This model is a Data Question Answering model based on T5-small, that answers questions given a structured table as input. It is actually a component of QuestEval metric but can be used independently as it is, for QA only.

How to use

python

from transformers import T5Tokenizer, T5ForConditionalGeneration

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

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

where CONTEXT is a structured table that is linearised this way:

CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ Â£ 2 0 - 2 5 ]"

Training data

The model was trained on synthetic data as described in Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation.

Citation info

bibtex

@article{rebuffel2021data,
  title={Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation},
  author={Rebuffel, Cl{\'e}ment and Scialom, Thomas and Soulier, Laure and Piwowarski, Benjamin and Lamprier, Sylvain and Staiano, Jacopo and Scoutheeten, Geoffrey and Gallinari, Patrick},
  journal={arXiv preprint arXiv:2104.07555},
  year={2021}
}