| --- |
| 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}, |
| } |
| ``` |
|
|
