If you choose this second option, there are three possibilities you can use to gather all the input Tensors
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.
It happens due to the fact that reaching the document boundary and stopping there means that an input sequence will contain less than 512 tokens. For having a similar number of tokens across all batches, the batch size in such cases needs to be augmented. This leads to variable batch size and more complex comparisons which researchers wanted to avoid.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
This is useful if you want more control over how to convert input_ids indices into associated vectors
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In this article, we have examined an improved version of BERT which modifies the original training procedure by introducing the following aspects:
Attentions weights Confira after the attention softmax, used to compute the weighted average in the self-attention
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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.
, 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code. Subjects:
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 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.