@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T PuerAI at SemEval-2025 Task 9: Research on Food Safety Data Classification Using ModernBERT
%A Dao, Jiaxu
%A Li, Zhuoying
%A Tang, Xiuzhong
%A Su, Youbang
%A Zhou, Qingsong
%A He, Weida
%A Lan, Xiaoli
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F dao-etal-2025-puerai
%X 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.
%U https://aclanthology.org/2025.semeval-1.26/
%P 180-185
Markdown (Informal)
[PuerAI at SemEval-2025 Task 9: Research on Food Safety Data Classification Using ModernBERT](https://aclanthology.org/2025.semeval-1.26/) (Dao et al., SemEval 2025)
ACL