Create README.md

README.md

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+---
+language: en
+tags:
+- qa
+- question 
+- answering
+- SQuAD
+- data2text
+- metric
+- nlg
+- t5-small
+license: mit
+datasets:
+- squad_v2
+model-index:
+- name: t5-qa_webnlg_synth-en
+  results:
+  - task: 
+      name: Data Question Answering
+      type: extractive-qa
+widget:
+   - text: "What is the food type at The Eagle? </s> name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"
+---
+# t5-qa_webnlg_synth-en
+
+## Model description
+This model is a *Data Question Answering* model based on T5-small, that answer questions given a structured table as input. 
+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_webnlg_synth-en")
+
+model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qa_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 = "{QUESTION} </s> {CONTEXT}"`
+
+where CONTEXT is a structured table that is linearised this way:
+
+'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](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}
+}
+}
+```