Try list of feature types

vendiscore.py

@@ -74,11 +74,12 @@ def get_dtype(config_name):
         return datasets.Features({"samples": datasets.Value("string")})
     if config_name == "image":
         return datasets.Features({"samples": datasets.Image})
-    if config_name in ("X", "K"):
+    elif config_name in ("X", "K"):
         return datasets.Array2D
-    if config_name == "default":
-        return datasets.Features({"samples": datasets.Value("string")})
-    return datasets.Features({"samples": datasets.Value(config_name)})
+    elif config_name == "default":
+        return datasets.Value("string")
+    else:
+        return datasets.Value(config_name)
 
 
 @evaluate.utils.file_utils.add_start_docstrings(
@@ -95,7 +96,19 @@ class VendiScore(evaluate.Metric):
             description=_DESCRIPTION,
             citation=_CITATION,
             inputs_description=_KWARGS_DESCRIPTION,
-            features=get_dtype(self.config_name),
+            features=[
+                datasets.Features({"samples": datasets.Value("string")}),
+                datasets.Features({"samples": datasets.Value("int32")}),
+                datasets.Features(
+                    {"samples": datasets.Sequence(datasets.Value("int32"))}
+                ),
+                datasets.Features({"samples": datasets.Value("float")}),
+                datasets.Features(
+                    {"samples": datasets.Sequence(datasets.Value("float"))}
+                ),
+                datasets.Features({"samples": datasets.Image}),
+                datasets.Features({"samples": datasets.Array2D}),
+            ],
             homepage="http://github.com/Vertaix/Vendi-Score",
             codebase_urls=["http://github.com/Vertaix/Vendi-Score"],
             reference_urls=[],