README.md

---
library_name: transformers
tags: []
license: apache-2.0
pipeline_tag: audio-text-to-text
---

# 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.

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:** 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:** 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

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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 -->
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

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

```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

### Training Data

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[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### 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 -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```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:**

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]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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