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Overview. The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook’s RoBERTa model released in 2019. It is a large multi-lingual language model, trained on ...
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RoBERTa is an extension of BERT with changes to the pretraining procedure. The modifications include: training the model longer, with bigger batches, over more data removing the next sentence prediction objective training on longer sequences dynamically changing the masking pattern applied to the training data. The authors also collect a large new dataset ($\text{CC-News}$) of comparable size ...
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3 de sept. de 2019 · This paper shows that the original BERT model, if trained correctly, can outperform all of the improvements that have been proposed lately, raising questions...
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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 ...
