From TF-IDF to Transformers: Implementing Four Generations of Semantic Search

2026-05-25 · Source: Towards Data Science · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, extended

Summary

The evolution of semantic search is detailed through four progressive methods, using a synthetic dataset of student and expert art critiques. Initially, Method 1 combines TF-IDF ranking with handcrafted features like keyword overlap, length normalization (target 250 words), and recency weighting (10-year half-life) for interpretable scoring. Method 2 advances to classical machine learning, employing TF-IDF features with Logistic Regression to classify critiques as "expert-like" or "novice." Method 3 introduces embedding-based semantic search, utilizing Sentence Transformers to generate 384-dimensional dense vectors for semantic similarity, visualized via PCA. Finally, Method 4 fine-tunes a pretrained DistilBERT model, a smaller version of BERT, for supervised classification, tokenizing critiques with truncation and padding to a max_length of 128 tokens, demonstrating contextual understanding but highlighting overfitting risks with small datasets.

Key takeaway

For NLP Engineers designing semantic search systems, recognize that method choice depends on your specific needs. If interpretability and speed are critical, start with TF-IDF and rule-based scoring. For capturing deeper semantic meaning with moderate data, use Sentence Transformer embeddings. When you have significant labeled data and require nuanced contextual understanding, fine-tune a model like DistilBERT, but be aware of overfitting risks with limited datasets.

Key insights

Semantic search evolved from explicit rules to learned contextual representations, balancing interpretability with semantic depth.

Principles

• Interpretability often decreases as semantic models gain flexibility.
• Large transformer models require substantial training data to generalize reliably.
• Semantic understanding exists on a continuum, not as a binary state.

Method

The article details a progression from TF-IDF with rule-based scoring, to TF-IDF with Logistic Regression, then Sentence Transformer embeddings, and finally fine-tuned DistilBERT for classification.

In practice

• Combine TF-IDF with heuristics for interpretable ranking.
• Use Sentence Transformers for dense semantic embeddings.
• Fine-tune DistilBERT for task-specific classification.

Topics

• Semantic Search
• TF-IDF
• Transformer Models
• Sentence Embeddings
• DistilBERT
• Natural Language Processing

Code references

• theomitsa/Semantic-Search-Evolution

Best for: AI Engineer, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.