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README.md
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---
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language: en
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tags:
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- qa
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- question
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- generation
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- SQuAD
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- data2text
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- metric
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- nlg
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- t5-small
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license: mit
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datasets:
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- squad_v2
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model-index:
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- name: t5-qg_webnlg_synth-en
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results:
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- task:
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name: Data Question Generation
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type: Text To Text Generation
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widget:
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- text: "coffee shop </s> name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"
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---
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# t5-qg_webnlg_synth-en
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## Model description
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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.
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It is actually a component of [QuestEval](https://github.com/recitalAI/QuestEval) metric but can be used independently as it is, for QG only.
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## How to use
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```python
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qg_webnlg_synth-en")
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model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qg_webnlg_synth-en")
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```
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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):
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`text_input = "{ANSWER} </s> {CONTEXT}"`
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where CONTEXT is a structured table that is linearised this way:
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`CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"`
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## Training data
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The model was trained on synthetic data as described in [Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation](https://arxiv.org/abs/2104.07555).
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### Citation info
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```bibtex
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@article{rebuffel2021data,
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title={Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation},
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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},
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journal={arXiv preprint arXiv:2104.07555},
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year={2021}
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}
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}
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```
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