architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of

Ao longo da história, o nome Roberta possui sido usado por várias mulheres importantes em variados áreas, e isso Pode vir a dar uma ideia do Genero do personalidade e carreira qual as pessoas utilizando esse nome podem possibilitar deter.

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

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Dynamically changing the masking pattern: In BERT architecture, the masking is performed once during data preprocessing, resulting in a single static mask. To avoid using the single static mask, training data is duplicated and masked 10 times, each time with a different mask strategy over 40 epochs thus having 4 epochs with the same mask.

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model. Initializing with a config file does not load Entenda 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

As a reminder, the BERT base model was trained on a batch size of 256 sequences for a million steps. The authors tried training BERT on batch sizes of 2K and 8K and the latter value was chosen for training RoBERTa.

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention

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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

If you choose this second option, there are three possibilities you can use to gather all the input Tensors

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