Update README.md

README.md

@@ -28,4 +28,107 @@ configs:
   data_files:
   - split: ccl
     path: data/ccl-*
+license: mit
 ---
+
+# 📜 NitiBench-Statute: Thai Legal Corpus for RAG
+
+**Part of the [NitiBench Project](https://github.com/vistec-AI/nitibench/)**
+
+This dataset contains the complete corpus of legal sections used in the **NitiBench** benchmark (CCL and Tax subset). It comprises **5,127 legal sections** extracted from **35 Thai legislations** (primarily focusing on Corporate and Commercial Law).
+
+It is designed to be used as a **Context Pool (Knowledge Base)** for Retrieval-Augmented Generation (RAG) pipelines. Researchers and developers can load this dataset to populate vector databases or search indices to reproduce NitiBench baselines or evaluate new retrieval strategies.
+
+## 🚀 Quick Start
+
+### Loading the Dataset
+You can easily load this dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the statute corpus
+dataset = load_dataset("vistec-AI/nitibench-statute", split="train")
+
+# Example: Print the first section
+print(dataset[0])
+```
+
+### Usage for RAG (Context Pool)
+To use this as a retrieval source, you typically iterate through the `section_content` to create embeddings:
+
+```python
+documents = []
+ids = []
+
+for row in dataset:
+    # Use 'section_content' as the text chunk to be indexed
+    documents.append(row['section_content'])
+    # Use 'law_code' or a combination of name+section as ID
+    ids.append(row['law_code'])
+
+# ... Proceed to pass `documents` to your VectorDB or Retriever (e.g., FAISS, ChromaDB, BM25)
+```
+
+## 📊 Dataset Statistics
+
+*   **Total Documents:** 5,127 sections
+*   **Total Legislations:** 35 Legislation (Corporate and Commercial Law)
+*   **Language:** Thai
+
+## 📂 Data Structure
+
+Each row represents a specific section of a law.
+
+| Column Name | Type | Description |
+|:--- |:--- |:--- |
+| `law_code` | `str` | Unique identifier for the specific law section (e.g., `ก0123-1B-0001`). |
+| `law_name` | `str` | The official full name of the legislation (e.g., `พระราชบัญญัติการประกอบกิจการพลังงาน พ.ศ. 2550`). |
+| `section_num` | `str` | The specific section number within the Act (e.g., `26`). |
+| `section_content` | `str` | The full text content to be used for retrieval. This includes the law name, section number, and the provision text combined. |
+| `reference` | `list` | A list of cross-references to other laws (if applicable). |
+
+### Example Data Point
+```json
+{
+ "law_code": "ก0123-1B-0001",
+ "law_name": "พระราชบัญญัติการประกอบกิจการพลังงาน พ.ศ. 2550",
+ "section_num": "26",
+ "section_content": "พระราชบัญญัติการประกอบกิจการพลังงาน พ.ศ. 2550 มาตรา 26 ก่อนการออกระเบียบ ข้อบังคับ ประกาศ หรือข้อกำหนดใดของคณะกรรมการซึ่งจะมีผลกระทบต่อบุคคล...",
+ "reference": []
+}
+```
+
+## 📝 Citation
+
+If you use this dataset in your research, please cite the NitiBench paper:
+
+```bibtex
+@inproceedings{akarajaradwong-etal-2025-nitibench,
+    title = "{N}iti{B}ench: Benchmarking {LLM} Frameworks on {T}hai Legal Question Answering Capabilities",
+    author = "Akarajaradwong, Pawitsapak  and
+      Pothavorn, Pirat  and
+      Chaksangchaichot, Chompakorn  and
+      Tasawong, Panuthep  and
+      Nopparatbundit, Thitiwat  and
+      Pratai, Keerakiat  and
+      Nutanong, Sarana",
+    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2025",
+    publisher = "Association for Computational Linguistics",
+}
+
+@misc{akarajaradwong2025nitibenchcomprehensivestudiesllm,
+      title={NitiBench: A Comprehensive Studies of LLM Frameworks Capabilities for Thai Legal Question Answering}, 
+      author={Pawitsapak Akarajaradwong and Pirat Pothavorn and Chompakorn Chaksangchaichot and Panuthep Tasawong and Thitiwat Nopparatbundit and Sarana Nutanong},
+      year={2025},
+      eprint={2502.10868},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.10868}, 
+}
+```
+
+## ⚖️ License
+This dataset is provided under the **MIT License**.