catalyst

✨ Getting Started

Using catalyst is as simple as installing its NuGet Package, and setting the storage to use our online repository. This way, models will be lazy loaded either from disk or downloaded from our online repository. Check out also some of the sample projects for more examples on how to use catalyst.

Catalyst.Models.English.Register(); //You need to pre-register each language (and install the respective NuGet Packages)

Storage.Current = new DiskStorage("catalyst-models");
var nlp = await Pipeline.ForAsync(Language.English);
var doc = new Document("The quick brown fox jumps over the lazy dog", Language.English);
nlp.ProcessSingle(doc);
Console.WriteLine(doc.ToJson());

You can also take advantage of C# lazy evaluation and native multi-threading support to process a large number of documents in parallel:

var docs = GetDocuments();
var parsed = nlp.Process(docs);
DoSomething(parsed);

IEnumerable<IDocument> GetDocuments()
{
    //Generates a few documents, to demonstrate multi-threading & lazy evaluation
    for(int i = 0; i < 1000; i++)
    {
        yield return new Document("The quick brown fox jumps over the lazy dog", Language.English);
    }
}

void DoSomething(IEnumerable<IDocument> docs)
{
    foreach(var doc in docs)
    {
        Console.WriteLine(doc.ToJson());
    }
}

Training a new FastText word2vec embedding model is as simple as this:

var nlp = await Pipeline.ForAsync(Language.English);
var ft = new FastText(Language.English, 0, "wiki-word2vec");
ft.Data.Type = FastText.ModelType.CBow;
ft.Data.Loss = FastText.LossType.NegativeSampling;
ft.Train(nlp.Process(GetDocs()));
ft.StoreAsync();

For fast embedding search, we have also released a C# version of the "Hierarchical Navigable Small World" (HNSW) algorithm on NuGet, based on our fork of Microsoft's HNSW.Net. We have also released a C# version of the "Uniform Manifold Approximation and Projection" (UMAP) algorithm for dimensionality reduction on GitHub and on NuGet.