ML.NET 2.0 Samples
July 5, 2023 ยท View on GitHub
| ML.NET version | Status | App Type | Data type | Scenario | ML Task | Algorithms |
|---|---|---|---|---|---|---|
| v2.0.0 | Up-to-date | Console App | .csv file | AutoML, Text Classification, Sentence Similarity | Regression,Text Classification,Sentence Similarity | Sdca, NAS-BERT |
This directory contains samples for ML.NET 2.0.
Data
The samples in this directory use the following datasets:
How to use these samples
To use these samples, download the datasets above and place them in the Data directory.
In Visual Studio, set any of the projects as the Startup project and run the application.
dotnet CLI
You may have to update the dataPath in the console apps. Then, in the terminal, navigate to the project directory and enter dotnet run.
Samples
AutoML
- AutoMLQuickStart - C# console application that shows how to get started with the AutoML API.
- AutoMLAdvanced - C# console application that shows the following concepts:
- Modifying column inference results
- Excluding trainers
- Configuring monitoring
- Choosing tuners
- Cancelling experiments
- AutoMLEstimators - C# console application that shows how to:
- Customize search spaces
- Create sweepable estimators
- AutoMLTrialRunner - C# console application that shows how to create your own trial runner for the Text Classification API.
Natural Language Processing (NLP)
- TextClassification - C# console app that shows how to use the Text Classification API for inferencing using code generated by Model Builder. The model is trained using Model Builder.
- TextClassification_Sentiment_Razor - ASP.NET Core Razor Pages application for sentiment analysis. Code sample for Analyze sentiment of website comments in a web application using ML.NET Model Builder tutorial. Model is trained using Model Builder.
- SentenceSimilarity - C# console app that shows how to use the Sentence Similarity API. Like the Text Classification API, the Sentence Similarity API uses a NAS-BERT transformer-based deep learning model built with TorchSharp to compare how similar two pieces of text are.