| | --- |
| | library_name: transformers |
| | license: mit |
| | --- |
| | |
| | # Model Card for GERM-T |
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| | <!-- Provide a quick summary of what the model is/does. --> |
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| | ## Model Details |
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| | ### Model Description |
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| | <!-- Provide a longer summary of what this model is. --> |
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| | This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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| | - **Developed by:** Haozheng Luo, ChengHao Qiu |
| | - **License:** MIT |
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| | ### Model Sources |
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| | <!-- Provide the basic links for the model. --> |
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| | - **Repository:** https://github.com/MAGICS-LAB/GERM |
| | - **Paper:** https://arxiv.org/abs/2505.00598 |
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| | ## Uses |
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| | <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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| | ### Direct Use |
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| | ``` |
| | from transformers import AutoTokenizer, AutoModelForMaskedLM |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("magicslabnu/GERM-T", trust_remote_code=True) |
| | model = AutoModelForMaskedLM.from_pretrained("magicslabnu/GERM-T", trust_remote_code=True) |
| | ``` |
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| | ## Training Details |
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| | ### Training Data |
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| | <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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| | GLUE |
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| | **BibTeX:** |
| | ``` |
| | @misc{luo2025fastlowcostgenomicfoundation, |
| | title={Fast and Low-Cost Genomic Foundation Models via Outlier Removal}, |
| | author={Haozheng Luo and Chenghao Qiu and Maojiang Su and Zhihan Zhou and Zoe Mehta and Guo Ye and Jerry Yao-Chieh Hu and Han Liu}, |
| | year={2025}, |
| | eprint={2505.00598}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.LG}, |
| | url={https://arxiv.org/abs/2505.00598}, |
| | } |
| | ``` |
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