---
license: mit
task_categories:
- text-classification
tags:
  - biology
  - genomics
  - long-context
configs:
- config_name: gene_classification
  data_files:
  - split: train
    path: "gener_tasks/gene_classification/train.parquet"
  - split: test
    path: "gener_tasks/gene_classification/test.parquet"
- config_name: taxonomic_classification
  data_files:
  - split: train
    path: "gener_tasks/taxonomic_classification/train.parquet"
  - split: test
    path: "gener_tasks/taxonomic_classification/test.parquet"
---

# Gener Tasks

## Abouts
The Gener Tasks currently includes 2 subtasks:
* The gene classification task assesses the model's ability to understand short to medium-length sequences, ranging from 100 to 5000 bp. It includes six different gene types and control samples drawn from non-gene regions, with balanced sampling from six distinct eukaryotic taxonomic groups in RefSeq. The classification goal is to predict the gene type. 
* The taxonomic classification task is designed to assess the model's comprehension of longer sequences, which include both gene and predominantly non-gene regions, ranging in length from 10,000 to 100,000 bp. Samples are similarly balanced and sourced from RefSeq across the same six taxonomic groups, with the objective being to predict the taxonomic group of each sample.

## How to use
```python
from datasets import load_dataset

# Load gene_classification task
datasets = load_dataset("GenerTeam/gener-tasks",name='gene_classification')

# Load taxonomic_classification task
datasets = load_dataset("GenerTeam/gener-tasks",name='taxonomic_classification')
```

## Citation
```
@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},
}
```