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--- |
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license: mit |
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task_categories: |
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- text-classification |
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tags: |
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- biology |
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- genomics |
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- long-context |
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configs: |
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- config_name: gene_classification |
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data_files: |
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- split: train |
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path: "gener_tasks/gene_classification/train.parquet" |
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- split: test |
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path: "gener_tasks/gene_classification/test.parquet" |
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- config_name: taxonomic_classification |
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data_files: |
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- split: train |
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path: "gener_tasks/taxonomic_classification/train.parquet" |
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- split: test |
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path: "gener_tasks/taxonomic_classification/test.parquet" |
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--- |
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# Gener Tasks |
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## Abouts |
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The Gener Tasks currently includes 2 subtasks: |
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* 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. |
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* 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. |
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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. |
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## How to use |
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```python |
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from datasets import load_dataset |
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# Load gene_classification task |
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datasets = load_dataset("GenerTeam/gener-tasks",name='gene_classification') |
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# Load taxonomic_classification task |
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datasets = load_dataset("GenerTeam/gener-tasks",name='taxonomic_classification') |
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``` |
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## Citation |
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``` |
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@misc{wu2025generator, |
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title={GENERator: A Long-Context Generative Genomic Foundation Model}, |
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author={Wei Wu and Qiuyi Li and Mingyang Li and Kun Fu and Fuli Feng and Jieping Ye and Hui Xiong and Zheng Wang}, |
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year={2025}, |
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eprint={2502.07272}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2502.07272}, |
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} |
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``` |
