private static void PredictWithModelLoadedFromFile(SentimentIssue sampleStatement)
        {
            // Test with Loaded Model from .zip file

            using (var env2 = new LocalEnvironment())
            {
                ITransformer loadedModel;
                using (var stream = new FileStream(ModelPath, FileMode.Open, FileAccess.Read, FileShare.Read))
                {
                    loadedModel = TransformerChain.LoadFrom(env2, stream);
                }

                // Create prediction engine and make prediction.

                var engine = loadedModel.MakePredictionFunction <SentimentIssue, SentimentPrediction>(env2);

                var predictionFromLoaded = engine.Predict(sampleStatement);

                Console.WriteLine();
                Console.WriteLine("=============== Test of model with a sample ===============");

                Console.WriteLine($"Text: {sampleStatement.Text} | Prediction: {(Convert.ToBoolean(predictionFromLoaded.Prediction) ? "Toxic" : "Nice")} sentiment | Probability: {predictionFromLoaded.Probability} ");
            }
        }
        static void Main(string[] args)
        {
            //1. Create ML.NET context/environment
            using (var env = new LocalEnvironment())
            {
                //2. Create DataReader with data schema mapped to file's columns
                var reader = new TextLoader(env,
                                            new TextLoader.Arguments()
                {
                    Separator = "tab",
                    HasHeader = true,
                    Column    = new[]
                    {
                        new TextLoader.Column("Label", DataKind.Bool, 0),
                        new TextLoader.Column("Text", DataKind.Text, 1)
                    }
                });

                //Load training data
                IDataView trainingDataView = reader.Read(new MultiFileSource(TrainDataPath));


                //3.Create a flexible pipeline (composed by a chain of estimators) for creating/traing the model.

                var pipeline = new TextTransform(env, "Text", "Features")  //Convert the text column to numeric vectors (Features column)
                               .Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments(),
                                                                       "Features",
                                                                       "Label"));
                //.Append(new LinearClassificationTrainer(env, "Features", "Label")); //(Simpler in ML.NET v0.7)



                //4. Create and train the model
                Console.WriteLine("=============== Create and Train the Model ===============");

                var model = pipeline.Fit(trainingDataView);

                Console.WriteLine("=============== End of training ===============");
                Console.WriteLine();


                //5. Evaluate the model and show accuracy stats

                //Load evaluation/test data
                IDataView testDataView = reader.Read(new MultiFileSource(TestDataPath));

                Console.WriteLine("=============== Evaluating Model's accuracy with Test data===============");
                var predictions = model.Transform(testDataView);

                var binClassificationCtx = new BinaryClassificationContext(env);
                var metrics = binClassificationCtx.Evaluate(predictions, "Label");

                Console.WriteLine();
                Console.WriteLine("Model quality metrics evaluation");
                Console.WriteLine("------------------------------------------");
                Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
                Console.WriteLine($"Auc: {metrics.Auc:P2}");
                Console.WriteLine($"F1Score: {metrics.F1Score:P2}");
                Console.WriteLine("=============== End of Model's evaluation ===============");
                Console.WriteLine();


                //6. Test Sentiment Prediction with one sample text
                var predictionFunct = model.MakePredictionFunction <SentimentIssue, SentimentPrediction>(env);

                SentimentIssue sampleStatement = new SentimentIssue
                {
                    Text = "This is a very rude movie"
                };

                var resultprediction = predictionFunct.Predict(sampleStatement);

                Console.WriteLine();
                Console.WriteLine("=============== Test of model with a sample ===============");

                Console.WriteLine($"Text: {sampleStatement.Text} | Prediction: {(Convert.ToBoolean(resultprediction.Prediction) ? "Toxic" : "Nice")} sentiment | Probability: {resultprediction.Probability} ");

                // Save model to .ZIP file
                SaveModelAsFile(env, model);

                // Predict again but now testing the model loading from the .ZIP file
                PredictWithModelLoadedFromFile(sampleStatement);

                Console.WriteLine("=============== End of process, hit any key to finish ===============");
                Console.ReadKey();
            }
        }