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In this paper, we present our approach for sentiment classification onSpanish-English code-mixed social media data in the SemEval-2020 Task 9. Weinvestigate performance of various pre-trained Transformer models by usingdifferent fine-tuning strategies. We explore both monolingual and multilingualmodels with the standard fine-tuning method. Additionally, we propose a custommodel that we fine-tune in two steps: once with a language modeling objective,and once with a task-specific objective. Although two-step fine-tuning improvessentiment classification performance over the base model, the largemultilingual XLM-RoBERTa model achieves best weighted F1-score with 0.537 ondevelopment data and 0.739 on test data. With this score, our team jupitterplaced tenth overall in the competition.

FiSSA at SemEval-2020 Task 9: Fine-tuned For Feelings