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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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It happens due to the fact that reaching the document boundary and stopping there means that an input sequence will contain less than 512 tokens. For having a similar number of tokens across all batches, the batch size in such cases needs to be augmented. This leads to variable batch size and more complex comparisons which researchers wanted to avoid.

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The "Open Roberta® Lab" is a freely available, cloud-based, open source programming environment that makes learning programming easy - from the first steps to programming intelligent robots with multiple sensors and capabilities.

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

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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.

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This results in 15M and 20M additional parameters for BERT base and BERT large models respectively. The introduced encoding version in RoBERTa demonstrates slightly worse results than before.

Ultimately, for the final RoBERTa implementation, the authors chose to keep the first two aspects and omit the third one. Despite the observed improvement behind the third insight, researchers did not not proceed with it because otherwise, it would have made the comparison between previous implementations more problematic.

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

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