Panlingo.LanguageIdentification.FastText.Native 0.0.0.17

There is a newer version of this package available.
See the version list below for details.
dotnet add package Panlingo.LanguageIdentification.FastText.Native --version 0.0.0.17                
NuGet\Install-Package Panlingo.LanguageIdentification.FastText.Native -Version 0.0.0.17                
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="Panlingo.LanguageIdentification.FastText.Native" Version="0.0.0.17" />                
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add Panlingo.LanguageIdentification.FastText.Native --version 0.0.0.17                
#r "nuget: Panlingo.LanguageIdentification.FastText.Native, 0.0.0.17"                
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install Panlingo.LanguageIdentification.FastText.Native as a Cake Addin
#addin nuget:?package=Panlingo.LanguageIdentification.FastText.Native&version=0.0.0.17

// Install Panlingo.LanguageIdentification.FastText.Native as a Cake Tool
#tool nuget:?package=Panlingo.LanguageIdentification.FastText.Native&version=0.0.0.17                

Panlingo.LanguageIdentification.FastText

Welcome to Panlingo.LanguageIdentification.FastText, a .NET wrapper for the FastText library by Facebook AI Research (FAIR). This package seamlessly integrates language identification capabilities into .NET applications, leveraging the powerful and efficient FastText library to recognize and classify texts in multiple languages. Ideal for applications that require fast and accurate language detection.

Requirements

  • .NET >= 5.0
  • Linux

Installation

To integrate the FastText functionality, follow these steps:

  1. Install the NuGet package:

    dotnet add package Panlingo.LanguageIdentification.FastText
    
  2. Download the Pretrained Models:

    Depending on your needs, download one of the pretrained language identification (LID) models provided by Facebook:

    • For the LID model with 176 languages:

      curl --location -o /models/fasttext176.bin https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.bin
      
    • For the LID model with 217 languages:

      curl --location -o /models/fasttext217.bin https://huggingface.co/facebook/fasttext-language-identification/resolve/main/model.bin?download=true
      

    Learn more about these models here:

Usage

Integrating the FastText library into your .NET application is straightforward. Here’s a quick guide to get you started:

  1. Install the Package: Ensure you have added the Panlingo.LanguageIdentification.FastText package to your project using the provided installation command.
  2. Download the Models: Follow the provided commands to download the pretrained language identification models.
  3. Initialize the Library: Follow the example snippet to initialize and use the FastText library for detecting languages.
using Panlingo.LanguageIdentification.FastText;

class Program
{
    static void Main()
    {
        using var fastText = new FastTextDetector();
        fastText.LoadModel("/models/fasttext217.bin");

        var predictions = fastText.Predict(
            text: "Привіт, як справи?", 
            count: 10
        );

        foreach (var prediction in predictions)
        {
            Console.WriteLine($"{prediction.Label}: {prediction.Probability}");
        }

        var dimensions = fastText.GetModelDimensions();
        var labels = fastText.GetLabels();
    }
}

Alternatives

If you are exploring other options, here are some alternatives to consider:


We value your feedback. Feel free to open issues or contribute to the repository. Let’s make language detection in .NET even more powerful and versatile! 🌍📝

Happy coding! 👩‍💻👨‍💻


Stay updated by following our repository. For any inquiries or support, reach out through the issues page.

Product Compatible and additional computed target framework versions.
.NET net5.0 was computed.  net5.0-windows was computed.  net6.0 was computed.  net6.0-android was computed.  net6.0-ios was computed.  net6.0-maccatalyst was computed.  net6.0-macos was computed.  net6.0-tvos was computed.  net6.0-windows was computed.  net7.0 was computed.  net7.0-android was computed.  net7.0-ios was computed.  net7.0-maccatalyst was computed.  net7.0-macos was computed.  net7.0-tvos was computed.  net7.0-windows was computed.  net8.0 was computed.  net8.0-android was computed.  net8.0-browser was computed.  net8.0-ios was computed.  net8.0-maccatalyst was computed.  net8.0-macos was computed.  net8.0-tvos was computed.  net8.0-windows was computed. 
.NET Core netcoreapp3.0 was computed.  netcoreapp3.1 was computed. 
.NET Standard netstandard2.1 is compatible. 
MonoAndroid monoandroid was computed. 
MonoMac monomac was computed. 
MonoTouch monotouch was computed. 
Tizen tizen60 was computed. 
Xamarin.iOS xamarinios was computed. 
Xamarin.Mac xamarinmac was computed. 
Xamarin.TVOS xamarintvos was computed. 
Xamarin.WatchOS xamarinwatchos was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.
  • .NETStandard 2.1

    • No dependencies.

NuGet packages (1)

Showing the top 1 NuGet packages that depend on Panlingo.LanguageIdentification.FastText.Native:

Package Downloads
Panlingo.LanguageIdentification.FastText

This is a .NET wrapper for the FastText library by Facebook AI Research (FAIR). This package facilitates the integration of language identification capabilities into .NET applications, leveraging the powerful and efficient FastText library to recognize and classify texts in multiple languages. Ideal for applications that require fast and accurate language detection.

GitHub repositories

This package is not used by any popular GitHub repositories.

Version Downloads Last updated
0.2.0 118 11/10/2024
0.1.1 211 10/22/2024
0.1.0 127 10/7/2024
0.0.0.22 177 9/22/2024
0.0.0.21 151 9/10/2024
0.0.0.20 142 9/8/2024
0.0.0.19 130 9/1/2024
0.0.0.18 132 8/26/2024
0.0.0.17 167 8/21/2024
0.0.0.16 158 8/9/2024
0.0.0.15 122 8/8/2024
0.0.0.14 99 8/3/2024
0.0.0.13 109 8/1/2024
0.0.0.12 122 7/20/2024
0.0.0.10 121 7/14/2024

- Initial release