From TF-IDF to Transformers: Implementing Four Generations of Semantic Search
The article discusses the evolution of semantic search from traditional methods to modern transformer-based systems. It highlights four key stages in this progression, including handcrafted retrieval features, classical machine learning, embedding-based search, and transformer fine-tuning. The author emphasizes the importance of understanding this evolution to grasp the current capabilities and limitations of semantic search technologies.
- ▪Semantic search has evolved from keyword matching and TF-IDF vectors to advanced transformer-based systems.
- ▪The article outlines four major stages in the evolution of semantic search methods.
- ▪A small synthetic dataset of art critiques is used to demonstrate the progression of retrieval systems.
Opening excerpt (first ~120 words) tap to expand
Deep Learning From TF-IDF to Transformers: Implementing Four Generations of Semantic Search Rule-based retrieval, classical ML, embeddings, and fine-tuned transformers in Python Dr. Theophano Mitsa May 25, 2026 23 min read Share Image created by Theophano Mitsa with ChatGPT. “Beauty will save the world”— Fyodor Dostoevsky A. Introduction Semantic search did not emerge overnight. Today’s transformer-based systems can feel almost magical, capable of capturing context and even subtle relationships between ideas. But the origin of today’s semantic search systems is actually gradual. Before embeddings, transformers, and large language models, researchers used keyword matching, TF–IDF vectors, and traditional machine learning methods to analyze text.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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