diff --git a/spacy/pipeline/spancat_exclusive.py b/spacy/pipeline/spancat_exclusive.py
index 9a52fc1d4..b5af27265 100644
--- a/spacy/pipeline/spancat_exclusive.py
+++ b/spacy/pipeline/spancat_exclusive.py
@@ -215,17 +215,18 @@ class SpanCategorizerExclusive(TrainablePipe):
         return list(self.labels)
 
     @property
-    def _negative_label(self):
-        """
-        Index of the negative label.
-        """
+    def label_map(self) -> Dict[str, int]:
+        """RETURNS (Dict[str, int]): The label map."""
+        return {label: i for i, label in enumerate(self.labels)}
+
+    @property
+    def _negative_label(self) -> int:
+        """RETURNS (int): Index of the negative label."""
         return len(self.label_data)
 
     @property
-    def _n_labels(self):
-        """
-        Number of labels including the negative label.
-        """
+    def _n_labels(self) -> int:
+        """RETURNS (int): Number of labels including the negative label."""
         return len(self.label_data) + 1
 
     def predict(self, docs: Iterable[Doc]):
@@ -339,7 +340,6 @@ class SpanCategorizerExclusive(TrainablePipe):
         spans = Ragged(
             self.model.ops.to_numpy(spans.data), self.model.ops.to_numpy(spans.lengths)
         )
-        label_map = {label: i for i, label in enumerate(self.labels)}
         target = numpy.zeros(scores.shape, dtype=scores.dtype)
         # Set negative class as target initially for all samples.
         negative_spans = numpy.ones((scores.shape[0]))
@@ -358,7 +358,7 @@ class SpanCategorizerExclusive(TrainablePipe):
                 key = (gold_span.start, gold_span.end)
                 if key in spans_index:
                     row = spans_index[key]
-                    k = label_map[gold_span.label_]
+                    k = self.label_map[gold_span.label_]
                     target[row, k] = 1.0
                     # delete negative label target.
                     negative_spans[row] = 0.0
@@ -438,14 +438,15 @@ class SpanCategorizerExclusive(TrainablePipe):
         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]]
+        positive = numpy.where(predicted != self._negative_label)[0]
+        predicted = predicted[positive]
+        indices = indices[positive]
 
         # Sort spans according to argmax probability
         if not allow_overlap:
+            # Get the probabilities
             argmax_probs = numpy.take_along_axis(
-                scores[positive[0]], numpy.expand_dims(predicted, 1), axis=1
+                scores[positive], numpy.expand_dims(predicted, 1), axis=1
             )
             argmax_probs = argmax_probs.squeeze()
             sort_idx = (argmax_probs * -1).argsort()