// Utilities --------------------------------- /// <summary> /// Trains a POS tag maximum entropy model. /// </summary> /// <param name="eventStream">Stream of training events</param> /// <param name="iterations">number of training iterations to perform</param> /// <param name="cut">cutoff value to use for the data indexer</param> /// <returns>Trained GIS model</returns> public static SharpEntropy.GisModel Train(SharpEntropy.ITrainingEventReader eventStream, int iterations, int cut) { var trainer = new SharpEntropy.GisTrainer(); trainer.TrainModel(iterations, new SharpEntropy.TwoPassDataIndexer(eventStream, cut)); return(new SharpEntropy.GisModel(trainer)); }
public static void Train(SharpEntropy.ITrainingEventReader eventReader, string outputFilename) { SharpEntropy.GisTrainer trainer = new SharpEntropy.GisTrainer(0.1); trainer.TrainModel(100, new SharpEntropy.TwoPassDataIndexer(eventReader, 5)); SharpEntropy.GisModel tokenizeModel = new SharpEntropy.GisModel(trainer); new SharpEntropy.IO.BinaryGisModelWriter().Persist(tokenizeModel, outputFilename); }
/// <summary> /// Trains the chunker. Training file should be one word per line where each line consists of a /// space-delimited triple of "word pos outcome". Sentence breaks are indicated by blank lines. /// </summary> /// <param name="eventReader"> /// The chunker event reader. /// </param> /// <returns> /// Trained model. /// </returns> public static SharpEntropy.GisModel Train(SharpEntropy.ITrainingEventReader eventReader) { return(Train(eventReader, 100, 5)); }
private static SharpEntropy.GisModel Train(SharpEntropy.ITrainingEventReader eventReader, int iterations, int cutoff) { SharpEntropy.GisTrainer trainer = new SharpEntropy.GisTrainer(); trainer.TrainModel(iterations, new SharpEntropy.TwoPassDataIndexer(eventReader, cutoff)); return(new SharpEntropy.GisModel(trainer)); }
public static SharpEntropy.GisModel TrainModel(SharpEntropy.ITrainingEventReader eventReader, int iterations, int cut) { SharpEntropy.GisTrainer trainer = new SharpEntropy.GisTrainer(); trainer.TrainModel(eventReader, iterations, cut); return new SharpEntropy.GisModel(trainer); }