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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
<!-- # Model Card for Model ID -->
<!-- Provide a quick summary of what the model is/does. -->
<!-- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Longformer-Encoder-Decoder (LED) model is designed to address the challenge of summarizing lengthy English texts, particularly legal documents. Utilizing a local-global attention mechanism, LED is capable of handling longer input sequences efficiently, making it highly suitable for legal document summarization tasks.
## Citation
**BibTeX:**
```
@misc{duc2023led,
author = {Chu Đình Đức},
title = {Longformer-Encoder-Decoder for Legal Document Summarization},
year = {2023},
}
``` |