Add Beat-age model card

README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+  - ecg
+  - biological-age
+  - cardiology
+  - pytorch
+  - uk-biobank
+pipeline_tag: tabular-regression
+---
+
+# Beat-age
+
+This is the official checkpoint release for the paper:
+
+**Beat-Level Electrocardiographic Biological Age and Its Variability as Digital Biomarkers for Cardiovascular Risk Stratification**
+
+Official GitHub repository: https://github.com/chiangfish/beat-age
+
+## Files
+
+- `v1_best.pth`: Beat-age beat-level Net1D checkpoint trained on the UK Biobank Development Cohort.
+- `ckpt_manifest.json`: checkpoint metadata, including file size, SHA-256 checksum, architecture, and intended use.
+
+## Model
+
+Beat-age is a beat-level ECG biological age model. It predicts biological age from individual segmented 12-lead cardiac cycles and aggregates beat-level predictions at the ECG-recording level.
+
+- Architecture: one-dimensional residual CNN (`Net1D`)
+- Input: segmented 12-lead ECG beats
+- Output: predicted biological age in years
+- Age gap: predicted age minus chronological age
+
+## Usage
+
+Download the checkpoint and place it under `ckpts/` in the GitHub repository:
+
+```bash
+mkdir -p ckpts
+hf download chiangfish/beat-age v1_best.pth --local-dir ckpts
+```
+
+Then run the inference scripts from the GitHub repository following its README.
+
+## Data
+
+The model was developed using controlled-access UK Biobank ECG data. Downstream external validation used MIMIC-IV-ECG. These datasets are not redistributed in this model repository.
+
+## Citation
+
+```bibtex
+@article{beatage2026,
+  title = {Beat-Level Electrocardiographic Biological Age and Its Variability as Digital Biomarkers for Cardiovascular Risk Stratification},
+  author = {Zirui Jiang, Guangkun Nie, Qinghao Zhao, and Shenda Hong},
+  year = {2026}
+}
+```