public void Word2Vec()
{
using (var fastText = new FastTextWrapper())
{
fastText.LoadModel(Path.Combine(dataDir, "dbpedia.ftz"));
var vector = fastText.GetSentenceVector("Can I use a larger crockpot than the recipe calls for?");
}
}
public static double[] Cosine(string src, string[] dst, string model)
{
using (var fastText = new FastTextWrapper())
{
fastText.LoadModel(model);
var vector = fastText.GetSentenceVector(src.ToLower());
return(dst.Select(x => CalCosine(vector, fastText.GetSentenceVector(x.ToLower()))).ToArray());
}
}
public void CanLoadSupervisedModel()
{
using var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
fastText.LoadModel(_fixture.FastText.ModelPath);
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
AssertLabels(fastText.GetLabels());
}
private static void LoadModel()
{
using (var fastText = new FastTextWrapper())
{
fastText.LoadModel(@"D:\__Models\cooking.bin");
var labels = fastText.GetLabels();
var prediction = fastText.PredictSingle("Can I use a larger crockpot than the recipe calls for?");
var predictions = fastText.PredictMultiple("Can I use a larger crockpot than the recipe calls for?", 4);
var vector = fastText.GetSentenceVector("Can I use a larger crockpot than the recipe calls for?");
}
}
static void Main(string[] args)
{
if ((args.FirstOrDefault() == "nn" && args.Length < 2) || (args.FirstOrDefault() != "nn" && args.Length < 3))
{
Console.WriteLine(Usage);
return;
}
using (var fastText = new FastTextWrapper())
{
switch (args[0])
{
case "train":
TrainSupervised(fastText, args[1], args[2]);
break;
case "trainlowlevel":
TrainLowLevel(fastText, args[1], args[2]);
break;
case "load":
fastText.LoadModel(args[2]);
break;
}
if (args[0] != "nn")
{
Test(fastText);
}
else
{
fastText.LoadModel(File.ReadAllBytes(args[1]));
TestNN(fastText);
}
}
}
public void CanQuantizeLoadedSupervisedModel()
{
using var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
fastText.LoadModel(_fixture.FastText.ModelPath);
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
AssertLabels(fastText.GetLabels());
string newPath = Path.Combine(Path.GetDirectoryName(_fixture.FastText.ModelPath), Path.GetFileNameWithoutExtension(_fixture.FastText.ModelPath));
fastText.Quantize();
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
fastText.ModelPath.Should().Be(newPath + ".ftz");
File.Exists(newPath + ".ftz").Should().BeTrue();
File.Exists(newPath + ".vec").Should().BeTrue();
}
static void Main(string[] args)
{
var model = Path.Combine(@"D:\SciSharp\CherubNLP\data", "dbpedia.bin");
using (var fastText = new FastTextWrapper())
{
fastText.LoadModel(model);
var vector1 = fastText.GetSentenceVector("Hello");
}
var similarities = Similarity.Cosine("Power Outage -Fifth & Park - JPMC150713", new[]
{
"Cosine Similarity algorithm function sample.",
"Power Restored -Fifth & Park - JPMC150713",
"Compute the similarity of two hardcoded lists.",
"We can compute the similarity of two hardcoded lists.",
"Coronavirus app could trace your contacts without sacrificing your privacy"
}, model);
// var test = new KaggleTest();
// test.SpookyAuthorIdentification();
}
