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README.md

@@ -108,6 +108,79 @@ Geometric spacing boundaries and physical placement constraints.
 
 ## 🚀 How to Load and Explore the Dataset
 
+### 🔍 Exploring Directly on Hugging Face Data Studio / SQL Explorer
+
+You can run SQL queries directly on your browser using DuckDB over the hosted Parquet tables:
+
+* **Query the train dataset split from tasks table**
+    ```sql
+    SELECT 
+        tasks_index,
+        duration,
+        efficiency,
+        sheet_width,
+        sheet_length,
+        sheet_type,
+        tasks_index,
+    FROM tasks 
+    WHERE is_train = true 
+    LIMIT 500;
+    ```
+    
+*   **Query parts and shapes geometry for a specific task:**
+    ```sql
+    SELECT 
+        p.tasks_index,
+        p.part_id,
+        p.shape_hash,
+        s.raw AS shape_vertices,
+        s.sizes AS vertex_sizes
+    FROM 
+        parts p
+    JOIN 
+        shapes s ON p.shape_hash = s.shape_hash
+    WHERE 
+        p.tasks_index = 228
+    ORDER BY 
+        p.part_id ASC;
+    ```
+
+*   **Query constraints parameters for a specific task:**
+    ```sql
+    SELECT 
+        tasks_index,
+        type AS constraint_type,
+        parts_1,
+        parts_2,
+        y_min,
+        y_max,
+        r1_start,
+        r1_end,
+        is_frozen
+    FROM 
+        constraints
+    WHERE 
+        tasks_index = 228;
+    ```
+
+### 🚀 Loading via the Hugging Face Datasets Library
+
+You can also download or stream these relational tables directly from the Hugging Face Hub using their specific dataset configuration names:
+
+```python
+from datasets import load_dataset
+
+# Load individual tables using configuration subsets
+tasks_ds = load_dataset("clallier/nesting-tasks-2d", name="tasks")
+parts_ds = load_dataset("clallier/nesting-tasks-2d", name="parts")
+shapes_ds = load_dataset("clallier/nesting-tasks-2d", name="shapes")
+constraints_ds = load_dataset("clallier/nesting-tasks-2d", name="constraints")
+
+print(tasks_ds)
+```
+
+### 🚀 Loading via Pandas
+
 Since the dataset is stored in standard Apache Parquet format, loading takes a single line of python code:
 
 ```python
@@ -131,25 +204,9 @@ val_df = tasks_df[tasks_df['is_val']]
 test_df = tasks_df[tasks_df['is_test']]
 
 print(f"Loaded {len(tasks_df)} irregular nesting tasks:")
-print(f"   🟢 Train instances      : {len(train_df)}")
-print(f"   🟡 Validation instances : {len(val_df)}")
-print(f"   🔴 Test instances       : {len(test_df)}")
-```
-
-### Loading Directly from Hugging Face Hub
-
-You can also download or stream these relational tables directly from the Hugging Face Hub using their specific dataset configuration names:
-
-```python
-from datasets import load_dataset
-
-# Load individual tables using configuration subsets
-tasks_ds = load_dataset("clallier/nesting-tasks-2d", name="tasks")
-parts_ds = load_dataset("clallier/nesting-tasks-2d", name="parts")
-shapes_ds = load_dataset("clallier/nesting-tasks-2d", name="shapes")
-constraints_ds = load_dataset("clallier/nesting-tasks-2d", name="constraints")
-
-print(tasks_ds)
+print(f"   Train instances      : {len(train_df)}")
+print(f"   Validation instances : {len(val_df)}")
+print(f"   Test instances       : {len(test_df)}")
 ```
 
 ---