roberta No Further um Mistério

roberta No Further um Mistério

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

This strategy is compared with dynamic masking in which different masking is generated  every time we pass data into the model.

Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding

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

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

The authors of the paper conducted research for finding an optimal way to model the next sentence prediction task. As a consequence, they found several valuable insights:

It more beneficial to construct input sequences by sampling contiguous sentences from a single document rather than from multiple documents. Normally, sequences are always constructed from contiguous full sentences of a single document so that the Perfeito length is at most 512 tokens.

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

The problem arises when we reach the end of a document. In this aspect, researchers compared whether it was worth stopping sampling sentences for such sequences or additionally sampling the first several sentences of the next document (and adding a corresponding separator token between documents). The results showed that the first option is better.

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

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 Veja mais control for training set size effects

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