---
language:
- zh
tags:
- law
- legal
- judgment-prediction
- chinese
- multi-defendant
task_categories:
- text-classification
- text-generation
pretty_name: Explainable Multidefendant Judgment Prediction (Chinese Criminal Judgments)
size_categories:
- unknown
---

# Explainable Multidefendant Judgment Prediction Dataset

> ⚠️ **Current status / 当前版本说明**  
> The current release of `SHerZH/IMLJP` is a **small subset** of the full dataset.  
> It is intended **only for visualizing the data structure and format**, and for small-scale experiments.  
> A larger / full version of the dataset may be released in future once further cleaning, de-identification, and checks are completed.  
> 当前版本仅包含**一小部分样例数据**，用于展示数据结构与字段格式，方便他人编写和调试预处理代码。  
> 完整或更大规模的数据集将在后续经过进一步清洗与审查后视情况逐步开放。

This repository hosts the **processed dataset** used in the paper:

> *"Explainable Multidefendant Judgment Prediction Enhanced by Judicial Logic Based on Large Language Models"*

The dataset consists of **Chinese criminal judgments** involving **multiple defendants**, with structured labels for each defendant (e.g., principal/accomplice, prison term, probation), and textual descriptions of case facts and court reasoning.

The dataset is designed for **judgment prediction**, **explainable legal AI**, and **multi-defendant reasoning** in Chinese criminal law.

> 🇨🇳 简要中文说明：  
> 本数据集选取了中国刑事案件中的多被告人判决书，抽取并脱敏了“事实摘要（FD）”、“裁判说理片段（CV）”以及按被告人划分的量刑、角色等标签。  
> **当前公开版本仅为小规模样例集**，主要用于展示数据结构和字段含义，便于复现与扩展。后续可能根据数据清洗和隐私审查情况，逐步开放更大规模数据。

---

## 1. Repository & Code

- **Code implementation** for the paper (model, training, experiments) is provided in the GitHub repository:

  👉 [GitHub: *Explainable Multidefendant Judgment Prediction Enhanced by Judicial Logic Based on Large Language Models*](https://github.com/XuZhang29/MMSI)  <!-- 请改成你自己的 GitHub 链接 -->

The Hugging Face dataset `SHerZH/IMLJP` is mainly intended to:

1. Provide a **public example** of the data format (`FD`, `CV`, `judgment`).
2. Help users understand how to **construct their own datasets** in the same structure.
3. Support **lightweight tests and visualization** of models and preprocessing pipelines.

In future, we plan to release a **larger / full dataset** version (with the same structure) once all ethical, privacy, and legal considerations are carefully addressed.