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tensorflow/lite/g3doc/examples/text_classification/overview.md

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+# Text classification
+
+Use a TensorFlow Lite model to category a paragraph into predefined groups.
+
+Note: (1) To integrate an existing model, try
+[TensorFlow Lite Task Library](https://www.tensorflow.org/lite/inference_with_metadata/task_library/nl_classifier).
+(2) To customize a model, try
+[TensorFlow Lite Model Maker](https://ai.google.dev/edge/litert/libraries/modify/text_classification).
+
+## Get started
+
+<img src="images/screenshot.gif" class="attempt-right" style="max-width: 300px">
+
+If you are new to TensorFlow Lite and are working with Android, we recommend
+exploring the guide of
+[TensorFLow Lite Task Library](../../inference_with_metadata/task_library/nl_classifier.md)
+to integrate text classification models within just a few lines of code. You can
+also integrate the model using the
+[TensorFlow Lite Interpreter Java API](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/guide/inference.md#load-and-run-a-model-in-java).
+
+The Android example below demonstrates the implementation for both methods as
+[lib_task_api](https://github.com/tensorflow/examples/tree/master/lite/examples/text_classification/android/lib_task_api)
+and
+[lib_interpreter](https://github.com/tensorflow/examples/tree/master/lite/examples/text_classification/android/lib_interpreter),
+respectively.
+
+<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/text_classification/android">Android
+example</a>
+
+If you are using a platform other than Android, or you are already familiar with
+the TensorFlow Lite APIs, you can download our starter text classification
+model.
+
+<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/text_classification/text_classification_v2.tflite">Download
+starter model</a>
+
+## How it works
+
+Text classification categorizes a paragraph into predefined groups based on its
+content.
+
+This pretrained model predicts if a paragraph's sentiment is positive or
+negative. It was trained on
+[Large Movie Review Dataset v1.0](http://ai.stanford.edu/~amaas/data/sentiment/)
+from Mass et al, which consists of IMDB movie reviews labeled as either positive
+or negative.
+
+Here are the steps to classify a paragraph with the model:
+
+1.  Tokenize the paragraph and convert it to a list of word ids using a
+    predefined vocabulary.
+1.  Feed the list to the TensorFlow Lite model.
+1.  Get the probability of the paragraph being positive or negative from the
+    model outputs.
+
+### Note
+
+*   Only English is supported.
+*   This model was trained on movie reviews dataset so you may experience
+    reduced accuracy when classifying text of other domains.
+
+## Performance benchmarks
+
+Performance benchmark numbers are generated with the tool
+[described here](https://www.tensorflow.org/lite/performance/benchmarks).
+
+<table>
+  <thead>
+    <tr>
+      <th>Model Name</th>
+      <th>Model size </th>
+      <th>Device </th>
+      <th>CPU</th>
+    </tr>
+  </thead>
+  <tr>
+    <td rowspan = 3>
+      <a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/text_classification/text_classification_v2.tflite">Text Classification</a>
+    </td>
+    <td rowspan = 3>
+      0.6 Mb
+    </td>
+    <td>Pixel 3 (Android 10) </td>
+    <td>0.05ms*</td>
+  </tr>
+   <tr>
+     <td>Pixel 4 (Android 10) </td>
+    <td>0.05ms*</td>
+  </tr>
+   <tr>
+     <td>iPhone XS (iOS 12.4.1) </td>
+    <td>0.025ms** </td>
+  </tr>
+</table>
+
+\* 4 threads used.
+
+\*\* 2 threads used on iPhone for the best performance result.
+
+## Example output
+
+| Text                                       | Negative (0) | Positive (1) |
+| ------------------------------------------ | ------------ | ------------ |
+| This is the best movie I’ve seen in recent | 25.3%        | 74.7%        |
+: years. Strongly recommend it!              :              :              :
+| What a waste of my time.                   | 72.5%        | 27.5%        |
+
+## Use your training dataset
+
+Follow this
+[tutorial](https://ai.google.dev/edge/litert/libraries/modify/text_classification)
+to apply the same technique used here to train a text classification model using
+your own datasets. With the right dataset, you can create a model for use cases
+such as document categorization or toxic comments detection.
+
+## Read more about text classification
+
+*   [Word embeddings and tutorial to train this model](https://www.tensorflow.org/tutorials/text/word_embeddings)