Model release
Browse files- .gitattributes +1 -0
- README.md +32 -0
- config.json +3 -0
- eval_results.txt +3 -0
- nbest_predictions.json +3 -0
- predictions.json +3 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +3 -0
- tokenizer_config.json +3 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
.gitattributes
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README.md
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# oBERT-3-downstream-pruned-unstructured-90-squadv1
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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).
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It corresponds to the model presented in the `Table 3 - 3 Layers - Sparsity 90% - unstructured`.
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```
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Pruning method: oBERT downstream unstructured
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Paper: https://arxiv.org/abs/2203.07259
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Dataset: SQuADv1
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Sparsity: 90%
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Number of layers: 3
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```
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The dev-set performance of this model:
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```
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EM = 73.61
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F1 = 82.50
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```
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Code: _coming soon_
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## BibTeX entry and citation info
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```bibtex
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@article{kurtic2022optimal,
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title={The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models},
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author={Kurtic, Eldar and Campos, Daniel and Nguyen, Tuan and Frantar, Elias and Kurtz, Mark and Fineran, Benjamin and Goin, Michael and Alistarh, Dan},
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journal={arXiv preprint arXiv:2203.07259},
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year={2022}
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}
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```
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config.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:c8486603b13aa568cb30a3f12f76029a3365344a234ace7353010ea02ea15338
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size 659
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eval_results.txt
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exact_match = 73.61400189214758
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f1 = 82.49717824558252
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epoch = 30.0
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nbest_predictions.json
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version https://git-lfs.github.com/spec/v1
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size 45878925
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predictions.json
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version https://git-lfs.github.com/spec/v1
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size 601085
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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size 180436839
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special_tokens_map.json
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version https://git-lfs.github.com/spec/v1
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size 112
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tokenizer_config.json
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version https://git-lfs.github.com/spec/v1
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size 362
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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size 2479
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vocab.txt
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