Updated the dataset details

README.md

@@ -8,17 +8,37 @@ The corpus was curated to enable systematic investigation of how data compositio
 
 ## Dataset Overview
 
-Dataset Name: HC4 (Healthcare Comprehensive Commons Corpus)  
-Size: 153GB (around 65 billion tokens)  
-Number of samples: 9.7+ million documents from diverse sources including peer-reviewed scientific literature collected from PubMed Central, Semantic Scholar, OpenAlex repositories
-Repository: m42-health/HC4  
-Purpose: Pretraining large language models for healthcare applications
-Format: `.parquet` files
-License: Open licenses for each data sample permitting commercial use and redistribution
-Organization: M42 (Abu Dhabi)
+**Dataset Name**: HC4 (Healthcare Comprehensive Commons Corpus)  
+
+**Size**: 153GB (around 65 billion tokens)  
+
+**Number of samples**: 9.7+ million documents from diverse sources including peer-reviewed scientific literature collected from PubMed Central, Semantic Scholar, OpenAlex repositories
+
+**Repository**: m42-health/HC4  
+
+**Purpose**: Pretraining large language models for healthcare applications
+
+**Format**: `.parquet` files
+
+**License**: Open licenses for each data sample permitting commercial use and redistribution
+
+**Organization**: M42 (Abu Dhabi)
+
+
+## How to load the dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("m42-health/HC4")
+```
+
+
 
 ## Details
 
 This dataset is accompanied by a peer-reviewed research paper accepted at EMNLP 2025 Conference, which presents comprehensive bias analysis methodology for clinical LLMs and provides transparency in dataset composition and curation.
 
-**Reference Paper:** "Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency" (EMNLP 2025)
+**Reference Paper:** "Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency" (EMNLP 2025)
+
+**Link to arxiv page**: https://arxiv.org/pdf/2510.18556