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This paper describes our system for SemEval-2023 Task 3 Subtask 2 on FramingDetection. We used a multi-label contrastive loss for fine-tuning largepre-trained language models in a multi-lingual setting, achieving verycompetitive results: our system was ranked first on the official test set andon the official shared task leaderboard for five of the six languages for whichwe had training data and for which we could perform fine-tuning. Here, wedescribe our experimental setup, as well as various ablation studies. The codeof our system is available at https://github.com/QishengL/SemEval2023

MarsEclipse at SemEval-2023 Task 3: Multi-Lingual and Multi-Label Framing Detection with Contrastive Learning