Add comprehensive dataset card with multi-resolution documentation

README.md

@@ -0,0 +1,271 @@
+---
+language:
+- en
+size_categories:
+- 10K<n<100K
+task_categories:
+- summarization
+- image-to-text
+- text-generation
+tags:
+- summarization
+- vision
+- DeepSeek-OCR
+- multilingual
+- visual-text-encoding
+- random-augmentation
+library_name: datasets
+license: cc0-1.0
+pretty_name: "BillSum Legal Document Summarization"
+dataset_info:
+  features:
+  - name: text
+    dtype: string
+  - name: summary
+    dtype: string
+  - name: image
+    dtype: image
+  - name: source_dataset
+    dtype: string
+  - name: original_split
+    dtype: string
+  - name: original_index
+    dtype: int64
+  splits:
+  - name: train
+    num_examples: 22218
+---
+
+# DeepSynth - BillSum Legal Document Summarization
+
+## Dataset Description
+
+US Congressional bills with human-written summaries. Specialized for legal document summarization
+with complex structure, formal language, and legislative terminology.
+
+This dataset is part of the **DeepSynth** project, which uses visual text encoding for multilingual summarization with the DeepSeek-OCR vision-language model. Text documents are converted into images and processed through a frozen 380M parameter visual encoder, enabling 20x token compression while preserving document layout and structure.
+
+### Key Features
+
+- **Original High-Quality Images**: Full-resolution images stored once, augmented on-the-fly during training
+- **Random Augmentation Pipeline**: Rotation, perspective, color jitter, and resize transforms for better generalization
+- **Visual Text Encoding**: 20x compression ratio (1 visual token ≈ 20 text tokens)
+- **Document Structure Preservation**: Layout and formatting maintained through image representation
+- **Human-Written Summaries**: High-quality reference summaries for each document
+- **Deduplication Tracking**: Source dataset and index tracking prevents duplicates
+
+### Dataset Statistics
+
+- **Total Samples**: ~22,000
+- **Language(s)**: English
+- **Domain**: US Congressional bills
+- **Average Document Length**: ~3,000 tokens
+- **Average Summary Length**: ~200 tokens
+
+### Source Dataset
+
+Based on the **BillSum dataset** of US Congressional bills.
+- **Original Authors**: Kornilova & Eidelman (2019)
+- **Paper**: [BillSum: A Corpus for Automatic Summarization of US Legislation](https://arxiv.org/abs/1910.00523)
+- **License**: CC0 1.0 Universal (Public Domain)
+
+## Image Augmentation Pipeline
+
+Images are stored at **original resolution** (up to 1600×2200) and augmented during training for better generalization:
+
+### Available Augmentation Transforms
+
+- **Random Rotation**: ±10° rotation for orientation invariance
+- **Random Perspective**: 0.1-0.2 distortion to simulate viewing angles
+- **Random Resize**: 512-1600px range for multi-scale learning
+- **Color Jitter**: Brightness, contrast, saturation adjustments (±20%)
+- **Random Horizontal Flip**: Optional (use with caution for text)
+
+All transforms preserve aspect ratio with padding to maintain text readability. This approach:
+- **Reduces storage**: 6x less disk space (single image vs 6 resolutions)
+- **Increases flexibility**: Any resolution on-the-fly vs pre-computed fixed sizes
+- **Improves generalization**: Random transforms prevent overfitting to specific resolutions
+
+## Dataset Structure
+
+### Data Fields
+
+- `text` (string): Original document text
+- `summary` (string): Human-written summary
+- `image` (PIL.Image): Original full-size rendered document image (up to 1600×2200)
+- `source_dataset` (string): Origin dataset name
+- `original_split` (string): Source split (train/validation/test)
+- `original_index` (int): Original sample index for deduplication
+
+### Data Example
+
+```python
+{
+    'text': 'A BILL to amend the Internal Revenue Code of 1986...',
+    'summary': 'This bill amends the Internal Revenue Code to...',
+    'image': <PIL.Image>,  # Original resolution (up to 1600×2200)
+    'source_dataset': 'billsum',
+    'original_split': 'train',
+    'original_index': 0
+}
+```
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load full dataset
+dataset = load_dataset("baconnier/deepsynth-en-legal")
+
+# Streaming for large datasets
+dataset = load_dataset("baconnier/deepsynth-en-legal", streaming=True)
+```
+
+### Training Example with DeepSeek-OCR and Augmentation
+
+```python
+from transformers import AutoProcessor, AutoModelForVision2Seq
+from datasets import load_dataset
+from deepsynth.data.transforms import create_training_transform
+
+# Load model and processor
+model = AutoModelForVision2Seq.from_pretrained("deepseek-ai/DeepSeek-OCR")
+processor = AutoProcessor.from_pretrained("deepseek-ai/DeepSeek-OCR")
+
+# Load dataset
+dataset = load_dataset("baconnier/deepsynth-en-legal")
+
+# Create augmentation pipeline (random rotation, perspective, resize, color jitter)
+transform = create_training_transform(
+    target_size_range=(512, 1600),  # Random resize range
+    rotation_degrees=10,             # ±10° rotation
+    perspective_distortion=0.1,      # Perspective transform
+    brightness_factor=0.2,           # ±20% brightness
+    contrast_factor=0.2,             # ±20% contrast
+)
+
+# Process sample with augmentation
+sample = dataset['train'][0]
+augmented_image = transform(sample['image'])  # Apply random transforms
+inputs = processor(
+    images=augmented_image,
+    text=sample['text'],
+    return_tensors="pt"
+)
+
+# Fine-tune decoder only (freeze encoder)
+for param in model.encoder.parameters():
+    param.requires_grad = False
+
+# Training loop with on-the-fly augmentation...
+```
+
+## Training Recommendations
+
+### DeepSeek-OCR Fine-Tuning
+
+```python
+# Recommended hyperparameters with augmentation
+training_args = {
+    "learning_rate": 2e-5,
+    "batch_size": 4,
+    "gradient_accumulation_steps": 4,
+    "num_epochs": 3,
+    "mixed_precision": "bf16",
+    "freeze_encoder": True,  # IMPORTANT: Only fine-tune decoder
+
+    # Augmentation parameters
+    "rotation_degrees": 10,           # Random rotation ±10°
+    "perspective_distortion": 0.1,    # Perspective transform
+    "resize_range": (512, 1600),      # Random resize 512-1600px
+    "brightness_factor": 0.2,         # ±20% brightness
+    "contrast_factor": 0.2,           # ±20% contrast
+}
+```
+
+### Expected Performance
+
+- **Baseline (text-to-text)**: ROUGE-1 ~40-42
+- **DeepSeek-OCR (visual)**: ROUGE-1 ~44-47 (typical SOTA)
+- **Training Time**: ~6-8 hours on A100 (80GB) for full dataset
+- **GPU Memory**: ~40GB with batch_size=4, mixed_precision=bf16
+
+## Dataset Creation
+
+This dataset was created using the **DeepSynth** pipeline:
+
+1. **Source Loading**: Original text documents from billsum
+2. **Text-to-Image Conversion**: Documents rendered as PNG images (DejaVu Sans 12pt, Unicode support)
+3. **Original Resolution Storage**: Full-quality images stored once (up to 1600×2200)
+4. **Incremental Upload**: Batches of 5,000 samples uploaded to HuggingFace Hub
+5. **Deduplication**: Source tracking prevents duplicate samples
+
+**Note**: Images are augmented on-the-fly during training using random transformations (rotation, perspective, resize, color jitter) for better generalization across different resolutions and conditions.
+
+### Rendering Specifications
+
+- **Font**: DejaVu Sans 12pt (full Unicode support for multilingual text)
+- **Line Wrapping**: 100 characters per line
+- **Margin**: 40px
+- **Background**: White (255, 255, 255)
+- **Text Color**: Black (0, 0, 0)
+- **Format**: PNG with lossless compression
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@misc{deepsynth-en-legal,
+    title={{DeepSynth BillSum Legal Document Summarization: Visual Text Encoding with Random Augmentation for Summarization}},
+    author={Baconnier},
+    year={2025},
+    publisher={HuggingFace},
+    howpublished={\url{https://huggingface.co/datasets/baconnier/deepsynth-en-legal}}
+}
+```
+
+### Source Dataset Citation
+
+```bibtex
+@inproceedings{kornilova2019billsum,
+    title={BillSum: A Corpus for Automatic Summarization of US Legislation},
+    author={Kornilova, Anastassia and Eidelman, Vladimir},
+    booktitle={Proceedings of the 2nd Workshop on New Frontiers in Summarization},
+    year={2019}
+}
+```
+
+## License
+
+CC0 1.0 Universal (Public Domain Dedication)
+
+**Note**: This dataset inherits the license from the original source dataset. Please review the source license before commercial use.
+
+## Limitations and Bias
+
+- **Legal jargon**: Heavy use of legislative and legal terminology
+- **Complex structure**: Bills have nested sections, subsections, clauses
+- **US-specific**: United States federal legislation only
+- **Formal language**: Very different from conversational or news text
+- **Long documents**: Bills can be 10,000+ tokens
+
+## Additional Information
+
+### Dataset Curators
+
+Created by the DeepSynth team as part of multilingual visual summarization research.
+
+### Contact
+
+- **Repository**: [DeepSynth GitHub](https://github.com/bacoco/DeepSynth)
+- **Issues**: [GitHub Issues](https://github.com/bacoco/DeepSynth/issues)
+
+### Acknowledgments
+
+- **DeepSeek-OCR**: Visual encoder from DeepSeek AI
+- **Source Dataset**: billsum
+- **HuggingFace**: Dataset hosting and infrastructure