Update examples/movement-pruning/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>
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For reproducibility purposes, we share the detailed results presented in the paper. This [spreadsheet](TODO) exhaustively describes the individual hyper-parameters used for each data point.
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## Remarks
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## Inference speed
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Early experiments show that even though models fine-pruned with (soft) movement pruning are extremely sparse, they do not benefit from significant improvement in terms of inference speed when using the standard PyTorch inference.
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We are currently benchmarking and exploring inference setups specifically for sparse architectures.
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