task_categories:text-classification task_ids:entity-linking-classification annotations_creators:machine-generated language_creators:expert-generated multilinguality:multilingual size_categories:100K<n<1M source_datasets:original language:de language:fr language:it license:cc-by-sa-4.0 arxiv:2306.09237 doi:10.57967/hf/0773 region:us
---
annotations_creators:
- machine-generated
language:
- de
- fr
- it
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
pretty_name: 'Swiss Doc2doc Information Retrieval'
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- text-classification
task_ids:
- entity-linking-classification
---

https://huggingface.co/spaces/huggingface/datasets-tagging


# Dataset Card for Swiss Doc2doc Information Retrieval

## 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 Doc2doc Information Retrieval is a multilingual, diachronic dataset of 131K Swiss Federal Supreme Court (FSCS) cases annotated with law citations and ruling citations, posing a challenging text classification task. As unique label we are using decision_id of cited rulings and uuid of cited law articles, which can be found in the SwissCourtRulingCorpus. We also provide additional metadata, i.e., the publication year, the legal 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 Doc2Doc IR can be used as information retrieval task using documents in Swiss Legislation (https://huggingface.co/datasets/rcds/swiss_legislation) and Swiss Leading desicions (https://huggingface.co/datasets/rcds/swiss_leading_decisions).

### Languages

Switzerland has four official languages with three languages (German 86K, French 30k and Italian 10k) being represented. The decisions are written by the judges and clerks in the language of the proceedings.

## Dataset Structure

### Data Instances

```
{
  "decision_id": "000127ef-17d2-4ded-8621-c0c962c18fd5",
  "language": de,
  "year": 2018,
  "chamber": "CH_BGer_008",
  "region": "Federation",
  "origin_chamber": 47,
  "origin_court": 8,
  "origin_canton": 151,
  "law_area": "social_law",
  "law_sub_area": ,
  "laws": "['75488867-c001-4eb9-93b9-04264ea91f55', 'e6b06567-1236-4210-adb3-e11c26e497d5', '04bf6369-99cb-41fa-8aff-413679bc8c18', ...],
  "cited_rulings": "['fe8a76b3-8b0f-4f27-a277-2d887140e7ab', '16fef75e-e8d5-4a51-8230-a9ca3676c8a9', '6d21b282-3b23-41dd-9350-6ba5386df9b1', '302fd9f3-e78a-4a9f-9f8d-cde51fcbdfe7']",
  "facts": "Sachverhalt: A. A._, geboren 1954, war ab November 2002 als Pflegehilfe im Altersheim C._ angestellt. Am 23. Dezember 2002 meldete sie sich erstmals unter Hinweis auf Depressionen ...",
  "considerations": "Erwägungen: 1. 1.1. Die Beschwerde kann wegen Rechtsverletzung gemäss Art. 95 und Art. 96 BGG erhoben werden. Das Bundesgericht wendet das ...",
  "rulings": "Demnach erkennt das Bundesgericht: 1. Die Beschwerde wird abgewiesen. 2. Die Gerichtskosten von Fr. 800.- werden der Beschwerdeführerin ...",
}
```

### Data Fields

```
decision_id: (str) a unique identifier of the for the document
language: (str) one of (de, fr, it)
year: (int) the publication year
chamber: (str) the chamber of the case
region: (str) the region of the case
origin_chamber: (str) the chamber of the origin case
origin_court: (str) the court of the origin case
origin_canton:  (str) the canton of the origin case
law_area: (str) the law area of the case
law_sub_area:(str) the law sub area of the case
laws: (str) a list of law ids
cited rulings: (str) a list of cited rulings ids
facts: (str) the facts of the case
considerations: (str) the considerations of the case
rulings: (str) the rulings of the case
```

### Data Splits

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

| Language   | Subset     | Number of Documents (Training/Validation/Test) | 
|------------|------------|------------------------------------------------|  
| German     | **de**     | 86'832 (59'170 / 19'002 / 8'660)               |
| French     | **fr**     | 46'203 (30'513 / 10'816 / 4'874)               |
| Italian    | **it**     | 8'306 (5'673 / 1'855 / 778)                    |


## Dataset Creation

### Curation Rationale

The dataset was created by Stern et al. (2023).

### Source Data

#### Initial Data Collection and Normalization

The original data are available at 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 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. 

### Annotations

#### Annotation process

The decisions have been annotated with the citation ids using html tags and parsers.
For more details on laws (rcds/swiss_legislation) and rulings (rcds/swiss_rulings).

#### Who are the annotators?

Stern annotated the citations.
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.