Model release

.gitattributes

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README.md

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+# oBERT-3-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 3 layers from `neuralmagic/oBERT-12-upstream-pretrained-dense`, pretrained with knowledge distillation. This model is used as a starting point for downstream finetuning and pruning runs presented in the `Table 3 - 3 Layers`.
+The model can also be used for finetuning on any downstream task, as a starting point instead of the three times larger `bert-base-uncased` model.
+
+Finetuned and pruned versions of this model on the SQuADv1 downstream task, as described in the paper:
+- 0%: `neuralmagic/oBERT-3-downstream-dense-squadv1`
+- 80% unstructured: `neuralmagic/oBERT-3-downstream-pruned-unstructured-80-squadv1`
+- 80% block-4: `neuralmagic/oBERT-3-downstream-pruned-block4-80-squadv1`
+- 90% unstructured: `neuralmagic/oBERT-3-downstream-pruned-unstructured-90-squadv1`
+- 90% block-4: `neuralmagic/oBERT-3-downstream-pruned-block4-90-squadv1`
+
+```
+Training objective: masked language modeling (MLM) + knowledge distillation
+Paper: https://arxiv.org/abs/2203.07259
+Dataset: BookCorpus and English Wikipedia
+Sparsity: 0%
+Number of layers: 3
+```
+
+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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pytorch_model.bin

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training_args.bin

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