如何建立自己的 Hugging Face 排行榜：以 Vectara 幻覺排行榜為例的完整指南
Original: A guide to setting up your own Hugging Face leaderboard: an end-to-end example with Vectara's hallucination leaderboard
In the open-source AI community, the Hugging Face Open LLM Leaderboard serves as an important benchmark for evaluating model capabilities…
本教學詳細介紹如何從頭構建一個自訂的 Hugging Face 模型排行榜。文章以 Vectara 的「LLM 幻覺排行榜（Hallucination Leaderboard）」為實際案例，展示如何結合 Hugging Face Spaces（使用 Gradio）與 Datasets 儲存評測數據，並實現自動化更新與前端展示。這對於想要建立特定領域（如 RAG、安全、特定語言）模型評估標準的開發者與研究人員非常實用。
In the open-source AI community, the Hugging Face Open LLM Leaderboard serves as an important benchmark for evaluating model capabilities. However, many enterprises or research teams need to establish custom evaluation criteria tailored to specific tasks — such as RAG, hallucination detection, or domain-specific applications. Using Vectara's "LLM Hallucination Leaderboard" as an example, this article provides a complete end-to-end implementation guide to help developers quickly build their own leaderboards.
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