feat: add pretokenizer parameter to Tokenizer, update README#26
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AmitMY
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Apr 8, 2026
| "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?" | ||
| "|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?" | ||
| "|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" | ||
| ) |
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let's create a GPTPretokenizer = huggingface tokenizers Split(pattern=....)
that would then mean we always get a huggingface "Tokenizer" class (like words-segmentation as well is a tokenizer class)
We might need to create a RegexTokenizer(Tokenier) but that's fine as well
| units: str | Callable[[str], GraphVertex] = "utf8_clusters", | ||
| merge_size: int = 2, | ||
| connected: bool = False, | ||
| pretokenizer: str | Callable[[str], list[str]] | None = None, |
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always should be the huggingface tokenizer class type, default to GPTPretokenizer
- Tokenizer accepts pretokenizer as regex string, callable, or None (defaults to GPT pretokenization pattern) - Extract regex_pretokenizer() factory for creating regex-based splitters - Pass pretokenizer through to words() and SuperBPETokenizer phase 1 - Rewrite README with current API, remove stale WordsSegmentationTokenizer - 3 new pretokenizer tests Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
Tokenizer(pretokenizer=...)accepts a regex string, callable, orNone(GPT pattern default)BPETokenizer,BNETokenizer, etc.) pass it throughSuperBPETokenizerpasses pretokenizer to phase 1regex_pretokenizer()factory inwords.pyWordsSegmentationTokenizerreferenceUsage
Test plan
ruff check .passes🤖 Generated with Claude Code