metadata

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
🔗 Dataset on Hugging Face

📂 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:

{
  "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

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

@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.

🎉 Happy Researching! 🚀