diff --git a/website/docs/api/spancategorizer.mdx b/website/docs/api/spancategorizer.mdx
index 7ee487c16..c9ae8e483 100644
--- a/website/docs/api/spancategorizer.mdx
+++ b/website/docs/api/spancategorizer.mdx
@@ -13,12 +13,12 @@ A span categorizer consists of two parts: a [suggester function](#suggesters)
 that proposes candidate spans, which may or may not overlap, and a labeler model
 that predicts zero or more labels for each candidate.
 
-This component comes in two forms: `spancat` and `spancat_singlelabel` (added in spaCy v3.5.1). When you
-need to perform multi-label classification on your spans, use `spancat`. The
-`spancat` component uses a `Logistic` layer where the output class probabilities
-are independent for each class. However, if you need to predict at most one true
-class for a span, then use `spancat_singlelabel`.
-It uses a `Softmax` layer and treats the task as a multi-class problem.
+This component comes in two forms: `spancat` and `spancat_singlelabel` (added in
+spaCy v3.5.1). When you need to perform multi-label classification on your
+spans, use `spancat`. The `spancat` component uses a `Logistic` layer where the
+output class probabilities are independent for each class. However, if you need
+to predict at most one true class for a span, then use `spancat_singlelabel`. It
+uses a `Softmax` layer and treats the task as a multi-class problem.
 
 Predicted spans will be saved in a [`SpanGroup`](/api/spangroup) on the doc.
 Individual span scores can be found in `spangroup.attrs["scores"]`.