  Transformer-based deep NLP models are trained using hundreds of millions of
parameters, limiting their applicability in computationally constrained
environments. In this paper, we study the cause of these limitations by
defining a notion of Redundancy, which we categorize into two classes: General
Redundancy and Task-specific Redundancy. We dissect two popular pretrained
models, BERT and XLNet, studying how much redundancy they exhibit at a
representation-level and at a more fine-grained neuron-level. Our analysis
reveals interesting insights, such as: i) 85% of the neurons across the network
are redundant and ii) at least 92% of them can be removed when optimizing
towards a downstream task. Based on our analysis, we present an efficient
feature-based transfer learning procedure, which maintains 97% performance
while using at-most 10% of the original neurons.
