File size: 1,205 Bytes
5466d30 d930191 084db88 d930191 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 |
---
tags:
- bert
- oBERT
- sparsity
- pruning
- compression
language: en
datasets: squad
---
# oBERT-12-downstream-dense-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 - 12 Layers - 0% Sparsity`, and it represents an upper bound for performance of the corresponding pruned models:
- 80% unstructured: `neuralmagic/oBERT-12-downstream-pruned-unstructured-80-squadv1`
- 80% block-4: `neuralmagic/oBERT-12-downstream-pruned-block4-80-squadv1`
- 90% unstructured: `neuralmagic/oBERT-12-downstream-pruned-unstructured-90-squadv1`
- 90% block-4: `neuralmagic/oBERT-12-downstream-pruned-block4-90-squadv1`
SQuADv1 dev-set:
```
EM = 82.71
F1 = 89.48
```
## 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}
}
``` |