calibre
Calibration and confidence-based routing measured on Banking77: 80.2% accuracy at $0.103 per 500 decisions.

- GitHub stars
- 2 as of 2026-09-22
- Forks
- 0
- Language
- Python
- License
- MIT
- Created
- 2026-09-17
- Last updated on GitHub
- 2026-09-18
- Added to AionEdge
- 2026-09-19

What Jev does in calibre
The project describes itself this way: Calibration and confidence-based routing measured on Banking77: 80.2% accuracy at $0.103 per 500 decisions. 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
- benchmark
- calibration
- confidence
- deepseek
- jev
- llm
- routing
- typesafe
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: Routing & Cost Optimization.
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Python