| # oBERT-3-upstream-pretrained-dense | |
| 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 3 layers from `neuralmagic/oBERT-12-upstream-pretrained-dense`, pretrained with knowledge distillation. This model is used as a starting point for downstream finetuning and pruning runs presented in the `Table 3 - 3 Layers`. | |
| The model can also be used for finetuning on any downstream task, as a starting point instead of the three times larger `bert-base-uncased` model. | |
| Finetuned and pruned versions of this model on the SQuADv1 downstream task, as described in the paper: | |
| - 0%: `neuralmagic/oBERT-3-downstream-dense-squadv1` | |
| - 80% unstructured: `neuralmagic/oBERT-3-downstream-pruned-unstructured-80-squadv1` | |
| - 80% block-4: `neuralmagic/oBERT-3-downstream-pruned-block4-80-squadv1` | |
| - 90% unstructured: `neuralmagic/oBERT-3-downstream-pruned-unstructured-90-squadv1` | |
| - 90% block-4: `neuralmagic/oBERT-3-downstream-pruned-block4-90-squadv1` | |
| ``` | |
| Training objective: masked language modeling (MLM) + knowledge distillation | |
| Paper: https://arxiv.org/abs/2203.07259 | |
| Dataset: BookCorpus and English Wikipedia | |
| Sparsity: 0% | |
| Number of layers: 3 | |
| ``` | |
| 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} | |
| } | |
| ``` |