Microsoft has released ML.Net 2., a new model of its open source, cross-system device mastering framework for .Internet. The enhance characteristics abilities for textual content classification and automatic device finding out.
Unveiled November 10, ML.Web 2. arrived in tandem with a new version of the ML.Internet Design Builder, a visible developer device for developing equipment mastering products for .Net programs. The Design Builder introduces a textual content classification circumstance that is powered by the ML.Web Text Classification API.
Previewed in June, the Textual content Classification API enables developers to prepare tailor made products to classify uncooked text knowledge. The Text Classification API employs a pre-properly trained TorchSharp NAS-BERT model from Microsoft Investigation and the developer’s individual knowledge to fantastic-tune the product. The Model Builder situation supports area coaching on possibly CPUs or CUDA-compatible GPUs.
Also in ML.Web 2.:
- Binary classification, multiclass classification, and regression types employing preconfigured automated equipment mastering pipelines make it easier to begin working with machine studying.
- Facts preprocessing can be automated using the AutoML Featurizer.
- Developers can opt for which trainers are utilized as component of a teaching approach. They also can pick tuning algorithms utilized to locate ideal hyperparameters.
- Highly developed AutoML education choices are introduced to decide on trainers and select an evaluation metric to optimize.
- A sentence similarity API, employing the similar fundamental TorchSharp NAS-BERT design, calculates a numerical benefit representing the similarity of two phrases.
Potential programs for ML.Net incorporate expansion of deep mastering coverage and emphasizing use of the LightBGM framework for classical machine studying responsibilities these types of as regression and classification. The developers behind ML.Net also intend to improve the AutoML API to empower new eventualities and customizations and simplify equipment finding out workflows.
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