  Sentiment analysis for code-mixed social media text continues to be an
under-explored area. This work adds two common approaches: fine-tuning large
transformer models and sample efficient methods like ULMFiT. Prior work
demonstrates the efficacy of classical ML methods for polarity detection.
Fine-tuned general-purpose language representation models, such as those of the
BERT family are benchmarked along with classical machine learning and ensemble
methods. We show that NB-SVM beats RoBERTa by 6.2% (relative) F1. The best
performing model is a majority-vote ensemble which achieves an F1 of 0.707. The
leaderboard submission was made under the codalab username nirantk, with F1 of
0.689.
