diff --git a/spacy/pipeline/spancat_exclusive.py b/spacy/pipeline/spancat_exclusive.py
index fa6d8ae60..5605a40f5 100644
--- a/spacy/pipeline/spancat_exclusive.py
+++ b/spacy/pipeline/spancat_exclusive.py
@@ -266,26 +266,27 @@ class Exclusive_SpanCategorizer(SpanCategorizer):
     ) -> SpanGroup:
         scores = self.model.ops.to_numpy(scores)
         indices = self.model.ops.to_numpy(indices)
-        if scores.size != 0:
-            predicted = scores.argmax(axis=1)
 
-            # Remove samples where the negative label is the argmax
-            positive = numpy.where(predicted != self._negative_label)[0]
-            predicted = predicted[positive]
-            indices = indices[positive]
+        # Handle cases when there are zero suggestions
+        if scores.size == 0:
+            return SpanGroup(doc, name=self.key)
 
-            # Sort spans according to argmax probability
-            if not allow_overlap and predicted.size != 0:
-                # Get the probabilities
-                argmax_probs = numpy.take_along_axis(
-                    scores[positive], numpy.expand_dims(predicted, 1), axis=1
-                )
-                argmax_probs = argmax_probs.squeeze()
-                sort_idx = (argmax_probs * -1).argsort()
-                predicted = predicted[sort_idx]
-                indices = indices[sort_idx]
-        else:
-            predicted = []
+        predicted = scores.argmax(axis=1)
+        # Remove samples where the negative label is the argmax
+        positive = numpy.where(predicted != self._negative_label)[0]
+        predicted = predicted[positive]
+        indices = indices[positive]
+
+        # Sort spans according to argmax probability
+        if not allow_overlap and predicted.size != 0:
+            # Get the probabilities
+            argmax_probs = numpy.take_along_axis(
+                scores[positive], numpy.expand_dims(predicted, 1), axis=1
+            )
+            argmax_probs = argmax_probs.squeeze()
+            sort_idx = (argmax_probs * -1).argsort()
+            predicted = predicted[sort_idx]
+            indices = indices[sort_idx]
 
         seen = Ranges()
         spans = SpanGroup(doc, name=self.key)