Os roberta pires Diaries

Os roberta pires Diaries

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The original BERT uses a subword-level tokenization with the vocabulary size of 30K which is learned after input preprocessing and using several heuristics. RoBERTa uses bytes instead of unicode characters as the base for subwords and expands the vocabulary size up to 50K without any preprocessing or input tokenization.

model. Initializing with a config file does not load the weights associated with the model, only the configuration.

Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general

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model. Initializing with a config file does not load the weights associated with the model, only the configuration.

This is useful if you want more control over how to convert input_ids indices into associated vectors

This is useful if you want more control over how to convert input_ids indices into associated vectors

a dictionary with one or several input Tensors associated to the input names given in the docstring:

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, 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. Our best model achieves state-of-the-art results on GLUE, Veja mais RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code. Subjects:

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.

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