Update README.md

README.md

@@ -56,7 +56,7 @@ 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
 
 | Annotator    | Gender | Education                                     | Political  | Color  |
 |--------------|--------|-----------------------------------------------|------------|--------|
@@ -85,7 +85,7 @@ religious intolerance : 0, misogyny : 0, xenophobia : 0, other : 0
 
 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 non-aggressive and aggressive documents
 
 | Label                | Count  |
 |----------------------|--------|