Add dataset card with paper link, GitHub repository, and task categories

README.md

@@ -1,3 +1,55 @@
 ---
 license: apache-2.0
+task_categories:
+- other
+tags:
+- recommendation-systems
+- sequential-recommendation
+- ranking
 ---
+
+# UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction
+
+[**GitHub**](https://github.com/salmon1802/UniRank) | [**Paper**](https://huggingface.co/papers/2607.19987)
+
+UniRank is an open PyTorch benchmark and dataset suite for unified sequential modeling and feature interaction in large-scale recommendation ranking. It standardizes chronological point-wise autoregressive supervision, multi-feedback evaluation, model implementations, and distributed training in one reproducible pipeline.
+
+This repository hosts preprocessed datasets from short-video, advertising, and e-commerce scenarios used in the benchmark.
+
+## Datasets
+
+The UniRank benchmark evaluates models on five large-scale industrial datasets:
+
+| Dataset | Instances | Users | Items | Fields | Tasks | Avg. length | Max. length |
+|:--|--:|--:|--:|--:|--:|--:|--:|
+| QK-Video | 493,306,303 | 4,996,176 | 3,752,235 | 10 | 4 | 99 | 6,013 |
+| KuaiRand | 323,464,444 | 27,285 | 32,038,725 | 40 | 6 | 11,855 | 228,030 |
+| TAAC-25 | 757,207,146 | 7,706,778 | 15,707,425 | 30 | 2 | 98 | 100 |
+| Taobao | 23,601,301 | 470,570 | 831,643 | 23 | 4 | 50 | 3,756 |
+| MerRec | 172,304,959 | 1,697,072 | 42,577,610 | 20 | 5 | 102 | 26,576 |
+
+## Quick Start
+
+You can download a ready-to-use dataset from the UniRank benchmark via the Hugging Face CLI:
+
+```bash
+# Example: Download the KuaiRand dataset
+hf download salmon1802/KuaiRand \
+  --repo-type dataset \
+  --local-dir /path/to/data/KuaiRand_Video_Action
+```
+
+Please refer to the [GitHub repository](https://github.com/salmon1802/UniRank) for complete instructions on training, evaluating, and extending the benchmark.
+
+## Citation
+
+If you use this benchmark or dataset in your research, please cite the UniRank paper:
+
+```bibtex
+@article{unirank2026,
+  title={UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction},
+  author={UniRank Authors},
+  journal={arXiv preprint arXiv:2607.19987},
+  year={2026}
+}
+```