Update README.md

README.md

@@ -23,18 +23,9 @@ language:
 license: other
 configs:
   - config_name: default
-    data_files: "data/*.jsonl"
-    # Note: Files have variable schemas due to different language sets per repository
-  - config_name: enhanced_triple
-    data_files: "data/enhanced_enhanced_enhanced_*.jsonl"
-  - config_name: enhanced_double
-    data_files: "data/enhanced_enhanced_sustained_*.jsonl"
-  - config_name: enhanced_single
-    data_files: "data/enhanced_sustained_*.jsonl"
+    data_files: "data_csv/*.csv"
 ---
 
-
-
 # CodeReality: Evaluation Subset - Deliberately Noisy Code Dataset
 
 ![Dataset Status](https://img.shields.io/badge/status-complete-brightgreen)
@@ -84,10 +75,49 @@ codereality-1t/
 
 ### Loading the Dataset
 
+## 📊 **Unified CSV Format**
+
+**This dataset has been converted to CSV format with a unified schema** to ensure compatibility with Hugging Face's dataset viewer and eliminate schema inconsistencies that were present in the original JSONL format.
+
+### **How to Use This Dataset**
+
+**Option 1: Standard Hugging Face Datasets (Recommended)**
 ```python
-import json
-import os
+from datasets import load_dataset
+
+# Load the complete dataset
+dataset = load_dataset("vinsblack/CodeReality")
 
+# Access the data
+print(f"Total samples: {len(dataset['train'])}")
+print(f"Columns: {dataset['train'].column_names}")
+
+# Sample record
+sample = dataset['train'][0]
+print(f"Repository: {sample['repo_name']}")
+print(f"Language: {sample['primary_language']}")
+print(f"Quality Score: {sample['quality_score']}")
+```
+
+**Option 2: Direct CSV Access**
+```python
+import pandas as pd
+from huggingface_hub import snapshot_download
+
+# Download the dataset
+repo_path = snapshot_download(repo_id="vinsblack/CodeReality", repo_type="dataset")
+
+# Load CSV files
+import glob
+csv_files = glob.glob(f"{repo_path}/data_csv/*.csv")
+df = pd.concat([pd.read_csv(f) for f in csv_files], ignore_index=True)
+
+print(f"Total records: {len(df)}")
+print(f"Columns: {list(df.columns)}")
+```
+
+**Option 3: Metadata and Analysis**
+```python
 # Load evaluation subset metadata
 with open('eval_metadata.json', 'r') as f:
     metadata = json.load(f)