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

## Model description

This repository contains a model for Danish abstractive summarisation of medical text. 

This model is a fine-tuned version of mt5-large 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"], "pipeline_tag": "summarization", "model-index": [{"name": "DaMedSumT5-large", "results": []}]}
transformers pytorch tensorboard mt5 text2text-generation generated_from_trainer summarization da license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us