| --- |
| |
| license: mit |
| library_name: pytorch-lightning |
| pipeline_tag: tabular-classification |
| tags: |
| - biology |
| - genomics |
| datasets: |
| - Genentech/human-atac-catlas-data |
| base_model: |
| - Genentech/enformer-model |
| --- |
| |
| # 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() |
| ``` |
