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Table 2 - oBERT - MNLI 90% (in the upcoming updated version of the paper).Pruning method: oBERT upstream unstructured + sparse-transfer to downstream
Paper: https://arxiv.org/abs/2203.07259
Dataset: MNLI
Sparsity: 90%
Number of layers: 12(*)):| oBERT 90% | m-acc | mm-acc|
| ------------ | ----- | ----- |
| seed=42 | 83.45 | 84.13 |
| seed=3407 (*)| 83.45 | 83.72 |
| seed=12345 | 83.27 | 83.57 |
| seed=123 | 83.42 | 83.71 |
| ------------ | ----- | ----- |
| mean | 83.40 | 83.78 |
| stdev | 0.086 | 0.241 |1@article{kurtic2022optimal,
2 title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
3 author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
4 journal={arXiv preprint arXiv:2203.07259},
5 year={2022}
6}