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Code-switching occurs when more than one language is mixed in a givensentence or a conversation. This phenomenon is more prominent on social mediaplatforms and its adoption is increasing over time. Therefore code-mixed NLPhas been extensively studied in the literature. As pre-trainedtransformer-based architectures are gaining popularity, we observe that realcode-mixing data are scarce to pre-train large language models. We presentL3Cube-HingCorpus, the first large-scale real Hindi-English code mixed data ina Roman script. It consists of 52.93M sentences and 1.04B tokens, scraped fromTwitter. We further present HingBERT, HingMBERT, HingRoBERTa, and HingGPT. TheBERT models have been pre-trained on codemixed HingCorpus using masked languagemodelling objectives. We show the effectiveness of these BERT models on thesubsequent downstream tasks like code-mixed sentiment analysis, POS tagging,NER, and LID from the GLUECoS benchmark. The HingGPT is a GPT2 based generativetransformer model capable of generating full tweets. We also releaseL3Cube-HingLID Corpus, the largest code-mixed Hindi-English languageidentification(LID) dataset and HingBERT-LID, a production-quality LID model tofacilitate capturing of more code-mixed data using the process outlined in thiswork. The dataset and models are available athttps://github.com/l3cube-pune/code-mixed-nlp .

L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models