sparse-retrieval-eval
CommunityBenchmark sparse IR models on standard benchmarks
AuthorJoaquinCampo
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Evaluate and compare sparse retrieval models on standard IR benchmarks to quantify performance across diverse datasets and metrics.
Core Features & Use Cases
- Supports loading BEIR, MIRACL, and mMARCO benchmarks and computing full IR metrics (nDCG@k, Recall@k, MAP, MRR).
- Handles sparse encoding of document representations with CSR matrices, IDF-weight weighted retrieval, and caching for reproducibility.
- Interprets results with clear guidance for analysis and comparison across models and datasets.
Quick Start
Run the sparse retrieval evaluation workflow on your dataset to compare BEIR, MIRACL, and mMARCO benchmarks.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: sparse-retrieval-eval Download link: https://github.com/JoaquinCampo/Skills/archive/main.zip#sparse-retrieval-eval Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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