In this section we use the methods of Section3 to give a corresponding evaluation of threshold pruning. For both strategies the masks are determined with respect to full model functionality on a precise dataset. We are interested in getting the subnetworks that allow BERT to perform effectively on the full set of 9 GLUE tasks (Wang et al. 2018). GLUE test sets are not publicly obtainable, and we use the dev sets to each come across the pruning masks and to test the model. Considering that we are interested in BERT’s ‘reasoning strategies’ rather than generalization, this strategy lets us see the greatest/worst possible subnetworks for this precise information. This winning ticket would even generalize out of the training information domain, as it truly does compute the real sum of its two inputs.
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Rather, we create here a technique aimed at performing structured trimming. We show that this demands to rely on worldwide selection and introduce a particular criterion primarily based on mutual information and facts. Very first, we confirm the surprising outcome that smaller sized models offer larger accuracy than their massive counterparts.
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