README.md

---
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**.