void Initialize(List<Data> trainingData)
var data = new List<Data>
{
new Data() { Text = "ΠΠ²ΠΎΠ΄ΠΈΠΌΡΠΉ ΡΠ΅ΠΊΡΡ", Reponse = "ΠΠΎΠ·Π²ΡΠ°ΡΠ°Π΅ΠΌΡΠΉ ΡΠ΅ΠΊΡΡ", ReturnCode = -1 },
new Data() { Text = "ΠΠ²ΠΎΠ΄ΠΈΠΌΡΠΉ ΡΠ΅ΠΊΡΡ x2", Reponse = "ΠΠΎΠ·Π²ΡΠ°ΡΠ°Π΅ΠΌΡΠΉ ΡΠ΅ΠΊΡΡ x2", ReturnCode = 0 }
};
MultiAPI.ChatBot.Initialize(data);
ΠΠ½ΠΈΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΡΠ΅Ρ ΠΌΠΎΠ΄Π΅Π»Ρ.
ΠΠ° Π·Π°Π³ΡΡΠ·ΠΊΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΌΠΎΠΆΠ΅Ρ ΡΠΉΡΠΈ Π΄ΠΎ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
ΠΌΠΈΠ½ΡΡ! Π€Π°ΠΊΡΠΎΡ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π·Π°Π³ΡΡΠ·ΠΊΠΈ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΡΠ°Π·ΠΌΠ΅ΡΠ° Π·Π°Π³ΡΡΠΆΠ°Π΅ΠΌΡΡ
Π΄Π°Π½Π½ΡΡ
Π² ΠΌΠΎΠ΄Π΅Π»Ρ.
public static void Initialize(List<Data> trainingData)
{
ChatBot.trainingData = trainingData;
var trainingDataView = mlContext.Data.LoadFromEnumerable(trainingData);
var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey("Label", nameof(Data.Response))
.Append(mlContext.Transforms.Text.FeaturizeText("TextFeaturized", nameof(Data.Text)))
.Append(mlContext.Transforms.Concatenate("Features", "TextFeaturized"));
var trainer = mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy("Label", "Features")
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));
var trainingPipeline = dataProcessPipeline.Append(trainer);
var model = trainingPipeline.Fit(trainingDataView);
predictionEngine = mlContext.Model.CreatePredictionEngine<Data, Prediction>(model);
}