BM25 in Python: Rank Better with Boosts and Priors that Reorder
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YouTubeProfessor Py: Information Retrieval with Python
BM25 in Python: Rank Better with Boosts and Priors that Reorder
Understand search scores with BM25 and boosts—learn when to use it over dense embeddings. Build a Python pipeline for tokenization, term counts, simple IDF, BM25 (k1, b), query boosts, and document priors to rank top results fast. We use pure Python with regex tokenization, length normalization, and evaluate with NDCG—fast, interpretable ...
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Uma Abu | Software Engineer on Instagram: "Elasticsearch is one of the most powerful search solutions for developers. But it comes with trade-offs. Duplicating your data, maintaining sync pipelines, and running additional infrastructure adds complexity. Not to mention how expensive it gets at scale. If you’re developing on Postgres, there’s now a way to get BM25-ranked keyword search directly inside Postgres, the same ranking algorithm Elasticsearch uses, thanks to Tiger Data (creators of Timesc
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