roberta-large
The RoBERTa model was pre-trained on a dataset consisting of 11,038 books, English Wikipedia, 63 million news articles, and a dataset containing a subset of Common Crawl data. It achieved state-of-the-art results on Glue, SuperGLUE, and multi-task benchmarks while exhibiting less sensitivity to hyperparameter tuning compared to BERT. RoBERTa uses a robust optimization approach and dynamic masking, which changes during pre-training, unlike BERT.
The RoBERTa model was pre-trained on a dataset consisting of 11,038 books, English Wikipedia, 63 million news articles, and a dataset containing a subset of Common Crawl data. It achieved state-of-the-art results on Glue, SuperGLUE, and multi-task benchmarks while exhibiting less sensitivity to hyperparameter tuning compared to BERT. RoBERTa uses a robust optimization approach and dynamic masking, which changes during pre-training, unlike BERT.
Input
text prompt, should include exactly one <mask> token
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Output
where is my father? (0.09)
where is my mother? (0.08)
