EVIDENCE_REVIEW.md

Independent submission evidence review

Scope: the seven contributor folders selected for the normalized dataset at source commit 1a5cd2c2e4df2287b4c19b3dbf5051f5d460fdc1. This is a review of submitted evidence, documentation, and reproducibility—not a ranking of tokenizer quality. Author-reported compression values use different corpora, held-outs, vocabulary sizes, and word definitions and are therefore not compared across authors.

Rubric

Each dimension is scored 0–4 (maximum 20):

• Artifact usability: complete runtime artifact, loadability, round-trip/special-token readiness.
• Documentation: design, data, configuration, outputs, and limitations are explained.
• Evaluation protocol: held-out construction, denominators, controls, stress tests, and baselines.
• Reproducibility: committed code, pinned inputs, seeds/environment, and runnable evaluation.
• Claims discipline: conclusions match evidence; confounds and non-comparability are acknowledged.

The score measures strength of the evidence package only. It must not be combined with future common-corpus benchmark results as if it were a tokenizer-performance score.

Results

GitHub author	Artifact	Docs	Protocol	Repro	Claims	Total	Evidence judgment
KateMajzel	4	4	4	2	4	18	Strongest controlled-methodology package; excellent failure analysis and explicit limits.
janbanot	4	4	3	1	4	16	Broadest qualitative/stress analysis and a directly loadable final artifact; exact split/code absent.
Maggio333	2	4	4	1	4	15	Deepest research narrative and unusually good caveats; custom runtime and missing scripts prevent replay.
olajachymiak	2	4	4	1	4	15	Strong progression of controlled experiments and candid overfitting analysis; referenced scripts absent.
dawidmajewski	4	3	2	1	3	13	Seven loadable artifacts and concrete corpus/source tables; mostly exploratory, with no fixed replay harness.
ktalik	4	2	1	1	2	10	Loadable minimal tokenizer plus interactive plots; sparse protocol and referenced training/report code absent.
p4pryk	1	3	2	1	2	9	Useful design explanation and corpus accounting, but only a custom vocab/merge map was submitted.

Per-author findings

KateMajzel (source folder KasiaMP)

• Best evidence of experimental hygiene: identical stated corpus size (9,363,020 chars), matched total vocab (6,756), shared 2,696-character/371-word held-out, and the same word denominator.
• Documents and repairs serialization loss, boilerplate contamination, corpus reconstruction drift, and the 6,500-merges versus 6,756-total-vocab convention. Four HF artifacts load directly.
• Excellent claims discipline: calls the 1.3% spread inconclusive and explicitly lists limitations.
• Reproduction gap: no training/evaluation code, held-out text, corpus manifest/hash, seed, lockfile, or one-command replay is present. Character count is a useful check but not a cryptographic identity.

janbanot (source folder Janek)

• Directly loadable HF ByteLevel BPE (8,192); comprehensive discussion of corpus balancing, vocabulary sweep, utilization, morphology, multilingual/emoji/numeric/code stress cases, and limits.
• Small peer-baseline suite is clearly described as contextual rather than a definitive benchmark.
• Protocol gap: the exact held-out composition/identity and split procedure are not committed, and neither training nor evaluation code is present. Reported tables therefore cannot be replayed.

Maggio333 (source folder Arek)

• Most ambitious research account: matched-vocab pre-tokenizer comparisons, vocabulary curve, distribution-shift matrix, compute-head trade-off, Renyi efficiency, and morphology caveats.
• Explicitly warns that different held-outs cannot be compared and that compression is not downstream model quality. That restraint is exemplary.
• All 17 artifacts use a custom BPE serialization and fail direct HF Tokenizer.from_file loading. A loading sketch is documented, but the actual encoder/pre-tokenizer implementation is absent.
• Major replay gap: referenced vocab_cost.py, training/evaluation code, exact data manifests, held-out artifacts, and environment are not in the repository. Several broad empirical claims are supported only by prose/tables embedded in the README.

olajachymiak (source folder ola)

• Strong pedagogical chain: controls corpus at vocab 512, controls vocab on one corpus, exposes the in-domain Quo Vadis illusion, then builds an 8k diverse/pretokenized version.
• Clearly reports train/held-out boundary for the book experiment, exact held-out counts, round-trip, overfitting mechanisms, and remaining weaknesses.
• All four JSONs are custom experiment bundles rather than directly loadable HF serializations.
• Referenced homework_diverse.py and homework_vocab_sweep.py are absent, as are the exact corpora, corpus hashes, environment, and executable evaluator. The final Pan Tadeusz result is not a fully out-of-domain test because the training mix is still majority Polish literature.

dawidmajewski (source folder dawidm)

• Seven submitted tokenizer files load directly. The writeup gives source URLs/revisions for test snippets, corpus sizes, regex, vocab variants, round-trip claim, and raw token-count tables.
• The author accurately labels the work exploratory rather than research, which appropriately limits claims.
• Training-corpus fertility appears to be reported alongside short out-of-corpus token counts; there is no fixed held-out benchmark across every model, no vocabulary-utilization analysis, and no statistical treatment. Code, pinned training inputs, preprocessing, seeds, and evaluator are absent.

ktalik (source folder Konrad)

• Submitted 456-vocab HF ByteLevel BPE loads directly; three HTML plots preserve some experimental output.
• README states SJP scale, merge sweep, and an aggregate token-count trend.
• The claimed bpe.py and report.py are not committed. No train/eval split, exact word list, preprocessing, denominator, round-trip suite, corpus version/license, or reproducible command is given.

p4pryk (source folder patryk)

• Explains ByteLevel motivation, a Polish regex, balanced DynaWord/Wikipedia sampling, length/newline constraints, exact stated training character count, and a Pan Tadeusz evaluation.
• Submitted JSON is only a custom vocabulary/merge mapping, not a complete runtime tokenizer; it lacks normalizer, pre-tokenizer, decoder, added-token policy, and a runnable loader.
• No code, exact data manifest/hash, held-out artifact, environment, or evaluation command is present. Phrases such as "commercial standard", "ideal", and character compression as taking less storage overstate what round-trip and token-count results establish. The held-out may also be adjacent in literary domain to some training sources.

Cross-submission conclusions

1. Do not select a winner from reported metrics. Vocabulary ranges from 456 to 512,000 and evaluation domains range from the training corpus to held-out book tails, Pan Tadeusz, SpeakLeash, and tiny probe suites.
2. Artifact readiness differs sharply. KateMajzel, janbanot, dawidmajewski, and ktalik have at least one directly loadable HF artifact. The others need author-specific adapters or reconstruction.
3. No submission is fully reproducible from this repository. The repository contains no contributor training/evaluation scripts; exact evaluation texts and dependency environments are also absent.
4. Documentation distinction: strongest controlled-methodology evidence is KateMajzel; broadest research analysis is Maggio333; broadest practical stress analysis is janbanot; clearest controlled learning progression is olajachymiak.
5. Next judging step: run loadable artifacts (and validated adapters for custom formats) through one versioned, multi-domain Polish test manifest. Publish per-domain metrics and Pareto fronts within vocab bands; keep this evidence score as a separate reproducibility/documentation axis.

Minimum evidence upgrade requested from every author

• train.py and evaluate.py (or notebook exported with deterministic cells), dependency lock, and commands.
• Corpus source/version/license, preprocessing config, byte count plus SHA-256, and deterministic split rule.
• Committed held-out manifest or hashes, explicit word-count definition, round-trip/stress test corpus.
• Standard tokenizer.json plus special-token configuration, or a versioned adapter with parity tests.
• Machine-readable result JSON containing tokenizer hash, dataset hash, code commit, environment, and timings.