MuRIL Large Multilingual Representations for Indian Languages : A BERT Large (24L) model pre trained on 17 Indian languages, and their transliterated counterparts. Overview This model uses a BERT large architecture [1] pretrained from scratch using the Wikipedia [2], Common Crawl [3], PMINDIA [4] and Dakshina [5] corpora for 17 [6] Indian languages. We use a training paradigm similar to multilingual bert, with a few modifications as listed: We include translation and transliteration segment pairs in training as well. We keep an exponent value of 0.3 and not 0.7 for upsampling, shown to enhance low resource performance. [7] See the Training section for more details. Training The MuRIL model is pre trained on monolingual segments as well as parallel segments as detailed below : Monolingual Data : We make use of publicly available corpora from Wikipedia and Common Crawl for 17 Indian languages. Parallel Data : We have two types of parallel data : Translated Data : We obtain translations of the above monolingual corpora using the Google NMT pipeline. We feed translated segment pairs as input. We also make use of the publicly available PMINDIA corpus. Transliterated Data : We obtain tra…
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