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")# 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
Update README.md
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README.md
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It achieves the following results:
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- Loss: 2.0190
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- Rouge1: 19.
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- Rouge2:
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- Rougel: 16.
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- Rougelsum: 16.
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- Gen Len: 32.
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## Usage
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```python
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It achieves the following results:
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- Loss: 2.0190
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- Rouge1: 19.3739
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- Rouge2: 5.9753
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- Rougel: 16.691
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- Rougelsum: 16.7862
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- Gen Len: 32.5268
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## Usage
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```python
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