Try updating possible types.

vendiscore.py

@@ -85,7 +85,10 @@ class VendiScore(evaluate.Metric):
             inputs_description=_KWARGS_DESCRIPTION,
             features=datasets.Features(
                 {
-                    "samples": datasets.Value("string"),
+                    "sents": datasets.Value("string"),
+                    "imgs": datasets.Image,
+                    "X": datasets.Array2D,
+                    "K": datasets.Array2D,
                 }
             ),
             homepage="http://github.com/Vertaix/Vendi-Score",
@@ -100,7 +103,10 @@ class VendiScore(evaluate.Metric):
 
     def _compute(
         self,
-        samples,
+        sents=None,
+        imgs=None,
+        X=None,
+        K=None,
         k="ngram_overlap",
         score_K=False,
         score_X=False,
@@ -115,18 +121,16 @@ class VendiScore(evaluate.Metric):
         device="cpu",
     ):
         if score_K:
-            vs = vendi.score_K(samples, normalize=normalize)
+            vs = vendi.score_K(K, normalize=normalize)
         elif score_dual:
-            vs = vendi.score_dual(samples, normalize=normalize)
+            vs = vendi.score_dual(X, normalize=normalize)
         elif score_X:
-            vs = vendi.score_X(samples, normalize=normalize)
+            vs = vendi.score_X(X, normalize=normalize)
         elif type(k) == str and k == "ngram_overlap":
-            vs = text_utils.ngram_vendi_score(
-                samples, ns=ns, tokenizer=tokenizer
-            )
+            vs = text_utils.ngram_vendi_score(sents, ns=ns, tokenizer=tokenizer)
         elif type(k) == str and k == "text_embeddings":
             vs = text_utils.embedding_vendi_score(
-                samples,
+                sents,
                 model=model,
                 tokenizer=tokenizer,
                 batch_size=batch_size,
@@ -134,15 +138,17 @@ class VendiScore(evaluate.Metric):
                 model_path=model_path,
             )
         elif type(k) == str and k == "pixels":
-            vs = image_utils.pixel_vendi_score(samples)
+            vs = image_utils.pixel_vendi_score(imgs)
         elif type(k) == str and k == "image_embeddings":
             vs = image_utils.embedding_vendi_score(
-                samples,
+                imgs,
                 batch_size=batch_size,
                 device=device,
                 model=model,
                 transform=transform,
             )
+        elif sents is not None or imgs is not None or X is not None:
+            vs = vendi.score(sents or imgs or X, k)
         else:
-            vs = vendi.score(samples, k)
+            raise ValueError(f"Must provide one of `sents` or `imgs` or `X`.")
         return {"VS": vs}