jev-curate

sifts synthetic JSONL and Parquet rows using Jev Noul checks and calibrated confidence scores, streaming passed records and rejections straight to disk.

OWNER

AkashPriyadarshii

INDUSTRY

Dataset engineering

WHAT IT DECIDES

CHOICE

Picks one option from a fixed set

SCORE

Rates against ordered levels

NOUL

Answers a yes / no question

Inferred from this project's own one-line summary in the community list — it reads as a SCORE + NOUL decision. Read the source to confirm.

THE SHAPE OF A SCORE CALL

A generic skeleton for this question type, not this project's actual code.

from typesafe import TypeSafe

ts = TypeSafe()
result = ts.evaluate(
    state=candidate,
    questions={
        "relevance": {
            "type": "score",
            "levels": ["low", "medium", "high"],
            "instructions": "How relevant is this to the query?",
        }
    },
)
if result["relevance"] >= 0.7:
    keep(candidate)

MORE IN Data Labeling & Curation

jev-align (Sutro)evaluates CSV, Parquet, and JSONL rows with Jev `Choice`, `Score`, or `Boolean` decisions, sends ambiguous and audit samples to a human, and uses accepted human labels to optimize the saved definition with GEPA.typeful-triagemultiplayer triage dashboard where Jev answers a fixed set of typed questions per issue — kind, severity, urgency, duplicate, and next step — and every human correction is kept and shown back to the model on later runs.jlinklinks records under a plain-English match rule using Jev Noul pair judgments, with local candidate blocking and match resolution.jgrepfilters text, structured records, functions, and diff hunks against plain-English descriptions using Jev Noul judgments.