Verdict-open-jev
Non-autoregressive decision engine on ModernBERT (151M) with calibrated uncertainty (RLCD), TypeSafe AI Jev benchmark audit, and in-browser WebGPU playground.

- GitHub stars
- 68 as of 2026-09-22
- Forks
- 12
- Language
- Python
- License
- Check upstream
- Created
- 2026-09-17
- Last updated on GitHub
- 2026-09-20
- Added to AionEdge
- 2026-09-19

What Jev does in Verdict-open-jev
The project describes itself this way: Non-autoregressive decision engine on ModernBERT (151M) with calibrated uncertainty (RLCD), TypeSafe AI Jev benchmark audit, and in-browser WebGPU playground. In the Benchmarks & research category, Jev typically decides labels and probabilities on fixed test sets, so accuracy, calibration and latency can be measured.
Verification
Verified. We confirmed this repository and its Jev relevance through the GitHub API and the source lists below. A specific file link has not been recorded yet.
Topics
- brier-score
- calibration
- decision-engine
- edge-ai
- gliclass
- jev
- modernbert
- onnx
- rlcd
- structured-outputs
- system-1
- typesafe-ai
- webgpu
- zero-token-generation
How Benchmarks & research projects use Jev
Benchmarks and research projects test Jev rather than just use it. They compare it with language models and other classifiers, check whether its probabilities are well calibrated, and explore where the decision-model approach breaks down.
Also listed in: Browser & computer use.
Related Jev projects in Benchmarks & research
- typesafe-ai-benchmarkiammrduncan/typesafe-ai-benchmarkCompares Jev with other structured-output models on shared application tasks, recording errors, latency, Tokens, and estimated cost.★ 38
TypeScript - jev-curateAkashPriyadarshii/jev-curateA Rust dataset-filtering experiment using Jev scores to keep or reject text records.★ 26
Rust - jevifyaltryne/jevifyAn Agent Skill for finding suitable Jev decision points and designing questions and comparison experiments.★ 24
Python - jev-benchmarksAbdelStark/jev-benchmarksA benchmark comparing Jev and GLiNER on text classification, probability calibration and selective automation.★ 17
Python
jevcalabhixhek/jevcalA toolkit for evaluating Jev probabilities on labeled data, selecting confidence thresholds and checking model drift.★ 10
Python
convex-evalsget-convex/convex-evalsConvex's evaluation suite, including a decision track that asks Jev multiple-choice questions about the Convex platform.★ 129
TypeScript