---
license: mit
language:
- en
tags:
- dataset
- document-processing
- multimodal
- vision-language
- information-retrieval

---

# 📊 Banque_Vision: A Multimodal Dataset for Document Understanding

## 📌 Overview
**Banque_Vision** is a **multimodal dataset** designed for **document-based question answering (QA) and information retrieval**. It combines **textual data** and **visual document representations**, enabling research on **how vision models and language models** interact for document comprehension.

🔗 **Created by**: Matteo Khan   
🎓 **Affiliation**: TW3Partners 
📍 **License**: MIT  

🔗 [Connect with me on LinkedIn](https://www.linkedin.com/in/matteo-khan-a10309263/)  
🔗 [Dataset on Hugging Face](https://huggingface.co/datasets/YourProfile/banque_vision)  

## 📂 Dataset Structure
- **Document Text**: The full text of the document related to the query.
- **Query**: The question or request for information.
- **Document Page**: The specific page containing the answer.
- **Document Image**: The visual representation (scan or screenshot) of the document page.

This dataset allows models to process and retrieve information across both textual and visual modalities, making it highly relevant for **document AI research**.

## 🎯 Intended Use
This dataset is designed for:
- ✅ **Document-based QA** (e.g., answering questions based on scanned documents)
- ✅ **Information retrieval** from structured/unstructured sources
- ✅ **Multimodal learning** for combining text and vision-based features
- ✅ **OCR-based research** and benchmarking
- ✅ **Fine-tuning vision-language models** like Donut, LayoutLM, and BLIP

## ⚠️ Limitations & Considerations
While **Banque_Vision** is a powerful resource, users should be aware of:
- ❌ **OCR errors**: Text extraction may be imperfect due to document quality.
- ⚠️ **Bias in document sources**: Some domains may be over- or under-represented.
- 🔄 **Data labeling noise**: Possible inaccuracies in question-answer alignment.

## 📊 Dataset Format
The dataset is stored in **JSONL** format with the following structure:

```json
{
  "document_text": "... The standard interest rate for savings accounts is 2.5% ...",
  "document_page": 5,
  "query": "What is the interest rate for savings accounts?",
  "document_image": "path/to/image.jpg",
}
```

## 🚀 How to Use
```python
from datasets import load_dataset

dataset = load_dataset("YourProfile/banque_vision")

# Example
sample = dataset["train"][0]
print("Query:", sample["query"])
```

## 🌍 Why It Matters
- **Bridges the gap** between text and vision-based document processing.
- **Supports real-world applications** like legal document analysis, financial records processing, and automated document retrieval.
- **Encourages innovation** in hybrid models that combine **LLMs with vision transformers**.

## 📝 Citation
```bibtex
@misc{banquevision2025,
      title={Banque_Vision: A Multimodal Dataset for Document Understanding},
      author={Your Name},
      year={2025},
      eprint={arXiv:XXXX.XXXXX},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

📩 **Feedback & Contributions**: Feel free to collaborate or provide feedback via [Hugging Face](https://huggingface.co/datasets/YourProfile/banque_vision).

🎉 **Happy Researching!** 🚀