task_categories:text-classification size_categories:1K<n<10K language:de license:mit legal region:us
---
license: mit
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: file_number
    dtype: string
  - name: date
    dtype: timestamp[us]
  - name: type
    dtype: string
  - name: content
    dtype: string
  - name: tenor
    dtype: string
  - name: facts
    dtype: string
  - name: reasoning
    dtype: string
  - name: winner
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 159271707.27722773
    num_examples: 2660
  - name: test
    num_bytes: 8442598.017326733
    num_examples: 141
  download_size: 83977470
  dataset_size: 167714305.29455447
task_categories:
- text-classification
language:
- de
tags:
- legal
pretty_name: labeled German Court case decisions
size_categories:
- 1K<n<10K
---

# Dataset Card for openlegaldata.io bulk case data

## Dataset Description

This is a labeled version of my already edited data from [openlegaldata.io](https://de.openlegaldata.io/).

#### The Entire Dataset Is In German

- **Github Repository:** [uniArchive-legalis]](https://github.com/LennardZuendorf/uniArchive-legalis)
- **Processed Data:** [openlegaldata-processed](https://huggingface.co/datasets/LennardZuendorf/openlegaldata-processed)
- **Original Bulk Data:** [Bulk Data](https://static.openlegaldata.io/dumps/de/)

## Edit Summary

- This Data is based on already processed data from openlegaldata. Repositories for both can be found on Huggingface (links above).

### Data Fields

| id | court | file_number | date | type | content | tenor | reasoning | facts | 
| - | - | - | - | - | - | - | - | - |
| numeric id | name of the court that made the decision | file number of the case ("Aktenzeichen") | decision date | type of the case decision | entire content (text) of the case decision | An abstract, legal summary of the cases decision | the entire rest of the decision, explaining in detail why the decision has been made | the facts and details of a case |

Additionally, I have added 2 field that label the data

#### label fields

- The labels are created using ChatGPT to extract/summarize the tenor (the summary of the decision) down to a winner. **This might lead to errors**. While I have checked the data occasionally, I have not check every single decision of the 2800 cases. But for my project, which was a proof of concept for University this is more than enough.
- see Github for the used Jupyter Notebook

| winner | label |
| - | - |
| Winner in text form - plaintiff("Kläger*in") or defendent ("Verklagte*r") | binary label: 1 if plaintiff won, 0 if defendent won |

### Languages

- German

## Additional Information

### Licensing/Citation Information

The [openlegaldata platform](https://github.com/openlegaldata/oldp) is licensed under the MIT license, you can access the dataset by citing the original source, [openlegaldata.io](https://de.openlegaldata.io/) and me, [Lennard Zündorf](https://github.com/LennardZuendorf) as the editor of this dataset.