Summarization
Transformers
PyTorch
Safetensors
Italian
t5
text2text-generation
text-generation-inference
Instructions to use ARTeLab/it5-summarization-fanpage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARTeLab/it5-summarization-fanpage 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-fanpage")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ARTeLab/it5-summarization-fanpage") model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/it5-summarization-fanpage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on the simple config and train split of scan
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\ Your model has been evaluated on the simple config and train split of the [scan](https://huggingface.co/datasets/scan) dataset by @test_yoon_0921, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-eval-scan-simple-0b9bd3-1528755178).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=scan).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=scan).2bb663d
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