test

app.py

@@ -10,13 +10,10 @@ metric = evaluate.load("Aye10032/top5_error_rate")
 
 
 def compute(data):
-    print(data)
-    # return metric.compute()
     result = {
         "predictions": [list(map(float, pred.split(","))) for pred in data["predictions"]],
         "references": data["references"].cast(pl.Int64).to_list()
     }
-    print(result)
     return metric.compute(**result)
 
 

top5_error_rate.py

@@ -63,6 +63,8 @@ class Top5ErrorRate(evaluate.Metric):
             references: list[int] = None,
             **kwargs,
     ) -> Dict[str, Any]:
+        print(predictions)
+        print(references)
         # to numpy array
         outputs = np.array(predictions, dtype=np.float32)
         labels = np.array(references)