Text classification guide for iOS

The Text Classifier task lets you classify text into a set of defined categories, such as positive or negative sentiment. The categories are determined based on the model you use and how that model was trained. These instructions show you how to use the Text Classifier in iOS apps. The code sample described in these instructions is available on GitHub.

You can see this task in action by viewing this Web demo. 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 basic implementation of a Text Classifier app for iOS.

You can use the app as a starting point for your own iOS app, or refer to it when modifying an existing app. You can refer to the Text Classifier example code 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/google-ai-edge/mediapipe-samples
   

2. Optionally, configure your git instance to use sparse checkout, so you have only the files for the Text Classifier example app:

   cd mediapipe
   git sparse-checkout init --cone
   git sparse-checkout set examples/text_classification/ios/
   

After creating a local version of the example code, you can install the MediaPipe task library, open the project using Xcode, and run the app. For instructions, see the Setup Guide for iOS.

Key components

The following files contain the crucial code for the Text Classifier example application:

• TextClassifierHelper.swift: Initializes the text classifier and handles the model selection.
• ViewController.swift: Implements the UI and formats the results.

Setup

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

Dependencies

Text Classifier uses the MediaPipeTasksText library, which must be installed using CocoaPods. The library is compatible with both Swift and Objective-C apps and does not require any additional language-specific setup.

For instructions to install CocoaPods on macOS, refer to the CocoaPods installation guide. For instructions on how to create a Podfile with the necessary pods for your app, refer to Using CocoaPods.

Add the MediaPipeTasksText pod in the Podfile using the following code:

target 'MyTextClassifierApp' do
  use_frameworks!
  pod 'MediaPipeTasksText'
end

If your app includes unit test targets, refer to the Set Up Guide for iOS for additional information on setting up your Podfile.

Model

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

Select and download a model, and add it to your project directory using Xcode. For instructions on how to add files to your Xcode project, refer to Managing files and folders in your Xcode project.

Use the BaseOptions.modelAssetPath property to specify the path to the model in your app bundle. For a code example, see the next section.

Create the task

You can create the Text Classifier task by calling one of its initializers. The TextClassifier(options:) initializer sets values for the configuration options.

If you don't need a Text Classifier initialized with customized configuration options, you can use the TextClassifier(modelPath:) initializer to create a Text Classifier with the default options. For more information about configuration options, see Configuration Overview.

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

Swift

import MediaPipeTasksText

let modelPath = Bundle.main.path(forResource: "model",
                                      ofType: "tflite")

let options = TextClassifierOptions()
options.baseOptions.modelAssetPath = modelPath
options.scoreThreshold = 0.6

let textClassifier = try TextClassifier(options: options)

Objective-C

@import MediaPipeTasksText;

NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"model"
                                                      ofType:@"tflite"];

MPPTextClassifierOptions *options = [[MPPTextClassifierOptions alloc] init];
options.baseOptions.modelAssetPath = modelPath;
options.scoreThreshold = 0.6;

MPPTextClassifier *textClassifier =
      [[MPPTextClassifier alloc] initWithOptions:options error:nil];

Configuration options

This task has the following configuration options for iOS apps:

Prepare data

Text Classifier works with text data. The task handles the data input preprocessing, including tokenization and tensor preprocessing.

All preprocessing is handled within the classify(text:) function. There is no need for additional preprocessing of the input text beforehand.

Swift

let text = "The input text to be classified."

Objective-C

NSString *text = @"The input text to be classified.";

Run the task

To run the Text Classifier, use the classify(text:) method. The Text Classifier returns the possible categories for the input text.

Swift

let result = try textClassifier.classify(text: text)

Objective-C

MPPTextClassifierResult *result = [textClassifier classifyText:text
                                                          error:nil];

Note: The task blocks the current thread until it finishes running inference on the text. To avoid blocking the current thread, execute the processing in a background thread using iOS Dispatch or NSOperation frameworks.

Handle and display results

Upon running inference, the Text Classifier task returns a TextClassifierResult object which contains the list of possible categories for the input text. The categories are defined by the model you use, so if you want different categories, pick a different model, or retrain an existing one.

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

TextClassificationResult:
  Classification #0 (single classification head):
    ClassificationEntry #0:
      Category #0:
        category name: "positive"
        score: 0.8904
        index: 0
      Category #1:
        category name: "negative"
        score: 0.1096
        index: 1

This result has been obtained by running the BERT-classifier on the input text: "an imperfect but overall entertaining mystery".

The ViewController.swift file in the example code demonstrates how to display the detection results returned from the task.