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metadata
language: en
tags:
  - qa
  - question
  - generation
  - SQuAD
  - data2text
  - metric
  - nlg
  - t5-small
license: mit
datasets:
  - squad_v2
model-index:
  - name: t5-qg_webnlg_synth-en
    results:
      - task:
          name: Data Question Generation
          type: Text To Text Generation
widget:
  - text: >-
      The Eagle </s> name [ The Eagle ] , eatType [ coffee shop ] , food [
      French ] , priceRange [ £ 2 0 - 2 5 ]

t5-qg_webnlg_synth-en

Model description

This model is a Data Question Generation model based on T5-small, that generates questions given a structured table as input and the conditioned answer. 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_webnlg_synth-en")

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

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

CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 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

@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}
}
}