Model release

.gitattributes

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README.md

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+# oBERT-12-upstream-pruned-unstructured-97-finetuned-mnli
+
+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 - MNLI 97%`.
+
+```
+Pruning method: oBERT upstream unstructured + sparse-transfer to downstream
+Paper: https://arxiv.org/abs/2203.07259
+Dataset: MNLI
+Sparsity: 97%
+Number of layers: 12
+```
+
+The dev-set performance reported in the paper is averaged over three seeds, and we release the best model (marked with `(*)`):
+
+```
+| oBERT 97%    | m-acc | mm-acc|
+| ------------ | ----- | ----- |
+| seed=42      | 78.55 | 79.90 |
+| seed=3407    | 78.88 | 79.78 |
+| seed=54321(*)| 79.11 | 79.71 |
+| ------------ | ----- | ----- |
+| mean         | 78.85 | 79.80 |
+| stdev        | 0.281 | 0.096 |
+```
+
+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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eval_results.json

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tokenizer_config.json

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