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.
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Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/from-tf-idf-to-transformers-implementing-four-generations-of-semantic-search/ |
| Publication time | Mon, 25 May 2026 13:30:00 +0000 |
| Retrieval time | 2026-05-25T13:32:37.788Z |
| Last seen | 2026-05-25T13:32:37.788Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | pZkgd_Yaq4FO |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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