Update README.md

README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+task_categories:
+  - text-classification
+  - image-classification
+  - multimodal
+tags:
+  - web-security
+  - phishing-detection
+  - html-analysis
+  - screenshot
+  - manually-verified
+dataset_info:
+  features:
+    - name: url
+      dtype: string
+      description: Website URL and folder name for resource access
+    - name: html_content
+      dtype: string
+      description: "Path to HTML file (format: {url}/page.html)"
+    - name: screenshot_content
+      dtype: image
+      description: "Path to screenshot image (format: {url}/screenshot.png)"
+    - name: label
+      dtype: string
+      description: Classification label from metadata.txt (phishing/legitimate)
+  splits:
+    - name: train
+      num_bytes: 1024000000
+      num_examples: 1000
+  configs:
+    - config_name: default
+      data_files:
+        - split: train
+          path: data/dataset.csv
+
+---
+# Web Content Classification Dataset with HTML and Screenshots
+
+## Dataset
+
+Description: This dataset contains 1,000 carefully curated examples of web content designed for phishing detection classification tasks. Each example consists of a website URL along with its complete HTML content, a visual screenshot, and a manually verified classification label.
+
+Important Note: This entire dataset was meticulously created through manual processes. Each website was individually visited, analyzed, and verified to ensure the highest quality and accuracy of labels.
+
+## Dataset Structure
+
+The dataset is organized with a main CSV file that references all resources using a consistent folder structure:
+
+### CSV Columns:
+- url: Website URL and folder name for resource access
+- html_content: Path to HTML file (`{url}/page.html`)
+- screenshot_content: Path to screenshot image (`{url}/screenshot.png`) 
+- label: Classification label (phishing/legitimate)
+
+### Directory Structure:
+
+dataset:
+  files:
+    - dataset.csv
+  folders:
+    - name: example-domain-1.com
+      files:
+        - page.html
+        - screenshot.png
+        - metadata.txt
+    - name: example-domain-2.com
+      files:
+        - page.html
+        - screenshot.png
+        - metadata.txt
+
+## File Reference System
+
+The dataset uses a consistent referencing system where each URL serves as both the website address and folder name:
+
+- HTML Content: "{url}/page.html (e.g., `example.com/page.html`)"
+- Screenshot: "{url}/screenshot.png (e.g., `example.com/screenshot.png`) "
+- Label: Value contained in `{url}/metadata.txt`
+
+## Collection Methodology
+
+### Manual Curation Process
+This dataset was created through an extensive manual verification process:
+
+1. Individual Website Visits: Each of the 1,000 websites was personally visited and analyzed
+2. Manual Verification: Every classification label was manually assigned after careful examination
+3. Quality Control: Each entry was individually checked for accuracy and completeness
+4. Content Capture: HTML and screenshots were captured while ensuring proper rendering
+
+### Data Points Collected:
+
+data_points:
+  URL: "The exact folder name and web address"
+  HTML content: "Full source code saved as {url}/page.html"
+  Visual representation: "High-quality screenshot saved as `{url}/screenshot.png`"
+  Classification label: "Value stored in `{url}/metadata.txt` (verified as phishing/legitimate)"
+
+## Usage
+
+using_with_hugging_face_datasets: |
+  ```python
+  from datasets import load_dataset
+  
+  dataset = load_dataset("your-username/your-dataset-name")
+