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Model description. RoBERTa is a transformers model pretrained on a large corpus of English data 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 ...
26 de jul. de 2019 · TLDR. A new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems, including cloze-style word reconstruction task, and a detailed analysis of a number of factors that contribute to effective pretraining. Expand. 188.
10 de ene. de 2023 · RoBERTa (short for “Robustly Optimized BERT Approach”) is a variant of the BERT (Bidirectional Encoder Representations from Transformers) model, which was developed by researchers at Facebook AI. Like BERT, RoBERTa is a transformer-based language model that uses self-attention to process input sequences and generate contextualized ...
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Overview¶. The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google’s BERT model released in 2018. It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and ...
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