| --- |
| license: mit |
| pretty_name: SlayerLab Tokenizers |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| - config_name: benchmark |
| data_files: |
| - split: test |
| path: results/tokenizer_benchmark.parquet |
| - config_name: evidence_review |
| data_files: |
| - split: review |
| path: results/author_evidence_scores.csv |
| - config_name: leaderboard |
| data_files: |
| - split: test |
| path: results/provisional_leaderboard.parquet |
| --- |
| |
| # SlayerLab Tokenizers |
|
|
| Normalized tokenizer artifacts collected from the contributor directories in |
| [`slayerlabs/tokenizer`](https://github.com/slayerlabs/tokenizer/tree/1a5cd2c2e4df2287b4c19b3dbf5051f5d460fdc1), |
| 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. |
|
|
