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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-efficient-small-el32-wiki-summarization") model = AutoModelForMultimodalLM.from_pretrained("gsarti/it5-efficient-small-el32-wiki-summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a59a2fe73793cea8988d92b7c20211eec66636ae20b7e2103d14c3fcc89b0f4f
- Size of remote file:
- 623 Bytes
- SHA256:
- 374a18984601ceaa197ac91e3793e0f053af79436628e1cb2546ccc04e9050dc
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