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Announcing ML.NET 0.4

Announcing ML.NET 0.4

A few months ago we released ML.NET 0.1 at //Build 2018., ML.NET is a cross-platform, open source machine learning framework for .NET developers. We’ve gotten great feedback so far and would like to thank the community for your engagement as we continue to develop ML.NET together in the open.

We are happy to announce the latest version: ML.NET 0.4. In this release we’ve improved support for natural language processing (NLP) scenarios by adding the Word Embedding Transform, improved the speed of linear learners like binary classification and linear regression by adding support for the SymSGD learner, made improvements to the F# API and samples for ML.NET, bug fixes and more.

Additionally, we really want your feedback on making ML.NET really easy to use. We are working on a new API which improves flexibility and ease of use. When the new API is ready and good enough, we plan to deprecate the current “pipeline” API. Because this will be a significant change we want to share our proposals for the multiple API options and comparisons in a future blog post and start an open discussion with you where you can provide your feedback and help shape the long-term API for ML.NET.

The blog post below provides more details about the additions in the 0.4 release.

Word Embeddings Transform for Text Scenarios

Word embeddings is a technique for mapping words to numeric vectors that are intended to capture some of the meaning of the words, so they can be used for visualization or model training.

The word embedding transform added to ML.NET enables using pretrained word embedding models in pipelines. “Pretrained” means you can use existing embeddings instead of needing to create your own (which takes a lot of data and time).  Several different pretrained models are available (GloVefastText, and SSWE).

By adding this transform in addition to existing transforms for working with text (like the TextFeaturizer), you can improve the model’s metrics.

For example, we can improve the accuracy of the sentiment analysis sample by 5% if we change the line with TextFeaturizer to: