How to Build a Benchmark with a Private Test Set on Hugging Face

The article provides a comprehensive guide on setting up a challenge or benchmark using Hugging Face, where users can submit their model predictions to be evaluated against a private test set, and the results are displayed on a public leaderboard. It outlines the architecture needed for this setup, involving a public leaderboard, a private evaluator, a submissions dataset, and a results dataset, all interconnected to maintain privacy and provide a clean interface for users. The guide emphasizes the importance of planning the schema for datasets upfront to avoid future complications and provides detailed instructions for creating and managing the necessary repositories and spaces on Hugging Face. Additionally, it includes practical tips for handling common issues such as schema consistency, error handling, rate limiting, and caching to ensure smooth operation.

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