Train an Embedding Model: From the Transformer Architecture to Hybrid Search in Production with Python, UV and PyTorch (Intelligence Artificielle & Machine Learning)
English | May 6, 2026 | ASIN: B0GZNGC9XY | 325 pages | PDF | 5.24 MB
Behind every semantic search engine, every RAG assistant, and every relevant recommendation system, lies the same discrete component: the embedding model. It transforms sentences into vectors, and those vectors into geometry - a geometry where proximity signifies similarity, and searching becomes a matter of measuring distance.Yet, the literature on the subject remains fragmented: tutorials that teach you how to call model.encode() without explaining what happens under the hood, or research papers that are inaccessible without advanced training in optimization. This book occupies the space between the two.What You Will DiscoverAcross six progressive parts and twenty chapters, this book guides you through the entire lifecycle of an embedding model:The Modern Workspace - A reproducible, versioned, and structured Python project using UV and PyTorch.Transformer Architecture for Encoders - Tokenization, positional encoding, bidirectional attention, pooling: every building block explained, formalized, and implemented.Contrastive Loss Functions - Triplet loss, Multiple Negative Ranking Loss, Matryoshka Representation Learning: why every choice impacts the learned vector space.Training Strategies - Pre-training on noisy corpora, supervised fine-tuning, negative mining (random, semi-hard, hard, curriculum), and modern reinforcement-learning-inspired approaches.End-to-End RAG - Source preprocessing, advanced chunking strategies, vector databases, IVF-PQ compression, ColBERT, and late interaction.Production Deployment - Quantization, serving with Ollama, TEI, and vLLM, hybrid search (BM25 + semantic), reranking, self-correcting RAG, monitoring, and observability.How You Will Discover ItEach chapter follows a four-step progression: a conceptual introduction that defines the problem, an analogy to ground the intuition, a mathematical deep dive to formalize the mechanism, and a complete Python implementation integrated into the book's project architecture. You won't read the formula before understanding its meaning - and you won't code before understanding the formula.Who This Book Is ForThis book is written for machine learning engineers and practitioners who possess an intermediate level of Python, are comfortable with NumPy and PyTorch, have a solid foundation in linear algebra, and want to understand the why - not just the how. It does not assume advanced expertise in optimization but requires the curiosity to look under the hood.It constitutes the second volume in a series dedicated to deep-dive language model training and can be read as a standalone work.
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