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
Download pytorch_model.bin from RyeAI/DaMedSum-large: direct link, hf CLI and curl.
- Browser
- Download file 4.92 GB
-
https://huggingface.co/RyeAI/DaMedSum-large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RyeAI/DaMedSum-large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RyeAI/DaMedSum-large/resolve/main/pytorch_model.bin
4.92 GB
- Xet hash:
- 58034e472aec2c48e017012ad0af503d9c0abc94ae55895e94bc20b683ebffc8
- Size of remote file:
- 4.92 GB
- SHA256:
- 50f272c0d3c6c465d490ba5f9bbdf9c42d8fb08d5a0e9ce4d2322a906cc29fe3
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