diff --git a/lit_nlp/examples/models/tfx_model.py b/lit_nlp/examples/models/tfx_model.py
deleted file mode 100644
index 29df2ab9..00000000
--- a/lit_nlp/examples/models/tfx_model.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Wrapper for using TFX-generated models within LIT."""
-from collections.abc import Iterator
-
-import attr
-from lit_nlp.api import model as lit_model
-from lit_nlp.api import types as lit_types
-import tensorflow as tf
-import tensorflow_text as tf_text  # pylint: disable=unused-import
-
-_SERVING_DEFAULT_SIGNATURE = 'serving_default'
-
-
-@attr.s(auto_attribs=True)
-class TFXModelConfig(object):
-  """Configuration object for TFX Models."""
-  path: str
-  input_spec: lit_types.Spec
-  output_spec: lit_types.Spec
-  signature: str = _SERVING_DEFAULT_SIGNATURE
-
-
-# TODO(b/188036366): Revisit the assumed mapping between input values and
-# TF.Examples.
-def _inputs_to_serialized_example(input_dict: lit_types.JsonDict):
-  """Converts the input dictionary to a serialized tf example."""
-  feature_dict = {}
-  for k, v in input_dict.items():
-    if not isinstance(v, list):
-      v = [v]
-    if isinstance(v[0], int):
-      feature_dict[k] = tf.train.Feature(int64_list=tf.train.Int64List(value=v))
-    elif isinstance(v[0], float):
-      feature_dict[k] = tf.train.Feature(float_list=tf.train.FloatList(value=v))
-    else:
-      feature_dict[k] = tf.train.Feature(
-          bytes_list=tf.train.BytesList(value=[bytes(i, 'utf-8') for i in v]))
-  result = tf.train.Example(features=tf.train.Features(feature=feature_dict))
-  return result.SerializeToString()
-
-
-class TFXModel(lit_model.BatchedModel):
-  """Wrapper for querying a TFX-generated SavedModel."""
-
-  def __init__(self, config: TFXModelConfig):
-    self._model = tf.saved_model.load(config.path)
-    self._signature = config.signature
-    self._input_spec = config.input_spec
-    self._output_spec = config.output_spec
-
-  def predict_minibatch(  # pytype: disable=signature-mismatch  # overriding-return-type-checks
-      self, inputs: list[lit_types.JsonDict]
-  ) -> Iterator[lit_types.JsonDict]:
-    for i in inputs:
-      filtered_inputs = {k: v for k, v in i.items() if k in self._input_spec}
-      result = self._model.signatures[self._signature](
-          tf.constant([_inputs_to_serialized_example(filtered_inputs)]))
-      result = {
-          k: tf.squeeze(v).numpy().tolist()
-          for k, v in result.items()
-          if k in self._output_spec
-      }
-      for k, v in result.items():
-        # If doing Multiclass Prediction for a Binary Classifier.
-        if (isinstance(self._output_spec[k], lit_types.MulticlassPreds) and
-            not isinstance(v, list)):
-          result[k] = [1 - v, v]
-      yield result
-
-  def input_spec(self) -> lit_types.Spec:
-    return self._input_spec
-
-  def output_spec(self) -> lit_types.Spec:
-    return self._output_spec
diff --git a/lit_nlp/examples/models/tfx_model_test.py b/lit_nlp/examples/models/tfx_model_test.py
deleted file mode 100644
index e7b977c9..00000000
--- a/lit_nlp/examples/models/tfx_model_test.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""Tests for lit_nlp.components.tfx_model."""
-import tempfile
-
-from lit_nlp.api import types as lit_types
-from lit_nlp.examples.models import tfx_model
-import tensorflow as tf
-
-
-class TfxModelTest(tf.test.TestCase):
-
-  def setUp(self):
-    super(TfxModelTest, self).setUp()
-    self._path = tempfile.mkdtemp()
-    input_layer = tf.keras.layers.Input(
-        shape=(1,), dtype=tf.string, name='example'
-    )
-    parsed_input = tf.io.parse_example(
-        tf.reshape(input_layer, [-1]),
-        {'input_0': tf.io.FixedLenFeature([1], dtype=tf.float32)})
-    output_layer = tf.keras.layers.Dense(
-        1, name='output_0')(
-            parsed_input['input_0'])
-    model = tf.keras.Model(input_layer, output_layer)
-    model.compile(
-        optimizer=tf.keras.optimizers.Adam(lr=.001),
-        loss=tf.keras.losses.binary_crossentropy)
-    model.save(self._path)
-
-  def testTfxModel(self):
-    input_spec = {'input_0': lit_types.Scalar()}
-    output_spec = {
-        'output_0':
-            lit_types.MulticlassPreds(vocab=['0', '1'], parent='input_0')
-    }
-    config = tfx_model.TFXModelConfig(self._path, input_spec, output_spec)
-    lit_model = tfx_model.TFXModel(config)
-    result = list(lit_model.predict([{'input_0': 0.5}]))
-    self.assertLen(result, 1)
-    result = result[0]
-    self.assertListEqual(list(result.keys()), ['output_0'])
-    self.assertLen(result['output_0'], 2)
-    self.assertIsInstance(result['output_0'][0], float)
-    self.assertIsInstance(result['output_0'][1], float)
-    self.assertDictEqual(lit_model.input_spec(), input_spec)
-    self.assertDictEqual(lit_model.output_spec(), output_spec)
-
-
-if __name__ == '__main__':
-  tf.test.main()