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")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ARTeLab/it5-summarization-fanpage") model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/it5-summarization-fanpage") - Notebooks
- Google Colab
- Kaggle
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README.md
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This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on Fanpage dataset for Abstractive Summarization.
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It achieves the following results:
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- Loss: 1.
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- Rouge1:
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- Rouge2: 15.
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- Rougel:
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- Rougelsum: 28.
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- Gen Len: 69.
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## Usage
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This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on Fanpage dataset for Abstractive Summarization.
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It achieves the following results:
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- Loss: 1.5348
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- Rouge1: 34.1882
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- Rouge2: 15.7866
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- Rougel: 25.141
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- Rougelsum: 28.4882
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- Gen Len: 69.3041
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## Usage
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