tensorflow/lite/g3doc/examples/smart_reply/overview.md
Our smart reply model generates reply suggestions based on chat messages. The suggestions are intended to be contextually relevant, one-touch responses that help the user to easily reply to an incoming message.
<a class="button button-primary" href="https://tfhub.dev/tensorflow/lite-model/smartreply/1/default/1?lite-format=tflite">Download starter model</a>
There is a TensorFlow Lite sample application that demonstrates the smart reply model on Android.
<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/smart_reply/android">View Android example</a>
Read the GitHub page to learn how the app works. Inside this project, you will also learn how to build an app with custom C++ ops.
The model generates reply suggestions to conversational chat messages.
The on-device model comes with several benefits. It is:
<ul> <li>Fast: The model resides on the device and does not require internet connectivity. Thus, inference is very fast and has an average latency of only a few milliseconds.</li> <li>Resource efficient: The model has a small memory footprint on the device.</li> <li>Privacy-friendly: User data never leaves the device.</li> </ul>