Text embedding guide for Android

The MediaPipe Text Embedder task lets you create a numeric representation of text data to capture its semantic meaning. These instructions show you how to use the Text Embedder with Android apps.

For more information about the capabilities, models, and configuration options of this task, see the Overview.

Code example

The MediaPipe Tasks example code is a simple implementation of a Text Embedder app for Android. The example evaluates the semantic similarities between two pieces of text, and requires either a physical Android device or an Android emulator.

You can use the app as a starting point for your own Android app, or refer to it when modifying an existing app. The Text Embedder example code is hosted on GitHub.

Download the code

The following instructions show you how to create a local copy of the example code using the git command line tool.

To download the example code:

  1. Clone the git repository using the following command:
    git clone https://github.com/googlesamples/mediapipe
  2. Optionally, configure your git instance to use sparse checkout, so you have only the files for the Text Embedder example app:
    cd mediapipe
    git sparse-checkout init --cone
    git sparse-checkout set examples/text_embedder/android

After creating a local version of the example code, you can import the project into Android Studio and run the app. For instructions, see the Setup Guide for Android.

Key components

The following files contain the crucial code for this text embedder example application:

  • TextEmbedderHelper.kt: Initializes the text embedder and handles the model and delegate selection.
  • MainActivity.kt: Implements the application and assembles the user interface components.


This section describes key steps for setting up your development environment and code projects specifically to use Text Embedder. For general information on setting up your development environment for using MediaPipe tasks, including platform version requirements, see the Setup guide for Android.


Text Embedder uses the com.google.mediapipe:tasks-text libraries. Add this dependency to the build.gradle file of your Android app development project. You can import the required dependencies with the following code:

dependencies {
    implementation 'com.google.mediapipe:tasks-text:latest.release'


The MediaPipe Text Embedder task requires a trained model that is compatible with this task. For more information on available trained models for Text Embedder, see the task overview Models section.

Select and download the model, and then store it within your project directory:


Specify the path of the model within the ModelAssetPath parameter. In the example code, the model is defined in the setupTextEmbedder() function in TextEmbedderHelper.kt file:

Use the BaseOptions.Builder.setModelAssetPath() function to specify the path used by the model. This method is referred to in the code example in the next section.

Create the task

You can use one of the createFrom...() functions to create the task. The createFromOptions() function accepts configuration options to set the embedder options. You can also initialize the task using the createFromFile() factory function. The createFromFile() function accepts a relative or absolute path to the trained model file. For more information on configuration options, see Configuration options.

The following code demonstrates how to build and configure this task.

val baseOptions = baseOptionsBuilder.build()
val optionsBuilder =
val options = optionsBuilder.build()
textEmbedder = TextEmbedder.createFromOptions(context, options)

The example code implementation sets the text embedder options in the setupTextEmbedder() function in the TextEmbedderHelper.kt file.

Configuration options

This task has the following configuration options for Android apps:

Option Name Description Value Range Default Value
l2_normalize Whether to normalize the returned feature vector with L2 norm. Use this option only if the model does not already contain a native L2_NORMALIZATION TFLite Op. In most cases, this is already the case and L2 normalization is thus achieved through TFLite inference with no need for this option. Boolean False
quantize Whether the returned embedding should be quantized to bytes via scalar quantization. Embeddings are implicitly assumed to be unit-norm and therefore any dimension is guaranteed to have a value in [-1.0, 1.0]. Use the l2_normalize option if this is not the case. Boolean False

Prepare data

Text Embedder works with text (String) data. The task handles the data input preprocessing, including tokenization and tensor preprocessing. All preprocessing is handled within the embed() function. There is no need for additional preprocessing of the input text beforehand.

val inputText = "The input text to be embedded."

Run the task

The Text Embedder uses the embed function to trigger inferences. For text embedding, this means returning the embedding vectors for the input text.

The following code demonstrates how execute the processing with the task model.

textEmbedder?.let {
    val firstEmbed =
    val secondEmbed =

In the example code, the embed function is called in the TextEmbedderHelper.kt file.

Handle and display results

Upon running inference, the Text Embedder task returns an TextEmbedderResult object that contains a list of embeddings (either floating point or scalar-quantized) for the input text.

The following shows an example of the output data from this task:

  Embedding #0 (sole embedding head):
    float_embedding: {0.2345f, 0.1234f, ..., 0.6789f}
    head_index: 0

You can compare the semantic similarity of two embeddings using the TextEmbedder.cosineSimilarity function. See the following code for an example.

val similarity = TextEmbedder.cosineSimilarity(firstEmbed, secondEmbed)

In the example code, the TextEmbedder.cosineSimilarity() function is called in the TextEmbedderHelper.kt file.