code: package zemberek.apps.fasttext;
  
  import com.beust.jcommander.Parameter;
  import java.io.IOException;
  import java.nio.file.Path;
  import java.nio.file.Paths;
  import zemberek.classification.FastTextClassifier;
  import zemberek.classification.FastTextClassifierTrainer;
  import zemberek.classification.FastTextClassifierTrainer.LossType;
  import zemberek.core.embeddings.FastText;
  import zemberek.core.logging.Log;
  
  public class TrainClassifier extends FastTextAppBase {
  
    @Parameter(names = {"--input", "-i"},
        required = true,
        description = "Classifier training file. each line should contain a single document and "
            + "one or more class labels. "
            + "Document class label needs to have __label__ prefix attached to it.")
    Path input;
  
    @Parameter(names = {"--output", "-o"},
        required = true,
        description = "Output model file.")
    Path output;
  
    @Parameter(names = {"--lossType", "-l"},
        description = "Model type.")
    LossType lossType = LossType.SOFTMAX;
  
    @Parameter(names = {"--applyQuantization", "-q"},
        description = "If used, applies quantization to model. This way model files will be "
            + " smaller. Underlying algorithm uses 8 bit values for weights instead of 32 bit floats."
            + " Quantized model will be saved same place of output with name [output].q ")
    boolean applyQuantization = false;
  
    @Parameter(names = {"--cutOff", "-c"},
        description = "Reduces dictionary size with given threshold value. "
            + "Dictionary entries are sorted with l2-norm values and top `cutOff` are selected. "
            + "This greatly reduces model size. This option is only available if"
            + "applyQuantization flag is used.")
    int cutOff = -1;
  
    @Parameter(names = {"--epochCount", "-ec"},
        description = "Epoch Count.")
    int epochCount = FastTextClassifierTrainer.DEFAULT_EPOCH;
  
    @Parameter(names = {"--learningRate", "-lr"},
        description = "Learning rate. Should be between 0.01-2.0")
    float learningRate = FastTextClassifierTrainer.DEFAULT_LR;
  
    @Override
    public String description() {
      return "Generates a text classification model from a training set. Classification algorithm"
          + " is based on Java port of fastText library. It is usually suggested to apply "
          + "tokenization, lower-casing and other specific text operations to the training set"
          + " before training the model. "
          + "Algorithm may be more suitable for sentence and short paragraph"
          + " level texts rather than long documents.\n "
          + "In the training set, each line should contain a single document. Document class "
          + "label needs to have __label__ prefix attached to it. Such as "
          + "[__label__sports Match ended in a draw.]\n"
          + "Each line (document) may contain more than one label.\n"
          + "If there are a lot of labels, LossType can be chosen `HIERARCHICAL_SOFTMAX`. "
          + "This way training and runtime speed will be faster with a small accuracy loss.\n "
          + "For generating compact models, use -applyQuantization and -cutOff [dictionary-cut-off] "
          + "parameters.";
    }
  
    @Override
    public void run() throws IOException {
  
      Log.info("Generating classification model from %s", input);
  
      FastTextClassifierTrainer trainer = FastTextClassifierTrainer.builder()
          .epochCount(epochCount)
          .learningRate(learningRate)
          .lossType(lossType)
          .quantizationCutOff(cutOff)
          .minWordCount(minWordCount)
          .threadCount(threadCount)
          .wordNgramOrder(wordNGrams)
          .dimension(dimension)
          .contextWindowSize(contextWindowSize)
          .build();
  
      Log.info("Training Started.");
      trainer.getEventBus().register(this);
  
      FastTextClassifier classifier = trainer.train(input);
  
      Log.info("Saving classification model to %s", output);
      FastText fastText = classifier.getFastText();
      fastText.saveModel(output);
  
      if (applyQuantization) {
        Log.info("Applying quantization.");
        if (cutOff > 0) {
          Log.info("Quantization dictionary cut-off value = %d", cutOff);
        }
        Path parent = output.getParent();
        String name = output.toFile().getName() + ".q";
        Path quantizedModel = parent == null ? Paths.get(name) : parent.resolve(name);
        Log.info("Saving quantized classification model to %s", quantizedModel);
        FastText quantized = fastText.quantize(output, fastText.getArgs());
        quantized.saveModel(quantizedModel);
      }
    }
  
    public static void main(String[] args) {
      new TrainClassifier().execute(args);
    }
  }
  