MTEB Leaderboard: From a slow demo to feature-rich leaderboard

The newly released version of the MTEB leaderboard significantly enhances user experience by addressing previous issues of speed and reliability. Built on a scalable framework using FastAPI and Svelte, the updated leaderboard offers improved filtering, model comparison, and transparency, enabling users to deeply explore and customize benchmarks to suit specific needs. Notably faster than its predecessors, it allows users to filter on domains, language, modality, and individual tasks, and provides transparency by allowing inspection of datasets and task metadata. The update encourages broader improvements across models, not just top performers, by highlighting factors like size, memory usage, and runtime. Users can easily compare models by pinning them for tailored analysis, and can fetch scores locally via CSV or API. The development team encourages user feedback for further enhancements, reflecting a commitment to continuous improvement and community engagement.

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