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Model description

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.

How to use

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}"

Training data

The model was trained on SQuAD.

Citation info

  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},
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This model can be loaded on the Inference API on-demand.

Dataset used to train ThomasNLG/t5-qg_squad1-en

Evaluation results

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