Datasets:

rcds
/

swiss_law_area_prediction

	---
	license: cc-by-sa-4.0
	annotations_creators:
	- machine-generated
	language:
	- de
	- fr
	- it
	language_creators:
	- expert-generated
	multilinguality:
	- multilingual
	pretty_name: Law Area Prediction
	size_categories:
	- 100K<n<1M
	source_datasets:
	- original
	task_categories:
	- text-classification
	---

	# Dataset Card for Law Area Prediction

	## 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

	The dataset contains cases to be classified into the four main areas of law: Public, Civil, Criminal and Social

	These can be classified further into sub-areas:
	```
	"public": ['Tax', 'Urban Planning and Environmental', 'Expropriation', 'Public Administration', 'Other Fiscal'],
	"civil": ['Rental and Lease', 'Employment Contract', 'Bankruptcy', 'Family', 'Competition and Antitrust', 'Intellectual Property'],
	'criminal': ['Substantive Criminal', 'Criminal Procedure']
	```

	### Supported Tasks and Leaderboards

	Law Area Prediction can be used as 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 \| 127K \|
	\| French \| fr \| 156K \|
	\| Italian \| it \| 46K \|


	## Dataset Structure

	- decision_id: unique identifier for the decision
	- facts: facts section of the decision
	- considerations: considerations section of the decision
	- law_area: label of the decision (main area of law)
	- law_sub_area: sub area of law of the decision
	- language: language of the decision
	- year: year of the decision
	- court: court of the decision
	- chamber: chamber of the decision
	- canton: canton of the decision
	- region: region of the decision

	### Data Fields
	[More Information Needed]
	### Data Instances
	[More Information Needed]
	### Data Fields
	[More Information Needed]
	### Data Splits

	The dataset was split date-stratisfied
	- Train: 2002-2015
	- Validation: 2016-2017
	- Test: 2018-2022

	## Dataset Creation
	### Curation Rationale
	### 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?
	### 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