This model was created using the nn_pruning python library: the linear layers contains 37% of the original weights.
The model contains 51% of the original weights overall (the embeddings account for a significant part of the model, and they are not pruned by this method).
In terms of perfomance, its accuracy is 91.17.
This model was fine-tuned from the HuggingFace model checkpoint on task, and distilled from the model textattack/bert-base-uncased-SST-2. This model is case-insensitive: it does not make a difference between english and English.
A side-effect of the block pruning method is that some of the attention heads are completely removed: 88 heads were removed on a total of 144 (61.1%). Here is a detailed view on how the remaining heads are distributed in the network after pruning.
Pytorch model file size:
351MB (original BERT:
|Metric||# Value||# Original (Table 2)||Variation|
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