Improve model card for MELP: add paper link, description, and pipeline tag

This PR improves the model card for the MELP model by:

- Adding the `pipeline_tag: audio-text-to-text` to the metadata, ensuring the model can be found at https://huggingface.co/models?pipeline_tag=audio-text-to-text.
- Populating the model description and direct use cases with information from the paper abstract.
- Adding a direct link to the paper at https://huggingface.co/papers/2506.21803.
- Adding the `license: apache-2.0` to the metadata.
- Including a basic `transformers` usage example.
- Adding the BibTeX and APA citations for the paper.

Please review and merge.

README.md

@@ -1,37 +1,39 @@
 ---
 library_name: transformers
 tags: []
+license: apache-2.0
+pipeline_tag: audio-text-to-text
 ---
 
-# Model Card for Model ID
+# Model Card for MELP
 
 <!-- Provide a quick summary of what the model is/does. -->
-
-
+MELP (Multi-scale ECG-Language Pretraining) is a novel model presented in the paper "From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining". It aims to overcome limitations in traditional ECG analysis by leveraging hierarchical supervision from ECG-text pairs to align ECG signals with textual reports.
 
 ## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+Electrocardiograms (ECGs) play a vital role in monitoring cardiac health and diagnosing heart diseases. However, traditional deep learning approaches for ECG analysis rely heavily on large-scale manual annotations, which are both time-consuming and resource-intensive to obtain. To overcome this limitation, self-supervised learning (SSL) has emerged as a promising alternative, enabling the extraction of robust ECG representations that can be efficiently transferred to various downstream tasks.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+MELP introduces a novel Multi-scale ECG-Language Pretraining (MELP) model that fully leverages hierarchical supervision from ECG-text pairs. MELP first pretrains a cardiology-specific language model to enhance its understanding of clinical text. It then applies three levels of cross-modal supervision—at the token, beat, and rhythm levels—to align ECG signals with textual reports, capturing structured information across different time scales. Experimental results demonstrate that MELP outperforms existing SSL methods, underscoring its effectiveness and adaptability across diverse clinical applications.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+-   **Developed by:** The authors of the paper.
+-   **Funded by [optional]:** [More Information Needed]
+-   **Shared by [optional]:** [More Information Needed]
+-   **Model type:** Multimodal ECG-Language Pretraining Model
+-   **Language(s) (NLP):** English (clinical text)
+-   **License:** Apache-2.0
+-   **Finetuned from model [optional]:** [More Information Needed]
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+-   **Repository:** The code is available as mentioned in the paper's abstract. Please refer to the paper for the exact URL.
+-   **Paper [optional]:** [From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining](https://huggingface.co/papers/2506.21803)
+-   **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
@@ -40,14 +42,12 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+MELP can be directly used for self-supervised learning of robust ECG representations. These representations can be efficiently transferred to various downstream tasks, such as zero-shot ECG classification, linear probing, and other transfer learning applications on ECG data.
 
 ### Downstream Use [optional]
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be fine-tuned for diverse clinical applications, including but not limited to tasks that require aligning ECG signals with textual reports, thereby assisting in cardiac health monitoring and heart disease diagnosis.
 
 ### Out-of-Scope Use
 
@@ -71,7 +71,22 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+from transformers import AutoConfig, AutoModel
+
+# This is a placeholder for the actual model ID on the Hugging Face Hub.
+# Replace "your_model_id" with the correct model identifier.
+model_id = "your_model_id" # e.g., "org/melp-model"
+
+# Load configuration
+config = AutoConfig.from_pretrained(model_id)
+
+# Load model
+model = AutoModel.from_pretrained(model_id, config=config)
+
+# For detailed usage instructions and examples, please refer to the paper's
+# official code repository mentioned in the abstract.
+```
 
 ## Training Details
 
@@ -92,7 +107,7 @@ Use the code below to get started with the model.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+-   **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
 #### Speeds, Sizes, Times [optional]
 
@@ -144,11 +159,11 @@ Use the code below to get started with the model.
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+-   **Hardware Type:** [More Information Needed]
+-   **Hours used:** [More Information Needed]
+-   **Cloud Provider:** [More Information Needed]
+-   **Compute Region:** [More Information Needed]
+-   **Carbon Emitted:** [More Information Needed]
 
 ## Technical Specifications [optional]
 
@@ -174,11 +189,18 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@article{zhou2025token,
+  title={From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining},
+  author={Zhou, Zijian and Liu, Shikun and Han, Xiao and Liu, Haozhe and Ng, Kam Woh and Xie, Tian and Cong, Yuren and Li, Hang and Xu, Mengmeng and P{\'e}rez-R{\'u}a, Juan-Manuel and Patel, Aditya and Xiang, Tao and Shi, Miaojing and He, Sen},
+  journal={arXiv preprint arXiv:2506.21803},
+  year={2025}
+}
+```
 
 **APA:**
 
-[More Information Needed]
+Zhou, Z., Liu, S., Han, X., Liu, H., Ng, K. W., Xie, T., Cong, Y., Li, H., Xu, M., Pérez-Rúa, J.-M., Patel, A., Xiang, T., Shi, M., & He, S. (2025). *From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining*. arXiv preprint arXiv:2506.21803.
 
 ## Glossary [optional]