readme: ---
  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 [ Â£ 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](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QG only.
  
  
  ## How to use
  ```python
  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 [ 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](https://arxiv.org/abs/2104.07555).
  
  ### 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}
  }
  ```