README.md

---
license: apache-2.0
language:
- vi
tags:
- legal
- corpus
- pre-training
- legal-documents
- vilegallm
- 16GB
- vietnamese
pretty_name: ViLegalText
size_categories:
- 100K<n<1M
---

# ViLegalTexts: A 16GB Vietnamese Legal Pre-training Corpus

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![HuggingFace](https://img.shields.io/badge/🤗%20HuggingFace-ntphuc149%2FViLegalText-yellow)](https://huggingface.co/datasets/ntphuc149/ViLegalText)

This repository contains the pre-training corpus used to train **ViLegalLM**. The corpus was crawled from four publicly available Vietnamese legal repositories. For full details, please refer to the paper: [Read paper](https://aclanthology.org/2026.findings-acl.1801/)

---

## 📋 Overview

| Property | Value |
|---|---|
| Language | Vietnamese |
| Domain | Legal |
| Corpus size | **16 GB** |
| Format | `.zip` containing multiple `.txt` files |
| Sources | 4 public Vietnamese legal repositories |
| License | Apache 2.0 |

---

## 🔧 Data Processing Pipeline

The raw corpus underwent a 4-stage preprocessing pipeline:

**Content Extraction → Text Cleaning → Language Identification → Deduplication**

For full details of each stage, please refer to the [paper](https://aclanthology.org/2026.findings-acl.1801/).

The final corpus is distributed as `.zip` files, each containing multiple `.txt` files (one document per file), with a total uncompressed size of **16 GB**.

---

## 🚀 Usage

### Clone this repository

```bash
apt-get install -y git-lfs
git lfs install

git clone https://your_username:YOUR_HF_TOKEN@huggingface.co/datasets/ntphuc149/ViLegalText
```

### Unzip pre-training corpus

```python
import os

os.makedirs("/process", exist_ok=True)
zip_path = f"/ViLegalText/ViLegalText.zip"
print(f"📦 Giải nén: ViLegalText.zip")
os.system(f'unzip -q "{zip_path}" "*.txt" -d /process/')

print("✅ Xong!")
```

### Preview a sample file

```python
with open("/process/doc_0.txt", encoding="utf-8") as f:
    print(f.read())
```

---

## 🤖 Models Trained on This Corpus

This corpus was used to continually pretrain the following models, all publicly available:

| Model | Architecture | Parameters | Max Length | HuggingFace |
|---|---|---|---|---|
| ViLegalBERT | Encoder-only (MLM) | 135M | 256 | [ntphuc149/ViLegalBERT](https://huggingface.co/ntphuc149/ViLegalBERT) |
| ViLegalQwen2.5-1.5B-Base | Decoder-only (CLM) | 1.54B | 2,048 | [ntphuc149/ViLegalQwen2.5-1.5B-Base](https://huggingface.co/ntphuc149/ViLegalQwen2.5-1.5B-Base) |
| ViLegalQwen3-1.7B-Base | Decoder-only (CLM) | 1.72B | 4,096 | [ntphuc149/ViLegalQwen3-1.7B-Base](https://huggingface.co/ntphuc149/ViLegalQwen3-1.7B-Base) |

---

## 📄 Citation

If you use this corpus in your research, please cite:

```bibtex
@inproceedings{nguyen-etal-2026-vilegallm,
    title = "{V}i{L}egal{LM}: Language Models for {V}ietnamese Legal Text",
    author = "Nguyen, Truong-Phuc  and
      Nguyen, Quy-Nhan  and
      Nguyen, Minh-Tien",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.1801/",
    pages = "36136--36150",
    ISBN = "979-8-89176-395-1",
    abstract = "We present **ViLegalLM**, comprising **ViLegalBERT** and **ViLegalQwen**, the first suite of Vietnamese pretrained language models for legal text understanding and generation. It includes one encoder-only model (ViLegalBERT, 135M parameters) and two decoder-only models (ViLegalQwen2.5-1.5B-Base and ViLegalQwen3-1.7B-Base), all continually pretrained on a newly curated 16GB Vietnamese legal corpus, significantly larger than previous work. To mitigate data scarcity, we construct three synthetic datasets using LLM-based generation and hard negative mining for True/False QA, Multiple Choice QA, and Natural Language Inference. We establish state-of-the-art results among open-source models on four main Vietnamese legal downstream tasks spanning ten benchmarks, demonstrating that continual pretraining from base models consistently outperforms instruction-tuned adaptation. Source codes, corpus, datasets, and model checkpoints are publicly available at https://github.com/ntphuc149/ViLegalLM."
}
```

---

## ⚠️ Intended Use & Limitations

- This corpus is intended for **research purposes** in Vietnamese legal NLP.
- Documents are collected from **publicly available** Vietnamese legal repositories. No personally identifiable information is included.
- Models trained on this corpus are **not intended to replace professional legal counsel**. Users should not rely solely on model outputs for consequential legal decisions without qualified professional consultation.
- The corpus may inherit biases present in the source repositories, including temporal bias, regional variations, and domain coverage imbalances.

---

## 🔗 Related Resources

- **GitHub**: [ViLegalLM](https://github.com/ntphuc149/ViLegalLM)
- **ViLegalLM Models**: [ViLegalBERT](https://huggingface.co/ntphuc149/ViLegalBERT), [ViLegalQwen2.5-1.5B-Base](https://huggingface.co/ntphuc149/ViLegalQwen2.5-1.5B-Base), [ViLegalQwen3-1.7B-Base](https://huggingface.co/ntphuc149/ViLegalQwen3-1.7B-Base), 
- **Synthetic Datasets**: [ViLegalTF](https://huggingface.co/datasets/ntphuc149/ViLegalTF), [ViLegalMCQ](https://huggingface.co/datasets/ntphuc149/ViLegalMCQ), [ViLegalNLI](https://huggingface.co/datasets/ntphuc149/ViLegalNLI)