The Map of Meaning: How Embedding Models “Understand” Human Language

A practical introduction to embedding models explaining how they map text into vector spaces to capture semantic meaning. Covers the step-by-step process from tokenization to vector search, with code examples using BERT, SentenceTransformers, and Qdrant. Also demonstrates fine-tuning an embedding model using contrastive learning with TripletLoss, and introduces alignment and uniformity as evaluation metrics for embedding quality.