
# 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.
task_categories:text-classification size_categories:1K<n<10K language:de license:mit legal region:us
{"language": ["de"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "labeled German Court case decisions", "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}, "tags": ["legal"]}
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-German #license-mit #legal #region-us 

Dataset Card for URL bulk case data
===================================


Dataset Description
-------------------


This is a labeled version of my already edited data from URL.


#### The Entire Dataset Is In German


* Github Repository: [uniArchive-legalis]](URL
* Processed Data: openlegaldata-processed
* Original Bulk Data: Bulk Data


Edit Summary
------------


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


### Data Fields



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



### Languages


* German


Additional Information
----------------------


### Licensing/Citation Information


The openlegaldata platform is licensed under the MIT license, you can access the dataset by citing the original source, URL and me, Lennard Zündorf as the editor of this dataset.



#### The Entire Dataset Is In German


* Github Repository: [uniArchive-legalis]](URL
* Processed Data: openlegaldata-processed
* Original Bulk Data: Bulk Data


Edit Summary
------------


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



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 ### Languages


* German


Additional Information
---------------------- ### Licensing/Citation Information


The openlegaldata platform is licensed under the MIT license, you can access the dataset by citing the original source, URL and me, Lennard Zündorf as the editor of this dataset.
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-German #license-mit #legal #region-us 
 #### The Entire Dataset Is In German


* Github Repository: [uniArchive-legalis]](URL
* Processed Data: openlegaldata-processed
* Original Bulk Data: Bulk Data


Edit Summary
------------


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



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 ### Languages


* German


Additional Information
---------------------- ### Licensing/Citation Information


The openlegaldata platform is licensed under the MIT license, you can access the dataset by citing the original source, URL and me, Lennard Zündorf as the editor of this dataset.
passage: TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-German #license-mit #legal #region-us 
#### The Entire Dataset Is In German


* Github Repository: [uniArchive-legalis]](URL
* Processed Data: openlegaldata-processed
* Original Bulk Data: Bulk Data


Edit Summary
------------


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



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### Languages


* German


Additional Information
----------------------### Licensing/Citation Information


The openlegaldata platform is licensed under the MIT license, you can access the dataset by citing the original source, URL and me, Lennard Zündorf as the editor of this dataset.