In July 2019, the Facebook research team introduced the Robustly Optimized BERT Pretraining Approach (RoBERTa)–an improvement over the Bidirectional Encoder Representations from Transformers (BERT), a self-supervised method for NLP tasks released by Facebook in 2018.
Two researchers, Nipun Sadvilkar and Haswanth Aekula, have now pretrained the RoBERTa model on Marathi language using a masked language modelling (MLM) objective in a self-supervised manner. The duo unveiled the model at Hugging Face’s community week.
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The model is primarily aimed at tasks that use the whole sentences (potentially masked) to make decisions, such as sequence classification, token classification or question-answer. The duo used this model to fine-tune text classification tasks like iNLTK and indicNLP. Since the Marathi mc4 dataset is made up of text from Marathi newspapers, it might involve biases that can affect all fine-tuned versions of the model, the team warned.