diff --git a/mediapipe/model_maker/python/text/text_classifier/preprocessor.py b/mediapipe/model_maker/python/text/text_classifier/preprocessor.py
index 24130f6f8a..5c3b769c8f 100644
--- a/mediapipe/model_maker/python/text/text_classifier/preprocessor.py
+++ b/mediapipe/model_maker/python/text/text_classifier/preprocessor.py
@@ -145,7 +145,6 @@ def get_vocab(self) -> Mapping[str, int]:
     """Returns the vocab of the AverageWordEmbeddingClassifierPreprocessor."""
     return self._vocab
 
-  # TODO: Align with MediaPipe's RegexTokenizer.
   def _regex_tokenize(self, text: str) -> Sequence[str]:
     """Splits `text` by words but does not split on single quotes.
 
diff --git a/mediapipe/model_maker/python/text/text_classifier/text_classifier.py b/mediapipe/model_maker/python/text/text_classifier/text_classifier.py
index 623edbc386..348f4cfb63 100644
--- a/mediapipe/model_maker/python/text/text_classifier/text_classifier.py
+++ b/mediapipe/model_maker/python/text/text_classifier/text_classifier.py
@@ -353,7 +353,6 @@ def _get_metadata_writer(self, tflite_model: bytearray, vocab_filepath: str):
     return text_classifier_writer.MetadataWriter.create_for_regex_model(
         model_buffer=tflite_model,
         regex_tokenizer=metadata_writer.RegexTokenizer(
-            # TODO: Align with MediaPipe's RegexTokenizer.
             delim_regex_pattern=self._DELIM_REGEX_PATTERN,
             vocab_file_path=vocab_filepath),
         labels=metadata_writer.Labels().add(list(self._label_names)))