Model release
Browse files- .gitattributes +1 -0
- README.md +32 -0
- all_results.json +3 -0
- config.json +3 -0
- eval_results.json +3 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +3 -0
- tokenizer.json +3 -0
- tokenizer_config.json +3 -0
- train_results.json +3 -0
- trainer_state.json +3 -0
- training_args.bin +3 -0
- vocab.txt +0 -0
.gitattributes
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README.md
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# oBERT-12-upstream-pruned-unstructured-90-v2
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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 upstream pruned model used as a starting point for sparse-transfer learning to downstream tasks presented in the `Table 2 - oBERT - {SQuADv1, MNLI, QQP} - 90%` (in the upcoming updated version of the paper).
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Finetuned versions of this model for each downstream task are:
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- SQuADv1: `neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-squadv1-v2`
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- MNLI: `neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-mnli-v2`
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- QQP: `neuralmagic/oBERT-12-upstream-pruned-unstructured-90-finetuned-qqp-v2`
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```
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Pruning method: oBERT upstream unstructured
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Paper: https://arxiv.org/abs/2203.07259
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Dataset: BookCorpus and English Wikipedia
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Sparsity: 90%
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Number of layers: 12
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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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all_results.json
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config.json
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eval_results.json
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pytorch_model.bin
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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train_results.json
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trainer_state.json
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training_args.bin
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vocab.txt
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