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--- |
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tags: |
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- bert |
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- oBERT |
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- sparsity |
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- pruning |
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- compression |
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language: en |
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datasets: mnli |
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--- |
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# oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli |
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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 2 - oBERT - MNLI 97%`. |
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``` |
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Pruning method: oBERT upstream unstructured + sparse-transfer to downstream |
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Paper: https://arxiv.org/abs/2203.07259 |
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Dataset: MNLI |
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Sparsity: 97% |
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Number of layers: 12 |
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``` |
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The dev-set performance reported in the paper is averaged over three seeds, and we release the best model (marked with `(*)`): |
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``` |
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| oBERT 97% | m-acc | mm-acc| |
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| ------------ | ----- | ----- | |
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| seed=42 | 78.55 | 79.90 | |
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| seed=3407 | 78.88 | 79.78 | |
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| seed=54321(*)| 79.11 | 79.71 | |
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| ------------ | ----- | ----- | |
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| mean | 78.85 | 79.80 | |
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| stdev | 0.281 | 0.096 | |
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``` |
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Code: [https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT](https://github.com/neuralmagic/sparseml/tree/main/research/optimal_BERT_surgeon_oBERT) |
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If you find the model useful, please consider citing our work. |
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## 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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``` |
