Datasets:
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---
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. 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},
}
``` |