Transformers
PyTorch
TensorFlow
JAX
TensorBoard
Italian
mt5
text2text-generation
italian
sequence-to-sequence
squad_it
text2text-question-answering
Eval Results (legacy)
Instructions to use gsarti/mt5-small-question-answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/mt5-small-question-answering with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/mt5-small-question-answering") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/mt5-small-question-answering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be19eaf6eef3147c31af64e85a018dffc2816c7c081b881b0f6a4285dbc344e8
- Size of remote file:
- 1.2 GB
- SHA256:
- e2cdf9192339fc3b6192b2273d244024cc8885c97e81e53cb3cdd273f8a8c487
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.