constants.py

DND_HEADER = """
<style>
.header-gradient {
    top: 40%;
    bottom: 40%;
    padding: 10px 0px;
    font-weight: bold;
    font-size: 40px;
    font-family: Inter, Arial, Helvetica, sans-serif;
    background: linear-gradient(to right, #67a102, #c0dc90);
    -webkit-text-fill-color: transparent;
    -webkit-background-clip: text;
}

.header-normal {
    top: 40%;
    bottom: 40%;
    padding: 10px 0px;
    font-weight: bold;
    font-size: 40px;
    font-family: Inter, Arial, Helvetica, sans-serif;
}
</style>

<div align="center">
    <span class="header-gradient"> Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights </span>
</div>
<p align="center">
| <a href=""><b>Documentation</b></a> | <a href=""><b>Github</b></a> | <a href="https://arxiv.org/abs/2506.16406"><b>Paper </b> </a> | <a href="https://x.com/VictorKaiWang1/status/1935905121659240513"><b>Twitter/X</b> </a> |
</p>"""

DND_INTRODUCTION = """
🚀 Welcome to the Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights! 

> Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights is a zero-shot prompt-to-weights model that can generate a model from a prompt.

- **Zero-Shot**: Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights can generate a model from a prompt without any training data.
- **Prompt-to-Weights**: Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights can generate a model from a prompt.
- **Easy-to-use**: Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights provides a unified interface for prompt-to-weights model generation.

"""

TASK_LIST = ["🧠 Commonsense Reasoning", "🔢 Math", "💻 Coding"]
TASK_DATASET_LIST = {
    "🧠 Commonsense Reasoning": ["ARC-c", "OBQA"],
    "🔢 Math": ["GSM-8K"],
    "💻 Coding": ["HumanEval"],
}
EXAMPLE_LIST = ["Example 1", "Example 2", "Example 3", "Example 4", "Example 5"]

TASK_PATH_MAPPING = {
    "🧠 Commonsense Reasoning": "common",
    "🔢 Math": "math",
    "💻 Coding": "coding",
}
DATASET_PATH_MAPPING = {
    "ARC-c": "arc_c",
    "OBQA": "obqa",
    "GSM-8K": "gsm8k",
    "HumanEval": "humaneval",
}
EXAMPLE_PATH_MAPPING = {
    "Example 1": "1",
    "Example 2": "2",
    "Example 3": "3",
    "Example 4": "4",
    "Example 5": "5",
}

COLUMN_NAMES = ["Prompt", "Pass@1", "Pass@5", "Pass@10", "Correctness"]
DATA_TITLE_TYPE = ['markdown', 'number', 'number', 'number', 'markdown']

CITATION_BUTTON_LABEL = "NOTE: We only use preloaded samples. Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
@article{liang2025drag,
  title={Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights},
  author={Liang, Zhiyuan and Tang, Dongwen and Zhou, Yuhao and Zhao, Xuanlei and Shi, Mingjia and Zhao, Wangbo and Li, Zekai and Wang, Peihao and Sch{\"u}rholt, Konstantin and Borth, Damian and others},
  journal={arXiv preprint arXiv:2506.16406},
  year={2025}
}
"""