These gists are part of the blog post: https://blog.georgekosmidis.net/2020/07/22/a-hello-world-with-microsoft-machine-learning-framework-ml-net/

AveragedPerceptron.cs

var trainer = mlContext.BinaryClassification.Trainers
    .AveragedPerceptron(labelColumnName: "Sentiment", featureColumnName: "Features"));

var trainingPipeline = dataTransformPipeline.Append(trainer);

FeaturizeText.cs

// Convert sentiment text into numeric features
var dataTransformPipeline = mlContext.Transforms.Text
    .FeaturizeText("Features", "SentimentText");

Fit.cs

var model = pipeline.Fit(trainingData);

LoadFromTextFile.cs

var trainingData = mlContext.Data
    .LoadFromTextFile<SentimentInput>(dataPath, separatorChar: ',', hasHeader: true);

MLContext.cs

var mlContext = new MLContext();

Predict.cs

var mlContext = new MLContext();
DataViewSchema predictionPipelineSchema;
var model = mlContext.Model.Load("model.zip", out predictionPipelineSchema);

var predEngine = mlContext.Model.CreatePredictionEngine(model); 
var sampleComment = new SentimentInput{ SentimentText = "This is very rude!" }; 
var result = predEngine.Predict(sampleComment);
Console.WriteLine(result.Prediction);

Transform.cs

// Make predictions on test data
IDataView predictions = model.Transform(testDataView);

// Evaluate model and return metrics
var metrics = mlContext.BinaryClassification
    .Evaluate(predictions, labelColumnName: "Sentiment"); 

// Print out accuracy metric
Console.WriteLine("Accuracy" + metrics.Accuracy);