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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- google/byt5-small |
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tags: |
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- theorem-proving |
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- lean |
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- byt5 |
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- mathematics |
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- code |
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--- |
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# Lean-ByT5 |
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**Lean-ByT5** is a ByT5-small model fine-tuned on the augmented Lean-Mathlib data for **Lean theorem proving**. |
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The model performs **sequence-to-sequence generation**, generating Lean tactics for a given |
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statement goal. |
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## Base Model |
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- google/byt5-small (Apache License 2.0) |
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## Training |
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- PyTorch Lightning |
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## Related Paper & Citation |
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For more details on the training methodology, data augmentation strategy, and dynamic sampling method used for Lean theorem proving, please refer to our paper: |
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**Enhancing Neural Theorem Proving through Data Augmentation and Dynamic Sampling Method** |
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Rahul Vishwakarma, Subhankar Mishra |
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*arXiv:2312.14188 (cs.AI, cs.LG, cs.LO)* |
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**Paper:** https://arxiv.org/abs/2312.14188 |
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If you use this model or build upon this work, please cite: |
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```bibtex |
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@article{vishwakarma2023enhancing, |
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title={Enhancing Neural Theorem Proving through Data Augmentation and Dynamic Sampling Method}, |
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author={Vishwakarma, Rahul and Mishra, Subhankar}, |
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journal={arXiv preprint arXiv:2312.14188}, |
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year={2023}, |
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primaryClass={cs.AI} |
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} |