diff --git a/tensorflow/lite/g3doc/convert/python_api.md b/tensorflow/lite/g3doc/convert/python_api.md
index b914a34fa87..4d2c7361c9f 100644
--- a/tensorflow/lite/g3doc/convert/python_api.md
+++ b/tensorflow/lite/g3doc/convert/python_api.md
@@ -19,9 +19,9 @@ be targeted to devices with mobile.
 
 ## API
 
-The API for converting TensorFlow models to TensorFlow Lite as of TensorFlow 1.9
-is `tf.lite.TFLiteConverter`. The API for calling the Python intepreter
-is `tf.lite.Interpreter`.
+The API for converting TensorFlow models to TensorFlow Lite is
+`tf.lite.TFLiteConverter`. The API for calling the Python interpreter is
+`tf.lite.Interpreter`.
 
 `TFLiteConverter` provides class methods based on the original format of the
 model. `TFLiteConverter.from_session()` is available for GraphDefs.
diff --git a/tensorflow/lite/tutorials/post_training_quant.ipynb b/tensorflow/lite/tutorials/post_training_quant.ipynb
index 3ff145d9ce9..394ab0760b5 100644
--- a/tensorflow/lite/tutorials/post_training_quant.ipynb
+++ b/tensorflow/lite/tutorials/post_training_quant.ipynb
@@ -235,9 +235,9 @@
         "id": "AT8BgkKmljOy"
       },
       "source": [
-        "Using the python `TocoConverter`, the saved model can be converted into a TFLite model.\n",
+        "Using the python `TFLiteConverter`, the saved model can be converted into a TFLite model.\n",
         "\n",
-        "First load the model using the `TocoConverter`:"
+        "First load the model using the `TFLiteConverter`:"
       ]
     },
     {
@@ -252,7 +252,7 @@
       "source": [
         "import tensorflow as tf\n",
         "tf.enable_eager_execution()\n",
-        "converter = tf.lite.TocoConverter.from_saved_model(saved_model_dir)\n",
+        "converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\n",
         "tflite_model = converter.convert()"
       ]
     },
@@ -648,7 +648,7 @@
         "graph_def_file = pathlib.Path(archive_path).parent/\"resnet_v2_101_299_frozen.pb\"\n",
         "input_arrays = [\"input\"] \n",
         "output_arrays = [\"output\"]\n",
-        "converter = tf.lite.TocoConverter.from_frozen_graph(\n",
+        "converter = tf.lite.TFLiteConverter.from_frozen_graph(\n",
         "  str(graph_def_file), input_arrays, output_arrays, input_shapes={\"input\":[1,299,299,3]})\n",
         "converter.post_training_quantize = True\n",
         "resnet_tflite_file = graph_def_file.parent/\"resnet_v2_101_quantized.tflite\"\n",