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This model is a Question Generation model based on T5-small. It is actually a component of QuestEval metric but can be used independently as it is, for QG only.
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qg_squad1-en")
model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qg_squad1-en")
You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model):
text_input = "sv1 </s> {ANSWER} </s> {CONTEXT}"
The model was trained on SQuAD.
@article{scialom2020QuestEval,
title={QuestEval: Summarization Asks for Fact-based Evaluation},
author={Scialom, Thomas and Dray, Paul-Alexis and Gallinari, Patrick and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo and Wang, Alex},
journal={arXiv preprint arXiv:2103.12693},
year={2021}
}
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