Integrate Natural language classifier

The Task Library's NLClassifier API classifies input text into different categories, and is a versatile and configurable API that can handle most text classification models.

Key features of the NLClassifier API

  • Takes a single string as input, performs classification with the string and outputs pairs as classification results.

  • Optional Regex Tokenization available for input text.

  • Configurable to adapt different classification models.

Supported NLClassifier models

The following models are guaranteed to be compatible with the NLClassifier API.

Run inference in Java

See the Text Classification reference app for an example of how to use NLClassifier in an Android app.

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 Vision Library dependency (NNAPI is included)
    implementation 'org.tensorflow:tensorflow-lite-task-text:0.4.4'
    // Import the GPU delegate plugin Library for GPU inference
    implementation 'org.tensorflow:tensorflow-lite-gpu-delegate-plugin:0.4.4'

Step 2: Run inference using the API

// Initialization, use NLClassifierOptions to configure input and output tensors
NLClassifierOptions options =
NLClassifier classifier =
    NLClassifier.createFromFileAndOptions(context, modelFile, options);

// Run inference
List<Category> results = classifier.classify(input);

See the source code for more options to configure NLClassifier.

Run inference in Swift

Step 1: Import CocoaPods

Add the TensorFlowLiteTaskText pod in Podfile

target 'MySwiftAppWithTaskAPI' do
  pod 'TensorFlowLiteTaskText', '~> 0.4.4'

Step 2: Run inference using the API

// Initialization
var modelOptions:TFLNLClassifierOptions = TFLNLClassifierOptions()
modelOptions.inputTensorName = inputTensorName
modelOptions.outputScoreTensorName = outputScoreTensorName
let nlClassifier = TFLNLClassifier.nlClassifier(
      modelPath: modelPath,
      options: modelOptions)

// Run inference
let categories = nlClassifier.classify(text: input)

See the source code for more details.

Run inference in C++

// Initialization
NLClassifierOptions options;
std::unique_ptr<NLClassifier> classifier = NLClassifier::CreateFromOptions(options).value();

// Run inference with your input, `input_text`.
std::vector<core::Category> categories = classifier->Classify(input_text);

See the source code for more details.

Run inference in Python

Step 1: Install the pip package

pip install tflite-support

Step 2: Using the model

# Imports
from tflite_support.task import text

# Initialization
classifier = text.NLClassifier.create_from_file(model_path)

# Run inference
text_classification_result = classifier.classify(text)

See the source code for more options to configure NLClassifier.

Example results

Here is an example of the classification results of the movie review model.

Input: "What a waste of my time."


category[0]: 'Negative' : '0.81313'
category[1]: 'Positive' : '0.18687'

Try out the simple CLI demo tool for NLClassifier with your own model and test data.

Model compatibility requirements

Depending on the use case, the NLClassifier API can load a TFLite model with or without TFLite Model Metadata. See examples of creating metadata for natural language classifiers using the TensorFlow Lite Metadata Writer API.

The compatible models should meet the following requirements:

  • Input tensor: (kTfLiteString/kTfLiteInt32)

    • Input of the model should be either a kTfLiteString tensor raw input string or a kTfLiteInt32 tensor for regex tokenized indices of raw input string.
    • If input type is kTfLiteString, no Metadata is required for the model.
    • If input type is kTfLiteInt32, a RegexTokenizer needs to be set up in the input tensor's Metadata.
  • Output score tensor: (kTfLiteUInt8/kTfLiteInt8/kTfLiteInt16/kTfLiteFloat32/kTfLiteFloat64)

    • Mandatory output tensor for the score of each category classified.

    • If type is one of the Int types, dequantize it to double/float to corresponding platforms

    • Can have an optional associated file in the output tensor's corresponding Metadata for category labels, the file should be a plain text file with one label per line, and the number of labels should match the number of categories as the model outputs. See the example label file.

  • Output label tensor: (kTfLiteString/kTfLiteInt32)

    • Optional output tensor for the label for each category, should be of the same length as the output score tensor. If this tensor is not present, the API uses score indices as classnames.

    • Will be ignored if the associated label file is present in output score tensor's Metadata.