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

Note: The taxonomic classification dataset is substantial (2GB), which may result in extended training and evaluation time. To accommodate the model's maximum context length, we implement **right** truncation for sequences that exceed this limit.

## 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},
}
```