February 28, 2024


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ML.NET 2.0 enhances text classification

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Microsoft has launched ML.Net 2., a new model of its open resource, cross-platform device understanding framework for .Web. The update features capabilities for textual content classification and automated machine studying.

Unveiled November 10, ML.Web 2. arrived in tandem with a new edition of the ML.Internet Product Builder, a visible developer instrument for building machine studying types for .Internet applications. The Model Builder introduces a text classification state of affairs that is driven by the ML.Internet Text Classification API.

Previewed in June, the Textual content Classification API allows developers to coach customized products to classify raw textual content knowledge. The Textual content Classification API uses a pre-skilled TorchSharp NAS-BERT design from Microsoft Investigation and the developer’s own details to fantastic-tune the model. The Product Builder scenario supports nearby coaching on both CPUs or CUDA-appropriate GPUs.

Also in ML.Web 2.:

  • Binary classification, multiclass classification, and regression designs making use of preconfigured automatic machine studying pipelines make it less complicated to start off utilizing machine mastering.
  • Knowledge preprocessing can be automatic employing the AutoML Featurizer.
  • Developers can decide on which trainers are utilised as component of a instruction method. They also can select tuning algorithms employed to come across optimum hyperparameters.
  • Sophisticated AutoML coaching options are introduced to decide on trainers and select an analysis metric to enhance.
  • A sentence similarity API, applying the exact same fundamental TorchSharp NAS-BERT product, calculates a numerical worth representing the similarity of two phrases.

Long term plans for ML.Web consist of growth of deep learning protection and emphasizing use of the LightBGM framework for classical machine mastering responsibilities such as regression and classification. The builders driving ML.Net also intend to make improvements to the AutoML API to help new situations and customizations and simplify equipment discovering workflows.

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