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Model description

This model is a Classifier model based on T5-small, that predicts if a answer / question couple is considered as important fact or not (Is this answer enough relevant to appear in a plausible summary?). It is actually a component of QuestEval metric but can be used independently as it is.

How to use

from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-weighter_cnndm-en")

model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-weighter_cnndm-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 = "{ANSWER} </s> {QUESTION} </s> {CONTEXT}"

Training data

The model was trained on synthetic data as described in Questeval: Summarization asks for fact-based evaluation.

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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Dataset used to train ThomasNLG/t5-weighter_cnndm-en

Evaluation results

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