diff --git a/keras/src/metrics/probabilistic_metrics.py b/keras/src/metrics/probabilistic_metrics.py
index 1abcd55623f..2f719d84630 100644
--- a/keras/src/metrics/probabilistic_metrics.py
+++ b/keras/src/metrics/probabilistic_metrics.py
@@ -69,9 +69,7 @@ class Poisson(reduction_metrics.MeanMetricWrapper):
         name: (Optional) string name of the metric instance.
         dtype: (Optional) data type of the metric result.
 
-    Example:
-
-    Example:
+    Examples:
 
     >>> m = keras.metrics.Poisson()
     >>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
@@ -119,9 +117,7 @@ class BinaryCrossentropy(reduction_metrics.MeanMetricWrapper):
             e.g. `label_smoothing=0.2` means that we will use
             a value of 0.1 for label "0" and 0.9 for label "1".
 
-    Example:
-
-    Example:
+    Examples:
 
     >>> m = keras.metrics.BinaryCrossentropy()
     >>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
@@ -195,9 +191,7 @@ class CategoricalCrossentropy(reduction_metrics.MeanMetricWrapper):
         axis: (Optional) Defaults to `-1`.
             The dimension along which entropy is computed.
 
-    Example:
-
-    Example:
+    Examples:
 
     >>> # EPSILON = 1e-7, y = y_true, y` = y_pred
     >>> # y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)
@@ -282,9 +276,7 @@ class SparseCategoricalCrossentropy(reduction_metrics.MeanMetricWrapper):
         axis: (Optional) Defaults to `-1`.
             The dimension along which entropy is computed.
 
-    Example:
-
-    Example:
+    Examples:
 
     >>> # y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]
     >>> # logits = log(y_pred)