Instructions to use sarakolding/daT5-summariser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sarakolding/daT5-summariser 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="sarakolding/daT5-summariser")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sarakolding/daT5-summariser") model = AutoModelForSeq2SeqLM.from_pretrained("sarakolding/daT5-summariser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#2)
- Adding `safetensors` variant of this model (2da12851cc8c3a5578c83a9c3d098da8d060d6f2) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>fc01f28
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New text for readme (#1)
- New text for readme (f778f1c553cdbd29cba631aebbc20691ef71f3f9) Co-authored-by: Nymann <Katrine@users.noreply.huggingface.co>190e150
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updates
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add metadata
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preparing model card
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