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.

The paper summarizes our research on multilingual detection of persuasion techniques in memes for the SemEval-2024 Task 4. Our work focused on English-Subtask 1, implemented based on a roberta-large pre-trained model provided by the transforms tool that was fine-tuned into a corpus of social media posts. Our method significantly outperforms the officially released baseline method, and ranked 7th in English-Subtask 1 for the test set. This paper also compares the performances of different deep learning model architectures, such as BERT, ALBERT, and XLM-RoBERTa, on multilingual detection of persuasion techniques in memes. The experimental source code covered in the paper will later be sourced from Github.

This paper delineates our investigation into the application of BioLinkBERT for enhancing clinical trials, presented at SemEval-2024 Task 2. Centering on the medical biomedical NLI task, our approach utilized the BioLinkBERT-large model, refined with a pioneering mixed loss function that amalgamates contrastive learning and cross-entropy loss. This methodology demonstrably surpassed the established benchmark, securing an impressive F1 score of 0.72 and positioning our work prominently in the field. Additionally, we conducted a comparative analysis of various deep learning architectures, including BERT, ALBERT, and XLM-RoBERTa, within the context of medical text mining. The findings not only showcase our method’s superior performance but also chart a course for future research in biomedical data processing. Our experiment source code is available on GitHub at: https://github.com/daojiaxu/semeval2024_task2.