Featuring Every Eval Ever Results on Hugging Face Model Pages
Featuring Every Eval Ever Results on Hugging Face Model Pages
EEE launched in February 2026 as a project of the EvalEval Coalition, the first cross-institutional effort to improve how AI evaluation results get reported by both first and third party evaluators. Hugging Face launched Community Evals in February 2026 to decentralize how benchmark scores get reported on the Hub. Combined, they patch gaps in how users, researchers, and policymakers trust, understand, and choose evaluations and models. Evaluation results are how we measure model capabilities, compare models against each other, and reason about safety and governance, and yet they are scattered and hard to compare. They live in papers, leaderboards, blog posts, and harness logs, among others, each in its own format. The same model on the same benchmark often returns different scores depending on who ran it and how; LLaMA 65B, for one, has been reported at both 63.7 and 48.8 on MMLU. These gaps can arise from evaluation settings that we found are commonly unreported. EEE is our fix for the reporting side. It’s one JSON schema for an evaluation result that records: The schema was built with feedback from researchers and policy researchers, and it takes in results from any source, so harness logs, leaderboard scrapes, and paper numbers all end up in the same shape. The GitHub repository has the converters, examples, and a contributor guide. Since launching, the datastore on Hugging Face has grown to around 229,000 evaluation results across more than 22,000 models and 2,200 benchmarks, pulled from 31 different reporting formats. Reproducing just those runs from scratch would cost somewhere in the hundreds of thousands of dollars, which is a reasonable argument for not letting the data scatter once someone has paid to generate it. Learn more about the schema and how to contribute here. Now, it comes with better integration and attribution. Contributors can now send EEE results to Hugging Face Community Evals.
