Update README.md

README.md

@@ -90,6 +90,18 @@ religious intolerance : 0, misogyny : 0, xenophobia : 0, other : 0
 
 # Dataset content
 
+The table 1 delineates the quantity of documents annotated in TuPyE, systematically categorized by the respective researchers.
+
+#### Table 1 - TuPyE composition
+
+| Label                | Count  |Source   |
+|----------------------|--------|---------|
+| Leite et al.         | 21,000 |Twitter  |
+| TuPy                 | 10,000 |Twitter  |
+| Vargas et al.        |  7,000 |Instagram|
+| Fortuna et al.       |  5,668 |Twitter  |
+| Total                | 43668            |
+
 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 non-aggressive and aggressive documents
@@ -120,20 +132,6 @@ Table 3 provides a detailed analysis of the dataset, delineating the data volume
 | Other                    | 4476  |
 | Total                    | 9367  |
 
-# BibTeX citation
-
-This dataset can be cited as follows:
-
-```pyyhon
-@misc {silly-machine_2023,
-	author       = { {Silly-Machine} },
-	title        = { TuPy-Dataset (Revision de6b18c) },
-	year         = 2023,
-	url          = { https://huggingface.co/datasets/Silly-Machine/TuPy-Dataset },
-	doi          = { 10.57967/hf/1529 },
-	publisher    = { Hugging Face }
-}
-```
 
 # Acknowledge
 The TuPy project is the result of the development of Felipe Oliveira's thesis and the work of several collaborators. This project is financed by the Federal University of Rio de Janeiro ([UFRJ](https://ufrj.br/)) and the Alberto Luiz Coimbra Institute for Postgraduate Studies and Research in Engineering ([COPPE](https://coppe.ufrj.br/)).