---
title: PLawBench
canonical_url: "https://www.modelscope.cn/datasets/evalscope/PLawBench"
md_url: "https://www.modelscope.cn/datasets/evalscope/PLawBench.md"
repository: evalscope/PLawBench
last_updated: 2026-08-06
license: apache-2.0
downloads: 465
stars: 0
---

# PLawBench

> PLawBench - evalscope 在 ModelScope 开源的数据集。PLawBench is a rubric-based benchmark for evaluating large language models on real-world Chinese legal practice. It covers the three hierarchical levels of a practitioner's workflow: public legal consultation,…

evalscope/PLawBench 是 ModelScope 魔搭社区上的数据集，采用 apache-2.0 许可。

- **Repository**: evalscope/PLawBench
- **License**: apache-2.0
- **Downloads**: 465
- **Stars**: 0
- **Last updated**: 2026-08-06

Source: https://www.modelscope.cn/datasets/evalscope/PLawBench

---

# PLawBench

PLawBench is a rubric-based benchmark for evaluating large language models on real-world Chinese legal practice.
It covers the three hierarchical levels of a practitioner's workflow: public legal consultation, practical case
analysis, and legal document drafting.

This repository re-packages the official release
([skylenage/PLawbench](https://github.com/skylenage/PLawbench)) into a ModelScope dataset with one config per task,
so each PLawBench task can be loaded as an independent subset.

## Subsets

| Config (subset) | Split | Samples | PLawBench task |
| --- | --- | --- | --- |
| `case_analysis` | `test` | 250 | Practical case analysis (conclusion / facts / reasoning / statutes) |
| `legal_consultation` | `test` | 18 | Public legal consultation (fact-eliciting question lists, "mid" difficulty) |
| `plaintiff_statement` | `test` | 6 | Legal document drafting - statement of complaint |
| `defendant_statement` | `test` | 6 | Legal document drafting - statement of defense |

## Fields

| Field | Type | Description |
| --- | --- | --- |
| `id` | string | Unique sample id, prefixed with the task name. |
| `task` | string | Task / subset name. |
| `judge_type` | string | Rubric grading protocol: `case_analysis`, `legal_qa`, or `document_generation`. |
| `category` | string | Legal domain label (`case_analysis`) or cause-of-action tag (drafting tasks). |
| `context` | string | Case background, only populated for `case_analysis`. |
| `question` | string | The question, or the client's statement for consultation / drafting tasks. |
| `rubrics` | string | Expert-annotated rubric. A JSON array string for `case_analysis`, plain text otherwise. |
| `max_points` | int64 | Total points available for the sample. |

## Usage

```python
from modelscope import MsDataset

ds = MsDataset.load('evalscope/PLawBench', subset_name='case_analysis', split='test')
```

Or evaluate directly with EvalScope:

```bash
evalscope eval --model <your-model> --datasets plawbench --judge-strategy llm
```

## Citation

Please cite the original PLawBench release: https://github.com/skylenage/PLawbench