A candidate-scoring head returns distributions for gates, routes and scores without decoding prose or JSON answers. The README compares a benchmark-trained AgentJev specialist with Jev 1.13.0 evaluated zero-shot; these use different training conditions.
jev-korean-benchmark: Reproducible early-access evaluation of Jev
mahlernim/jev-korean-benchmark
VerifiedResearchDocumented
jev-korean-benchmark: Reproducible early-access evaluation of Jev on Korean understanding and medical text, with runtime and cost evidence. See Jev references and source code.
Repository description: Reproducible early-access evaluation of Jev on Korean understanding and medical text, with runtime and cost evidence
What you can build with it
Get started
Clone the repository and follow its README for install and usage. JevHunt has not run this project.
git clone https://github.com/mahlernim/jev-korean-benchmark.gitProject facts
- Relationship to Jev
- Research
- Evidence
- Documented
- Language
- Python
- License
- Not reported; check the repository
- Origin
- Original repository
- Repository status
- Not archived
- Created
- 2026-09-17
- Last code update
- 2026-09-17
- GitHub stars
- 7
- Evidence checked
- Sep 29, 2026
- Metadata checked
- Sep 29, 2026
- Check status
- Up to date
Stars measure the whole repository, including work unrelated to Jev.
Evidence and scope
Documented records the linked documentation or source. JevHunt has not independently run or benchmarked this project.
Can you use TypeSafe Jev on Korean text, or should you translate everything to English first? This is a small, frozen, reproducible check that tries to answer that — 100 questions per cell, drawn from four public test sets, with every response recorded. It is a sample check, not a benchmark: 100 questions give roughly ±8 points of uncertainty, so small differences here are not findings.
Evidence commit: 2c983b7f6e7f1c2baf7e078161eae3698420f3de
Discovered through: awesome-jev.
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