This paper describes the system developed by the PEU Lab for SemEval-2026 Task 4, specifically focusing on Track A: Comparative Narrative Similarity. To address the pairwise nature of the task, a lightweight contrastive ranking approach is proposed. Specifically, the pretrained RoBERTa-Large model is utilized to encode the anchor and candidate stories. Rather than employing standard cross-entropy, a margin ranking loss is introduced, which allows the relative narrative proximity between different candidate stories to be explicitly modeled. Furthermore, a 5-fold cross-validation ensemble strategy is integrated to stabilize predictions on unseen data. Evaluated on the official dataset, the optimal configuration achieved an overall accuracy of 64.50%, demonstrating the effectiveness of relative order modeling. The code for this system is available at: https://github.com/mhchhh/SemEval2026-Task-4.

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.