Before revision:  Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best macro F1-score with 0.498 on development data and 0.739 on test data.
After revision:  Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best macro F1-score with 0.537 on development data and 0.739 on test data.