Update README.md

README.md

@@ -57,20 +57,11 @@ To generate the binary matrices, we employed a straightforward voting process. T
 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 ToLD-Br dataset is depicted below:
 
 ```python
-{text: "e tem pobre de direita imbecil que ainda defendia a manutenção da política de preços atrelada ao dólar link",
-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}
+{
+text: "e tem pobre de direita imbecil que ainda defendia a manutenção da política de preços atrelada ao dólar link",
+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
+}
 ```