5 técnicas simples para roberta pires

5 técnicas simples para roberta pires

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

RoBERTa has almost similar architecture as compare to BERT, but in order to improve the results on BERT architecture, the authors made some simple design changes in its architecture and training procedure. These changes are:

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

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

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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

As a reminder, the BERT base model was trained on a batch size of 256 sequences for Explore 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

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 control for training set size effects

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