@inproceedings{dao-etal-2025-puerai,
    title = "{P}uer{AI} at {S}em{E}val-2025 Task 9: Research on Food Safety Data Classification Using {M}odern{BERT}",
    author = "Dao, Jiaxu  and
      Li, Zhuoying  and
      Tang, Xiuzhong  and
      Su, Youbang  and
      Zhou, Qingsong  and
      He, Weida  and
      Lan, Xiaoli",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.26/",
    pages = "180--185",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our research in the SemEval-2025 Task 9: Food Hazard Detection Challenge, with a focus on the application of ModernBERT for food safety data classification. We applied the ModernBERT model for the food hazard classification task, achieving a score of 0.7952 on the validation set and 0.7729 on the final test set, outperforming other models. Through comparative experiments with various deep learning architectures, we further confirmed the superiority of ModernBERT in food hazard detection. The results demonstrate the significant potential of ModernBERT in food safety management, providing strong support for its practical applications in the field. The code of this paper is available at: https://github.com/daojiaxu/semeval{\_}2025{\_}Task-9."
}