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dataset_info:
- config_name: ip5-markush
features:
- name: id
dtype: string
- name: page_image_path
dtype: string
- name: annotation
dtype: string
- name: cxsmiles_dataset
dtype: string
- name: cxsmiles
dtype: string
- name: cxsmiles_opt
dtype: string
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list: float64
- name: text
dtype: string
- name: page_image
dtype: image
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- config_name: uspto-mol-m-54k
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configs:
- config_name: ip5-markush
data_files:
- split: test
path: ip5-markush/test-*
- config_name: m2s
data_files:
- split: test
path: m2s/test-*
- config_name: uspto-markush
data_files:
- split: test
path: uspto-markush/test-*
- config_name: uspto-mol-m-54k
data_files:
- split: train
path: uspto-mol-m-54k/train-*
- 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")
# Load a benchmark subset
benchmark = load_dataset("docling-project/MarkushGrapher-2-Datasets", "m2s")
```
## Note
**MarkushGrapher-2** is also trained on the following datasets:
- **Phase 1:** 243k real-world image–SMILES pairs from [MolScribe](https://huggingface.co/yujieq/MolScribe)
- **Phase 2:**
- 235k synthetically generated image–CXSMILES pairs from [MarkushGrapher-Datasets (v1)](https://huggingface.co/datasets/docling-project/MarkushGrapher-Datasets/viewer/markushgrapher-synthetic-training)
- 91k samples from [MolParser Dataset](https://huggingface.co/datasets/UniParser/MolParser-7M/viewer/sft_real)
## Citation
If you use this dataset, please cite:
```bibtex
@inproceedings{strohmeyer2026markushgrapher2,
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}
}
```
### License
This Dataset is released under the Creative Commons Attribution 4.0 License.
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