Add dataset card and link to paper

README.md

@@ -2,4 +2,62 @@
 license: other
 license_name: multiple-original-licenses
 license_link: LICENSE
+task_categories:
+- zero-shot-image-classification
 ---
+
+# VL-Compositionality-Benchmarks
+
+This repository contains a collection of evaluation benchmarks used to assess the compositional understanding of Vision-Language Models (VLMs), as presented in the paper [Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality](https://huggingface.co/papers/2606.13288).
+
+Official GitHub Repository: [hiker-lw/MACCO](https://github.com/hiker-lw/MACCO)
+
+## Dataset Summary
+
+These benchmarks are designed to evaluate how well models like CLIP capture object relations, attribute-object bindings, and word order dependencies, moving beyond "bag-of-words" behavior. 
+
+The collection includes the following benchmarks:
+- **ARO** (Attributes, Relations, and Order)
+- **VL-Checklist** (including HAKE, SWiG, and VG)
+- **Sugar-Crepe**
+- **VALSE**
+- **What's Up**
+
+## Data Preparation
+
+According to the official repository, datasets should be organized under a `datasets/` directory. 
+
+### Notes for VL-Checklist
+The VL-Checklist files are split into multiple parts and must be concatenated before extraction. You can use the following commands:
+
+```bash
+cat hake.tar.gz.part-* > hake.tar.gz
+cat swig.tar.gz.part-* > swig.tar.gz
+cat vg.tar.gz.part-* > vg.tar.gz
+```
+
+After extraction, the expected structure for VL-Checklist is:
+```text
+datasets/
+└── VL_checklist/
+    ├── VL_checklist_datasets/
+    │   ├── data/
+    │   ├── hake/
+    │   ├── swig/
+    │   └── vg/
+    └── VL_checklist_json_data/
+```
+
+## Citation
+
+```bibtex
+@misc{li2026crossmodalmaskedcompositionalconcept,
+      title={Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality}, 
+      author={Wei Li and Zhen Huang and Xinmei Tian},
+      year={2026},
+      eprint={2606.13288},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2606.13288}, 
+}
+```