Summarization
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
Italian
t5
text2text-generation
italian
sequence-to-sequence
wikipedia
efficient
wits
Eval Results (legacy)
text-generation-inference
Instructions to use gsarti/it5-efficient-small-el32-wiki-summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/it5-efficient-small-el32-wiki-summarization 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="gsarti/it5-efficient-small-el32-wiki-summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-efficient-small-el32-wiki-summarization") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-efficient-small-el32-wiki-summarization") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- type: bertscore
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value: 0.513
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name: "Test BERTScore"
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args:
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- model_type: "dbmdz/bert-base-italian-xxl-uncased"
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- lang: "it"
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- num_layers: 10
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- rescale_with_baseline: True
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- baseline_path: "bertscore_baseline_ita.tsv"
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---
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# IT5 Cased Small Efficient EL32 for Wikipedia Summarization 📑 🇮🇹
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- type: bertscore
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value: 0.513
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name: "Test BERTScore"
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---
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# IT5 Cased Small Efficient EL32 for Wikipedia Summarization 📑 🇮🇹
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