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Model release

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README.md ADDED
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+ # oBERT-12-downstream-pruned-unstructured-80-squadv1
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+
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+ 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).
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+
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+ It corresponds to the model presented in the `Table 1 - 30 Epochs - oBERT - SQuADv1 80%`.
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+
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+ ```
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+ Pruning method: oBERT downstream unstructured
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+ Paper: https://arxiv.org/abs/2203.07259
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+ Dataset: SQuADv1
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+ Sparsity: 80%
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+ Number of layers: 12
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+ ```
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+
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+ The dev-set performance reported in the paper is averaged over three seeds, and we release the best model (marked with `(*)`):
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+
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+ ```
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+ | oBERT 80% | F1 | EM |
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+ | ------------ | ----- | ----- |
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+ | seed=42 | 88.95 | 82.08 |
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+ | seed=3407 (*)| 89.16 | 82.05 |
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+ | seed=54321 | 89.01 | 82.12 |
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+ | ------------ | ----- | ----- |
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+ | mean | 89.04 | 82.08 |
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+ | stdev | 0.108 | 0.035 |
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+ ```
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+
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+ Code: _coming soon_
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+
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+ ## BibTeX entry and citation info
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+ ```bibtex
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+ @article{kurtic2022optimal,
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+ title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
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+ author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
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+ journal={arXiv preprint arXiv:2203.07259},
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+ year={2022}
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+ }
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+ ```
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+ exact_match = 82.05298013245033
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+ f1 = 89.15639642737877
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+ epoch = 30.0
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