Model card error
There’s an error in the yaml metadata in this model card. If you’re the model author, please log in to check the list of errors and warnings.
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}
}
- Downloads last month
- 175
Dataset used to train ThomasNLG/t5-qg_webnlg_synth-en
Evaluation results
Model card error
This model's model-index metadata is invalid: Schema validation error. "[0].results[0].metrics" is required