Instructions to use Danish-summarisation/DanSumT5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Danish-summarisation/DanSumT5-base 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="Danish-summarisation/DanSumT5-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Danish-summarisation/DanSumT5-base") model = AutoModelForSeq2SeqLM.from_pretrained("Danish-summarisation/DanSumT5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.d766ee7
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adding basic model card (#1)
- adding basic model card (a91447c99976f1f6eb6e245efad5e2f954f0bcd1) Co-authored-by: Ross Kristensen-McLachlan <rdkm89@users.noreply.huggingface.co>48ab84a
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Create README.md
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Upload tokenizer
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Upload MT5ForConditionalGeneration
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initial commit
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