  Code-mixing is a well-studied linguistic phenomenon when two or more
languages are mixed in text or speech. Several works have been conducted on
building datasets and performing downstream NLP tasks on code-mixed data.
Although it is not uncommon to observe code-mixing of three or more languages,
most available datasets in this domain contain code-mixed data from only two
languages. In this paper, we introduce OffMix-3L, a novel offensive language
identification dataset containing code-mixed data from three different
languages. We experiment with several models on this dataset and observe that
BanglishBERT outperforms other transformer-based models and GPT-3.5.
