Unpack average result

sbert_cosine.py

@@ -128,11 +128,11 @@ class sbert_cosine(evaluate.Metric):
             res = cosine(embeds[0, :], embeds[1, :]).item()
             return res
 
-        avg = lambda x: sum(x) / len(x)
+        # avg = lambda x: sum(x) / len(x)
 
         with torch.no_grad():
-            score = avg([calculate(pred, ref) for pred, ref in zip(predictions, references)])
+            scores = [calculate(pred, ref) for pred, ref in zip(predictions, references]
 
         return {
-            "score": score,
+            "score": scores,
         }