| # oBERT-12-upstream-pruned-unstructured-97 | |
| 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). | |
| It corresponds to the upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the `Table 2 - oBERT - {SQuADv1, MNLI, QQP} - 97%`. | |
| Finetuned versions of this model for each downstream task are: | |
| - SQuADv1: `neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-squadv1` | |
| - MNLI: `neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli` | |
| - QQP: `neuralmagic/oBERT-12-upstream-pruned-unstructured-97-finetuned-qqp` | |
| ``` | |
| Pruning method: oBERT upstream unstructured | |
| Paper: https://arxiv.org/abs/2203.07259 | |
| Dataset: BookCorpus and English Wikipedia | |
| Sparsity: 97% | |
| Number of layers: 12 | |
| ``` | |
| Code: _coming soon_ | |
| ## BibTeX entry and citation info | |
| ```bibtex | |
| @article{kurtic2022optimal, | |
| title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models}, | |
| author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan}, | |
| journal={arXiv preprint arXiv:2203.07259}, | |
| year={2022} | |
| } | |
| ``` |