results highlight the importance of previously overlooked design choices, and raise questions about the source
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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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model. Initializing with a config file does not load the weights associated with the model, only the configuration.
As researchers found, it is slightly better to use dynamic masking meaning that masking is generated uniquely every time a sequence is passed to BERT. Overall, this results in less duplicated data during the training giving an opportunity for a model to work with more various data and masking patterns.
Pelo entanto, às vezes podem ser obstinadas e teimosas e precisam aprender a ouvir os outros e a considerar variados perspectivas. Robertas também podem ser bastante sensíveis e empáticas e gostam de ajudar ESTES outros.
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model. Initializing with a config file does not load the weights associated with the model, only the configuration.
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 roberta 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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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.