Irene Dunne
Stephanie
Search results
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 ...
Jul 26, 2019 · We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it.
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke...
- arXiv:1907.11692 [cs.CL]
- 2019
- Computation and Language (cs.CL)
4 days ago · The name Roberta is a girl's name of English origin meaning "bright fame". Roberta has been one of the most successful feminization names, up at #64 in 1936. It's a name that's found all over children's lit, often nicknamed Bobbie or Robbie, though Bertie is another possibility.
Jul 29, 2019 · Facebook AI’s RoBERTa is a new training recipe that improves on BERT, Google’s self-supervised method for pretraining natural language processing systems. By training longer, on more data, and dropping BERT’s next-sentence prediction RoBERTa topped the GLUE leaderboard.
Sep 24, 2023 · 5 min read. ·. Sep 24, 2023. Introduction. The appearance of the BERT model led to significant progress in NLP. Deriving its architecture from Transformer, BERT achieves state-of-the-art results on various downstream tasks: language modeling, next sentence prediction, question answering, NER tagging, etc.
People also ask
What does Roberta do?
What is Roberta model?
Why is Roberta better than Bert?
What is Roberta architecture?
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.
RoBERTa builds on BERT’s language masking strategy and modifies key hyperparameters in BERT, including removing BERT’s next-sentence pretraining objective, and training with much larger mini-batches and learning rates. RoBERTa was also trained on an order of magnitude more data than BERT, for a longer amount of time.
