Create scorer.py

scorer.py

@@ -0,0 +1,34 @@
+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_subgroup = "subgroup" in p or "responder" in p
+    has_non = "non" in p and "responder" in p
+    has_gate = "gate" in p or "context" in p or "only if" in p
+    has_class = any(k in p for k in ["masked", "stage", "adherence", "toxicity", "context"])
+    has_salvage = "salvage" in p or "niche" in p or "use in" in p
+    has_plan = "plan" in p or "trial" in p or "verify" in p
+
+    raw = (
+        0.15 * int(words_ok) +
+        0.25 * int(has_subgroup) +
+        0.10 * int(has_non) +
+        0.20 * int(has_gate) +
+        0.10 * int(has_class) +
+        0.10 * int(has_salvage) +
+        0.10 * int(has_plan)
+    )
+    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)}