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The research on code-mixed data is limited due to the unavailability ofdedicated code-mixed datasets and pre-trained language models. In this work, wefocus on the low-resource Indian language Marathi which lacks any prior work incode-mixing. We present L3Cube-MeCorpus, a large code-mixed Marathi-English(Mr-En) corpus with 10 million social media sentences for pretraining. We alsorelease L3Cube-MeBERT and MeRoBERTa, code-mixed BERT-based transformer modelspre-trained on MeCorpus. Furthermore, for benchmarking, we present threesupervised datasets MeHate, MeSent, and MeLID for downstream tasks likecode-mixed Mr-En hate speech detection, sentiment analysis, and languageidentification respectively. These evaluation datasets individually consist ofmanually annotated \url{~}12,000 Marathi-English code-mixed tweets. Ablationsshow that the models trained on this novel corpus significantly outperform theexisting state-of-the-art BERT models. This is the first work that presentsartifacts for code-mixed Marathi research. All datasets and models are publiclyreleased at https://github.com/l3cube-pune/MarathiNLP .

My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks