Fix typo.

PiperOrigin-RevId: 322960082

official/nlp/modeling/layers/masked_lm.py

@@ -34,7 +34,7 @@ class MaskedLM(tf.keras.layers.Layer):
   Arguments:
     embedding_table: The embedding table of the targets.
     activation: The activation, if any, for the dense layer.
-    initializer: The intializer for the dense layer. Defaults to a Glorot
+    initializer: The initializer for the dense layer. Defaults to a Glorot
       uniform initializer.
     output: The output style for this network. Can be either 'logits' or
       'predictions'.

official/nlp/modeling/networks/classification.py

@@ -37,8 +37,8 @@ class Classification(tf.keras.Model):
     num_classes: The number of classes that this network should classify to. If
       equal to 1, a regression problem is assumed.
     activation: The activation, if any, for the dense layer in this network.
-    initializer: The intializer for the dense layer in this network. Defaults to
-      a Glorot uniform initializer.
+    initializer: The initializer for the dense layer in this network. Defaults
+      to a Glorot uniform initializer.
     output: The output style for this network. Can be either 'logits' or
       'predictions'.
   """

official/nlp/modeling/networks/span_labeling.py

@@ -33,8 +33,8 @@ class SpanLabeling(tf.keras.Model):
   Arguments:
     input_width: The innermost dimension of the input tensor to this network.
     activation: The activation, if any, for the dense layer in this network.
-    initializer: The intializer for the dense layer in this network. Defaults to
-      a Glorot uniform initializer.
+    initializer: The initializer for the dense layer in this network. Defaults
+      to a Glorot uniform initializer.
     output: The output style for this network. Can be either 'logits' or
       'predictions'.
   """

official/nlp/modeling/networks/token_classification.py

@@ -34,8 +34,8 @@ class TokenClassification(tf.keras.Model):
     input_width: The innermost dimension of the input tensor to this network.
     num_classes: The number of classes that this network should classify to.
     activation: The activation, if any, for the dense layer in this network.
-    initializer: The intializer for the dense layer in this network. Defaults to
-      a Glorot uniform initializer.
+    initializer: The initializer for the dense layer in this network. Defaults
+      to a Glorot uniform initializer.
     output: The output style for this network. Can be either 'logits' or
       'predictions'.
   """

official/nlp/xlnet/xlnet_modeling.py

@@ -55,7 +55,7 @@ def rel_shift(x, klen=-1):
 
 
 def _get_initializer(flags):
-  """Get variable intializer."""
+  """Get variable initializer."""
   if flags.init_method == 'uniform':
     initializer = tf.keras.initializers.RandomUniform(
         minval=-flags.init_range, maxval=flags.init_range)