bm25-corpus-sampling
Skill0 starsChanged 6 months agoNo licenceView on GitHub
Representative corpus sampling for BM25 fitting. When the full corpus would take > 2 hours to index, extract a ~2000-document sample whose WordNet token distribution approximates the full corpus. Stratifies on a 2D joint feature space (document-length bin × topic), normalizes cell counts via log-normal → median/MAD scale → Yeo-Johnson → shift-positive / linear normalize, then allocates quota proportionally. The Yeo-Johnson z-scores are a reusable reliability signal: the same cell weights that drive corpus sampling also serve as a BM25 reranker (penalise documents from poorly-calibrated cells) and as an ensemble mixing coefficient between BM25 and dense retrieval. Use before fitting any BM25 index when parity (convergence on global IDF mean) is the goal, not exhaustive coverage.
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