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. For Subtask 1 we obtained a weighted F1 score of 0.43 and placed 12 in the leaderboard. We investigate two distinct approaches: Masked Language Modeling (MLM) and Causal Language Modeling (CLM). For MLM%2C we employ pre-trained BERT-like models in a multilingual setting%2C fine-tuning them with a classifier to predict emotions. Experiments with varying input lengths%2C classifier architectures%2C and fine-tuning strategies demonstrate the effectiveness of this approach. Additionally%2C we utilize Mistral 7B Instruct V0.2%2C a state-of-the-art model%2C applying zero-shot and few-shot prompting techniques. Our findings indicate that while Mistral shows promise%2C MLMs currently outperform them in sentence-level emotion classification.?quality=80&w=800)
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