XLM-R, pretrained on 100 languages with 2TB of CommonCrawl data, improves average XNLI accuracy by 14.6 points and MLQA F1 by 13 points over mBERT while matching strong monolingual models on GLUE.
arXiv preprint arXiv:1910.07475 , year=
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A tutorial synthesizing foundations, recent models such as PALO and Maya, and low-cost methods for tri-modal multilingual AI in resource-constrained settings.
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Unsupervised Cross-lingual Representation Learning at Scale
XLM-R, pretrained on 100 languages with 2TB of CommonCrawl data, improves average XNLI accuracy by 14.6 points and MLQA F1 by 13 points over mBERT while matching strong monolingual models on GLUE.
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Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages
A tutorial synthesizing foundations, recent models such as PALO and Maya, and low-cost methods for tri-modal multilingual AI in resource-constrained settings.