Upload benchmark_eval.py with huggingface_hub

benchmark_eval.py

@@ -27,7 +27,7 @@ def get_capitalized_ngrams(text, n=3):
     """Extract n-grams where at least one token starts with uppercase."""
     tokens = text.split()
     ngrams = [tuple(tokens[i:i+n]) for i in range(len(tokens)-n+1)]
-    return [ng for ng in ngrams if any(t[0].isupper() for t in ng)]
+    return [ng for ng in ngrams if any(len(t) > 0 and t[0].isupper() for t in ng)]
 
 def crr3(gold_records, predictions):
     """Capitalized 3-gram survival rate."""
@@ -77,8 +77,21 @@ def main():
     with open(args.pred, "r", encoding="utf-8") as f:
         predictions = [json.loads(line) for line in f]
         
-    if len(gold_records) != len(predictions):
-        raise ValueError(f"Mismatch in number of records! Gold: {len(gold_records)}, Pred: {len(predictions)}")
+    unknown_entity_count = 0
+    total_entity_count = 0
+    
+    for g, p in zip(gold_records, predictions):
+        if g.get("id") != p.get("id"):
+            raise ValueError(f"ID mismatch: {g.get('id')} vs {p.get('id')}")
+            
+        for ent in g.get("entities", []):
+            total_entity_count += 1
+            if ent.get("type", "UNKNOWN") == "UNKNOWN":
+                unknown_entity_count += 1
+                
+    if total_entity_count > 0 and unknown_entity_count > 0:
+        pct = (unknown_entity_count / total_entity_count) * 100
+        print(f"[NOTE] {pct:.1f}% of entities in evaluation are typed as UNKNOWN.")
         
     print(f"Evaluating {len(predictions)} records...")