Summarization
Transformers
PyTorch
Safetensors
Italian
t5
text2text-generation
text-generation-inference
Instructions to use ARTeLab/it5-summarization-mlsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARTeLab/it5-summarization-mlsum 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="ARTeLab/it5-summarization-mlsum")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ARTeLab/it5-summarization-mlsum") model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/it5-summarization-mlsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ARTeLab/it5-summarization-mlsum: direct link, hf CLI and curl.
- Browser
- Download file 990 MB
-
https://huggingface.co/ARTeLab/it5-summarization-mlsum/resolve/1f0275ac6e07517767ff277dac7e8326ba444e4a/pytorch_model.bin
- Command line
-
hf download hf://ARTeLab/it5-summarization-mlsum@1f0275ac6e07517767ff277dac7e8326ba444e4a/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ARTeLab/it5-summarization-mlsum/resolve/1f0275ac6e07517767ff277dac7e8326ba444e4a/pytorch_model.bin
990 MB
- Xet hash:
- ea04c1c8f0a13c0bf1fb3c89790ded2a2f4134fbfe838b00b5ec04c8ba8e5a8c
- Size of remote file:
- 990 MB
- SHA256:
- f5a2e861d40ecd3b41c78c6d58bb53271e4ee48a2db66998a4d975039b91eee4
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