| # 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() | |
| ``` |