Improve dataset card: Add task category, sample usage, and descriptive intro

This PR enhances the dataset card for `MultiTableQA-HybridQA` by:

- Adding `task_categories: ['table-question-answering']` to the metadata, improving discoverability.
- Including a descriptive introductory sentence to clarify that this repository hosts the `MultiTableQA-HybridQA` dataset, part of the larger MultiTableQA benchmark.
- Incorporating a comprehensive "Sample Usage" section with installation instructions, data preparation steps, and examples for running T-RAG retrieval and downstream LLM inference, directly extracted from the associated `T-RAG` GitHub repository.
- Updating the BibTeX citation to reflect the correct paper title as provided in the GitHub repository.

README.md

@@ -1,15 +1,19 @@
 ---
+license: mit
 configs:
 - config_name: table
-  data_files: "hybridqa_table.jsonl"
+  data_files: hybridqa_table.jsonl
 - config_name: test_query
-  data_files: "hybridqa_query.jsonl"
-
-license: mit
+  data_files: hybridqa_query.jsonl
+task_categories:
+- table-question-answering
 ---
+
+This Hugging Face dataset repository contains **MultiTableQA-HybridQA**, one of the datasets released as part of the comprehensive **MultiTableQA** benchmark, introduced in the paper [RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking](https://arxiv.org/abs/2504.01346).
+
 📄 [Paper](https://arxiv.org/abs/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: 
+For MultiTableQA, we release a comprehensive benchmark, including five different datasets covering table fact-checking, single-hop QA, and multi-hop QA:
 | Dataset              | Link |
 |-----------------------|------|
 | MultiTableQA-TATQA    | 🤗 [dataset link](https://huggingface.co/datasets/jiaruz2/MultiTableQA_TATQA)   |
@@ -21,19 +25,93 @@ 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 section provides a quick guide to setting up the environment, preparing the MultiTableQA data, running T-RAG retrieval, and performing downstream inference with LLMs, based on the official [T-RAG GitHub repository](https://github.com/jiaruzouu/T-RAG).
+
+#### 1. Installation
+
+First, clone the repository and install the necessary dependencies:
+
+```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
+```
+
+#### 2. MultiTableQA Data Preparation
+
+To download and preprocess the MultiTableQA benchmark:
+
+```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.
+
+#### 3. Run T-RAG Retrieval
+
+To run hierarchical index construction and multi-stage retrieval:
+
+**Stage 1 & 2: Table to Graph Construction & Coarse-grained Multi-way Retrieval**
+
+```bash
+cd src
+cd table2graph
+bash scripts/table_cluster_run.sh #  or python scripts/table_cluster_run.py
+```
+
+**Stage 3: Fine-grained sub-graph Retrieval**
+
+```bash
+cd src
+cd table2graph
+python scripts/subgraph_retrieve_run.py
+```
+
+*Note: Our method supports different embedding methods such as E5, contriever, sentence-transformer, etc.*
+
+#### 4. Downstream Inference with LLMs
+
+Evaluate T-RAG with an (open/closed-source) LLM of your choice (e.g., GPT-4o, Claude-3.5, Qwen):
+
+For Closed-source LLM, please first insert your key under `key.json`:
+```json
+{
+    "openai": "<YOUR_OPENAI_API_KEY>",
+    "claude": "<YOUR_CLAUDE_API_KEY>"
+}
+```
+
+To run end-to-end model inference and evaluation:
+
+```bash
+cd src
+cd downstream_inference
+bash scripts/overall_run.sh
+```
+
 ---
 # 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},
       archivePrefix={arXiv},
       primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2504.01346}, 
+      url={https://arxiv.org/abs/2504.01346},
 }
-```
+```