ML Model Leaderboard Navigator

概要

Equip your workflow with a rigorous methodology for navigating the rapidly evolving landscape of machine learning benchmarks, focusing specifically on the Massive Text Embedding Benchmark (MTEB) and HuggingFace leaderboards. This skill provides strategies for identifying authoritative data sources, accessing live data through raw endpoints when standard scraping fails, and ensuring strict temporal alignment for time-sensitive requests. By implementing specific cross-referencing techniques and validation checks, it helps avoid the common pitfalls of relying on outdated academic papers or misinterpreting model eligibility criteria in interactive web environments.

主な機能

• Techniques for extracting raw data from interactive or JavaScript-heavy leaderboards
• Systematic search patterns for finding benchmark results in JSON or CSV formats
• Identification of authoritative primary sources versus outdated secondary literature
• 16 GitHub stars
• Cross-referencing strategies to validate model rankings across multiple sources
• Temporal verification workflows to match data with specific dates or periods

ユースケース

• Finding top-performing embedding models for specific languages or tasks on the MTEB leaderboard
• Conducting temporal performance comparisons to see how model rankings have changed over time
• Extracting current benchmark data from interactive HuggingFace Spaces or GitHub-based repositories