Model release

.gitattributes

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README.md

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+# oBERT-6-downstream-dense-squadv1
+
+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 3 - 6 Layers - 0% Sparsity`, and it represents an upper bound for performance of the corresponding pruned models:
+- 80% unstructured: `neuralmagic/oBERT-6-downstream-pruned-unstructured-80-squadv1`
+- 80% block-4: `neuralmagic/oBERT-6-downstream-pruned-block4-80-squadv1`
+- 90% unstructured: `neuralmagic/oBERT-6-downstream-pruned-unstructured-90-squadv1`
+- 90% block-4: `neuralmagic/oBERT-6-downstream-pruned-block4-90-squadv1`
+
+SQuADv1 dev-set:
+```
+EM = 81.17
+F1 = 88.32
+```
+
+## 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}
+}
+```

config.json

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eval_results.txt

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+exact_match = 81.17313150425733
+f1 = 88.31708304864826
+epoch = 30.0

nbest_predictions.json

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predictions.json

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pytorch_model.bin

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tokenizer_config.json

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training_args.bin

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