mirror of
https://github.com/mii443/tokenizers.git
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50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
import argparse
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import glob
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from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers
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parser = argparse.ArgumentParser()
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parser.add_argument("--files",
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default=None,
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metavar="path",
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type=str,
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required=True,
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help="The files to use as training; accept '**/*.txt' type of patterns \
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if enclosed in quotes")
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parser.add_argument("--out",
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default="./",
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type=str,
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help="Path to the output directory, where the files will be saved")
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parser.add_argument("--name",
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default="bpe-bytelevel",
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type=str,
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help="The name of the output vocab files")
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args = parser.parse_args()
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files = glob.glob(args.files)
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if not files:
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print(f"File does not exist: {args.files}")
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exit(1)
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# Initialize an empty tokenizer
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tokenizer = Tokenizer(models.BPE.empty())
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# Customize pre-tokenization and decoding
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tokenizer.with_pre_tokenizer(pre_tokenizers.ByteLevel.new(add_prefix_space=False))
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tokenizer.with_decoder(decoders.ByteLevel.new())
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# And then train
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trainer = trainers.BpeTrainer.new(
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vocab_size=50000,
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min_frequency=2,
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show_progress=True,
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special_tokens=[ "<s>", "<pad>", "</s>" ],
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initial_alphabet=pre_tokenizers.ByteLevel.alphabet()
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)
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tokenizer.train(trainer, files)
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# Save the files
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tokenizer.model.save(args.out, args.name)
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