---
configs:
- config_name: emp_H3
  data_files:
  - split: train
    path: "GUE/emp_H3/train.csv"
  - split: test
    path: "GUE/emp_H3/test.csv"
  - split: dev
    path: "GUE/emp_H3/dev.csv"
- config_name: emp_H3K14ac
  data_files:
  - split: train
    path: "GUE/emp_H3K14ac/train.csv"
  - split: test
    path: "GUE/emp_H3K14ac/test.csv"
  - split: dev
    path: "GUE/emp_H3K14ac/dev.csv"
- config_name: emp_H3K36me3
  data_files:
  - split: train
    path: "GUE/emp_H3K36me3/train.csv"
  - split: test
    path: "GUE/emp_H3K36me3/test.csv"
  - split: dev
    path: "GUE/emp_H3K36me3/dev.csv"
- config_name: emp_H3K4me1
  data_files:
  - split: train
    path: "GUE/emp_H3K4me1/train.csv"
  - split: test
    path: "GUE/emp_H3K4me1/test.csv"
  - split: dev
    path: "GUE/emp_H3K4me1/dev.csv"
- config_name: emp_H3K4me2
  data_files:
  - split: train
    path: "GUE/emp_H3K4me2/train.csv"
  - split: test
    path: "GUE/emp_H3K4me2/test.csv"
  - split: dev
    path: "GUE/emp_H3K4me2/dev.csv"
- config_name: emp_H3K4me3
  data_files:
  - split: train
    path: "GUE/emp_H3K4me3/train.csv"
  - split: test
    path: "GUE/emp_H3K4me3/test.csv"
  - split: dev
    path: "GUE/emp_H3K4me3/dev.csv"
- config_name: emp_H3K79me3
  data_files:
  - split: train
    path: "GUE/emp_H3K79me3/train.csv"
  - split: test
    path: "GUE/emp_H3K79me3/test.csv"
  - split: dev
    path: "GUE/emp_H3K79me3/dev.csv"
- config_name: emp_H3K9ac
  data_files:
  - split: train
    path: "GUE/emp_H3K9ac/train.csv"
  - split: test
    path: "GUE/emp_H3K9ac/test.csv"
  - split: dev
    path: "GUE/emp_H3K9ac/dev.csv"
- config_name: emp_H4
  data_files:
  - split: train
    path: "GUE/emp_H4/train.csv"
  - split: test
    path: "GUE/emp_H4/test.csv"
  - split: dev
    path: "GUE/emp_H4/dev.csv"
- config_name: emp_H4ac
  data_files:
  - split: train
    path: "GUE/emp_H4ac/train.csv"
  - split: test
    path: "GUE/emp_H4ac/test.csv"
  - split: dev
    path: "GUE/emp_H4ac/dev.csv"
- config_name: human_tf_0
  data_files:
  - split: train
    path: "GUE/human_tf_0/train.csv"
  - split: test
    path: "GUE/human_tf_0/test.csv"
  - split: dev
    path: "GUE/human_tf_0/dev.csv"
- config_name: human_tf_1
  data_files:
  - split: train
    path: "GUE/human_tf_1/train.csv"
  - split: test
    path: "GUE/human_tf_1/test.csv"
  - split: dev
    path: "GUE/human_tf_1/dev.csv"
- config_name: human_tf_2
  data_files:
  - split: train
    path: "GUE/human_tf_2/train.csv"
  - split: test
    path: "GUE/human_tf_2/test.csv"
  - split: dev
    path: "GUE/human_tf_2/dev.csv"
- config_name: human_tf_3
  data_files:
  - split: train
    path: "GUE/human_tf_3/train.csv"
  - split: test
    path: "GUE/human_tf_3/test.csv"
  - split: dev
    path: "GUE/human_tf_3/dev.csv"
- config_name: human_tf_4
  data_files:
  - split: train
    path: "GUE/human_tf_4/train.csv"
  - split: test
    path: "GUE/human_tf_4/test.csv"
  - split: dev
    path: "GUE/human_tf_4/dev.csv"
- config_name: mouse_0
  data_files:
  - split: train
    path: "GUE/mouse_0/train.csv"
  - split: test
    path: "GUE/mouse_0/test.csv"
  - split: dev
    path: "GUE/mouse_0/dev.csv"
- config_name: mouse_1
  data_files:
  - split: train
    path: "GUE/mouse_1/train.csv"
  - split: test
    path: "GUE/mouse_1/test.csv"
  - split: dev
    path: "GUE/mouse_1/dev.csv"
- config_name: mouse_2
  data_files:
  - split: train
    path: "GUE/mouse_2/train.csv"
  - split: test
    path: "GUE/mouse_2/test.csv"
  - split: dev
    path: "GUE/mouse_2/dev.csv"
- config_name: mouse_3
  data_files:
  - split: train
    path: "GUE/mouse_3/train.csv"
  - split: test
    path: "GUE/mouse_3/test.csv"
  - split: dev
    path: "GUE/mouse_3/dev.csv"
- config_name: mouse_4
  data_files:
  - split: train
    path: "GUE/mouse_4/train.csv"
  - split: test
    path: "GUE/mouse_4/test.csv"
  - split: dev
    path: "GUE/mouse_4/dev.csv"
- config_name: prom_300_all
  data_files:
  - split: train
    path: "GUE/prom_300_all/train.csv"
  - split: test
    path: "GUE/prom_300_all/test.csv"
  - split: dev
    path: "GUE/prom_300_all/dev.csv"
- config_name: prom_300_notata
  data_files:
  - split: train
    path: "GUE/prom_300_notata/train.csv"
  - split: test
    path: "GUE/prom_300_notata/test.csv"
  - split: dev
    path: "GUE/prom_300_notata/dev.csv"
- config_name: prom_300_tata
  data_files:
  - split: train
    path: "GUE/prom_300_tata/train.csv"
  - split: test
    path: "GUE/prom_300_tata/test.csv"
  - split: dev
    path: "GUE/prom_300_tata/dev.csv"
- config_name: prom_core_all
  data_files:
  - split: train
    path: "GUE/prom_core_all/train.csv"
  - split: test
    path: "GUE/prom_core_all/test.csv"
  - split: dev
    path: "GUE/prom_core_all/dev.csv"
- config_name: prom_core_notata
  data_files:
  - split: train
    path: "GUE/prom_core_notata/train.csv"
  - split: test
    path: "GUE/prom_core_notata/test.csv"
  - split: dev
    path: "GUE/prom_core_notata/dev.csv"
- config_name: prom_core_tata
  data_files:
  - split: train
    path: "GUE/prom_core_tata/train.csv"
  - split: test
    path: "GUE/prom_core_tata/test.csv"
  - split: dev
    path: "GUE/prom_core_tata/dev.csv"
- config_name: splice_reconstructed
  data_files:
  - split: train
    path: "GUE/splice_reconstructed/train.csv"
  - split: test
    path: "GUE/splice_reconstructed/test.csv"
  - split: dev
    path: "GUE/splice_reconstructed/dev.csv"
- config_name: virus_covid
  data_files:
  - split: train
    path: "GUE/virus_covid/train.csv"
  - split: test
    path: "GUE/virus_covid/test.csv"
  - split: dev
    path: "GUE/virus_covid/dev.csv"
---
This is a copy of the Genome Understanding Evaluation (GUE) that was presented in

DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

Zhihan Zhou and Yanrong Ji and Weijian Li and Pratik Dutta and Ramana Davuluri and Han Liu

and is available to download directly from

https://github.com/MAGICS-LAB/DNABERT_2

If you use this dataset, please cite

@misc{zhou2023dnabert2,
      title={DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome}, 
      author={Zhihan Zhou and Yanrong Ji and Weijian Li and Pratik Dutta and Ramana Davuluri and Han Liu},
      year={2023},
      eprint={2306.15006},
      archivePrefix={arXiv},
      primaryClass={q-bio.GN}
}


**Instructions to Load Dataset in Google Colab**
```
# choose the dataset that you wish to load, ex: prom_core_all
from datasets import load_dataset, get_dataset_config_names
config_names = get_dataset_config_names("leannmlindsey/GUE")
print(config_names)
prom_core_all = load_dataset("leannmlindsey/GUE", name="prom_core_all")
prom_core_all
prom_core_all["train"][0]
```