
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

```
 _____    ______   __    __   ______   _____    ______   __  __   __    __    
/\  __-. /\  __ \ /\ "-./  \ /\  ___\ /\  __-. /\  ___\ /\ \/\ \ /\ "-./  \   
\ \ \/\ \\ \  __ \\ \ \-./\ \\ \  __\ \ \ \/\ \\ \___  \\ \ \_\ \\ \ \-./\ \  
 \ \____- \ \_\ \_\\ \_\ \ \_\\ \_____\\ \____- \/\_____\\ \_____\\ \_\ \ \_\ 
  \/____/  \/_/\/_/ \/_/  \/_/ \/_____/ \/____/  \/_____/ \/_____/ \/_/  \/_/ 
                                                                                                                                                                        
```

## Model description
This repository contains a model for Danish abstractive summarisation of medicaltext.

This model is a fine-tuned version of DanSumT5-base trained on a danish medical text dataset.

The model was trained on LUMI using 1 AMD MI250X GPU. 

## Authors
Nicolaj Larsen   
Mikkel Kildeberg   
Emil Schledermann  

### Framework versions

- Transformers 4.30.2
- Pytorch 1.12.1+git7548e2f
- Datasets 2.13.2
- Tokenizers 0.13.3
{"language": ["da"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "DaMedSum-base", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation generated_from_trainer da license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us