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: >-
coffee shop </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}
}
}