metadata
license: mit
library_name: pytorch-lightning
pipeline_tag: tabular-classification
tags:
- biology
- genomics
datasets:
- Genentech/human-atac-catlas-data
human-atac-catlas-model
Model Description
This model is a multi-task binary classifier trained to predict chromatin accessibility across 204 cell types. It was trained by fine-tuning the Enformer model using the grelu library on top of the CATlas human enhancer dataset.
- Architecture: Fine-tuned Enformer
- Input: Genomic sequences (hg38)
- Output: Binary accessibility predictions for 204 cell type tasks.
Repository Content
model.ckpt: The trained model weights and hyperparameters (PyTorch Lightning checkpoint).2_train.ipynb: Jupyter notebook containing the training logic, architecture definition, and evaluation loops.output.log: Training logs.
How to use
To load this model for inference or fine-tuning, use the grelu interface:
from grelu.lightning import LightningModel
from huggingface_hub import hf_hub_download
ckpt_path = hf_hub_download(
repo_id="Genentech/human-atac-catlas-model",
filename="model.ckpt"
)
model = LightningModel.load_from_checkpoint(ckpt_path)
model.eval()