jevbooks

← All projects

jev-benchmarks

AbdelStark/jev-benchmarks · Homepage

Probability-aware evaluation for typed decision models: calibration, selective risk, latency.

Evaluation & Benchmarking89%Runner-up: Classification & RoutingQuestion health measured

What it is

jev-benchmarks is a reproducible evaluation harness for typed decision models. It measures calibration, selective automation at a fixed error budget, latency, and integrity, and ships a pilot comparing TypeSafe Jev with GLiNER2.5 on zero-shot single-label text classification.

How it uses Jev

Jev is run as a hosted backend through a Choice adapter that returns per-label probabilities for each example. The pipeline validates probability vectors, records resolved model identity, and feeds the predictions into discrimination, calibration, and risk/latency reports. The README does not describe the adapter's internal prompt or state construction.

Primitives:choice

Technique worth stealing

Paired target-stratified bootstrap intervals over pinned revisions, with a uniform negative control and append-only prediction logs.

Try it

uv sync --extra benchmark --dev; uv run jev-bench prepare --config configs/pilot-v1.yaml; then run each backend and report.

View on GitHub

judged by Jevjev-1.13.0

Evidence

Each line is one question put to Jev about the README. ≥ 0.60 reads as yes, ≤ 0.40 as no; in between Jev is not making a call.

  • Jev-centricno0.25
  • Shows a System One patternno0.28
  • Handles uncertaintyno0.09
  • Measuredno0.10
  • Runnableno0.13
  • Worth recommendingno0.26
  • Model replicano0.08
  • Problem scopescore on a 0–2 scale1.50
  • About Jevyes0.97

Signals by Jev jev-1.13.0, card written by DeepSeek V4.1 Flash from the README on 20 Sept 2026.