Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+---
+
+# Dataset Card for "JDWebProgrammer/arg-agi-augmented"
+
+## Dataset Description
+
+### Overview
+This dataset is an augmented version of grids extracted from the [ARC-AGI dataset](https://huggingface.co/datasets/dataartist/arc-agi) (Abstraction and Reasoning Corpus). It focuses on **individual grids** rather than full tasks or games, providing an expanded collection for pretraining and testing models like autoencoders (AEs) or latent-space reasoners.
+
+- **Source**: Derived from the `training` split of ARC-AGI (all demonstration and test grids).
+- **Augmentations**: Each original grid is expanded with 5 transformations (horizontal flip, vertical flip, 90°/180°/270° rotations), resulting in 6 variants per grid (original + 5 augments).
+- **Key Note**: This is **not the full games/tasks** from ARC-AGI. It contains only the raw, augmented grids (as 2D lists of integers 0-10) for standalone use in perceptual pretraining or reconstruction testing. Use the original ARC-AGI for full few-shot reasoning tasks.
+
+### Dataset Structure
+- **Format**: Hugging Face `Dataset` object.
+- **Splits**: Single split (`train`) with one field:
+  - `augmented_grids`: List of 2D lists (grids). Each grid is `[[int, ...], ...]` (H x W, values 0-10).
+- **Size**: ~48,000 grids (from ~400 ARC training tasks × ~4 grids/task × 6 augments).
+- **Metadata**: See `metadata.json` for stats (original grids, augmentation factor).
+
+Example grid entry:
+```python
+augmented_grids[0] = [[0, 1, 0], [1, 0, 1], [0, 1, 0]]  # Example 3x3 grid
+```
+
+### Usage
+Load and use for AE pretraining:
+```python
+from datasets import load_dataset
+ds = load_dataset("JDWebProgrammer/arc-agi-augmented")
+grids = ds['train']['augmented_grids']  # List of all grids
+
+# Example: Batch grids for AE
+def grid_to_tensor(grid):
+    h, w = len(grid), len(grid[0])
+    return torch.tensor(grid, dtype=torch.float).view(1, -1) / 10.0  # Normalize 0-1
+
+batch = torch.cat([grid_to_tensor(g) for g in grids[:32]])  # Batch of 32
+# Feed to AE: z = ae.encode(batch); recon = ae.decode(z)
+```
+
+Ideal for:
+- Pretraining perceptual models.
+- Testing reconstruction accuracy (compare original vs. augmented).
+- Data augmentation for fluid intelligence tasks (e.g., ARC-like pattern inference).
+
+### Generation
+- Extracted all input/output grids from ARC-AGI `training` split demos/tests.
+- Applied deterministic augmentations (flips/rotations) to expand variety without labels.
+- No synthetic generation — pure augmentation of real ARC data.
+
+### Limitations
+- Grids only (no task structure/context) — not for end-to-end ARC solving.
+- Augmentations preserve structure but may introduce artifacts (e.g., rotations on asymmetric grids).
+- Values 0-10 (ARC standard); normalize for models.
+
+### License
+- Based on ARC-AGI (CC BY-SA 4.0) — inherits same license.
+- Augmentations: MIT (free for research/commercial).
+
+### Citation
+```bibtex
+@misc{dataartist/arc-agi,
+  title = {Augmented ARC-AGI Grids for Pretraining},
+  author = {dataartist},
+  year = {2025},
+  url = {https://huggingface.co/datasets/your_username/arc-augmented-grids}
+}
+```
+
+---
+
+*Generated for pretraining perceptual models on ARC-style puzzles. Not a substitute for full ARC tasks.*