Transformers
PyTorch
TensorFlow
JAX
TensorBoard
Italian
t5
text2text-generation
Italian
efficient
sequence-to-sequence
question-generation
squad_it
Eval Results (legacy)
text-generation-inference
Instructions to use gsarti/it5-efficient-small-el32-question-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/it5-efficient-small-el32-question-generation with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-efficient-small-el32-question-generation") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-efficient-small-el32-question-generation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from gsarti/it5-efficient-small-el32-question-generation: direct link, hf CLI and curl.
- Browser
- Download file 817 kB
-
https://huggingface.co/gsarti/it5-efficient-small-el32-question-generation/resolve/main/spiece.model
- Command line
-
hf download hf://gsarti/it5-efficient-small-el32-question-generation/spiece.model
-
curl -L -o spiece.model https://huggingface.co/gsarti/it5-efficient-small-el32-question-generation/resolve/main/spiece.model
817 kB
- Xet hash:
- 16dc1ca84fe0970785c9e36e67a4bca7350e1e7eb2bcdcf7a6056e39d5f59414
- Size of remote file:
- 817 kB
- SHA256:
- 2dffd01fc009b7e92d98eddff8853983e271b41302ed0d363000e8581df12000
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