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-base-question-answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarti/mt5-base-question-answering with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("gsarti/mt5-base-question-answering") model = AutoModelForSeq2SeqLM.from_pretrained("gsarti/mt5-base-question-answering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6592767b5bdf210646a68cc2e8a50dc375cb1ce07316c3b7fcc8a58fff906c3
- Size of remote file:
- 2.33 GB
- SHA256:
- 802238cebb019c0ca69194205f2afc55973945471338d9396c1e1ccdaef0bb79
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