XLM RoBERTa (base sized model) XLM RoBERTa model pre trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross lingual Representation Learning at Scale by Conneau et al. and first released in this repository. Disclaimer: The team releasing XLM RoBERTa did not write a model card for this model so this model card has been written by the Hugging Face team. Model description XLM RoBERTa is a multilingual version of RoBERTa. It is pre trained on 2.5TB of filtered CommonCrawl data containing 100 languages. RoBERTa is a transformers model pretrained on a large corpus in a self supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RN…
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