Update README.md

README.md

@@ -10,12 +10,24 @@ language:
 - en
 ---
 
-# Uploaded finetuned  model
+## Model Description
 
-- **Developed by:** nuriyev
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
+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:
 
-This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+- **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](https://github.com/MahammadNuriyev62/text2mcdm) for the full pipeline.
+
+## Training
+
+- **Base model**: Qwen/Qwen3-4B-Instruct-2507
+- **Method**: LoRA fine-tuning with Unsloth
+- **Data**: [nuriyev/text2mcdm](https://huggingface.co/datasets/nuriyev/text2mcdm) (~600 synthetic decision narratives generated via Gemini API)
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)