Rename README_hf.md to README.md
Browse files- README_hf.md → README.md +1 -5
README_hf.md → README.md
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| **This dataset (Hub)** | [https://huggingface.co/datasets/EMAS4Rescue/ema-bench-26](https://huggingface.co/datasets/EMAS4Rescue/ema-bench-26) | Scenario configuration files and dataset card. |
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| **Platform guidelines** | [Google Doc (EMA-Bench)](https://docs.google.com/document/d/ema-bench) | Experiment and collaboration guidelines for the broader EMA-Bench platform. |
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| **Code & tooling** | [https://
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The Hub repository holds **configuration data**. The Unreal-based simulator build, Docker/UE deployment, and evaluation harnesses live in the **GitHub** repository and your own infrastructure.
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- [`RAI.md`](RAI.md) — Responsible AI: intended use, out-of-scope use, limitations, biases, human data, ethics, maintenance.
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## 中文摘要(可选)
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**EMA-Bench-26** 在 Hugging Face 上提供 **8,000+** 条**合成 JSON 场景配置**,用于动态灾害类(如室内蔓延火场)高保真仿真研究;每条记录包含场景编号、火点数量、NPC 数量及火势相关缩放参数等。数据为**学术仿真**用途,**不含**可运行仿真器二进制;完整工程与客户端见 [GitHub 仓库](https://github.com/ZUWQ/ema-bench-nips-26)。更完整的中文项目说明见仓库根目录 [README_zh.md](https://github.com/ZUWQ/ema-bench-nips-26/blob/main/README_zh.md)。
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| **This dataset (Hub)** | [https://huggingface.co/datasets/EMAS4Rescue/ema-bench-26](https://huggingface.co/datasets/EMAS4Rescue/ema-bench-26) | Scenario configuration files and dataset card. |
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| **Platform guidelines** | [Google Doc (EMA-Bench)](https://docs.google.com/document/d/ema-bench) | Experiment and collaboration guidelines for the broader EMA-Bench platform. |
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| **Code & tooling** | [https://anonymous.4open.science/r/ema-bench-nips-26-4E30](https://anonymous.4open.science/r/ema-bench-nips-26-4E30) | Open-source client, examples, and automation that **consume** scene configs (e.g. writing `SceneID` / `FireNum` / `NPCNum` into host `SceneConfig.json`). |
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The Hub repository holds **configuration data**. The Unreal-based simulator build, Docker/UE deployment, and evaluation harnesses live in the **GitHub** repository and your own infrastructure.
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- [`RAI.md`](RAI.md) — Responsible AI: intended use, out-of-scope use, limitations, biases, human data, ethics, maintenance.
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