Improve model card: Add paper link, pipeline tag, specific license, and sample usage (#3)

- Improve model card: Add paper link, pipeline tag, specific license, and sample usage (656984de2f31020d04db0098dab96221323220f8)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,19 +1,29 @@
 ---
-license: cc-by-4.0
 language:
 - en
+license: cc-by-nc-nd-4.0
 tags:
 - ecg
 - student-teacher
 - echocardiograms
+- medical
+pipeline_tag: other
 ---
-# EchoingECG
+
+# EchoingECG: An Electrocardiogram Cross-Modal Model for Echocardiogram Tasks
+
+The model was presented in the paper [EchoingECG: An Electrocardiogram Cross-Modal Model for Echocardiogram Tasks](https://huggingface.co/papers/2509.25791).
 
 EchoingECG is a probabilistic student-teacher model designed to improve cardiac function prediction from electrocardiograms (ECGs) by distilling knowledge from echocardiograms (ECHO). This approach leverages uncertainty-aware ECG embeddings and ECHO supervision, integrating Probabilistic Cross-Modal Embeddings (PCME++) and ECHO-CLIP, a vision-language pretrained model, to transfer ECHO knowledge into ECG representations.
 
-Please refer to our github for use: https://github.com/mcintoshML/EchoingECG
+You can find the official code and further details on our [GitHub repository](https://github.com/mcintoshML/EchoingECG).
 
-![EchoingECG Overview](assets/fig1_overview.png)
+## Features
+- ECHO-CLIP knowledge distillation
+- Probabilistic contrastive learning with PCME++
+- Outperforms state-of-the-art ECG models for ECHO prediction
+
+![EchoingECG Overview](https://huggingface.co/mcintoshML/EchoingECG/resolve/main/assets/fig1_overview.png)
 
 ## Installation
 Clone the repository and install dependencies:
@@ -23,6 +33,62 @@ cd EchoingECG
 pip install -r requirements.txt
 ```
 
+## Quick Start: Run EchoingECG in Jupyter Notebook
+Below is an example workflow using the provided demo notebook:
+
+```python
+import sys
+import yaml
+import torch
+from src.model.echoingecg_model import EchoingECG
+
+# Load model config
+with open("src/configs/model.yaml") as f:
+    model_cfg = yaml.safe_load(f)
+model = EchoingECG(model_cfg)
+model_weights = torch.load("echoingecg.pt", weights_only=True, map_location="cpu")
+model.load_state_dict(model_weights)
+
+# Example ECG input
+dummy_ecg = torch.zeros((1, 12, 1000)) # 10 seconds at 100Hz, 12 leads
+input = {"ecg": dummy_ecg}
+output = model(input)
+print(output["ecg"].keys()) # 'mean' and 'std' (probabilistic)
+print(output["ecg"]["mean"].shape, output["ecg"]["std"].shape)
+
+# Example text input
+from transformers import AutoTokenizer
+text_example = "ecg is normal"
+tokenizer = AutoTokenizer.from_pretrained("dmis-lab/biobert-v1.1", return_pt=True)
+tok_dict = tokenizer(text_example)
+input_model = {
+    "text": torch.tensor(tok_dict["input_ids"]).unsqueeze(0),
+    "attention_mask": torch.tensor(tok_dict["attention_mask"]).unsqueeze(0)
+}
+output = model(input_model)
+print(output["text"].keys()) # 'mean' and 'std'
+print(output["text"]["mean"].shape, output["text"]["std"].shape)
+
+# Load and scale an ECG properly
+from src.datasets.helpers import scale_ecg
+import joblib
+import numpy as np
+sc = joblib.load("ecg_scaler.pkl")
+_center = torch.from_numpy(sc.mean_.astype(np.float32))
+_scale = torch.from_numpy(sc.scale_.astype(np.float32)).clamp_min(1e-8)
+dummy_ecg = torch.zeros((1,12,1000))
+scaled_output = scale_ecg(_center, _scale, dummy_ecg)
+```
+
+## License
+This work is licensed under the **Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)**.
+
+You may share this work for non-commercial purposes, with proper attribution, but you may not modify it or use it commercially.
+
+[![Creative Commons License](https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)](https://creativecommons.org/licenses/by-nc-nd/4.0/)
+
+[View Full License Details](https://creativecommons.org/licenses/by-nc-nd/4.0/)
+
 ## Citation
 If you use EchoingECG in your research, please cite:
 ```