A guide to setting up your own Hugging Face leaderboard: an end-to-end example with Vectara’s hallucination leaderboard

building your own leaderboard, like the hallucination leaderboard example, is useful because it forces transparency. it stops us from just trusting whatever vague evaluation metrics some vendor spits out. we need to own the metrics we use to judge model performance, or we're just chasing noise.

setting up the end-to-end example shows that you can actually track performance metrics without heavy proprietary tooling. it forces you to define what 'good' looks like for specific tasks. stop relying on black boxes and start benchmarking what matters.