Update README.md

README.md

@@ -53,7 +53,7 @@ root.
 To generate the binary matrices, we employed a straightforward voting process. Three distinct evaluations were assigned to each document. In cases where a document received two or more identical classifications, the adopted value is set to 1; otherwise, it is marked as 0.Raw data can be checked into the repository in the [project repository](https://github.com/Silly-Machine/TuPy-Dataset)
 The subsequent table provides a concise summary of the annotators' profiles and qualifications:
 
-**Table 1 – Annotators’ profiles and qualifications.**
+#### Table 1 – Annotators’ profiles and qualifications
 
 | Annotator    | Gender | Education                                     | Political  | Color  |
 |--------------|--------|-----------------------------------------------|------------|--------|
@@ -78,11 +78,11 @@ religious intolerance : 0, misogyny : 0, xenophobia : 0, other : 0
 }
 ```
 
-## Data counts
+## Dataset content
 
 Table 2 provides a detailed breakdown of the dataset, delineating the volume of data based on the occurrence of aggressive speech and the manifestation of hate speech within the documents
 
-**Table 2 - Count of documents for categories non-aggressive and aggressive.**
+#### Table 2 - Count of documents for categories non-aggressive and aggressive
 
 | Label                | Count  |
 |----------------------|--------|
@@ -93,7 +93,7 @@ Table 2 provides a detailed breakdown of the dataset, delineating the volume of
 
 Table 3 provides a detailed analysis of the dataset, delineating the data volume in relation to the occurrence of distinct categories of hate speech.
 
-**Table 3 - Count of documents for hate categories.**
+#### Table 3 - Count of documents for hate categories
 
 | Label                    | Count |
 |--------------------------|-------|