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ARTeLab
/
it5-summarization-mlsum

Summarization
Transformers
PyTorch
Safetensors
Italian
t5
text2text-generation
text-generation-inference
Model card Files Files and versions
xet
Community
5

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")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("ARTeLab/it5-summarization-mlsum")
    model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/it5-summarization-mlsum")
  • Notebooks
  • Google Colab
  • Kaggle
it5-summarization-mlsum
992 MB
Ctrl+K
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  • 5 contributors
History: 9 commits
autoevaluator's picture
autoevaluator HF Staff
Add evaluation results on the ARTeLab--mlsum-it config and test split of ARTeLab/mlsum-it
12c58fb over 3 years ago
  • .gitattributes
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    initial commit over 4 years ago
  • README.md
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    Add evaluation results on the ARTeLab--mlsum-it config and test split of ARTeLab/mlsum-it over 3 years ago
  • all_results.json
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  • config.json
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  • eval_results.json
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  • generated_predictions.txt
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  • predict_results.json
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  • pytorch_model.bin
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  • special_tokens_map.json
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  • tokenizer.json
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  • tokenizer_config.json
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  • train_results.json
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  • trainer_state.json
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  • training_args.bin
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