diff --git a/spacy/pipeline/spancat_exclusive.py b/spacy/pipeline/spancat_exclusive.py
index 3d169b199..03bcc7b83 100644
--- a/spacy/pipeline/spancat_exclusive.py
+++ b/spacy/pipeline/spancat_exclusive.py
@@ -272,7 +272,7 @@ class SpanCategorizerExclusive(TrainablePipe):
         """Modify a batch of Doc objects, using pre-computed scores.
 
         docs (Iterable[Doc]): The documents to modify.
-        scores: The scores to set, produced by SpanCategorizer.predict.
+        scores: The scores to set, produced by SpanCategorizerExclusive.predict.
 
         DOCS: https://spacy.io/api/spancategorizerexclusive#set_annotations
         """
@@ -446,10 +446,12 @@ class SpanCategorizerExclusive(TrainablePipe):
         scores = self.model.ops.to_numpy(scores)
         indices = self.model.ops.to_numpy(indices)
         predicted = scores.argmax(axis=1)
+
         # Remove samples where the negative label is the argmax
         positive = numpy.where(predicted != self._negative_label)
         predicted = predicted[positive[0]]
         indices = indices[positive[0]]
+
         # Sort spans according to argmax probability
         if not allow_overlap:
             argmax_probs = numpy.take_along_axis(
@@ -459,16 +461,20 @@ class SpanCategorizerExclusive(TrainablePipe):
             sort_idx = (argmax_probs * -1).argsort()
             predicted = predicted[sort_idx]
             indices = indices[sort_idx]
+
         seen = Ranges()
         spans = SpanGroup(doc, name=self.key)
         for i in range(len(predicted)):
             label = predicted[i]
             start = indices[i, 0]
             end = indices[i, 1]
+
             if not allow_overlap:
                 if (start, end) in seen:
                     continue
                 else:
                     seen.add(start, end)
+
             spans.append(Span(doc, start, end, label=labels[label]))
+
         return spans