
# Dataset Card for openlegaldata.io bulk case data

## Dataset Description

This is a edit/cleanup of Bulk Data of [openlegaldata.io](https://de.openlegaldata.io/), which I also brought onto Huggingface [here](LennardZuendorf/openlegaldata-bulk-data).

#### The Entire Dataset Is In German

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

## Edit Summary

I have done some cleaning and splitting of the data and filtered out large parts that were not (easily) usable, cutting down the number of cases to at max 4000 - from 250000. This results in two different splits. Which is because German Courts don't format their case decision the same way.

### Data Fields

Independent of the split, most fields are the same, they are:

| id | court | file_number | date | type | content
| - | - | - | - | - | - |
| 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

Additionally, I added 3 more fields because of the splitting of the content:

#### Two Split

- Case Decision I could split into two parts: tenor and reasoning.
- Which means the three fields tenor, content and facts contain the following:

| tenor | reasoning | facts
| - | - | - |
| An abstract, legal summary of the cases decision | the entire rest of the decision, explaining in detail why the decision has been made | an empty text field |

#### Three Split

- Case Decision I could split into three parts: tenor, reasoning and facts
- This Data I have used to create binary labels with the help of ChatGPT, see [legalis](https://huggingface.co/datasets/LennardZuendorf/legalis) for that
- The three fields tenor, content and facts contain the following:

| tenor | reasoning | facts
| - | - | - |
| 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 |

### 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": "Edited German Court case decision", "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "court", "struct": [{"name": "id", "dtype": "int64"}, {"name": "jurisdiction", "dtype": "string"}, {"name": "level_of_appeal", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "state", "dtype": "int64"}]}, {"name": "file_number", "dtype": "string"}, {"name": "date", "dtype": "timestamp[s]"}, {"name": "type", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "tenor", "dtype": "string"}, {"name": "facts", "dtype": "string"}, {"name": "reasoning", "dtype": "string"}], "splits": [{"name": "three", "num_bytes": 169494251, "num_examples": 2828}, {"name": "two", "num_bytes": 183816899, "num_examples": 4954}], "download_size": 172182482, "dataset_size": 353311150}, "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 edit/cleanup of Bulk Data of URL, which I also brought onto Huggingface here.


#### The Entire Dataset Is In German


* Github Repository: [uniArchive-legalis]](URL
* Repository: Bulk Data


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


I have done some cleaning and splitting of the data and filtered out large parts that were not (easily) usable, cutting down the number of cases to at max 4000 - from 250000. This results in two different splits. Which is because German Courts don't format their case decision the same way.


### Data Fields


Independent of the split, most fields are the same, they are:



Additionally, I added 3 more fields because of the splitting of the content:


#### Two Split


* Case Decision I could split into two parts: tenor and reasoning.
* Which means the three fields tenor, content and facts contain the following:


tenor: An abstract, legal summary of the cases decision, reasoning: the entire rest of the decision, explaining in detail why the decision has been made, facts: an empty text field


#### Three Split


* Case Decision I could split into three parts: tenor, reasoning and facts
* This Data I have used to create binary labels with the help of ChatGPT, see legalis for that
* The three fields tenor, content and facts contain the following:


tenor: An abstract, legal summary of the cases decision, reasoning: the entire rest of the decision, explaining in detail why the decision has been made, facts: the facts and details of a case


### 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
* Repository: Bulk Data


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


I have done some cleaning and splitting of the data and filtered out large parts that were not (easily) usable, cutting down the number of cases to at max 4000 - from 250000. This results in two different splits. Which is because German Courts don't format their case decision the same way. ### Data Fields


Independent of the split, most fields are the same, they are:



Additionally, I added 3 more fields because of the splitting of the content: #### Two Split


* Case Decision I could split into two parts: tenor and reasoning.
* Which means the three fields tenor, content and facts contain the following:


tenor: An abstract, legal summary of the cases decision, reasoning: the entire rest of the decision, explaining in detail why the decision has been made, facts: an empty text field #### Three Split


* Case Decision I could split into three parts: tenor, reasoning and facts
* This Data I have used to create binary labels with the help of ChatGPT, see legalis for that
* The three fields tenor, content and facts contain the following:


tenor: An abstract, legal summary of the cases decision, reasoning: the entire rest of the decision, explaining in detail why the decision has been made, facts: the facts and details of a case ### 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
* Repository: Bulk Data


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


I have done some cleaning and splitting of the data and filtered out large parts that were not (easily) usable, cutting down the number of cases to at max 4000 - from 250000. This results in two different splits. Which is because German Courts don't format their case decision the same way. ### Data Fields


Independent of the split, most fields are the same, they are:



Additionally, I added 3 more fields because of the splitting of the content: #### Two Split


* Case Decision I could split into two parts: tenor and reasoning.
* Which means the three fields tenor, content and facts contain the following:


tenor: An abstract, legal summary of the cases decision, reasoning: the entire rest of the decision, explaining in detail why the decision has been made, facts: an empty text field #### Three Split


* Case Decision I could split into three parts: tenor, reasoning and facts
* This Data I have used to create binary labels with the help of ChatGPT, see legalis for that
* The three fields tenor, content and facts contain the following:


tenor: An abstract, legal summary of the cases decision, reasoning: the entire rest of the decision, explaining in detail why the decision has been made, facts: the facts and details of a case ### 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.