Datasets:
Leaderboard question: the current #1 was trained on test-split rows (Phocinae-Largha-150M-v1)
Hi maintainers, and congratulations to the Phocinae team. A 144M model at 21 ms per decision is genuinely impressive.
While reading the Phocinae-Largha-150M-v1 model card, we found this in its evaluation-overlap disclosure (corrected 2026-10-10): the fine-tune mix includes a 500-item error-correction pool, with gold soft targets, "mined from model predictions on the typed-decisions test rows".
Credit to the authors for disclosing it openly. Still, it means the 0.906 at the top of the Hub leaderboard was measured on a test split the model has partly studied, answer distributions included. Even a spotted seal swims faster in water it has already mapped. 🦭
Three short questions, asked in good faith:
- Should entries with test-split exposure be flagged on the Hub leaderboard and in Table 2, or listed separately? Table 2 already separates "fitted on train" from zero-shot, and "fitted on test" seems like a third category.
- Would it help to add one line to the dataset card, for example: "Do not train on test rows, test labels, or pairs derived from test predictions", and to ask submitters to state this in their
.eval_resultsnotes? - @perrylink , would you consider re-scoring a checkpoint trained without the test-derived pool? That number would be interesting in its own right, and it would make the comparison fair for everyone.
Thanks for running this benchmark. Clean splits are what make the numbers worth chasing.