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---
dataset_info:
features:
- name: law_code
dtype: string
- name: law_name
dtype: string
- name: section_num
dtype: string
- name: section_content
dtype: string
- name: reference
list:
- name: include
dtype: bool
- name: law_name
dtype: string
- name: section_num
dtype: string
splits:
- name: ccl
num_bytes: 8145015
num_examples: 5127
download_size: 1777237
dataset_size: 8145015
configs:
- config_name: default
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="ccl")
# 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(f"row['law_code']-row['section_num']")
# ... 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**. |