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README.md

TensorFlow Lite Text Classification Android Demo

Overview

This sample will accept text entered into a field and classify it as either positive or negative with a provided confidence score. The supported classification models include Word Vector and MobileBERT, both of which are generated using TensorFlow's Model Maker. These instructions walk you through building and running the demo on an Android device.

The model files are downloaded via Gradle scripts when you build and run the app. You don't need to do any steps to download TFLite models into the project explicitly.

Build the demo using Android Studio

Prerequisites

  • The Android Studio IDE. This sample has been tested on Android Studio Chipmunk.

  • A physical or emulated Android device with a minimum OS version of SDK 21 (Android 5.0) with developer mode enabled. The process of enabling developer mode may vary by device.

Building

  • Open Android Studio. From the Welcome screen, select Open an existing Android Studio project.

  • From the Open File or Project window that appears, navigate to and select the tensorflow-lite/examples/text_classification/android directory. Click OK.

  • If it asks you to do a Gradle Sync, click OK.

  • With your Android device connected developer mode enabled, click on the green Run arrow in Android Studio.

Models used

Downloading, extraction, and placing the models into the assets folder is managed automatically by the download_model.gradle file.