Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,116 @@
+---
+license: cc0-1.0
+task_categories:
+- image-to-text
+tags:
+- chemistry
+- molecular-structure
+- smiles
+- ocr
+- computer-vision
+- webdataset
+- lightonocr
+size_categories:
+- 1M<n<10M
+---
+
+# BMS Molecular Translation - WebDataset Shards
+
+This dataset contains pre-processed WebDataset shards of the BMS Molecular Translation dataset, 
+optimized for fast data loading during model training.
+
+## Dataset Summary
+
+- **Total Size**: 3.8 GB
+- **Training shards**: 236 files (3.7 GB) - 2.36M molecular structure images with SMILES
+- **Validation shards**: 5 files (0.1 GB) - 48K samples for model validation  
+- **Test shards**: 3 files (0.0 GB) - 24K held-out samples for final evaluation
+
+## Format
+
+Shards are in [WebDataset](https://github.com/webdataset/webdataset) format:
+- Sequential tar archives for fast I/O
+- 10,000 samples per shard
+- Training data pre-shuffled
+- Val/test data in original order
+- **Tar files are preserved** (not extracted) - perfect for WebDataset!
+
+## Usage
+
+### Download the Dataset
+
+```bash
+# Using HuggingFace Hub
+pip install huggingface_hub
+
+# Download entire dataset
+python download_shards_from_huggingface.py --username jeffdekerj
+
+# Or use HuggingFace Hub directly
+from huggingface_hub import snapshot_download
+snapshot_download(
+    repo_id="jeffdekerj/bms-images-shards",
+    repo_type="dataset",
+    local_dir=".data/webdataset_shards"
+)
+```
+
+### Load with WebDataset
+
+```python
+from webdataset_loader import BMSWebDataset
+from transformers import AutoProcessor
+
+processor = AutoProcessor.from_pretrained("lightonai/LightOnOCR-1B-1025")
+
+train_dataset = BMSWebDataset(
+    shard_dir=".data/webdataset_shards/train/",
+    processor=processor,
+    user_prompt="Return the SMILES string for this molecule.",
+    shuffle_buffer=1000,
+)
+```
+
+### Train Your Model
+
+```bash
+python finetune_lightocr.py \
+  --train_shards .data/webdataset_shards/train/ \
+  --val_shards .data/webdataset_shards/val/ \
+  --per_device_train_batch_size 4 \
+  --num_train_epochs 3 \
+  --fp16
+```
+
+## Benefits
+
+- **2-5x faster** data loading vs individual files
+- **Better I/O** performance for network filesystems
+- **Lower overhead** with sequential reads
+- **Built-in shuffling** without memory overhead
+- **Tar files preserved** - no auto-extraction like Kaggle
+
+## Source Repository
+
+GitHub: https://github.com/JeffDeKerj/lightocr
+
+Complete documentation available in the repository:
+- `docs/WEBDATASET_GUIDE.md` - Complete usage guide
+- `docs/HUGGINGFACE_GUIDE.md` - HuggingFace-specific guide
+- `docs/FINETUNE_GUIDE.md` - Fine-tuning guide
+- `README.md` - Project overview
+
+## Original Dataset
+
+Based on the BMS Molecular Translation competition dataset:
+https://www.kaggle.com/c/bms-molecular-translation
+
+## Citation
+
+If you use this dataset, please cite both:
+1. The original BMS Molecular Translation competition
+2. The LightOnOCR model (if applicable to your work)
+
+## License
+
+CC0: Public Domain. Free to use for any purpose.