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Featuring Every Eval Ever Results on Hugging Face Model Pages

Blog post from HuggingFace

Post Details
Company
Date Published
Author
Sree Harsha Nelaturu, Avijit Ghosh, Nathan Habib, Jan Batzner, Leshem Choshen, Irene Solaiman, and Julien Chaumond
Word Count
1,434
Company Posts That Month
90
Language
-
Hacker News Points
-
Summary

Every Eval Ever (EEE) and Hugging Face Community Evals have become intercompatible to enhance the reporting and interpretation of AI evaluation results, aiming to address the inconsistencies and scattered nature of current evaluation practices. Launched in February 2026, these initiatives allow cross-posting and provide a unified standardized metadata store, improving trust and comprehension for users, researchers, and policymakers. EEE offers a JSON schema to standardize evaluation reporting, capturing details like generation settings and metrics, while Hugging Face Community Evals decentralizes the reporting of benchmark scores, compiling them into a comprehensive leaderboard system. The collaboration enables contributors to submit evaluation data that appear both on Hugging Face model pages and within the EEE records, ensuring results are accessible and interpretable through integrated Eval Cards. This system facilitates efficient evaluation result management, mitigating duplication and enhancing transparency by linking each score to its detailed source record, with the converter tool simplifying the process of integrating data into both platforms.

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