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:
# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gsarti/mt5-base-question-answering: direct link, hf CLI and curl.
- Browser
- Download file 2.33 GB
-
https://huggingface.co/gsarti/mt5-base-question-answering/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gsarti/mt5-base-question-answering/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gsarti/mt5-base-question-answering/resolve/main/pytorch_model.bin
2.33 GB
- Xet hash:
- 7fa48072b23fa332c6c9b55c7c319030747a86c91885aaab3f27f3cf48631654
- Size of remote file:
- 2.33 GB
- SHA256:
- f4f5faa3798e1be310a51c86368c86240c9761c080d57523ce90d6250f85f0e2
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