Model release

.gitattributes

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README.md

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+# oBERT-12-upstream-pruned-unstructured-90
+
+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 upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the `Table 2 - oBERT - {SQuADv1, MNLI, QQP} - 90%`.
+
+Finetuned versions of this model for each downstream task are:
+
+- SQuADv1: `neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1`
+- MNLI: `neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli`
+- QQP: `neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp`
+
+```
+Pruning method: oBERT upstream unstructured
+Paper: https://arxiv.org/abs/2203.07259
+Dataset: BookCorpus and English Wikipedia
+Sparsity: 90%
+Number of layers: 12
+```
+
+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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pytorch_model.bin

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train_results.json

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