mirror of
https://github.com/mii443/tokenizers.git
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55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
import argparse
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import glob
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from os.path import join
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from tokenizers import ByteLevelBPETokenizer
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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 = ByteLevelBPETokenizer(add_prefix_space=True)
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# And then train
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tokenizer.train(
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files,
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vocab_size=10000,
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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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)
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# Save the files
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tokenizer.save(args.out, args.name)
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# Restoring model from learned vocab/merges
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tokenizer = ByteLevelBPETokenizer(
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join(args.out, "{}-vocab.json".format(args.name)),
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join(args.out, "{}-merges.txt".format(args.name)),
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add_prefix_space=True
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)
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# Test encoding
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print(tokenizer.encode("Training ByteLevel BPE is very easy").tokens)
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