diff --git a/mediapipe/model_maker/python/text/text_classifier/preprocessor.py b/mediapipe/model_maker/python/text/text_classifier/preprocessor.py
index 2a31bbd09..68a5df2fd 100644
--- a/mediapipe/model_maker/python/text/text_classifier/preprocessor.py
+++ b/mediapipe/model_maker/python/text/text_classifier/preprocessor.py
@@ -25,7 +25,6 @@ import tensorflow_hub
 
 from mediapipe.model_maker.python.core.data import cache_files as cache_files_lib
 from mediapipe.model_maker.python.text.text_classifier import dataset as text_classifier_ds
-from official.nlp.data import classifier_data_lib
 from official.nlp.tools import tokenization
 
 
@@ -290,6 +289,23 @@ class BertClassifierPreprocessor:
         ds_cache_files.num_shards,
     )
 
+  def _process_bert_features(self, text: str) -> Mapping[str, Sequence[int]]:
+    tokens = self._tokenizer.tokenize(text)
+    tokens = tokens[0 : (self._seq_len - 2)]  # account for [CLS] and [SEP]
+    tokens.insert(0, "[CLS]")
+    tokens.append("[SEP]")
+    input_ids = self._tokenizer.convert_tokens_to_ids(tokens)
+    input_mask = [1] * len(input_ids)
+    while len(input_ids) < self._seq_len:
+      input_ids.append(0)
+      input_mask.append(0)
+    segment_ids = [0] * self._seq_len
+    return {
+        "input_ids": input_ids,
+        "input_mask": input_mask,
+        "segment_ids": segment_ids,
+    }
+
   def preprocess(
       self, dataset: text_classifier_ds.Dataset
   ) -> text_classifier_ds.Dataset:
@@ -310,18 +326,7 @@ class BertClassifierPreprocessor:
       size = 0
       for index, (text, label) in enumerate(dataset.gen_tf_dataset()):
         _validate_text_and_label(text, label)
-        example = classifier_data_lib.InputExample(
-            guid=str(index),
-            text_a=text.numpy()[0].decode("utf-8"),
-            text_b=None,
-            # InputExample expects the label name rather than the int ID
-            # label=dataset.label_names[label.numpy()[0]])
-            label=label.numpy()[0],
-        )
-        feature = classifier_data_lib.convert_single_example(
-            index, example, None, self._seq_len, self._tokenizer
-        )
-
+        feature = self._process_bert_features(text.numpy()[0].decode("utf-8"))
         def create_int_feature(values):
           f = tf.train.Feature(
               int64_list=tf.train.Int64List(value=list(values))
@@ -329,10 +334,10 @@ class BertClassifierPreprocessor:
           return f
 
         features = collections.OrderedDict()
-        features["input_ids"] = create_int_feature(feature.input_ids)
-        features["input_mask"] = create_int_feature(feature.input_mask)
-        features["segment_ids"] = create_int_feature(feature.segment_ids)
-        features["label_ids"] = create_int_feature([feature.label_id])
+        features["input_ids"] = create_int_feature(feature["input_ids"])
+        features["input_mask"] = create_int_feature(feature["input_mask"])
+        features["segment_ids"] = create_int_feature(feature["segment_ids"])
+        features["label_ids"] = create_int_feature([label.numpy()[0]])
         tf_example = tf.train.Example(
             features=tf.train.Features(feature=features)
         )