Create README.md

README.md

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+---
+language: en
+tags:
+- qa
+- question 
+- answering
+- SQuAD
+- metric
+- nlg
+- summarization
+- 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/recitalAI/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")
+```
+
+The text input format should follow this template, accordingly to its training stage:
+
+`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}
+}
+```