Text Embeddings: Similarity, Search, and Classification

This chapter delves into the use of text embeddings for similarity, search, and classification. It explores the dimensions of different language models, explains how embeddings can map text data in a semantic space, and discusses their applications in tasks like handwritten digit classification, sentiment analysis, and fraud detection. The chapter also touches upon the limitations of embeddings and the potential risks of using large language models in chat interfaces.