|
|
--- |
|
|
|
|
|
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() |
|
|
``` |
