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- Tokenizer(units, merge_size, connected) — configurable base class
- BPETokenizer, BNETokenizer, BoundlessBPETokenizer, SuperBPETokenizer
- Units can be string ("utf8_clusters", "utf8", "characters") or callable
- 10 tests covering all variants, custom units, error handling
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- FastBPETrainer flattens words into byte tuples and counts word frequencies, avoiding repeated graph traversal - Pair counting operates on word-freq dict instead of full corpus - Produces identical merges to graph-based BPE (tested) - Significantly faster on repeated text patterns Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Test training on plain Hebrew, nikkud text, mixed text - Verify dagesh/qamats appear in early merges for repeated patterns - Verify bytes preservation and pretokenization of Hebrew text Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- from complex_tokenization import BPETokenizer, Tokenizer, etc. - Add __all__ for explicit export control - Add import tests Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Test empty text, single char, all same chars, whitespace-only, multiple empty texts for all 4 tokenizer variants - Test emoji, mixed scripts, newlines for all variants - Parametrized across BPE, BNE, Boundless BPE, Super BPE Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- bench_scaling.py compares graph BPE vs FastBPE across text sizes (5k-270k chars) and merge counts (50-200) - FastBPE consistently 7-12x faster, identical merge output - Add scaling tests: 270k chars in <5s, identical merges across sizes Results (270k chars, 200 merges): Graph 1.8s vs Fast 0.23s (7.9x) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Optional on_merge(step, total, token, nodes) callback called after each merge, enabling progress bars and logging - Backward compatible — callback is None by default Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Return self if merged subgraphs are identical to originals - Avoids creating new tuples and UnconnectedGraphs on no-op merges Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Track per-step stats: pair, frequency, total tokens, compression ratio - Accessible via trainer.stats after training - Tests verify: monotonic compression, decreasing token count Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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- Standalone reference BPE and Boundless BPE implementations as ground truth (no graph abstraction, just byte sequences + counters) - Verify graph BPE, FastBPE, and Boundless BPE all produce identical merges to reference on same input - 5 tests across small, medium, and multi-implementation comparisons Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Absorbed into #12 — all review feedback changes are now in the clean API PR directly. |
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Summary
Addresses review feedback from PR #4 and #6:
graphviz+Pillowto[draw]optional deps, included in[dev]super_bpe.pyto usetrain_bne_tokenizerfor phase 1 instead of duplicating settingstest_bnedoes)Stacked on #22.
Test plan
ruff check .passes🤖 Generated with Claude Code