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 |
Results
Pytorch model file size: 377MB (original BERT: 420MB)
| Metric | # Value |
|---|---|
| F1 | 87.87 |
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