🧠 Multimodal Data Extraction using Qwen2.5-VL

This repository contains a Jupyter Notebook for extracting structured information from images (e.g., scanned prescriptions) using the Qwen2.5-VL-7B-Instruct model.
It uses HuggingFace Transformers + Outlines library for structured generation and evaluates extracted data quality using standard NLP similarity metrics.

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📄 Notebook Details

• Filename: data-extraction-llm.ipynb
• Main Goal: Extract structured fields from images and save the outputs to an Excel file.
• Approach:
  • Load and quantize the Qwen2.5-VL-7B multimodal model.
  • Extract text from images.
  • Parse and structure the extracted outputs using Outlines and Pydantic schemas.
  • Save results into an Excel file.

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🛠️ Libraries Used

• transformers
• qwen_vl_utils
• outlines
• bitsandbytes
• torch
• PIL
• pydantic
• pandas

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📊 Evaluation Metrics

Metric	Description	Interpretation
Cosine Similarity	Measures angle between text vectors.	Closer to 1 = more similar.
BERTScore	Contextual similarity using BERT.	Higher score = better semantic overlap.
Levenshtein Distance	Minimum edits to match strings.	Lower score = more similar (0 = exact match).

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