update

top5_error_rate.py

@@ -17,7 +17,7 @@ Top-5 Error Rate = (Number of incorrect top-5 predictions) / (Total number of ca
 
 _KWARGS_DESCRIPTION = """
 Args:
-    predictions (`list` of `int`): Predicted labels.
+    predictions (`list` of `list` of `int`): Predicted labels.
     references (`list` of `int`): Ground truth labels.
 Returns:
     accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input.
@@ -42,9 +42,7 @@ class Top5ErrorRate(evaluate.Metric):
             inputs_description=_KWARGS_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "predictions": datasets.Sequence(
-                        datasets.Sequence(datasets.Value("int32"))
-                    ),
+                    "predictions": datasets.Sequence(list[datasets.Value("int32")]),
                     "references": datasets.Sequence(datasets.Value("int32")),
                 }
                 if self.config_name == "multilabel"