Update README.md

README.md

@@ -75,14 +75,16 @@ we adhered to stringent guidelines for text integration, detailed as follows:
 Through the application of these rigorous integration guidelines, we have succeeded in establishing a robust, unified database that stands as a valuable resource for the development and refinement of automatic hate speech detection systems in the Portuguese language.
 
 ## Data structure
-A data point comprises the tweet text (a string) along with thirteen categories, each category is assigned a value of 0 when there is an absence of aggressive or hateful content and a value of 1 when such content is present. These values represent the consensus of annotators regarding the presence of aggressive, hate, ageism, aporophobia, body shame, capacitism, lgbtphobia, political, racism, religious intolerance, misogyny, xenophobia, and others. An illustration from the multilabel TuPy dataset is depicted below:
+A data point comprises the tweet text (a string) along with thirteen categories, each category is assigned a value of 0 when there is an 
+absence of aggressive or hateful content and a value of 1 when such content is present. These values represent the consensus of 
+annotators regarding the presence of aggressive, hate, ageism, aporophobia, body shame, capacitism, lgbtphobia, political, racism, 
+religious intolerance, misogyny, xenophobia, and others. An illustration from the multilabel TuPyE dataset is depicted below:
 
 ```python
 {
 source:"twitter",
 text: "e tem pobre de direita imbecil que ainda defendia a manutenção da política de preços atrelada ao dólar link",
-researcher:"leite et al",
-year:2020,
+researcher:"leite et al", year:2020,
 aggressive: 1, hate: 1, ageism: 0, aporophobia: 1, body shame: 0, capacitism: 0, lgbtphobia: 0, political: 1, racism : 0,
 religious intolerance : 0, misogyny : 0, xenophobia : 0, other : 0
 }