Update README.md

README.md

@@ -48,95 +48,53 @@ The dataset was generated using the following filtering pipeline:
 
 ```
 .
-├── main_metadata.csv         # Metadata (CSV) for loading
-├── gen_vqa_v2-images.zip     # ZIP containing images and additional files
+├── main_metadata.csv              # PRIMARY DATA FILE - Use this!
+├── gen_vqa_v2-images.zip          # Images (10GB)
 │   └── gen_vqa_v22/
-│       ├── images/           # 135k+ COCO images (JPG)
-│       ├── metadata.csv      # Original metadata (not used by dataset loader)
-│       └── qa_pairs.json     # Full QA pairs with all annotations
+│       ├── images/                # 135k+ COCO images
+│       ├── metadata.csv           # (IGNORE - old version)
+│       └── qa_pairs.json          # (IGNORE - raw annotations)
+├── Generative-VQA-V2-Curated.py   # Custom loading script
 └── README.md
 ```
 
-## Metadata Fields
-
-- `image_id`: Original COCO Image ID.
-- `question_id`: Original VQA v2 Question ID.
-- `question`: The natural language question.
-- `answer`: The curated ground-truth answer.
-- `file_name`: Path to image relative to extracted zip root.
+**Note:** The `metadata.csv` and `qa_pairs.json` files inside the zip are NOT used by the dataset loader. The dataset uses `main_metadata.csv` at the repository root.
 
 ## Usage
 
-### Method 1: Load with Manual Extraction (Recommended for Dataset Viewer)
-
-Since the dataset uses a zip file for images, you'll need to manually extract it first:
+### Load with HuggingFace Datasets (Recommended)
 
 ```python
 from datasets import load_dataset
-from huggingface_hub import hf_hub_download
-import zipfile
-import os
-
-# Download the zip file
-zip_path = hf_hub_download(
-    repo_id="Deva8/Generative-VQA-V2-Curated",
-    filename="gen_vqa_v2-images.zip",
-    repo_type="dataset"
-)
-
-# Extract it
-extract_dir = "./gen_vqa_data"
-with zipfile.ZipFile(zip_path, 'r') as zip_ref:
-    zip_ref.extractall(extract_dir)
-
-# Now load the dataset
-dataset = load_dataset(
-    "Deva8/Generative-VQA-V2-Curated",
-    data_files="main_metadata.csv"
-)
-
-# The dataset loader will now be able to find the images
+
+# Load the dataset using the custom loading script
+dataset = load_dataset("Deva8/Generative-VQA-V2-Curated")
+
+# Access examples
 for item in dataset['train']:
     print(f"Q: {item['question']}")
     print(f"A: {item['answer']}")
-    # Note: You'll need to manually load images using the file_name path
+    item['image'].show()
 ```
 
-### Method 2: Direct CSV Loading
+### Load Metadata Only
 
 ```python
 import pandas as pd
-from PIL import Image
-import os
 
-# Load metadata
 df = pd.read_csv("hf://datasets/Deva8/Generative-VQA-V2-Curated/main_metadata.csv")
-
-# After extracting gen_vqa_v2-images.zip to a local directory
-base_path = "./gen_vqa_data"
-
-# Load an example
-row = df.iloc[0]
-img_path = os.path.join(base_path, row['file_name'])
-img = Image.open(img_path)
-
-print(f"Question: {row['question']}")
-print(f"Answer: {row['answer']}")
-img.show()
+print(df.head())
+print(f"\nDataset size: {len(df)} examples")
+print(f"\nTop 10 answers:\n{df['answer'].value_counts().head(10)}")
 ```
 
-### Check Answer Distribution
-
-```python
-import pandas as pd
-
-df = pd.read_csv("hf://datasets/Deva8/Generative-VQA-V2-Curated/main_metadata.csv")
-print(df['answer'].value_counts().head(10))  # Top 10 most common answers
-```
-
-## Known Limitations
+## Metadata Fields
 
-**Dataset Viewer**: The HuggingFace dataset viewer may not work automatically because images are stored in a separate zip file. Users should manually extract the zip and load images programmatically as shown above.
+- `image_id`: Original COCO Image ID
+- `question_id`: Original VQA v2 Question ID
+- `question`: The natural language question
+- `answer`: The curated ground-truth answer
+- `file_name`: Path to image (relative to extracted zip)
 
 ## License & Attribution
 
@@ -147,8 +105,6 @@ This dataset is a derivative work of the VQA v2 Dataset and the COCO Dataset.
 
 ## Citation
 
-If you use this dataset in your research or project, please cite it as follows:
-
 ```bibtex
 @misc{devarajan_genvqa_2026,
   author = {Devarajan},