Transformers
PyTorch
TensorFlow
JAX
TensorBoard
Italian
t5
text2text-generation
seq2seq
lm-head
text-generation-inference
Instructions to use gsarti/it5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/it5-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/it5-base") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/it5-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 363228c3208e868f160206925c06f9975a553630570da30bc8b97dc9b15109c2
- Size of remote file:
- 990 MB
- SHA256:
- 9a3eb59ed06456ada18c6c1fdab752832862c2e51d10d5f83a5a107abf734987
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.