readme: ---
  tags:
  - bert
  - oBERT
  - sparsity
  - pruning
  - compression
  language: en
  datasets: squad
  ---
  # oBERT-12-downstream-pruned-unstructured-97-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 1 - 30 Epochs - oBERT - SQuADv1 97%`.
  
  ```
  Pruning method: oBERT downstream unstructured
  Paper: https://arxiv.org/abs/2203.07259
  Dataset: SQuADv1
  Sparsity: 97%
  Number of layers: 12
  ```
  
  The dev-set performance reported in the paper is averaged over three seeds, and we release the best model (marked with `(*)`):
  
  ```
  | oBERT 97%    | F1    | EM    |
  | ------------ | ----- | ----- |
  | seed=42   (*)| 86.06 | 78.28 |
  | seed=3407    | 86.04 | 78.12 |
  | seed=54321   | 85.85 | 77.93 |
  | ------------ | ----- | ----- |
  | mean         | 85.98 | 78.11 |
  | stdev        | 0.115 | 0.175 |
  ```
  
  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}
  }
  ```