Update README.md

README.md

@@ -1,196 +1,205 @@
----
-license: apache-2.0
----
-
-# ARC-AGI-V1 Dataset (A Take On Format)
-
-This dataset is a reorganized version of the [ARC-AGI v1](https://github.com/fchollet/ARC-AGI) (Abstraction and Reasoning Corpus) benchmark, formatted for HuggingFace Datasets.
-
-## Dataset Structure
-
-The original ARC-AGI dataset has been transformed from its file-based JSON structure into a standardized HuggingFace dataset with two splits:
-
-- **train** (400 examples): Tasks from the original `training` directory
-- **test** (400 examples): Tasks from the original `evaluation` directory
-
-### Original Structure
-
-The original ARC-AGI dataset consisted of:
-- A `training` directory with JSON files (one per task)
-- An `evaluation` directory with JSON files (one per task)
-- Each JSON file named with a task ID (e.g., `007bbfb7.json`)
-- Each file containing:
-  - `train`: Array of input/output example pairs for learning the pattern
-  - `test`: Array of input/output pairs representing the actual task to solve
-
-### Transformed Structure
-
-Each row in this dataset represents a single ARC-AGI task with the following schema:
-
-```
-{
-    "id": string,           // Task ID from the original filename
-    "list": [               // Combined training examples and test inputs
-        [                   // Training example inputs (from original 'train')
-            [[int]], [[int]], ...
-        ],
-        [                   // Training example outputs (from original 'train')
-            [[int]], [[int]], ...
-        ],
-        [                   // Test inputs (from original 'test')
-            [[int]], [[int]], ...
-        ]
-    ],
-    "label": [              // Test outputs (from original 'test')
-        [[int]], [[int]], ...
-    ]
-}
-```
-
-#### Field Descriptions
-
-- **`id`**: The unique task identifier from the original filename
-- **`list`**: A nested list containing three components in order:
-  1. **Example inputs** (`list[0]`): All input grids from the original `train` array
-  2. **Example outputs** (`list[1]`): All output grids from the original `train` array (paired with example inputs)
-  3. **Test inputs** (`list[2]`): All input grids from the original `test` array
-- **`label`**: The correct output grids for the test inputs (from original `test` array outputs)
-
-### Data Format
-
-Each grid is represented as a 2D array of integers (0-9), where:
-- Values range from 0 to 9 (representing different colors/states)
-- Grid dimensions vary from 1×1 to 30×30
-- Each integer represents a colored cell in the grid
-
-### Example
-
-```json
-{
-    "id": "007bbfb7",
-    "list": [
-        [
-            [[0, 7, 7],                            // Example input 1
-            [7, 7, 7],                             // 
-            [0, 7, 7]],                            // 
-            [[4, 0, 4], [0, 0, 0], [0, 4, 0]],     // Example input 2
-            [[0, 0, 0], [0, 0, 2], [2, 0, 2]]      // Example input 3
-        ],
-        [
-            [[0, 0, 0, 0, 7, 7, 0, 7, 7],          // Example output 1
-            [0, 0, 0, 7, 7, 7, 7, 7, 7],
-            [0, 0, 0, 0, 7, 7, 0, 7, 7],
-            [0, 7, 7, 0, 7, 7, 0, 7, 7],
-            [7, 7, 7, 7, 7, 7, 7, 7, 7],
-            [0, 7, 7, 0, 7, 7, 0, 7, 7],
-            [0, 0, 0, 0, 7, 7, 0, 7, 7],
-            [0, 0, 0, 7, 7, 7, 7, 7, 7],
-            [0, 0, 0, 0, 7, 7, 0, 7, 7]],
-            [[], [], [], [], [], [], [], [], []],  // etc..
-        ],
-        [
-            [[7, 0, 7], [7, 0, 7], [7, 7, 0]]      // Test input 1
-        ]
-    ],
-    "label": [
-        [[7, 0, 7, 0, 0, 0, 7, 0, 7],              // Test output 1 (ground truth)
-        [7, 0, 7, 0, 0, 0, 7, 0, 7],
-        [7, 7, 0, 0, 0, 0, 7, 7, 0],
-        [7, 0, 7, 0, 0, 0, 7, 0, 7],
-        [7, 0, 7, 0, 0, 0, 7, 0, 7],
-        [7, 7, 0, 0, 0, 0, 7, 7, 0],
-        [7, 0, 7, 7, 0, 7, 0, 0, 0],
-        [7, 0, 7, 7, 0, 7, 0, 0, 0],
-        [7, 7, 0, 7, 7, 0, 0, 0, 0]]
-    ]
-}
-```
-
-## Usage Philosophy
-
-pprint(dataset['train']['list'][0][0][0])
-pprint(dataset['train']['list'][0][1][0])
-print('')
-pprint(dataset['train']['list'][0][2][0])
-pprint(dataset['train']['label'][0][0])
-
-This ARC-AGI dataset format allows (me at least) to think about the tasks in this way:
-1. **Learn from examples**: Study the input/output pairs:
-   - input: `dataset['train']['list'][0][0][0]`
-   - output: `dataset['train']['list'][0][1][0]`
-   - input: `dataset['train']['list'][0][0][1]`
-   - output: `dataset['train']['list'][0][1][1]`
-   - where:
-     - 1st num: `task number`
-     - 2nd num: `either 0: example input || 1: example output`
-     - 3rd num: `which example?`
-2.  **Then 'Get the tests'**:
-    - `dataset['train']['list'][0][2][0]`
-3. **Apply the pattern**: Use the learned rule to make your two guesses
-4. **Evaluate performance**: Compare model predictions against the `label` field
-   - `dataset['train']['label'][0][0]`
-
-### Training Split
-- Contains all tasks from the original `training` directory
-- Intended for model training and development
-- Both example pairs and test solutions are provided
-
-### Test Split
-- Contains all tasks from the original `evaluation` directory
-- Intended for final model evaluation
-- In competition settings, test labels may be withheld
-
-## Dataset Features
-
-```python
-Features({
-    'id': Value('string'),
-    'list': List(List(List(List(Value('int64'))))),
-    'label': List(List(List(Value('int64'))))
-})
-```
-
-## Loading the Dataset
-
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("ardea/arc_agi_v1")
-
-# Access splits
-train_data = dataset['train']
-test_data = dataset['test']
-
-# Example: Get a single task
-task = train_data[0]
-task_id = task['id']
-example_inputs = task['list'][0]
-example_outputs = task['list'][1]
-test_inputs = task['list'][2]
-test_outputs = task['label']
-
-# Example: Get a task by id
-task = list(filter(lambda t: t['id'] == '007bbfb7', train_data))
-```
-
-## Transparency
-I've left the script I used on the original dataset here as `arc_to_my_hf.py`
-
-## Citation
-
-If you use this dataset, please cite the original ARC-AGI work:
-
-```bibtex
-@misc{chollet2019measure,
-    title={On the Measure of Intelligence},
-    author={François Chollet},
-    year={2019},
-    eprint={1911.01547},
-    archivePrefix={arXiv},
-    primaryClass={cs.AI}
-}
-```
-
-## License
-
-This dataset maintains the Apache 2.0 license from the original ARC-AGI corpus.
+---
+license: apache-2.0
+task_categories:
+- table-question-answering
+tags:
+- arc
+- agi
+- arc-agi
+pretty_name: ARC AGI v1
+size_categories:
+- 1K<n<10K
+---
+
+# ARC-AGI-V1 Dataset (A Take On Format)
+
+This dataset is a reorganized version of the [ARC-AGI v1](https://github.com/fchollet/ARC-AGI) (Abstraction and Reasoning Corpus) benchmark, formatted for HuggingFace Datasets.
+
+## Dataset Structure
+
+The original ARC-AGI dataset has been transformed from its file-based JSON structure into a standardized HuggingFace dataset with two splits:
+
+- **train** (400 examples): Tasks from the original `training` directory
+- **test** (400 examples): Tasks from the original `evaluation` directory
+
+### Original Structure
+
+The original ARC-AGI dataset consisted of:
+- A `training` directory with JSON files (one per task)
+- An `evaluation` directory with JSON files (one per task)
+- Each JSON file named with a task ID (e.g., `007bbfb7.json`)
+- Each file containing:
+  - `train`: Array of input/output example pairs for learning the pattern
+  - `test`: Array of input/output pairs representing the actual task to solve
+
+### Transformed Structure
+
+Each row in this dataset represents a single ARC-AGI task with the following schema:
+
+```
+{
+    "id": string,           // Task ID from the original filename
+    "list": [               // Combined training examples and test inputs
+        [                   // Training example inputs (from original 'train')
+            [[int]], [[int]], ...
+        ],
+        [                   // Training example outputs (from original 'train')
+            [[int]], [[int]], ...
+        ],
+        [                   // Test inputs (from original 'test')
+            [[int]], [[int]], ...
+        ]
+    ],
+    "label": [              // Test outputs (from original 'test')
+        [[int]], [[int]], ...
+    ]
+}
+```
+
+#### Field Descriptions
+
+- **`id`**: The unique task identifier from the original filename
+- **`list`**: A nested list containing three components in order:
+  1. **Example inputs** (`list[0]`): All input grids from the original `train` array
+  2. **Example outputs** (`list[1]`): All output grids from the original `train` array (paired with example inputs)
+  3. **Test inputs** (`list[2]`): All input grids from the original `test` array
+- **`label`**: The correct output grids for the test inputs (from original `test` array outputs)
+
+### Data Format
+
+Each grid is represented as a 2D array of integers (0-9), where:
+- Values range from 0 to 9 (representing different colors/states)
+- Grid dimensions vary from 1×1 to 30×30
+- Each integer represents a colored cell in the grid
+
+### Example
+
+```json
+{
+    "id": "007bbfb7",
+    "list": [
+        [
+            [[0, 7, 7],                            // Example input 1
+            [7, 7, 7],                             // 
+            [0, 7, 7]],                            // 
+            [[4, 0, 4], [0, 0, 0], [0, 4, 0]],     // Example input 2
+            [[0, 0, 0], [0, 0, 2], [2, 0, 2]]      // Example input 3
+        ],
+        [
+            [[0, 0, 0, 0, 7, 7, 0, 7, 7],          // Example output 1
+            [0, 0, 0, 7, 7, 7, 7, 7, 7],
+            [0, 0, 0, 0, 7, 7, 0, 7, 7],
+            [0, 7, 7, 0, 7, 7, 0, 7, 7],
+            [7, 7, 7, 7, 7, 7, 7, 7, 7],
+            [0, 7, 7, 0, 7, 7, 0, 7, 7],
+            [0, 0, 0, 0, 7, 7, 0, 7, 7],
+            [0, 0, 0, 7, 7, 7, 7, 7, 7],
+            [0, 0, 0, 0, 7, 7, 0, 7, 7]],
+            [[], [], [], [], [], [], [], [], []],  // etc..
+        ],
+        [
+            [[7, 0, 7], [7, 0, 7], [7, 7, 0]]      // Test input 1
+        ]
+    ],
+    "label": [
+        [[7, 0, 7, 0, 0, 0, 7, 0, 7],              // Test output 1 (ground truth)
+        [7, 0, 7, 0, 0, 0, 7, 0, 7],
+        [7, 7, 0, 0, 0, 0, 7, 7, 0],
+        [7, 0, 7, 0, 0, 0, 7, 0, 7],
+        [7, 0, 7, 0, 0, 0, 7, 0, 7],
+        [7, 7, 0, 0, 0, 0, 7, 7, 0],
+        [7, 0, 7, 7, 0, 7, 0, 0, 0],
+        [7, 0, 7, 7, 0, 7, 0, 0, 0],
+        [7, 7, 0, 7, 7, 0, 0, 0, 0]]
+    ]
+}
+```
+
+## Usage Philosophy
+
+pprint(dataset['train']['list'][0][0][0])
+pprint(dataset['train']['list'][0][1][0])
+print('')
+pprint(dataset['train']['list'][0][2][0])
+pprint(dataset['train']['label'][0][0])
+
+This ARC-AGI dataset format allows (me at least) to think about the tasks in this way:
+1. **Learn from examples**: Study the input/output pairs:
+   - input: `dataset['train']['list'][0][0][0]`
+   - output: `dataset['train']['list'][0][1][0]`
+   - input: `dataset['train']['list'][0][0][1]`
+   - output: `dataset['train']['list'][0][1][1]`
+   - where:
+     - 1st num: `task number`
+     - 2nd num: `either 0: example input || 1: example output`
+     - 3rd num: `which example?`
+2.  **Then 'Get the tests'**:
+    - `dataset['train']['list'][0][2][0]`
+3. **Apply the pattern**: Use the learned rule to make your two guesses
+4. **Evaluate performance**: Compare model predictions against the `label` field
+   - `dataset['train']['label'][0][0]`
+
+### Training Split
+- Contains all tasks from the original `training` directory
+- Intended for model training and development
+- Both example pairs and test solutions are provided
+
+### Test Split
+- Contains all tasks from the original `evaluation` directory
+- Intended for final model evaluation
+- In competition settings, test labels may be withheld
+
+## Dataset Features
+
+```python
+Features({
+    'id': Value('string'),
+    'list': List(List(List(List(Value('int64'))))),
+    'label': List(List(List(Value('int64'))))
+})
+```
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ardea/arc_agi_v1")
+
+# Access splits
+train_data = dataset['train']
+test_data = dataset['test']
+
+# Example: Get a single task
+task = train_data[0]
+task_id = task['id']
+example_inputs = task['list'][0]
+example_outputs = task['list'][1]
+test_inputs = task['list'][2]
+test_outputs = task['label']
+
+# Example: Get a task by id
+task = list(filter(lambda t: t['id'] == '007bbfb7', train_data))
+```
+
+## Transparency
+I've left the script I used on the original dataset here as `arc_to_my_hf.py`
+
+## Citation
+
+If you use this dataset, please cite the original ARC-AGI work:
+
+```bibtex
+@misc{chollet2019measure,
+    title={On the Measure of Intelligence},
+    author={François Chollet},
+    year={2019},
+    eprint={1911.01547},
+    archivePrefix={arXiv},
+    primaryClass={cs.AI}
+}
+```
+
+## License
+
+This dataset maintains the Apache 2.0 license from the original ARC-AGI corpus.