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

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The resulting RoBERTa model appears to be superior to its ancestors on top benchmarks. Despite a more complex configuration, RoBERTa adds only 15M additional parameters maintaining comparable inference speed with BERT.

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In this article, we have examined an improved version of BERT which modifies the original training Ver mais procedure by introducing the following aspects:

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

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

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

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

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View PDF Abstract:Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al.