.token_to_id(sep_token)
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if sep_token_id is None:
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raise TypeError("sep_token not found in the vocabulary")
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cls_token_id = tokenizer.token_to_id(cls_token)
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if cls_token_id is None:
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raise TypeError("cls_token not found in the vocabulary")
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tokenizer.post_processor = BertProcessing(
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(sep_token, sep_token_id), (cls_token, cls_token_id)
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)
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tokenizer.decoder = decoders.WordPiece(prefix=wordpieces_prefix)
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parameters = {
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"model": "BertWordPiece",
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"add_special_tokens": add_special_tokens,
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"unk_token": unk_token,
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"sep_token": sep_token,
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"cls_token": cls_token,
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"clean_text": clean_text,
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"handle_chinese_chars": handle_chinese_chars,
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"strip_accents": strip_accents,
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"lowercase": lowercase,
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"wordpieces_prefix": wordpieces_prefix,
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}
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super().__init__(tokenizer, parameters)
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def train(
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self,
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files: Union[str, List[str]],
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vocab_size: int = 30000,
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min_frequency: int = 2,
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limit_alphabet: int = 1000,
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initial_alphabet: List[str] = [],
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special_tokens: List[str] = ["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]"],
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show_progress: bool = True,
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wordpieces_prefix: str = "##",
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):
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""" Train the model using the given files """
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trainer = trainers.WordPieceTrainer(
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vocab_size=vocab_size,
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min_frequency=min_frequency,
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limit_alphabet=limit_alphabet,
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initial_alphabet=initial_alphabet,
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special_tokens=special_tokens,
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show_progress=show_progress,
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continuing_subword_prefix=wordpieces_prefix,
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)
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if isinstance(files, str):
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files = [files]
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self._tokenizer.train(trainer, files)
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