---
license: mit
task_categories:
  - text-generation
tags:
  - biology
  - genomics
  - long-context
---

# 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")
```

## 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},
}
```