Update README.md

README.md

@@ -135,3 +135,55 @@ configs:
   - split: test
     path: uspto-mol-m-54k/test-*
 ---
+
+# MarkushGrapher 2 Datasets
+
+Datasets for training and evaluating **MarkushGrapher 2**, a model for converting patent Markush structure images into CXSMILES representations.
+
+## Dataset Subsets
+
+| Subset | Train | Test | Description | OCR |
+|---|---|---|---|---|
+| `uspto-mol-m-54k-new` | 54,785 | 200 | USPTO-MOL-M Markush samples | ChemicalOCR predictions |
+| `uspto-markush` | — | 74 | USPTO Markush structures benchmark | Ground Truth OCR |
+| `m2s` | — | 103 | Mol2Smiles (M2S) benchmark | Ground Truth OCR |
+| `IP5-markush` | — | 878 | IP5 Markush structures benchmark | Ground Truth OCR |
+
+## Features
+
+Each sample contains:
+
+- **`page_image`** — Input patent image (PIL Image, typically 1024×1024)
+- **`cells`** — OCR-detected text cells with bounding boxes (`bbox` in normalized coordinates, `text`)
+- **`cxsmiles`** — Ground truth CXSMILES representation
+- **`cxsmiles_opt`** — Optimized (tokenizer-friendly) CXSMILES representation
+- **`cxsmiles_dataset`** — Original CXSMILES from the source dataset
+- **`annotation`** — Annotation metadata (used to train model)
+- **`image_name`** — Source image filename
+- **`id`** — Sample identifier
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load a specific subset
+dataset = load_dataset("docling-project/MarkushGrapher-2-Datasets", "uspto-mol-m-54k-new")
+
+# Load a benchmark subset
+benchmark = load_dataset("docling-project/MarkushGrapher-2-Datasets", "m2s")
+```
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@inproceedings{strohmeyer2026markushgrapher2,
+  title={MarkushGrapher 2: End-to-End Markush Structure Parsing from Patent Images},
+  title     = {MarkushGrapher-2: End-to-end Multimodal Recognition of Chemical Structures},
+  author    = {Strohmeyer, Tim and Morin, Lucas and Meijer, Gerhard Ingmar and Weber, Valery and Nassar, Ahmed and Staar, Peter W. J.},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year      = {2026}
+}
+```