README.md

---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-6-downstream-pruned-unstructured-90-squadv1

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 model presented in the `Table 3 - 6 Layers - Sparsity 90% - unstructured`.

```
Pruning method: oBERT downstream unstructured
Paper: https://arxiv.org/abs/2203.07259
Dataset: SQuADv1
Sparsity: 90%
Number of layers: 6
```

The dev-set performance of this model:
```
EM = 79.16
F1 = 86.78
```

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)

If you find the model useful, please consider citing our work.

## 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}
}
```