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

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.

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.

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

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A tua personalidade condiz com algufoim satisfeita e Gozado, que gosta de olhar a vida através perspectiva1 positiva, enxergando a todos os momentos este lado positivo de tudo.

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

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

and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication

training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of

De acordo utilizando o paraquedista Paulo Zen, administrador e apenascio do Sulreal Wind, a equipe passou 2 anos dedicada ao estudo de viabilidade do empreendimento.

dynamically changing the masking pattern applied to the training data. The authors also collect a large new dataset ($text CC-News $) of comparable size to other privately used datasets, to better control for training set size effects

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