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
- Xet hash:
- 08740fa558d4a0a21cde522103756c4e40df87f5449eb1a241d59fce3bf14592
- Size of remote file:
- 569 MB
- SHA256:
- 86e599334580a042cd3c00573eef9d1bc821f2479a8a3ec889ac6c0d8b5cd906
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