Model release

.gitattributes

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README.md

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+# 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: _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}
+}
+```

all_results.json

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eval_results.json

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