mediapipe_text

Description:

mediapipe text

MediaPipe Text for Flutter #

A Flutter plugin to use the MediaPipe Text API, which contains multiple text-based Mediapipe tasks.
To learn more about MediaPipe, please visit the MediaPipe website
Getting Started #
To get started with MediaPipe, please see the documentation.
Supported Tasks #


Task
Android
iOS
Web
Windows
macOS
Linux


Classification








Embedding








Language Detection








Usage #
To get started with this plugin, you must be on the master channel.
Second, you will need to opt-in to the native-assets experiment,
using the --enable-experiment=native-assets flag whenever you run any commands
using the $ dart command line tool.
To enable this globally in Flutter, run:
$ flutter config --enable-native-assets
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To disable this globally in Flutter, run:
$ flutter config --no-enable-native-assets
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Add dependencies #
Add mediapipe_text and mediapipe_core to your pubspec.yaml file:
dependencies:
flutter:
sdk: flutter
mediapipe_core: latest
mediapipe_text: latest
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Add tflite models #
Add the necessary models to your assets directory for each task you
intend to use:
flutter:
assets:
- assets/bert_classifier.tflite
- assets/language_detector.tflite
- assets/universal_sentence_encoder.tflite
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These models can be downloaded at the following locations:

bert_classifier.tflife (for Text Classification)
language_detector.tflite (for Language Detection)
universal_sentence_encoder.tflife (for Text Embedding)

Initialize your task worker #
Text classification example:
import 'package:mediapipe_text/mediapipe_text.dart';

// Load your text classifier tflite model into memory
ByteData? classifierBytes = await DefaultAssetBundle.of(context)
.load('assets/bert_classifier.tflite');

// Create a `TextClassifier`
final classifier = TextClassifier(
TextClassifierOptions.fromAssetBuffer(
classifierBytes.buffer.asUint8List(),
),
);

// Classify some text!
final result = await classifier.classify('Hello, world!');
print(result.classifications.first);
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Language detection example:
import 'package:mediapipe_text/mediapipe_text.dart';

// Load your language detection tflite model into memory
ByteData? bytes = await DefaultAssetBundle.of(context)
.load('assets/language_detector.tflite');

// Create a `LanguageDetector`
final detector = LanguageDetector(
LanguageDetectorOptions.fromAssetBuffer(
bytes.buffer.asUint8List(),
),
);

// Language-detect some text!
final result = await detector.detect('Hello, world!');
print(result.predictions.first);
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Text embedding example:
import 'package:mediapipe_text/mediapipe_text.dart';

// Load your text embedding tflite model into memory
ByteData? embedderBytes = await DefaultAssetBundle.of(context)
.load('assets/universal_sentence_encoder.tflite');

// Create a `TextEmbedder`
final embedder = TextEmbedder(
TextEmbedderOptions.fromAssetBuffer(
embedderBytes.buffer.asUint8List(),
),
);

// Embed some text!
final result = await embedder.embed('Hello, world!');
final result2 = await embedder.embed('Hello, moon!');

// Compare the results
final similarity = embedder.cosineSimilarity(
result.embeddings.first,
result2.embeddings.first,
);
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Running the example #
To run the example project, download the models associated with whatever tasks
you want to explore, place them in the packages/mediapipe_task_text/example/assets
directory, and run the project on one of the supported platforms.
Issues and feedback #
Please file Flutter-MediaPipe specific issues, bugs, or feature requests in our issue tracker.
Issues that are specific to Flutter can be filed in the Flutter issue tracker.
To contribute a change to this plugin,
please review our contribution guide
and open a pull request.