Update README.md

README.md

@@ -36,4 +36,68 @@ configs:
     path: data/train-*
   - split: val
     path: data/val-*
+license: apache-2.0
+task_categories:
+- image-to-image
+language:
+- en
+- vi
+tags:
+- font
+- diffusion
+- deep-learning
+- computer-vision
+pretty_name: NomGenie - Font Diffusion for Sino-Nom Language
+size_categories:
+- 10K<n<100K
 ---
+
+# NomGenie: Font Diffusion for Sino-Nom Language
+
+**NomGenie** is a specialized image-to-image dataset designed for font generation and style transfer within the **Sino-Nom (Hán-Nôm)** script system. This dataset facilitates the training of deep learning models—particularly Diffusion Models and GANs—to preserve the historical and structural integrity of Vietnamese Nom characters while applying diverse typographic styles.
+
+## Dataset Description
+
+The dataset consists of paired images: a **content image** (representing the skeletal or standard structure of a character) and a **target image** (representing the character rendered in a specific artistic or historical font style).
+
+### Key Features
+* **character**: The specific Sino-Nom character represented.
+* **style/font**: Metadata identifying the aesthetic transformation applied.
+* **content_image**: The source glyph used as the structural reference.
+* **target_image**: The ground truth stylized glyph for model supervision.
+* **Hashing**: `content_hash` and `target_hash` are provided to ensure data integrity and assist in deduplication.
+
+## Dataset Structure
+
+### Data Splits
+The dataset is organized into three distinct splits to support various training stages:
+
+| Split | Examples | Size | Description |
+| :--- | :--- | :--- | :--- |
+| **train_original** | 8,235 | 124.79 MB | The full original training set. |
+| **train** | 5,172 | 79.72 MB | A curated subset optimized for standard training. |
+| **val** | 318 | 4.48 MB | Validation set for hyperparameter tuning and evaluation. |
+
+## Quick Start
+
+To use this dataset with the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("path/to/NomGenie")
+
+# Access a training sample
+sample = dataset['train'][0]
+display(sample['content_image'])
+display(sample['target_image'])
+
+## Technical Details
+- Task Category: image-to-image
+
+- Languages: Vietnamese (vi), English (en)
+
+- License: Apache 2.0
+
+- Primary Use Case: Generative AI for cultural heritage preservation and digital typography.