task_categories:text-classification annotations_creators:machine-generated language_creators:expert-generated multilinguality:multilingual size_categories:10K<n<100K source_datasets:original language:de language:it language:fr license:cc-by-sa-4.0 arxiv:2306.09237 region:us
---
license: cc-by-sa-4.0
language:
- de
- it
- fr
size_categories:
- 10K<n<100K
annotations_creators:
- machine-generated
language_creators:
- expert-generated
multilinguality:
- multilingual
source_datasets:
- original
task_categories:
- text-classification
pretty_name: Swiss Leading Decisions
---
# Dataset Card for Swiss Leading Decisions

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

Swiss Leading Decisions is a multilingual, diachronic dataset of 21K Swiss Federal Supreme Court (FSCS) cases. This dataset is part of a challenging text classification task. We also provide additional metadata as the publication year, the law area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.

### Supported Tasks and Leaderboards

Swiss Leading Decisions hepled in a text classification task

### Languages

Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.


| Language   | Subset     | Number of Documents  | 
|------------|------------|----------------------|  
| German     | **de**     | 14K                  |
| French     | **fr**     | 6K                   |
| Italian    | **it**     | 1K                   |

## Dataset Structure

### Data Fields

```
decision_id: (str) a unique identifier of the for the document
language: (int64) one of (0,1,2)
chamber_id: (int64) id to identfy chamber
file_id: (int64) id to identify file
date: (int64)
topic: (string)
year: (float64)
language: (string)
facts: (string) text section of the full text
facts_num_tokens_bert: (int64)
facts_num_tokens_spacy: (int64)
considerations: (string) text section of the full text
considerations_num_tokens_bert: (int64)
considerations_num_tokens_spacy: (int64)
rulings: (string)  text section of the full text
rulings_num_tokens_bert: (int64)
rulings_num_tokens_spacy: (int64)
chamber (string):
court: (string)
canton: (string)
region: (string)
file_name: (string)
html_url: (string)
pdf_url: (string)
file_number: (string)
```

### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
## Dataset Creation
### Curation Rationale

The dataset was created by Stern (2023).

### Source Data
#### Initial Data Collection and Normalization

The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. 

#### Who are the source language producers?

The decisions are written by the judges and clerks in the language of the proceedings.

### Annotations
#### Annotation process

#### Who are the annotators?

Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).

### Personal and Sensitive Information

The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.

## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information

We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022

The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf

### Citation Information

Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
      title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation}, 
      author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
      year={2023},
      eprint={2306.09237},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

### Contributions

Thanks to [@Stern5497](https://github.com/stern5497) for adding this dataset.