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scripts/train_tokenizer.py
CLI entry point for tokenizer training. Thin wrapper over
brain.tokenizer.tokenizer.BrainTokenizer.train() -- kept separate from the
tokenizer implementation itself so the implementation stays importable
without pulling in argparse/CLI concerns (same separation-of-concerns
principle as the model/API boundary elsewhere in this repo).
Usage:
PYTHONPATH=. python3 scripts/train_tokenizer.py \\
--corpus data/sample_corpus.txt \\
--vocab-size 32768 \\
--output data/brain_tokenizer.json
"""
import argparse
from brain.tokenizer.tokenizer import BrainTokenizer
def main() -> None:
parser = argparse.ArgumentParser(description="Train a Brain byte-level BPE tokenizer.")
parser.add_argument("--corpus", nargs="+", required=True, help="One or more corpus text files.")
parser.add_argument("--vocab-size", type=int, default=32768)
parser.add_argument("--min-frequency", type=int, default=2)
parser.add_argument("--output", required=True, help="Path to save the trained tokenizer JSON.")
args = parser.parse_args()
print(f"Training on: {args.corpus}")
print(f"Target vocab size: {args.vocab_size}")
tok = BrainTokenizer.train(
corpus_paths=args.corpus,
vocab_size=args.vocab_size,
min_frequency=args.min_frequency,
save_path=args.output,
)
print(f"Saved to: {args.output}")
print(f"Actual vocab size: {tok.vocab_size}")
if tok.vocab_size < args.vocab_size:
print(
f"NOTE: actual vocab ({tok.vocab_size}) is smaller than requested "
f"({args.vocab_size}) -- the corpus doesn't contain enough distinct "
f"merges to reach the target. This is expected on small corpora; it "
f"is the same behavior the original Brain Trail tokenizer had "
f"(requested 500, got 344 on the 21-line sample corpus)."
)
if __name__ == "__main__":
main()
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