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README.md

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 ---
-dataset_info:
-  features:
-  - name: pid
-    dtype: string
-  - name: question
-    dtype: string
-  - name: image
-    dtype: string
-  - name: decoded_image
-    dtype: image
-  - name: choices
-    list: string
-  - name: answer
-    dtype: string
-  - name: question_type
-    dtype: string
-  - name: answer_type
-    dtype: string
-  - name: metadata
-    dtype: string
-  - name: query
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 97695767
-    num_examples: 1678
-  - name: test
-    num_bytes: 11358159
-    num_examples: 187
-  download_size: 109148909
-  dataset_size: 109053926
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: cc-by-sa-4.0
+task_categories:
+  - visual-question-answering
+  - question-answering
+language:
+  - en
+tags:
+  - azure
+  - cloud-architecture
+  - solution-architect
+  - architecture-diagrams
+  - vision-language-model
+  - fine-tuning
+  - VQA
+size_categories:
+  - 1K<n<10K
 ---
+
+# Azure Architecture Visual Question Answering Dataset
+
+A comprehensive visual question answering (VQA) dataset for fine-tuning vision language models to become Azure Cloud Solution Architects. Created from the [Azure Architecture Center](https://learn.microsoft.com/en-us/azure/architecture/).
+
+## Dataset Description
+
+This dataset contains Q&A pairs paired with Azure architecture diagrams, designed for fine-tuning vision language models (like Qwen 3.5 VL) to understand and reason about cloud architecture patterns.
+
+### Source
+- **Azure Architecture Center**: 373 architecture pages scraped
+- **Architecture Diagrams**: 500+ high-quality architecture diagrams (PNG format)
+- **Q&A Generation**: Generated using Qwen 2.5 72B Instruct via HuggingFace Inference API
+
+### Question Categories
+Each architecture generates 5 Q&A pairs across these categories:
+
+1. **Architecture Overview** (free_form): High-level design purpose and patterns
+2. **Component Identification** (free_form): Azure services used and their roles
+3. **Design Decision** (multi_choice): Design choices and best practices
+4. **Visual Understanding** (free_form): Diagram layout, data flow, and connections
+5. **Scenario Application** (multi_choice): When and how to apply the pattern
+
+### Architecture Categories Covered
+- Reference Architectures (containers, networking, identity, etc.)
+- Example Scenarios (IoT, data, mainframe, SAP, etc.)
+- Design Patterns (CQRS, Event Sourcing, Gateway, etc.)
+- Solution Ideas (analytics, AI/ML, hybrid, etc.)
+- Best Practices and Guides
+- Cloud migration patterns
+- Microservices architectures
+- Serverless patterns
+- Hybrid and multi-cloud designs
+
+## Dataset Structure
+
+### Data Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `pid` | string | Unique question ID |
+| `question` | string | The question text |
+| `image` | string | Image filename reference |
+| `decoded_image` | Image | The architecture diagram (PIL Image) |
+| `choices` | list[string] | Answer choices for multi_choice, empty for free_form |
+| `answer` | string | The correct answer |
+| `question_type` | string | "free_form" or "multi_choice" |
+| `answer_type` | string | Answer format type |
+| `metadata` | string | JSON with category, skills, source, page info |
+| `query` | string | Formatted query with hints |
+
+### Splits
+
+| Split | Rows |
+|-------|------|
+| train | 1,678 |
+| test | 187 |
+
+## Intended Use
+
+### Fine-tuning Vision Language Models
+This dataset is specifically designed for fine-tuning models like:
+- **Qwen 3.5 VL** (0.8B, 3B, 8B variants)
+- Other vision-language models that support image+text input
+
+### Target Capability
+Train a model to act as an **Azure Cloud Solution Architect** that can:
+- Analyze architecture diagrams
+- Identify Azure services and their roles
+- Explain design decisions and trade-offs
+- Recommend architectures for given scenarios
+- Understand data flow and system interactions
+
+## Example
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("thegovind/azure-architecture-vqa")
+
+# View a sample
+sample = ds['train'][0]
+print(f"Question: {sample['question']}")
+print(f"Answer: {sample['answer']}")
+print(f"Type: {sample['question_type']}")
+if sample['decoded_image']:
+    sample['decoded_image'].show()
+```
+
+## Citation
+
+```bibtex
+@dataset{azure_architecture_vqa_2026,
+  title={Azure Architecture Visual Question Answering Dataset},
+  author={thegovind},
+  year={2026},
+  url={https://huggingface.co/datasets/thegovind/azure-architecture-vqa},
+  note={Created from Azure Architecture Center documentation}
+}
+```
+
+## License
+
+This dataset is released under CC-BY-SA-4.0. The source content is from Microsoft's Azure Architecture Center documentation.