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from tokenizers.decoders import WordPiece as WordPieceDecoder |
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from tokenizers.pre_tokenizers import BertPreTokenizer |
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from tokenizers.normalizers import BertNormalizer |
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from tokenizers.trainers import WordPieceTrainer |
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from tokenizers.models import WordPiece as WordPieceModel |
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from tokenizers import Tokenizer |
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import itertools |
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from datasets import load_dataset |
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from datasets.utils.logging import set_verbosity_error |
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set_verbosity_error() |
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from utils import SampleBatch |
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def unpack_samples( |
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batch: SampleBatch |
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): |
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iterator = ( |
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sample.values() |
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for sample in batch['translation'] |
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) |
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return list( |
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itertools.chain |
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.from_iterable(iterator) |
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) |
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def build_tokenizer( |
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clean_text: bool = True, |
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strip_accents: bool = True, |
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lowercase: bool = True |
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) -> Tokenizer: |
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tokenizer = Tokenizer( |
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model=WordPieceModel( |
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unk_token='<UNK>' |
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) |
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) |
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tokenizer.normalizer = BertNormalizer( |
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clean_text=clean_text, |
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handle_chinese_chars=True, |
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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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tokenizer.decoder = WordPieceDecoder() |
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return tokenizer |
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train_dset = load_dataset( |
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path='nordmann2023', |
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name='balanced', |
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split='train' |
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) |
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tokenizer = build_tokenizer( |
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clean_text=True, |
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strip_accents=False, |
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lowercase=False |
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) |
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tokenizer.train_from_iterator( |
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iterator=( |
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unpack_samples(batch) |
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for batch in train_dset.iter( |
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batch_size=10000 |
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) |
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), |
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trainer=WordPieceTrainer( |
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vocab_size=40000, |
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special_tokens=[ |
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'<UNK>', '<CLS>', '<SEP>', '<PAD>', '<MASK>' |
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] |
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), |
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length=train_dset.num_rows * 2 |
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) |
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tokenizer.save( |
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path='tokenizer.json' |
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) |
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