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:
+    # Expect a single float coherence_score in [0,1]
+    try:
+        pred = float((prediction or "").strip())
+    except Exception:
+        pred = -1.0
+
+    true_raw = sample.get("coherence_score", "")
+    try:
+        true = float(true_raw) if true_raw not in ("", None) else None
+    except Exception:
+        true = None
+
+    if true is None:
+        ok = 0.0 <= pred <= 1.0
+        return ScoreResult(1.0 if ok else 0.0, {"mode": "format_only", "id": sample.get("id"), "pred": pred})
+
+    err = abs(true - pred)
+    return ScoreResult(max(0.0, 1.0 - err), {"id": sample.get("id"), "pred": pred, "true": true, "abs_error": err})
+
+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)}