In this article, you will learn how to build a text clustering pipeline by combining large language model embeddings with HDBSCAN, a density-based clustering algorithm, to automatically discover topics in unlabeled text data.
Topics we will cover include:
How to generate text embeddings for raw documents using a pre-trained sentence-transformers model.
How to reduce the dimensionality of those embeddings with UMAP to prepare them for clustering.
How to apply HDBSCAN to automatically discover topic clusters and visualize the results.
