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When BERT Plays the Lottery, All Tickets Are Winning
[article]
2020
arXiv
pre-print
Large Transformer-based models were shown to be reducible to a smaller number of self-attention heads and layers. We consider this phenomenon from the perspective of the lottery ticket hypothesis, using both structured and magnitude pruning. For fine-tuned BERT, we show that (a) it is possible to find subnetworks achieving performance that is comparable with that of the full model, and (b) similarly-sized subnetworks sampled from the rest of the model perform worse. Strikingly, with structured
arXiv:2005.00561v2
fatcat:n4h3va3rdnevxmmw3c4ealynpm