Models such as mBERT and XLMR have shown success in solving Code-Mixed NLP tasks even though they were not exposed to such text during pretraining. Code-Mixed NLP models have relied on using synthetically generated data along with naturally occurring data to improve their performance. Finetun-ing mBERT on such data improves it's code-mixed performance, but the benefits of using the different types of Code-Mixed data aren't clear. In this paper, we study the impact of fine-tuning with different types of code-mixed data and outline the changes that occur to the model during such finetuning. Our findings suggest that using naturally occurring code-mixed data brings in the best performance improvement after finetuning and that finetuning with any type of code-mixed text improves the respon-sivity of it's attention heads to code-mixed text inputs.
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