public void CanTrainCbowWithProgressCallback()
{
using var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
string outPath = Path.Combine(_tempDir, "cooking");
int callNum = 0;
var args = new UnsupervisedArgs
{
TrainProgressCallback = (progress, loss, wst, lr, eta) =>
{
callNum++;
}
};
fastText.Unsupervised(UnsupervisedModel.CBow, "cooking.train.nolabels.txt", outPath, args);
callNum.Should().BeGreaterThan(0);
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
fastText.ModelPath.Should().Be(outPath + ".bin");
File.Exists(outPath + ".bin").Should().BeTrue();
File.Exists(outPath + ".vec").Should().BeTrue();
}
public void CanGetDefaultSkipgramArgs()
{
var args = new UnsupervisedArgs();
args.lr.Should().BeApproximately(0.05d, 10e-5);
args.bucket.Should().Be(2000000);
args.dim.Should().Be(100);
args.loss.Should().Be(LossName.NegativeSampling);
args.model.Should().Be(ModelName.SkipGram);
args.LabelPrefix.Should().Be("__label__");
// No need to check all of them here.
}
/// <summary>
/// Trains a new unsupervised model.
/// </summary>
/// <param name="model">Type of unsupervised model: Skipgram or Cbow.</param>
/// <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 Unsupervised(UnsupervisedModel model, string inputPath, string outputPath, UnsupervisedArgs args)
{
ValidatePaths(inputPath, outputPath, args.PretrainedVectors);
args.model = (ModelName)model;
var argsStruct = _mapper.Map <FastTextArgsStruct>(args);
CheckForErrors(Train(
_fastText,
inputPath,
outputPath,
argsStruct,
new AutotuneArgsStruct(),
args.TrainProgressCallback,
null,
args.LabelPrefix,
args.PretrainedVectors,
false));
_maxLabelLen = 0;
ModelPath = AdjustPath(outputPath, false);
}
