In this article, you will learn how to build a unified scikit-learn pipeline that combines text embeddings generated by a lightweight open-source language model with structured tabular features for classification tasks.
Topics we will cover include:
How to generate text embeddings using Hugging Face’s sentence-transformers library and wrap them in a custom scikit-learn transformer class.
How to use a ColumnTransformer to run parallel preprocessing branches for text, numeric, and categorical features simultaneously.
How to assemble and evaluate a complete, deployment-ready classification pipeline on a mixed dataset combining real text data with synthetic tabular features.
