readme: ---
  language: en
  tags:
  - qa
  - question 
  - answering
  - SQuAD
  - metric
  - nlg
  - t5-small
  license: mit
  datasets:
  - squad_v2
  model-index:
  - name: t5-qa_squad2neg-en
    results:
    - task: 
        name: Question Answering
        type: extractive-qa
  widget:
     - text: "Who was Louis 14? </s> Louis 14 was a French King."
  ---
  # t5-qa_squad2neg-en
  
  ## Model description
  This model is a *Question Answering* model based on T5-small. 
  It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QA only.
  
  
  ## How to use
  ```python
  from transformers import T5Tokenizer, T5ForConditionalGeneration
  
  tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qa_squad2neg-en")
  
  model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qa_squad2neg-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 = "{QUESTION} </s> {CONTEXT}"`
  
  ## Training data
  The model was trained on: 
  - SQuAD-v2
  - SQuAD-v2 neg: in addition to the training data of SQuAD-v2, for each answerable example, a negative sampled example has been added with the label *unanswerable*  to help the model learning when the question is not answerable given the context. For more details, see the [paper](https://arxiv.org/abs/2103.12693).
  
  
  ### Citation info
  
  ```bibtex
  @article{scialom2020QuestEval,
    title={QuestEval: Summarization Asks for Fact-based Evaluation},
    author={Scialom, Thomas and Dray, Paul-Alexis and Gallinari, Patrick and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo and Wang, Alex},
    journal={arXiv preprint arXiv:2103.12693},
    year={2021}
  }
  ```
  