{"@attributes":{"version":"2.0"},"channel":{"title":"Embedding-Models on Oriol Al\u00e0s Cerc\u00f3s","link":"https:\/\/oriolac.github.io\/tags\/embedding-models\/","description":"Recent content in Embedding-Models on Oriol Al\u00e0s Cerc\u00f3s","generator":"Hugo -- 0.150.0","language":"en-us","copyright":"Oriol Al\u00e0s Cerc\u00f3s","lastBuildDate":"Sun, 21 Dec 2025 11:10:23 +0100","item":[{"title":"Variational AutoEncoders (VAE) for Tabular Data","link":"https:\/\/oriolac.github.io\/posts\/20251210-vae-tabular\/","pubDate":"Sun, 21 Dec 2025 11:10:23 +0100","guid":"https:\/\/oriolac.github.io\/posts\/20251210-vae-tabular\/","description":"

The post of today is going to be a bit different. We have already talked about Variational Autoencoders (VAE)<\/strong>\nin the past<\/a>, but today we are going to see how to\nimplement it from scratch, train it on a dataset and see how it behaves with tabular data<\/strong>. Yes, VAEs can be used for\ntabular data as well. To do so, we will use the CRISP-DM framework<\/strong> to guide us through the process.<\/p>"},{"title":"From Words to Vectors: A Dive into Embedding Model Taxonomy","link":"https:\/\/oriolac.github.io\/posts\/20251025-embedding-models\/","pubDate":"Sat, 25 Oct 2025 12:31:23 +0100","guid":"https:\/\/oriolac.github.io\/posts\/20251025-embedding-models\/","description":"

Embedding models are foundational in modern NLP, turning raw text into numerical vectors that preserve semantic\nsignificance. These representations power everything from semantic search to Retrieval-Augmented Generation or Prompt\nEngineering for LLM Agents. With growing demand for domain-specific applications, understanding which is the best fit\nfor your system is more important than ever.<\/p>\n

Introduction<\/h1>\n

In modern NLP, a text embedding<\/em> is a vector that represents a piece of text in a mathematical space. The magic of\nembeddings is that they encode semantic meaning: texts with similar meaning end up with vectors that are close together.\nFor example, an embedding model might place “How to change a tier” near “Steps to fix a flat tire” in its vector space,\neven though the wording is different. This property makes embedding models incredibly useful for tasks like search,\nclustering or recommendation, where we care about semantic similarity<\/em> rather than exact keyword matches. By converting\ntext into vectors, embedding models allow computers to measure meaning and relevance via distances in vector space.<\/p>"}]}}