Update README.md

README.md

@@ -47,7 +47,7 @@ Based on the compilation process, each data instance is expected to contain the
 }
 
 
-How to Use & Integration Guide
+### How to Use & Integration Guide
 Since the dataset is quite large (ranging between 10 million and 100 million rows), loading it entirely into RAM might be inefficient depending on your hardware setup. Below are the recommended ways to handle the data.
 
 
@@ -58,7 +58,7 @@ If you want to download and cache the dataset automatically, make sure you have
 from datasets import load_dataset
 
 
-# Load the entire dataset into memory/cache
+### Load the entire dataset into memory/cache
 
 
 dataset = load_dataset("DLMveloper/DLM_DATASET")
@@ -66,7 +66,7 @@ print("Dataset loaded successfully:")
 print(dataset)
 
 
-# Access a specific slice or sample
+### Access a specific slice or sample
 
 
 first_sample = dataset['train'][0]
@@ -80,13 +80,13 @@ To avoid high memory overhead and start training or preprocessing immediately wi
 from datasets import load_dataset
 
 
-# Initialize a streaming instance of the dataset
+### Initialize a streaming instance of the dataset
 
 
 streaming_dataset = load_dataset("DLMveloper/DLM_DATASET", streaming=True)
 
 
-# Iterate through the data dynamically on-the-fly
+### Iterate through the data dynamically on-the-fly
 
 
 print("Streaming the first 5 examples:")
@@ -119,7 +119,7 @@ with open(file_path, "r", encoding="utf-8") as file:
 
 
 
-Intended Applications
+### Intended Applications
 This dataset is highly versatile and can be adapted for several downstream tasks:
 Multilingual Language Modeling: Enhancing model capabilities across English, Russian, and especially Kazakh (kk), which is highly valuable due to the scarcity of high-quality Kazakh datasets.
 Code Intelligence and Syntactical Modeling: Training models to generate, complete, or document python and javascript code snippets.