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model. Initializing with a config file does not load the weights associated with the model, only the configuration.
The corresponding number of training steps and the learning rate value became respectively 31K and 1e-3.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Dynamically changing the masking pattern: In BERT architecture, the masking is performed once during data preprocessing, resulting in a single static mask. To avoid using the single static mask, training data is duplicated and masked 10 times, each time with a different mask strategy over quarenta epochs thus having 4 epochs with the same mask.
Passing single conterraneo 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.
In this article, we have examined an improved version of BERT which modifies the original training procedure by introducing the following aspects:
This is useful if you want more control over how to convert input_ids indices into associated vectors
Apart from it, RoBERTa applies all four described aspects above with the same architecture parameters as BERT large. The Completa number of parameters of RoBERTa is 355M.
model. Initializing with a config file does not load the weights associated with the model, only the configuration.
This results in 15M and 20M additional parameters for BERT base and BERT large models respectively. The introduced encoding version in RoBERTa demonstrates slightly worse results than before.
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a dictionary with one or several input Tensors associated to the input names given in the docstring:
If you choose this second option, there are three possibilities you can use to gather all the input Tensors