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# oBERT-3-downstream-dense-squadv1 |
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This model is obtained with [The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models](https://arxiv.org/abs/2203.07259). |
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It corresponds to the model presented in the `Table 3 - 3 Layers - 0% Sparsity`, and it represents an upper bound for performance of the corresponding pruned models: |
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- 80% unstructured: `neuralmagic/oBERT-3-downstream-pruned-unstructured-80-squadv1` |
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- 80% block-4: `neuralmagic/oBERT-3-downstream-pruned-block4-80-squadv1` |
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- 90% unstructured: `neuralmagic/oBERT-3-downstream-pruned-unstructured-90-squadv1` |
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- 90% block-4: `neuralmagic/oBERT-3-downstream-pruned-block4-90-squadv1` |
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SQuADv1 dev-set: |
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``` |
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EM = 76.62 |
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F1 = 84.65 |
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``` |
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## BibTeX entry and citation info |
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```bibtex |
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@article{kurtic2022optimal, |
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title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, |
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author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, |
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journal={arXiv preprint arXiv:2203.07259}, |
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year={2022} |
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} |
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``` |