The ML.NET CLI has generated the trained model and code for you, so you can now use the model in your other .NET applications (e.g. your consumeModelApp console app) by following these steps:

1. From your consumeModelApp, add a reference to the generated library project (SampleBinaryClassification.Model).



In your terminal, run the following command (make sure you are in your consumeModelApp directory):

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cd consumeModelApp
dotnet add reference ../SampleBinaryClassification/SampleBinaryClassification.Model/

2. In your consumeModelApp, install the ML.NET NuGet package.



In your terminal, run the following command:

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dotnet add package Microsoft.ML --version 1.0.0

3. Copy the model (MLModel.zip) from SampleBinaryClassification.Model and paste it into the consumeModelApp directory.


4. Replace the Program.cs code in your consumeModelApp with the following code:
Program.csCopy

using System;
using SampleBinaryClassification.Model.DataModels;
using Microsoft.ML;

namespace consumeModelApp
{
    class Program
    {
        static void Main(string[] args)
        {
            ConsumeModel();
        }

        public static void ConsumeModel()
        {
            // Load the model
            MLContext mlContext = new MLContext();

            ITransformer mlModel = mlContext.Model.Load("MLModel.zip", out var modelInputSchema);

            var predEngine = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);

            // Use the code below to add input data
            var input = new ModelInput();
            input.SentimentText = "That is rude";

            // Try model on sample data
            // True is toxic, false is non-toxic
            ModelOutput result = predEngine.Predict(input);

            Console.WriteLine($"Text: {input.SentimentText} | Prediction: {(Convert.ToBoolean(result.Prediction) ? "Toxic" : "Non-toxic")} sentiment");
        }
    }
}

5. In your consumeModelApp, modify the sample text (input.SentimentText) in Program.cs.
Program.csCopy

input.SentimentText = "That is rude.";

6. Run your consumeModelApp.



In your terminal, run the following command (make sure you are in your consumeModelApp directory):

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dotnet run