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--- |
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license: mit |
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task_categories: |
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- text-generation |
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tags: |
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- biology |
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- genomics |
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- long-context |
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configs: |
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- config_name: bacteria |
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data_files: |
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- split: test |
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path: bacteria/test.parquet |
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- config_name: eukaryote |
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data_files: |
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- split: test |
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path: eukaryote/test.parquet |
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- config_name: others |
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data_files: |
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- split: test |
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path: others/test.parquet |
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--- |
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# Next K-mer Prediction |
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## Abouts |
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The Next K-mer Prediction task is a zero-shot evaluation method introduced in the **GENERator** paper to assess the quality of pretrained models. It involves inputting a sequence segment into the model and having it predict the next K base pairs. The predicted sequence is then compared to the actual sequence to assess accuracy. |
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* **Sequence**: The input sequence has a maximum length of 96k base pairs (bp). You can control the number of input tokens by applying **left** truncation. |
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* **Label**: The next 128 bp immediately following the end of the input sequence. |
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Note: Prediction time may increase significantly for longer input sequences. It is strongly recommended to begin testing with a smaller number of input tokens to optimize performance. |
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## How to use |
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```python |
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from datasets import load_dataset |
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datasets = load_dataset("GenerTeam/next-kmer-prediction", "eukaryote") # or "bacteria" or "others" |
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``` |
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## Citation |
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```bibtex |
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@misc{wu2025generator, |
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title={GENERator: A Long-Context Generative Genomic Foundation Model}, |
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author={Wei Wu and Qiuyi Li and Mingyang Li and Kun Fu and Fuli Feng and Jieping Ye and Hui Xiong and Zheng Wang}, |
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year={2025}, |
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eprint={2502.07272}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2502.07272}, |
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} |
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``` |
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