Datasetpublic

dnagpt/omnigene4-mm-corpus

OmniGene-4-MM unified corpus Multi-modal training corpus used for the OmniGene-4-MM Stages 1–3 (see https://github.com/maris205/omnigene4 ). Each row is a JSON object with messages (chat-format), images (list of relative image paths), and modality field. Vision rows reference images that live in the source datasets: Vis-CheBI20 (PharMolix/Vis-CheBI20) PubMedVision (FreedomIntelligence/PubMedVision) HPA10M (Human Protein Atlas microscopy) ChartQA (HuggingFaceM4/ChartQA)… See the full description on the dataset page: https://huggingface.co/datasets/dnagpt/omnigene4-mm-corpus.

sourceHugging Facecc-by-4.0updated 4mo agoView on Hugging Face

0likes25downloads

Dataset Card

OmniGene-4-MM unified corpus

Multi-modal training corpus used for the OmniGene-4-MM Stages 1–3 (see https://github.com/maris205/omnigene4 ).

Each row is a JSON object with messages (chat-format), images (list of relative image paths), and modality field. Vision rows reference images that live in the source datasets:

—Vis-CheBI20 (PharMolix/Vis-CheBI20)
—PubMedVision (FreedomIntelligence/PubMedVision)
—HPA10M (Human Protein Atlas microscopy)
—ChartQA (HuggingFaceM4/ChartQA)
—Synthetic biomedical visual tasks (project-internal)

To reproduce the MM training, download the source vision datasets to matching local paths and run omnigene5/scripts/02-fix_image_paths.py to remap.

Files

File	Rows	Notes
`train.jsonl`	~280K	Used by `30-train_omnigene5_stage1.py` (vision warmup) and the mixed-stage pools downstream
`val.jsonl`	~5K	Held-out validation slice
`meta.json`	—	Per-modality counts and split sizes
`B_chebi20/train.json`	~26K	Vis-CheBI20 train split (project copy of PharMolix/Vis-CheBI20). Five OCSU subtasks: `struct_recog`, `struct_cap`, `general_desp`, `trans_iupac`, `trans_smiles`.
`B_chebi20/test.json`	~3K	Vis-CheBI20 test split, same five subtasks. Used by `60-eval_stage2.py`, `91-eval_stage3v2.py`, `92-eval_stage3v3.py`, `95-qualitative_demo_stage3v3.py`.

Citation

bibtex

@article{wang2026omnigene4,
  author    = {Wang, Liang},
  title     = {{OmniGene-4}: A Unified Bio-Language MoE Model with Router-Level
               Interpretability and Modality-Invariant Transfer},
  year      = {2026},
  journal   = {bioRxiv},
  doi       = {10.64898/2026.05.12.724542}
}