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_corr = "correlation" in p or "convergence" in p
+    has_div = "diversity" in p or "response" in p
+    has_trend = "decline" in p or "trend" in p
+    has_time = "onset" in p or "year" in p
+    has_tip = "tipping" in p or "proximity" in p
+
+    raw = (
+        0.15 * int(words_ok) +
+        0.25 * int(has_corr) +
+        0.20 * int(has_div) +
+        0.20 * int(has_trend) +
+        0.10 * int(has_time) +
+        0.10 * int(has_tip)
+    )
+    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)}