Update README.md

README.md

@@ -26,60 +26,38 @@ This dataset is designed for training models to classify both textual and code d
 
 **Languages and Sources**
 Natural Languages:
-
-	English
-
-	French
-
-	Spanish
+	-English
+	-French
+	-Spanish
 Programming Languages:
-
-	Python
-
-	Java
-
-	JavaScript
-
-	C++
-
-	C
+	-Python
+	-Java
+	-JavaScript
+	-C++
+	-C
 Data Types:
-
-	NL: Textual Content
-
-	CL: Code Snippets
+	-NL: Textual Content
+	-CL: Code Snippets
 	
-
 **Dataset Features**
 Each entry in the dataset includes the following attributes:
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-	id (string): A unique identifier for each sample, represented as a SHA256 hash of the original text.
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-	text (string): The original content of the text or code snippet.
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-	lang (string): The language of the data, which can either be a natural language (English, French, Spanish) or a programming language (Python, Java, JavaScript, C++, C).
-
-	type (string): The type of the content. Use "NL" for natural language text and "CL" for code language (programming languages).
-	
+	-id (string): A unique identifier for each sample, represented as a SHA256 hash of the original text.
+	-text (string): The original content of the text or code snippet.
+	-lang (string): The language of the data, which can either be a natural language (English, French, Spanish) or a programming language (Python, Java, JavaScript, C++, C).
+	-type (string): The type of the content. Use "NL" for natural language text and "CL" for code language (programming languages).
 	
 **Intended Use**
 This dataset is ideal for training machine learning models to perform tasks such as:
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-	Text classification and sentiment analysis in multiple natural languages.
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-	Code classification, code completion, or other code-related tasks in various programming languages.
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-	Cross-language or multi-language classification for both text and code.
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-	It is intended to support the development of models that can handle both human languages and programming languages, making it versatile for a variety of NLP and code understanding tasks.
+	-Text classification and sentiment analysis in multiple natural languages.
+	-Code classification, code completion, or other code-related tasks in various programming languages.
+	-Cross-language or multi-language classification for both text and code.
+	
+    It is intended to support the development of models that can handle both human languages and programming languages, making it versatile for a variety of NLP and code understanding tasks.
 	
-
 **Limitations**
-	The dataset may not cover all edge cases for each language or code type.
-
-	The representation of languages may not be balanced; certain languages may have more samples than others.
-
-	Code snippets may vary in complexity, and some may not be representative of typical coding practices.
+	-The dataset may not cover all edge cases for each language or code type.
+	-The representation of languages may not be balanced; certain languages may have more samples than others.
+	-Code snippets may vary in complexity, and some may not be representative of typical coding practices.
 
 **Citation**
 Please cite or link back to this dataset on Hugging Face Hub if used in your projects.