File size: 1,247 Bytes
3bff6c9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
---
# 1. Metadata Block
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
1. `model.ckpt`: The trained model weights and hyperparameters (PyTorch Lightning checkpoint).
2. `2_train.ipynb`: Jupyter notebook containing the training logic, architecture definition, and evaluation loops.
3. `output.log`: Training logs.
## How to use
To load this model for inference or fine-tuning, use the `grelu` interface:
```python
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()
``` |