Fix minor comment typos in BERT and MobileBERT APIs.

PiperOrigin-RevId: 360529962

official/nlp/keras_nlp/encoders/bert_encoder.py

@@ -60,7 +60,7 @@ class BertEncoder(tf.keras.Model):
     initializer: The initialzer to use for all weights in this encoder.
     output_range: The sequence output range, [0, output_range), by slicing the
       target sequence of the last transformer layer. `None` means the entire
-      target sequence will attend to the source sequence, which yeilds the full
+      target sequence will attend to the source sequence, which yields the full
       output.
     embedding_width: The width of the word embeddings. If the embedding width is
       not equal to hidden size, embedding parameters will be factorized into two

official/nlp/modeling/layers/mobile_bert_layers_test.py

@@ -22,7 +22,7 @@ from official.nlp.modeling.networks import mobile_bert_encoder
 
 
 def generate_fake_input(batch_size=1, seq_len=5, vocab_size=10000, seed=0):
-  """Generate consisitant fake integer input sequences."""
+  """Generate consistent fake integer input sequences."""
   np.random.seed(seed)
   fake_input = []
   for _ in range(batch_size):

official/nlp/modeling/networks/bert_encoder.py

@@ -65,7 +65,7 @@ class BertEncoder(keras_nlp.encoders.BertEncoder):
       keyed by `encoder_outputs`.
     output_range: The sequence output range, [0, output_range), by slicing the
       target sequence of the last transformer layer. `None` means the entire
-      target sequence will attend to the source sequence, which yeilds the full
+      target sequence will attend to the source sequence, which yields the full
       output.
     embedding_width: The width of the word embeddings. If the embedding width is
       not equal to hidden size, embedding parameters will be factorized into two

official/nlp/modeling/networks/mobile_bert_encoder_test.py

@@ -21,7 +21,7 @@ from official.nlp.modeling.networks import mobile_bert_encoder
 
 
 def generate_fake_input(batch_size=1, seq_len=5, vocab_size=10000, seed=0):
-  """Generate consisitant fake integer input sequences."""
+  """Generate consistent fake integer input sequences."""
   np.random.seed(seed)
   fake_input = []
   for _ in range(batch_size):