Add unit-circle embedding benchmark project - #1
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erinepshovel-code merged 1 commit intoApr 14, 2026
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Full Replit-ready benchmark for testing whether a unit-circle / number-base coordinate remapping improves retrieval, clustering, and neighborhood preservation on top of proven embedding models. - src/transforms.py: full A0–A5 ablation ladder (identity, PCA, random rotation, UC direct, UC rank, UC broken-phase control) - src/benchmark.py: retrieval (Recall@k, MRR, nDCG), clustering (silhouette, NMI, ARI), geometry (neighbor overlap, Spearman, trustworthiness/continuity) - src/embed.py: SentenceTransformer wrapper with task-prefix support - src/config.py: model presets (nomic, bge-m3) and ablation configs - src/main.py: full ablation runner with JSON output and success checks - src/metrics.py: table formatting, delta-vs-baseline, success criteria - data/: 20-doc toy corpus (4 topics) + 10 queries + qrels - notebooks/sanity_checks.ipynb: bar charts, neighbor overlap, UC geometry - requirements.txt, pyproject.toml, .replit https://claude.ai/code/session_01EWFjWpMFhdAcdpST3syGPE
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April 14, 2026 20:07
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Full Replit-ready benchmark for testing whether a unit-circle /
number-base coordinate remapping improves retrieval, clustering, and
neighborhood preservation on top of proven embedding models.
random rotation, UC direct, UC rank, UC broken-phase control)
(silhouette, NMI, ARI), geometry (neighbor overlap, Spearman,
trustworthiness/continuity)
https://claude.ai/code/session_01EWFjWpMFhdAcdpST3syGPE