Add dataset card, link to paper and code

README.md

@@ -0,0 +1,37 @@
+---
+task_categories:
+- text-retrieval
+license: other
+---
+
+# AL-GR
+
+AL-GR is an industrial-scale dataset comprising 14 billion interactions and 250 million items with corresponding multimodal features collected from Taobao. It was introduced in the paper [FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets](https://huggingface.co/papers/2509.20904).
+
+The dataset is designed to benchmark the construction and evaluation of Semantic Identifiers (SIDs) for generative retrieval (GR) in recommendation systems.
+
+- **Paper:** [FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets](https://huggingface.co/papers/2509.20904)
+- **GitHub Repository:** [https://github.com/selous123/al_sid](https://github.com/selous123/al_sid)
+- **Project Page:** [https://huggingface.co/AL-GR](https://huggingface.co/AL-GR)
+
+## Sample Usage
+
+You can load the "Tiny" version of the dataset using the following code:
+
+```python
+from datasets import load_dataset
+
+# Login using e.g. `huggingface-cli login` to access this dataset
+dataset = load_dataset("AL-GR/AL-GR-Tiny", data_files="train_data/s1_tiny.csv", split="train")
+```
+
+## Citation
+
+```bibtex
+@article{fu2025forge,
+  title={FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets},
+  author={Fu, Kairui and Zhang, Tao and Xiao, Shuwen and Wang, Ziyang and Zhang, Xinming and Zhang, Chenchi and Yan, Yuliang and Zheng, Junjun and others},
+  journal={arXiv preprint arXiv:2509.20904},
+  year={2025}
+}
+```