readme: ---
  tags:
  - bert
  - oBERT
  - sparsity
  - pruning
  - compression
  language: en
  datasets:
  - bookcorpus
  - wikipedia
  ---
  # oBERT-12-upstream-pretrained-dense
  
  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 pretrained dense model used as a teacher for upstream pruning runs, as described in the paper. The model can be finetuned on any downstream task, just like the standard `bert-base-uncased` model which is used as initialization for training of this model.
  
  Sparse versions of this model:
  - 90% sparse: `neuralmagic/oBERT-12-upstream-pruned-unstructured-90`
  - 97% sparse: `neuralmagic/oBERT-12-upstream-pruned-unstructured-97`
  
  ```
  Training objective: masked language modeling (MLM)
  Paper: https://arxiv.org/abs/2203.07259
  Dataset: BookCorpus and English Wikipedia
  Sparsity: 0%
  Number of layers: 12
  ```
  
  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}
  }
  ```