Feature Engineering with LLM Embeddings: Enhancing Scikit-learn Models
Large language model embeddings, or LLM embeddings, are a powerful approach to capturing semantically rich information in text and utilizing it to leverage other machine learning models — like those trained using Scikit-learn — in tasks that require deep contextual understanding of text, such as intent recognition or sentiment analysis.
Machine learning models built with frameworks like scikit-learn can accommodate unstructured data like text, as long as this raw text is converted into a numerical representation that is understandable by algorithms, models, and machines in a broader sense.
In this article, you will learn how to build a multilingual text classification pipeline using multilingual large language model (LLM) embeddings and Scikit-learn, without training...