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Jev project · Benchmarks & research

Verdict-open-jev

Heman10x-NGU/Verdict-open-jev

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

View on GitHub ↗
Why Jev matters
Demo from the project README · view README ↗
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
Built by this developer
Hemant Kumar

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

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

  1. 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
  2. jev-curateAkashPriyadarshii/jev-curateA Rust dataset-filtering experiment using Jev scores to keep or reject text records.★ 26
    Rust
  3. jevifyaltryne/jevifyAn Agent Skill for finding suitable Jev decision points and designing questions and comparison experiments.★ 24
    Python
  4. jev-benchmarksAbdelStark/jev-benchmarksA benchmark comparing Jev and GLiNER on text classification, probability calibration and selective automation.★ 17
    Python
  5. jevcalabhixhek/jevcalA toolkit for evaluating Jev probabilities on labeled data, selecting confidence thresholds and checking model drift.★ 10
    Python
  6. convex-evalsget-convex/convex-evalsConvex's evaluation suite, including a decision track that asks Jev multiple-choice questions about the Convex platform.★ 129
    TypeScript

See all 107 Benchmarks & research projects →

Where we found it