bert-base-uncased model fine-tuned on QQP

This model was created using the nn_pruning python library: the linear layers contains 36% of the original weights.

The model contains 50% of the original weights overall (the embeddings account for a significant part of the model, and they are not pruned by this method).

Fine-Pruning details

This model was fine-tuned from the HuggingFace model checkpoint on task, and distilled from the model textattack/bert-base-uncased-QQP. This model is case-insensitive: it does not make a difference between english and English.

A side-effect of block pruning is that some of the attention heads are completely removed: 54 heads were removed on a total of 144 (37.5%).

Details of the QQP dataset

Dataset Split # samples
QQP train 364K
QQP eval 40K


Pytorch model file size: 377MB (original BERT: 420MB)

Metric # Value
F1 87.87
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Text Classification
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