readme: ---
  tags:
  - bert
  - oBERT
  - sparsity
  - pruning
  - compression
  language: en
  datasets: squad
  ---
  # oBERT-3-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 - 3 Layers - Sparsity 90% - unstructured`.
  
  ```
  Pruning method: oBERT downstream unstructured
  Paper: https://arxiv.org/abs/2203.07259
  Dataset: SQuADv1
  Sparsity: 90%
  Number of layers: 3
  ```
  
  The dev-set performance of this model:
  ```
  EM = 73.61
  F1 = 82.50
  ```
  
  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}
  }
  ```