SlayerLab Tokenizers

Normalized tokenizer artifacts collected from the contributor directories in slayerlabs/tokenizer, pinned to source commit 1a5cd2c2e4df2287b4c19b3dbf5051f5d460fdc1.

The dataset contains one row per tokenizer: the 38 workshop submissions plus the canonical SlayerLab Polish 32k tokenizer by kacperwikiel. Use the Dataset Viewer to sort, filter, and compare tokenizers without navigating folders.

Columns

• author: contributor's exact GitHub username, resolved from the source repository's pull-request history.
• size: tokenizer vocabulary size.
• name: original filename.
• quick_status: whether the artifact is ready to load or needs custom conversion.
• hf_loadable: whether it loads directly with tokenizers.Tokenizer.from_file(...).
• format: normalized artifact-format classification.
• model_type: tokenizer model type reported by the source.
• merge_count: number of BPE merge rules when available.
• normalizer, pre_tokenizer, decoder: quick configuration summary.
• unk_token, added_tokens_count: special-token readiness indicators.
• reported_metrics: evaluation numbers reported by the author, encoded as JSON.
• metadata: normalized metadata encoded as a JSON string.
• tokenizer_json: complete, lossless original JSON file contents.
• source_repo: repository containing the original artifact.
• source_path: original path in the source repository.
• source_commit: pinned source Git commit.
• bytes: original JSON file size.
• sha256: checksum of the original JSON file.

Formats

• hf_tokenizers (13 files): loadable with tokenizers.Tokenizer.from_file(...).
• custom_bpe (17 files): custom BPE artifacts with meta and model fields.
• custom_vocab (1 file): vocabulary/merge mapping rather than a full tokenizer runtime file.
• custom_experiment (7 files): custom experiment or result JSON containing tokenizer data and metrics.

All source files were parsed as valid JSON. A file being valid JSON does not imply it implements the Hugging Face Tokenizers serialization schema; check the format column before loading tokenizer_json.

Reported metrics were produced with different texts and procedures, so they are useful for inspecting an individual submission but not for ranking authors. A fair quality ranking requires running every compatible tokenizer against the same held-out Polish evaluation corpus.

Common diagnostic benchmark

The benchmark configuration evaluates all 39 artifacts on the same versioned, ten-domain Polish diagnostic suite. Fourteen run natively through the Rust tokenizers library and 25 run through the included custom-format adapters.

• 39/39 artifacts executed successfully.
• 32 are reconstructed exactly; 7 are core_only because their intended pre-tokenizer was not serialized.
• 37/39 preserve exact input; two normalized KateMajzel variants convert a tab to a space.
• No unknown tokens were observed in this suite.

The benchmark reports compression, round-trip behavior, unknown-token rate, per-domain results, serialized size, and local throughput. Throughput is only comparable within the same runtime; Python reference adapters must not be speed-ranked against native Rust tokenizers.

This is a small synthetic diagnostic corpus, not a statistically representative held-out benchmark and not evidence of downstream language-model quality. Raw compression must be compared within vocabulary-size bands.

Evidence review

The evidence_review configuration separately scores artifact usability, documentation, evaluation protocol, reproducibility, and claims discipline. These scores judge the submitted evidence package—not tokenizer performance—and must not be combined with compression metrics into a single winner score. See EVIDENCE_REVIEW.md for the full evidence and limitations.

Provisional scoring leaderboard

The leaderboard configuration provides a transparent 0–100 diagnostic quality score. Eligibility requires successful execution, exact adapter fidelity, exact round-trip on every suite record, and zero observed unknown tokens. Seven core_only reconstructions and two round-trip failures remain visible but are unranked.

The score adjusts compression for vocabulary size using author-balanced fits in each domain, then combines 80% mean domain percentile with 20% lower-quartile domain percentile. This rewards balanced performance and prevents an author with many variants from defining the baseline. Scores within two points share a rank tier.

Artifact readiness, evidence-package quality, traceability, file size, and speed are displayed separately and do not influence the quality rank. kacperwikiel is marked as a reference baseline. Best-of-many rows are explicitly labeled for selection bias. See LEADERBOARD_METHODOLOGY.md for the complete formula and limitations.