Summarization
Transformers
PyTorch
TensorBoard
Danish
mt5
text2text-generation
Generated from Trainer
Instructions to use RyeAI/DaMedSum-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RyeAI/DaMedSum-large with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="RyeAI/DaMedSum-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("RyeAI/DaMedSum-large") model = AutoModelForSeq2SeqLM.from_pretrained("RyeAI/DaMedSum-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from RyeAI/DaMedSum-large: direct link, hf CLI and curl.
- Browser
- Download file 1.19 kB
-
https://huggingface.co/RyeAI/DaMedSum-large/resolve/main/README.md
- Command line
-
hf download hf://RyeAI/DaMedSum-large/README.md
-
curl -L -o README.md https://huggingface.co/RyeAI/DaMedSum-large/resolve/main/README.md
1.19 kB
metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: DaMedSum-large
results: []
pipeline_tag: summarization
language:
- da
_____ ______ __ __ ______ _____ ______ __ __ __ __
/\ __-. /\ __ \ /\ "-./ \ /\ ___\ /\ __-. /\ ___\ /\ \/\ \ /\ "-./ \
\ \ \/\ \\ \ __ \\ \ \-./\ \\ \ __\ \ \ \/\ \\ \___ \\ \ \_\ \\ \ \-./\ \
\ \____- \ \_\ \_\\ \_\ \ \_\\ \_____\\ \____- \/\_____\\ \_____\\ \_\ \ \_\
\/____/ \/_/\/_/ \/_/ \/_/ \/_____/ \/____/ \/_____/ \/_____/ \/_/ \/_/
Model description
This repository contains a model for Danish abstractive summarisation of medical text.
This model is a fine-tuned version of DanSumT5-large 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