Improve dataset card: Add task category, update paper link, and add sample usage (#1)

- Improve dataset card: Add task category, update paper link, and add sample usage (13e13196a38214e065c7780a7b1391909c842e7e)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,14 +1,15 @@
 ---
+license: mit
+task_categories:
+- table-question-answering
 configs:
 - config_name: table
-  data_files: "sqa_table.jsonl"
+  data_files: sqa_table.jsonl
 - config_name: test_query
-  data_files: "sqa_query.jsonl"
-
-license: mit
+  data_files: sqa_query.jsonl
 ---
 
-📄 [Paper](https://arxiv.org/abs/2504.01346) | 👨🏻‍💻 [Code](https://github.com/jiaruzouu/T-RAG)
+📄 [Paper](https://huggingface.co/papers/2504.01346) | 👨🏻‍💻 [Code](https://github.com/jiaruzouu/T-RAG)
 
 For MultiTableQA, we release a comprehensive benchmark, including five different datasets covering table fact-checking, single-hop QA, and multi-hop QA: 
 | Dataset              | Link |
@@ -22,14 +23,40 @@ For MultiTableQA, we release a comprehensive benchmark, including five different
 
 MultiTableQA extends the traditional single-table QA setting into a multi-table retrieval and question answering benchmark, enabling more realistic and challenging evaluations.
 
+## Sample Usage
+
+This dataset (`MultiTableQA-SQA`) is part of the larger **MultiTableQA** benchmark. To prepare the full benchmark, you can follow these steps from the official T-RAG GitHub repository.
+
+First, clone the repository and set up the environment:
+
+```bash
+git clone https://github.com/jiaruzouu/T-RAG.git
+cd T-RAG
+
+conda create -n trag python=3.11.9
+conda activate trag
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+Then, navigate to the `table2graph` directory and run the data preparation script:
+
+```bash
+cd table2graph
+bash scripts/prepare_data.sh
+```
+
+This script will automatically fetch the source tables, apply decomposition (row/column splitting), and generate the benchmark splits, including the `MultiTableQA-SQA` data available in this repository.
+
 ---
 # Citation
 
 If you find our work useful, please cite:
 
 ```bibtex
-@misc{zou2025gtrgraphtableragcrosstablequestion,
-      title={GTR: Graph-Table-RAG for Cross-Table Question Answering}, 
+@misc{zou2025rag,
+      title={RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking}, 
       author={Jiaru Zou and Dongqi Fu and Sirui Chen and Xinrui He and Zihao Li and Yada Zhu and Jiawei Han and Jingrui He},
       year={2025},
       eprint={2504.01346},