---
title: DISC-Law-SFT
canonical_url: "https://www.modelscope.cn/datasets/AI-ModelScope/DISC-Law-SFT"
md_url: "https://www.modelscope.cn/datasets/AI-ModelScope/DISC-Law-SFT.md"
repository: AI-ModelScope/DISC-Law-SFT
last_updated: 2024-09-02
license: "Apache License 2.0"
storage_size: "375 MB"
domain:
  - configs
  - size_categories
  - tasks
  - language
tasks:
  - "{config_name=triplet, data_files=[{path=DISC-Law-SFT-Triplet-released.csv, split=train}]}"
  - "100M<n<1B"
  - chat
  - zh
downloads: 4761
stars: 13
---

# DISC-Law-SFT

> DISC-Law-SFT - AI-ModelScope 在 ModelScope 开源的数据集。本仓库拷贝自: https://huggingface.co/datasets/ShengbinYue/DISC-Law-SFT

AI-ModelScope/DISC-Law-SFT 是 ModelScope 魔搭社区上的{config_name=triplet, data_files=[{path=DISC-Law-SFT-Triplet-released.csv, split=train}]}、100M<n<1B、chat数据集，涉及 configs、size_categories、tasks 领域，存储大小 375 MB，采用 Apache License 2.0 许可。

- **Repository**: AI-ModelScope/DISC-Law-SFT
- **License**: Apache License 2.0
- **Tasks**: {config_name=triplet, data_files=[{path=DISC-Law-SFT-Triplet-released.csv, split=train}]}, 100M<n<1B, chat, zh
- **Domain**: configs, size_categories, tasks, language
- **Storage size**: 375 MB
- **Downloads**: 4761
- **Stars**: 13
- **Last updated**: 2024-09-02

Source: https://www.modelscope.cn/datasets/AI-ModelScope/DISC-Law-SFT

---

本仓库拷贝自: https://huggingface.co/datasets/ShengbinYue/DISC-Law-SFT

可以通过modelscope SDK下载
```python
from modelscope import MsDataset

train_dataset = MsDataset.load('AI-ModelScope/DISC-Law-SFT', 
                               subset_name='default', split='train').to_hf_dataset()
print(train_dataset)
"""Out[0]
Dataset({
    features: ['Unnamed: 0', 'id', 'input', 'output'],
    num_rows: 166758
})
"""
```

# DISC-Law-SFT Dataset

Legal Intelligent systems in Chinese require a combination of various abilities, including legal text understanding and generation. To achieve this, we have constructed a high-quality supervised fine-tuning dataset called DISC-Law-SFT, which covers different legal scenarios such as legal information extraction, legal judgment prediction, legal document summarization, and legal question answering. DISC-Law-SFT comprises two subsets, DISC-Law-SFT-Pair and DISC-Law-SFT-Triplet. The former aims to introduce legal reasoning abilities to the LLM, while the latter helps enhance the model's capability to utilize external legal knowledge. For more detailed information, please refer to our [technical report](https://arxiv.org/abs/2309.11325). The distribution of the dataset is:

<img src="" alt="" width=""/>

<table>
  <tr>
    <th>Dataset</th>
    <th>Task/Source</th>
    <th>Size</th>
    <th>Scenario</th>
  </tr>
  <tr>
    <td rowspan="10">DISC-Law-SFT-Pair</td>
    <td>Legal information extraction</td>
    <td>32K</td>
    <td rowspan="7">Legal professional assistant</td>
  </tr>
  <tr>
    <td>Legal event detection</td>
    <td>27K</td>
  </tr>
  <tr>
    <td>Legal case classification</td>
    <td>20K</td>
  </tr>
  <tr>
    <td>Legal judgement prediction</td>
    <td>11K</td>
  </tr>
  <tr>
    <td>Legal case matching</td>
    <td>8K</td>
  </tr>
  <tr>
    <td>Legal text summarization</td>
    <td>9K</td>
  </tr>
  <tr>
    <td>Judicial public opinion summarization</td>
    <td>6K</td>
  </tr>
  <tr>
    <td>Legal question answering</td>
    <td>93K</td>
    <td>Legal consultation services</td>
  </tr>
  <tr>
    <td>Legal reading comprehension</td>
    <td>38K</td>
    <td rowspan="2">Judicial examination assistant</td>
  </tr>
  <tr>
    <td>Judicial examination</td>
    <td>12K</td>
  </tr>
  <tr>
    <td rowspan="2">DISC-Law-SFT-Triple</td>
    <td>Legal judgement prediction</td>
    <td>16K</td>
    <td>Legal professional assistant</td>
  </tr>
  <tr>
    <td>Legal question answering</td>
    <td>23K</td>
    <td>Legal consultation services</td>
  </tr>
  <tr>
    <td rowspan="2">General</td>
    <td>Alpaca-GPT4</td>
    <td>48K</td>
    <td rowspan="2">General scenarios</td>
  </tr>
  <tr>
    <td>Firefly</td>
    <td>60K</td>
  </tr>
  <tr>
    <td>Total</td>
    <td colspan="3">403K</td>
  </tr>
</table>

We currently open-source most of the DISC-Law-SFT Dataset.

More detail and news check our [homepage](https://github.com/FudanDISC/DISC-LawLLM) !