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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: id |
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dtype: int64 |
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- name: file_number |
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dtype: string |
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- name: date |
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dtype: timestamp[us] |
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- name: type |
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dtype: string |
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- name: content |
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dtype: string |
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- name: tenor |
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dtype: string |
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- name: facts |
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dtype: string |
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- name: reasoning |
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dtype: string |
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- name: winner |
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dtype: string |
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- name: label |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 159271707.27722773 |
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num_examples: 2660 |
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- name: test |
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num_bytes: 8442598.017326733 |
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num_examples: 141 |
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download_size: 83977470 |
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dataset_size: 167714305.29455447 |
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task_categories: |
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- text-classification |
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language: |
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- de |
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tags: |
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- legal |
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pretty_name: labeled German Court case decisions |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Card for openlegaldata.io bulk case data |
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## Dataset Description |
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This is a labeled version of my already edited data from [openlegaldata.io](https://de.openlegaldata.io/). |
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#### The Entire Dataset Is In German |
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- **Github Repository:** [uniArchive-legalis]](https://github.com/LennardZuendorf/uniArchive-legalis) |
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- **Processed Data:** [openlegaldata-processed](https://huggingface.co/datasets/LennardZuendorf/openlegaldata-processed) |
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- **Original Bulk Data:** [Bulk Data](https://static.openlegaldata.io/dumps/de/) |
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## Edit Summary |
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- This Data is based on already processed data from openlegaldata. Repositories for both can be found on Huggingface (links above). |
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### Data Fields |
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| id | court | file_number | date | type | content | tenor | reasoning | facts | |
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| - | - | - | - | - | - | - | - | - | |
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| 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 | |
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Additionally, I have added 2 field that label the data |
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#### label fields |
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- 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. |
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- see Github for the used Jupyter Notebook |
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| winner | label | |
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| - | - | |
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| Winner in text form - plaintiff("Kläger*in") or defendent ("Verklagte*r") | binary label: 1 if plaintiff won, 0 if defendent won | |
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### Languages |
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- German |
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## Additional Information |
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### Licensing/Citation Information |
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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. |
