Create scorer.py

scorer.py

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+from dataclasses import dataclass
+from typing import Dict, Any, List
+import re
+
+@dataclass
+class ScoreResult:
+    score: float
+    details: Dict[str, Any]
+
+# Expect the model to output these fields in text
+REQ = ["interface_coherence_score", "baseline_failure_margin", "signal_paths"]
+
+_float_re = re.compile(r"(interface_coherence_score|baseline_failure_margin)\s*[:=]\s*(0(\.\d+)?|1(\.0+)?)", re.I)
+
+def _has_paths(text: str) -> bool:
+    # Accept either "A:..|B:.." style or "A:..>..|B:..>.." style
+    t = text.replace(" ", "")
+    return ("A:" in t and "B:" in t and ">" in t) or ("signal_paths" in t and ">" in t)
+
+def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
+    p = (prediction or "").strip()
+    pl = p.lower()
+    words_ok = len(p.split()) <= 900
+
+    field_words = sum(1 for k in REQ if k in pl)
+    float_hits = len(_float_re.findall(p))
+    has_paths = _has_paths(p)
+
+    raw = (
+        0.25 * int(words_ok) +
+        0.35 * min(1.0, field_words / len(REQ)) +
+        0.30 * min(1.0, float_hits / 2) +
+        0.10 * int(has_paths)
+    )
+    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id"), "field_word_hits": field_words})
+
+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)}