| --- |
| license: mit |
| task_categories: |
| - text-generation |
| tags: |
| - biology |
| - genomics |
| - long-context |
| configs: |
| - config_name: bacteria |
| data_files: |
| - split: test |
| path: bacteria/test.parquet |
| - config_name: eukaryote |
| data_files: |
| - split: test |
| path: eukaryote/test.parquet |
| - config_name: others |
| data_files: |
| - split: test |
| path: others/test.parquet |
| --- |
| |
| # Next K-mer Prediction |
|
|
| ## Abouts |
| 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. |
| * **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. |
| * **Label**: The next 128 bp immediately following the end of the input sequence. |
|
|
| 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. |
|
|
| ## How to use |
|
|
| ```python |
| from datasets import load_dataset |
| |
| datasets = load_dataset("GenerTeam/next-kmer-prediction", "eukaryote") # or "bacteria" or "others" |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{wu2025generator, |
| title={GENERator: A Long-Context Generative Genomic Foundation Model}, |
| author={Wei Wu and Qiuyi Li and Mingyang Li and Kun Fu and Fuli Feng and Jieping Ye and Hui Xiong and Zheng Wang}, |
| year={2025}, |
| eprint={2502.07272}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2502.07272}, |
| } |
| ``` |
|
|
