Create scorer.py

scorer.py

@@ -0,0 +1,32 @@
+from dataclasses import dataclass
+from typing import Dict, Any, List
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").lower()
+    words_ok = len(p.split()) <= 700
+
+    has_index = "coherence" in p or "index" in p
+    has_lag = "lag" in p or "delay" in p
+    has_scales = "scale" in p or "local" in p
+    has_time = "onset" in p or "year"
+    has_conf = "confidence" in p or "warning"
+
+    raw = (
+        0.15 * int(words_ok) +
+        0.25 * int(has_index) +
+        0.20 * int(has_lag) +
+        0.15 * int(has_scales) +
+        0.15 * int(has_time) +
+        0.10 * int(has_conf)
+    )
+    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id")})
+
+def aggregate(results: List[ScoreResult]) -> Dict[str, Any]:
+    if not results:
+        return {"mean": 0.0, "n": 0}
+    return {"mean": sum(r.score for r in results)/len(results), "n": len(results)}