Datasets:
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parquet
Languages:
English
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1K - 10K
Tags:
structured-decisions
calibration
probabilistic-classification
system-one
workflow-evaluation
Synthetic
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Commit History
Yesterday
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Add bench/score_with_jobs.{sh,py} referenced by the README section
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Add HF Jobs how-to for benchmarking a model
Verified signatureAdds a short README section and bench/score_with_jobs.{sh,py}: one command scores any /v1/systemone model on the test split with HF Jobs (accuracy, KL, Brier, ECE). See discussion #37 for the full 19-model run.ffabd96
6 days ago
7 days ago
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Leaderboard chart: add Amos 1 medium and large
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Add rony/ministral3-3b-reasoning-typed-decisions (fine-tuned on train, 0.808) from #30
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Add Amos 1 medium and large (general, zero-shot) and the large's fitted row (#32)
- Add Amos 1 medium and large (general, zero-shot) and the large's fitted row (1a9732640b9c9a40fea6a9804093580b0589ef9a) Co-authored-by: Zaid Mukaddam <mukaddamzaid@users.noreply.huggingface.co>b0084ae -
Leaderboard chart: add jev-vision-27b (zero-shot #5); generated by mini-jev/typed_decisions/make_leaderboard_chart.py
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Zero-shot table: add jev-vision-27b at #5 (0.714 acc, self-reported, #28)
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Table 2: add Qwen3.5-4B LoRA (#22), gemma3-270m specialist and general (#26, #27), ProKope-421M-8K (#23/#24)
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Table 2: list Phocinae-Largha-150M-v1 at its clean re-score (0.791), drop the test-exposed 0.906 (discussion #25)
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10 days ago
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Add Phocinae-Largha-150M-v1 (specialist, fitted on train) to Table 2 (#21)
- Add Phocinae-Largha-150M-v1 (specialist, fitted on train) to Table 2 (18d0e1477d4d3ba38af86cea3a4382b73ff47ec6) Co-authored-by: Perry Link <perrylink@users.noreply.huggingface.co>6a7927f
12 days ago
19 days ago
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Add Bekko System One v0 (17M, 68M, 400M) to the fine-tuned table, scored by us
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Leaderboard: zero-shot models only; add self-reported entries; second table for models fitted on train
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20 days ago
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Add eval.yaml to register as a Hub benchmark (#9)
- Add eval.yaml to register as a Hub benchmark (dc021733b120def5221fdce28d01049bb0216947) Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>561333a -
Restore the teacher description (4B-class); keep the request-shape note
Verified signatureReverts the wording change in 1111d14: the teacher is a roughly 4B-class model, as the card said before. The request-shape paragraph stays. No scores changed.e135720 -
Correct the teacher description; state the request shape
Verified signatureThe card called the gold teacher '4B-class', but the labeller only records the method (a routing proxy, 3 samples, temperature 0.7), not which model answered. Say that, and say closed models cannot be ruled out. Also state that every row sends the whole case in one request, and cite the third-party finding that request shape moves Jev's yes/no accuracy. No scores changed.1111d14
21 days ago
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Add Liquid AI d1 (0.742)
Verified signatureScored 2026-09-30 through api.liquid.ai (d1:free) with the same client code as the Jev row: 400 cases, 2,000 decisions, zero errors. Accuracy 0.742, KL 0.475, Brier 0.155, ECE 0.124, p50 525 ms.d51d993 -
Streamline the card: one leaderboard table, a chart, shorter notes
Verified signatureMerges the Leaderboard and Baseline results tables into one, adds a two-panel chart (accuracy vs KL, accuracy vs latency with price), and trims the prose from about 3,000 to 1,400 words. No numbers changed.f2491dd -
Tag meraGPT links with UTM parameters so card-driven visits can be attributed
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Add Jeff (0.8B, 2B, Gemma4-E2B) to the leaderboard, scored 2026-09-29 through the Jev client path
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25 days ago
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Leaderboard: add Featherless Simple Jev (Qwen3.6-35B-A3B classifier), 0.716 acc, measured on the 400-case test split
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28 days ago
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Latency footnote: say exactly how it was measured
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Leaderboard: Decider 1 beats Jev on accuracy, latency and price
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Leaderboard: meraGPT Decider 1 is state of the art (0.768 accuracy, KL 0.096)
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Replace the estimated System One row with a measured Jev 1.13.0 result
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card: point usage examples at the canonical org path
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card: explain each reference row, add ceilings and a saturation estimate
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card: correct the prior baseline, which had been fitted on the benchmark
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card: add Adaptive Classifier specialist baselines (MiniLM and ModernBERT)
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rewrite dataset card
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remove build-time audit from the published dataset
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full dataset: 400-case benchmark (test) + 1200-case training split, verified disjoint
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card: specialist vs generalist evaluation modes
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benchmark-only: single test split of 400; add measured score reference points
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card: remove personal attribution from citation
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typed-decisions: 400 cases, 2000 typed decisions across 4 workflows
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initial commit
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