nuriyev/text2mcdm

Model Description

This model extracts structured Z-number decision matrices from conversational text describing multi-criteria decision problems. Given a natural language narrative about alternatives, criteria, and preferences (often messy, subjective, or contradictory), the model outputs a markdown table with:

• Alternatives (e.g., train, flight, driving)
• Criteria (e.g., cost, comfort, reliability)
• Z-number ratings in value:confidence format (e.g., 4:3 = good rating with moderate confidence)

Z-numbers extend traditional fuzzy numbers by incorporating reliability/confidence, making them ideal for real-world decision-making under uncertainty.

Intended Use

The extracted matrix can be analyzed using Z-number-based MCDM methods (TOPSIS, PROMETHEE) to produce ranked alternatives. See text2mcdm for the full pipeline.

Training

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: LoRA fine-tuning with Unsloth
• Data: nuriyev/text2mcdm (~600 synthetic decision narratives generated via Gemini API)