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Language detection guide for Web

The MediaPipe Language Detector task lets you identify the language of a piece of text. These instructions show you how to use the Language Detector for web and JavaScript apps. The code sample described in these instructions is available on GitHub.

You can see this task in action by viewing the demo. For more information about the capabilities, models, and configuration options of this task, see the Overview.

Code example

The example code for Language Detector provides a complete implementation of this task in JavaScript for your reference. This code helps you test this task and get started on building your own language detector feature. You can view, run, and edit the Language Detector example code using just your web browser.


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

JavaScript packages

Language Detector code is available through the @mediapipe/tasks-text package. You can find and download these libraries from links provided in the platform Setup guide.

You can install the required packages with the following code for local staging using the following command:

npm install @mediapipe/tasks-text

If you want to deploy to a server, you can use a content delivery network (CDN) service, such as jsDelivr, to add code directly to your HTML page, as follows:

  <script src=""


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

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


Specify the path of the model with the baseOptions object modelAssetPath parameter, as shown below:

baseOptions: {
        modelAssetPath: `/app/shared/models/language_detector.tflite`

Create the task

Use one of the Language Detector LanguageDetector.createFrom...() functions to prepare the task for running inferences. You can use the createFromModelPath() function with a relative or absolute path to the trained model file. The code example below demonstrates using the createFromOptions() function. For more information on configuring tasks, see Configuration options.

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

async function createDetector() {
  const textFiles = await FilesetResolver.forTextTasks(
  languageDetector = await languageDetector.createFromOptions(
      baseOptions: {
        modelAssetPath: ``

Configuration options

This task has the following configuration options for Web and JavaScript applications:

Option Name Description Value Range Default Value
maxResults Sets the optional maximum number of top-scored language predictions to return. If this value is less than zero, all available results are returned. Any positive numbers -1
scoreThreshold Sets the prediction score threshold that overrides the one provided in the model metadata (if any). Results below this value are rejected. Any float Not set
categoryAllowlist Sets the optional list of allowed language codes. If non-empty, language predictions whose language code is not in this set will be filtered out. This option is mutually exclusive with categoryDenylist and using both results in an error. Any strings Not set
categoryDenylist Sets the optional list of language codes that are not allowed. If non-empty, language predictions whose language code is in this set will be filtered out. This option is mutually exclusive with categoryAllowlist and using both results in an error. Any strings Not set

Prepare data

Language Detector works with text (string) data. The task handles the data input preprocessing, including tokenization and tensor preprocessing. All preprocessing is handled within the detect function. There is no need for additional preprocessing of the input text beforehand.

const inputText = "The input text for the detector.";

Run the task

The Language Detector uses the detect function to trigger inferences. For language detection, this means returning the possible languages for the input text.

The following code demonstrates how to execute the processing with the task model:

// Wait to run the function until inner text is set
const detectionResult = languageDetector.detect(inputText);

Handle and display results

The Language Detector task outputs a LanguageDetectorResult consisting of a list of language predictions along with the probabilities for those predictions. The following shows an example of the output data from this task:

  LanguagePrediction #0:
    language_code: "fr"
    probability: 0.999781

This result has been obtained by running the model on the input text: "Il y a beaucoup de bouches qui parlent et fort peu de tĂȘtes qui pensent.".

For an example of the code required to process and visualize the results of this task, see the Web sample app.