Transformers
PyTorch
JAX
English
t5
text2text-generation
qg
question
generation
SQuAD
metric
nlg
t5-small
text-generation-inference
Instructions to use ThomasNLG/t5-qg_squad1-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThomasNLG/t5-qg_squad1-en with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ThomasNLG/t5-qg_squad1-en") model = AutoModelForSeq2SeqLM.from_pretrained("ThomasNLG/t5-qg_squad1-en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29f8dd033424b3f40a7c01993ee28d614ed0132297b62fd5a1bf93bedcb2062f
- Size of remote file:
- 892 MB
- SHA256:
- 24bbae093f13add7c6bd890ddb22a751ff4fe692b93406d49fe55ba28be8bc08
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.