Update README.md

README.md

@@ -1,3 +1,71 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- e-commerce
+size_categories:
+- 100K<n<1M
+---
+
+## Introduction
+EcomMMMU comprises 7 tasks, including
+answerability prediction, category classification, product relation prediction, 
+product substitute identification, multiclass product classification, 
+sentiment analysis, and sequential recommendation. 
+MMECInstruct is split into training sets, validation sets, IND
+test sets, and OOD test sets.
+
+EcomMMMU is a large-scale multimodal multitask understanding dataset for e-commerce applications, 
+containing 406,190 samples and 8,989,510 product images across 34 product categories. 
+It is designed to systematically evaluate how multimodal large language models (MLLMs) 
+utilize visual information in real-world shopping scenarios.
+
+Unlike prior datasets that treat all images equally, 
+EcomMMMU explicitly investigates when and how multiple product images contribute to understanding. 
+It includes a specialized vision-salient subset (VSS), 
+designed to test scenarios where textual information alone is insufficient and visuals are crucial.
+
+
+## Dataset Sources
+- **Repository:** [GitHub](https://github.com/ninglab/EcomMMMU)
+
+<!-- ## Data Split
+The statistics for the MMECInstruct Dataset are shown in the table below.
+
+| Split | Size |
+| --- | --- |
+| Train | 56,000 |
+| Validation | 7,000 |
+ -->
+
+## Quick Start
+
+Run the following command to get the data:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("NingLab/EcomMMMU")
+```
+
+## License
+Please check the license of each subset in our curated dataset ECInstruct.
+
+| Dataset | License Type |
+| --- | --- |
+| [Amazon Review](https://amazon-reviews-2023.github.io/) | Non listed |
+| [AmazonQA](https://github.com/amazonqa/amazonqa) | Non listed |
+| [Shopping Queries Dataset](https://github.com/amazon-science/esci-data) | Apache License 2.0 |
+
+## Citation
+```bibtex
+@article{ling2025ecommmmu,
+  title={EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-Commerce Models},
+  author={Ling, Xinyi and Du, Hanwen and Zhu, Zhihui and Ning, Xia},
+  journal={arXiv preprint arXiv:2508.15721},
+  year={2025}
+}
+```