Update README.md

README.md

@@ -2,7 +2,7 @@
 language: en
 tags:
 - qa
-- question 
+- question
 - answering
 - SQuAD
 - data2text
@@ -15,16 +15,16 @@ datasets:
 model-index:
 - name: t5-qa_webnlg_synth-en
   results:
-  - task: 
+  - 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 ]"
+   - 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. 
+This model is a *Data Question Answering* model based on T5-small, that answers 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.
 
 
@@ -43,7 +43,7 @@ You can play with the model using the inference API, the text input format shoul
 
 where CONTEXT is a structured table that is linearised this way:
 
-`CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"`
+`CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"`
 
 
 ## Training data
@@ -58,5 +58,4 @@ The model was trained on synthetic data as described in [Data-QuestEval: A Refer
   journal={arXiv preprint arXiv:2104.07555},
   year={2021}
 }
-}
 ```