text_content.py

HEAD_TEXT = """
Based on the DomainEval benchmark, we evaluate code generation ability of different LLMs across multiple domains.

Leaderboard on GitHub: [DomainEval Leaderboard on GitHub](https://domaineval.github.io/)

More details about how to evaluate the LLM are available in the [DomainEval GitHub repository](https://github.com/domaineval/DomainEval).

For a complete description of DomainEval benchmark and related experimental analysis, please refer to the paper: 
[DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation](https://arxiv.org/abs/2408.13204). [![](https://img.shields.io/badge/arXiv-2408.13204-b31b1b.svg)](https://arxiv.org/abs/2408.13204)

**_Latest News_** 🔥
- [24/08/26] We release our DomainEval benchmark, leaderboard and paper.
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"

CITATION_BUTTON_TEXT = r"""@misc{zhu2024domainevalautoconstructedbenchmarkmultidomain,
      title={DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation}, 
      author={Qiming Zhu and Jialun Cao and Yaojie Lu and Hongyu Lin and Xianpei Han and Le Sun and Shing-Chi Cheung},
      year={2024},
      eprint={2408.13204},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2408.13204}, 
}
"""

ACKNOWLEDGEMENT_TEXT = """
Inspired from the [🤗 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
"""


NOTES_TEXT = """
**Notes:**
- Evaluate using pass@k as the evaluation metric.
- `Mean` denotes the macro average results of pass@k across 6 different domains.
- `Std` denotes the standard deviation of pass@k across 6 different domains.
- You can choose differt pass@k in `⏬ Pass@k`.
- `⏬ Domains` can choose domains you want to show in the leaderboard.
"""