| # Polish tokenizer diagnostic suite |
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|
| `polish_suite.jsonl` is a fixed, synthetic stress suite written for this |
| repository on 2026-08-26. It has not been copied from a corpus and makes no |
| claim of being held out from tokenizer training data. Each domain contains two |
| short examples so domain metrics are useful for inspection, not statistical |
| inference. |
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| Run the benchmark with: |
|
|
| ```bash |
| python3 benchmark_tokenizers.py |
| ``` |
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| All dataset rows are retained. Native Hugging Face artifacts have |
| `adapter_status=native`; other formats use the local reference adapter. An |
| `adapter_fidelity` of `exact` means the serialized artifact contains enough |
| information to reproduce its behavior. `core_only` means the byte-BPE core is |
| lossless but intended pre-tokenization is missing, so its segmentation metrics |
| must not be ranked as if they represented the author's full tokenizer. |
|
|
| Metrics use Unicode code points for `chars`, UTF-8 bytes for `bytes`, and |
| Python Unicode `\\w+` spans for words. Special tokens are disabled during |
| encoding and retained during decoding. Throughput is the median of repeated |
| whole-suite batches after one warm-up and is only comparable within the same |
| run, machine, and `runtime`. In particular, `python_reference_adapter` speed |
| measures this diagnostic adapter and is not comparable to `rust_tokenizers`. |
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| This suite can catch broken round trips, unknown-token behavior, pathological |
| segmentation, and operational cost differences. It cannot establish model |
| quality. A defensible final ranking also needs a larger provenance-controlled |
| held-out corpus and identical downstream language-model experiments. |
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|
