/// <summary>
/// Trains a new model.
/// </summary>
/// <param name="inputPath">Path to a training set.</param>
/// <param name="outputPath">Path to write the model to (excluding extension).</param>
/// <param name="args">Training arguments.</param>
/// <remarks>Trained model will consist of two files: .bin (main model) and .vec (word vectors).</remarks>
public void Train(string inputPath, string outputPath, TrainingArgs args)
{
var argsStruct = new TrainingArgsStruct
{
Epochs = args.Epochs,
LearningRate = args.LearningRate,
MaxCharNGrams = args.MaxCharNGrams,
MinCharNGrams = args.MinCharNGrams,
Verbose = args.Verbose,
WordNGrams = args.WordNGrams
};
TrainSupervised(_fastText, inputPath, outputPath, argsStruct, args.LabelPrefix);
_maxLabelLen = GetMaxLabelLenght(_fastText);
}
/// <summary>
/// Trains a new model using low-level FastText arguments.
/// </summary>
/// <param name="inputPath">Path to a training set.</param>
/// <param name="outputPath">Path to write the model to (excluding extension).</param>
/// <param name="args">Low-level training arguments.</param>
/// <remarks>Trained model will consist of two files: .bin (main model) and .vec (word vectors).</remarks>
public void Train(string inputPath, string outputPath, FastTextArgs args)
{
ValidatePaths(inputPath, outputPath, args.PretrainedVectors);
var argsStruct = new TrainingArgsStruct
{
bucket = args.bucket,
cutoff = args.cutoff,
dim = args.dim,
dsub = args.dsub,
epoch = args.epoch,
loss = (loss_name)args.loss,
lr = args.lr,
lrUpdateRate = args.lrUpdateRate,
maxn = args.maxn,
minCount = args.minCount,
minCountLabel = args.minCountLabel,
minn = args.minn,
model = (model_name)args.model,
neg = args.neg,
qnorm = args.qnorm,
qout = args.qout,
retrain = args.retrain,
saveOutput = args.saveOutput,
t = args.t,
thread = args.thread,
verbose = args.verbose,
wordNgrams = args.wordNgrams,
ws = args.ws,
};
Train(_fastText, inputPath, outputPath, argsStruct, args.LabelPrefix, args.PretrainedVectors);
_maxLabelLen = GetMaxLabelLength(_fastText);
_modelLoaded = true;
}
private static extern void TrainSupervised(IntPtr hPtr, string input, string output, TrainingArgsStruct args, string labelPrefix);
private static extern void Train(IntPtr hPtr, string input, string output, TrainingArgsStruct args, string labelPrefix, string pretrainedVectors);
