Model release

.gitattributes

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README.md

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+# oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1-v2
+
+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 2 - oBERT - SQuADv1 90%` (in the upcoming updated version of the paper).
+
+```
+Pruning method: oBERT upstream unstructured + sparse-transfer to downstream
+Paper: https://arxiv.org/abs/2203.07259
+Dataset: SQuADv1
+Sparsity: 90%
+Number of layers: 12
+```
+
+The dev-set performance reported in the paper is averaged over four seeds, and we release the best model (marked with `(*)`):
+
+```
+| oBERT 90%    | F1    | EM    |
+| ------------ | ----- | ----- |
+| seed=42      | 88.55 | 81.48 |
+| seed=3407    | 88.34 | 81.25 |
+| seed=123  (*)| 88.64 | 81.57 |
+| seed=12345   | 88.44 | 81.43 |
+| ------------ | ----- | ----- |
+| mean         | 88.49 | 81.43 |
+| stdev        | 0.130 | 0.134 |
+```
+
+Code: _coming soon_
+
+## BibTeX entry and 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}
+}
+```

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