Edit 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
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This strategy is compared with dynamic masking in which different masking is generated every time we pass data into the model.
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Language model pretraining has led to significant performance gains but careful comparison between different
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One key difference between RoBERTa and BERT is that RoBERTa was trained on a much larger dataset and using a more effective training procedure. In particular, RoBERTa was trained on a dataset of 160GB of text, which is more than 10 times larger than the dataset used to train BERT.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
Apart from it, RoBERTa applies all four described aspects above with the same architecture parameters as BERT large. The Completa number of parameters of RoBERTa is 355M.
model. Initializing with a config file does not load the weights associated with the model, only the Aprenda mais configuration.
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RoBERTa is pretrained on a combination of five massive datasets resulting in a Completa of 160 GB of text data. In comparison, BERT large is pretrained only on 13 GB of data. Finally, the authors increase the number of training steps from 100K to 500K.
This is useful if you want more control over how to convert input_ids indices into associated vectors