Update README.md

README.md

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----
-dataset_info:
-  features:
-  - name: instruction
-    dtype: string
-  - name: input
-    dtype: string
-  - name: output
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 219748692
-    num_examples: 90148
-  - name: test
-    num_bytes: 121634
-    num_examples: 50
-  download_size: 39406500
-  dataset_size: 219870326
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
----
+# Dataset Card for Masked Floorplans (Alpaca-style)
+
+## Summary
+
+This dataset contains structured JSON representations of residential floorplans where **one room has been removed (masked)**. Each example follows the **Alpaca-style instruction format** to fine-tune a language model to **predict the missing room's geometric attributes** based on a partial plan and a semantic conditioning signal.
+
+## Format
+
+Each sample is a JSON object with the following fields:
+
+- **instruction**: A prompt like `"Predict the geometry of the missing room."`
+- **input**: A JSON-encoded partial floorplan with visible rooms and the type of the masked room.
+- **output**: The ground-truth geometry of the missing room, including polygon coordinates, area, orientation, and adjacency.
+
+## Use Cases
+- Instruction-tuning of LLMs for geometric reasoning
+- Floorplan infill or layout prediction
+- Generative design based on semantic layout context