Repo · Benchmarks & research
Jev Benchmarks
Measure calibration, latency, and selective risk.
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Probability-aware evaluation for typed decision models: calibration, selective risk, latency, and reproducible benchmarks.
benchmarkingcalibrationevaluationmachine-learningreproducibilityselective-classificationzero-shot-classification
How builders describe it
Probability-aware evaluation for typed decision models: calibration, selective risk, latency, and reproducible benchmarks.
A probability-aware evaluation harness that compares TypeSafe Jev with GLiNER2.5 on zero-shot single-label text classification, measuring calibration, coverage at a fixed error budget, latency, and token cost.
The decision Jev makes
Benchmark questions with known answers, to check accuracy and confidence.
Where it fits
Head-to-head tests, calibration studies and independent research into how well Jev decides, how fast, and at what cost. All 682 benchmarks & research projects →
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