# Weight Pruning

This model is pruned using the nn_pruning library, retaining 32% of the original weights, with a processing speed 2.15 times faster than the original version and an F1 score of 83.22

This is a sparse pre-trained model that can be fine-tuned for various language tasks, reducing computational overhead through weight pruning.

This is a sparsely pretrained BERT-Base model achieving 90% weight sparsity through one-shot pruning, suitable for fine-tuning on various language tasks.