Datasets:
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---
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},
}
```
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