src/about.py

from dataclasses import dataclass
from enum import Enum

@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
    # For MMLongBench-Doc (https://arxiv.org/abs/2407.01523), we use ACC as the main metric
    task0 = Task("mmlongbench_doc", "acc", "ACC")

NUM_FEWSHOT = 0 # Change with your few shot
# ---------------------------------------------------



# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">🥇 <a href="" target="_blank">UniGenBench</a> Leaderboard</h1>"""

# Links and conference info
LINKS_AND_INFO = """
<div align="center">
<p><a href="https://hunyuan.tencent.com/" target="_blank">Hunyuan</a>, Tencent</p> <br>

<a href="https://codegoat24.github.io/UnifiedReward/Pref-GRPO" target="_blank">🏠 Homepage</a> | 
<a href="https://arxiv.org/pdf/2508.20751" target="_blank">📄 arXiv Paper</a>

<a href="https://github.com/CodeGoat24/UniGenBench" target="_blank" rel="noopener noreferrer"><img alt="Code" src="https://img.shields.io/github/stars/CodeGoat24/UniGenBench.svg?style=social&amp;label=Official"></a>

</div>
"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
📚 [UniGenBench](https://github.com/CodeGoat24/UniGenBench) is a unified benchmark for T2I generation that integrates diverse prompt themes with a comprehensive suite of fine-grained evaluation criteria. 

🔧 You can use the official [GitHub repo](https://github.com/CodeGoat24/UniGenBench) to evaluate your model on [UniGenBench](https://github.com/CodeGoat24/UniGenBench).

📝 To add your own model to the leaderboard, please send an Email to [Yibin Wang](https://codegoat24.github.io/), then we will help with the evaluation and updating the leaderboard.
"""




CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@article{UniGenBench&Pref-GRPO,
  title={Pref-GRPO: Pairwise Preference Reward-based GRPO for Stable Text-to-Image Reinforcement Learning},
  author={Wang, Yibin and Li, Zhimin and Zang, Yuhang and Zhou, Yujie and Bu, Jiazi and Wang, Chunyu and Lu, Qinglin, and Jin, Cheng and Wang, Jiaqi},
  journal={arXiv preprint arXiv:2508.20751},
  year={2025}
}
"""