Microsoft has released a new version of ML.NET, its cross-platform, open source machine learning framework for .NET developers. The new version 2.0 adds a text classification API. A new version of Model Builder has also been released,

Developers can use ML.NET to develop custom AI machine learning models that can then be included in their apps. You can create and use machine learning models targeting common tasks such as classification, regression, clustering, ranking, recommendations and anomaly detection. It supports deep-learning frameworks such as TensorFlow and interoperability through ONNX.

ML.NET includes Infer.NET as 'part of the ML.NET family'. Infer.NET is a cross-platform framework for running Bayesian inference in graphical models that can also be used for probabilistic programming.

The new version adds a Text Classification scenario in Model Builder powered by the ML.NET Text Classification API. The API was released in preview a few months ago.

It can be used to train custom models that classify raw text data. It does so by integrating a TorchSharp implementation of NAS-BERT into ML.NET. Using a pre-trained version of this model, the Text Classification API uses your data to fine-tune the model.

The new release also adds a new API for sentence similarity that uses the same underlying TorchSharp NAS-BERT model as the Text Classification API. The main difference is that instead of predicting a category, the model calculates a numerical value that represents how similar two phrases are. 

Alongside the improvements to ML.NET, there are new options in Model Builder. There are now advanced training options that can be used to choose which trainers you want to use, and also choose the evaluation metric you want to optimize.

ML.NET 2 is available now. 

More Information

ML.NET

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