Difference between revisions of "Bidirectional Encoder Representations from Transformers (BERT)"

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** [http://arxiv.org/abs/1907.11692 RoBERTa: A Robustly Optimized BERT Pretraining Approach | Y. Li, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov]
 
** [http://arxiv.org/abs/1907.11692 RoBERTa: A Robustly Optimized BERT Pretraining Approach | Y. Li, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov]
 
** [http://github.com/pytorch/fairseq/tree/master/examples/roberta RoBERTa: A Robustly Optimized BERT Pretraining Approach | GitHub] - iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data.  
 
** [http://github.com/pytorch/fairseq/tree/master/examples/roberta RoBERTa: A Robustly Optimized BERT Pretraining Approach | GitHub] - iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data.  
** [http://venturebeat.com/2019/07/29/facebook-ais-roberta-improves-googles-bert-pretraining-methods/ Facebook AI’s RoBERTa improves Google’s BERT pretraining methods | Khari Johnson - VentureBeat]
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** [http://venturebeat.com/2019/07/29/facebook-ais-roberta-improves-googles-bert-pretraining-methods/ [[Meta|Facebook]] AI’s RoBERTa improves Google’s BERT pretraining methods | Khari Johnson - VentureBeat]
 
* Google's BERT - built on ideas from [[ULMFiT]], [[ELMo]], and [[OpenAI]]
 
* Google's BERT - built on ideas from [[ULMFiT]], [[ELMo]], and [[OpenAI]]
 
* [[Attention]] Mechanism/[[Transformer]] Model
 
* [[Attention]] Mechanism/[[Transformer]] Model

Revision as of 22:59, 8 February 2023

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BERT Research | Chris McCormick