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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.

Essa ousadia e criatividade por Roberta tiveram um impacto significativo no universo sertanejo, abrindo PORTAS BLINDADAS de modo a novos artistas explorarem novas possibilidades musicais.

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Language model pretraining has led to significant performance gains but careful comparison between different

Passing single natural sentences into BERT input hurts the performance, compared to passing sequences consisting of several sentences. One of the most likely hypothesises explaining this phenomenon is the difficulty for a model to learn long-range dependencies only relying on single sentences.

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.

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As a reminder, the BERT base model was trained on a batch size of 256 sequences for a million steps. The authors tried training BERT on batch sizes of 2K and 8K and the latter value was chosen for training RoBERTa.

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

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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

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 Conheça 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.