Integrate BERT question answerer

The Task Library BertQuestionAnswerer API loads a Bert model and answers questions based on the content of a given passage. For more information, see the example for the Question-Answer model.

Key features of the BertQuestionAnswerer API

• Takes two text inputs as question and context and outputs a list of possible answers.

• Performs out-of-graph Wordpiece or Sentencepiece tokenizations on input text.

Supported BertQuestionAnswerer models

The following models are compatible with the BertNLClassifier API.

• Models created by TensorFlow Lite Model Maker for BERT Question Answer.

• The pretrained BERT models on TensorFlow Hub.

• Custom models that meet the model compatibility requirements.

Run inference in Java

Step 1: Import Gradle dependency and other settings

Copy the .tflite model file to the assets directory of the Android module where the model will be run. Specify that the file should not be compressed, and add the TensorFlow Lite library to the module’s build.gradle file:

android {
    // Other settings

    // Specify tflite file should not be compressed for the app apk
    aaptOptions {
        noCompress "tflite"
    }

}

dependencies {
    // Other dependencies

    // Import the Task Text Library dependency
    implementation 'org.tensorflow:tensorflow-lite-task-text:0.4.4'
}

Step 2: Run inference using the API

// Initialization
BertQuestionAnswererOptions options =
    BertQuestionAnswererOptions.builder()
        .setBaseOptions(BaseOptions.builder().setNumThreads(4).build