The article discusses using Large Language Model (LLM) embeddings as features in traditional machine learning models built with scikit-learn. It covers the process of generating embeddings from text data using models like Sentence Transformers, and how these embeddings can be combined with existing features to improve model performance. It details practical steps including loading data, creating embeddings, and integrating them into a scikit-learn pipeline for tasks like classification.
emlearn is an open-source machine learning inference engine designed for microcontrollers and embedded devices. It supports various machine learning models for classification, regression, unsupervised learning, and feature extraction. The engine is portable, with a single header file include, and uses C99 code and static memory allocation. Users can train models in Python and convert them to C code for inference.
A Comprehensive Guide to Understand and Implement Text Classification in Python
