public void SetupSentimentPipeline()
{
_sentimentExample = new SentimentData()
{
SentimentText = "Not a big fan of this."
};
string _sentimentDataPath = Program.GetInvariantCultureDataPath("wikipedia-detox-250-line-data.tsv");
var env = new MLContext(seed: 1, conc: 1);
var reader = new TextLoader(env, columns: new[]
{
new TextLoader.Column("Label", DataKind.BL, 0),
new TextLoader.Column("SentimentText", DataKind.Text, 1)
},
hasHeader: true
);
IDataView data = reader.Read(_sentimentDataPath);
var pipeline = new TextFeaturizingEstimator(env, "SentimentText", "Features")
.Append(new SdcaBinaryTrainer(env, "Label", "Features", advancedSettings: (s) => { s.NumThreads = 1; s.ConvergenceTolerance = 1e-2f; }));
var model = pipeline.Fit(data);
_sentimentModel = model.CreatePredictionEngine <SentimentData, SentimentPrediction>(env);
}
public void SetupSentimentPipeline()
{
_sentimentExample = new SentimentData()
{
SentimentText = "Not a big fan of this."
};
string _sentimentDataPath = BaseTestClass.GetDataPath("wikipedia-detox-250-line-data.tsv");
var env = new MLContext(seed: 1, conc: 1);
var reader = new TextLoader(env, columns: new[]
{
new TextLoader.Column("Label", DataKind.BL, 0),
new TextLoader.Column("SentimentText", DataKind.Text, 1)
},
hasHeader: true
);
IDataView data = reader.Read(_sentimentDataPath);
var pipeline = new TextFeaturizingEstimator(env, "Features", "SentimentText")
.Append(env.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
new SdcaBinaryTrainer.Options {
NumThreads = 1, ConvergenceTolerance = 1e-2f,
}));
var model = pipeline.Fit(data);
_sentimentModel = model.CreatePredictionEngine <SentimentData, SentimentPrediction>(env);
}
public void SetupSentimentPipeline()
{
_sentimentExample = new SentimentData()
{
SentimentText = "Not a big fan of this."
};
string _sentimentDataPath = Program.GetInvariantCultureDataPath("wikipedia-detox-250-line-data.tsv");
using (var env = new ConsoleEnvironment(seed: 1, conc: 1, verbose: false, sensitivity: MessageSensitivity.None, outWriter: EmptyWriter.Instance))
{
var reader = new TextLoader(env,
new TextLoader.Arguments()
{
Separator = "\t",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("Label", DataKind.BL, 0),
new TextLoader.Column("SentimentText", DataKind.Text, 1)
}
});
IDataView data = reader.Read(_sentimentDataPath);
var pipeline = new TextFeaturizingEstimator(env, "SentimentText", "Features")
.Append(new SdcaBinaryTrainer(env, "Label", "Features", advancedSettings: (s) => { s.NumThreads = 1; s.ConvergenceTolerance = 1e-2f; }));
var model = pipeline.Fit(data);
_sentimentModel = model.MakePredictionFunction <SentimentData, SentimentPrediction>(env);
}
}
