数据集:
rcds/swiss_legislation
Swiss Legislation is a multilingual, diachronic dataset of 36K Swiss laws. This dataset is part of a challenging Information Retreival task.
The total number of texts in the dataset is 35,698. The dataset is saved in lexfind_v2.jsonl format. Switzerland has four official languages German, French, Italian and Romanch with some additional English laws being represenated. Laws are written by legal experts. 36K & 18K & 11K & 6K & 534 & 207
Language | Subset | Number of Documents |
---|---|---|
German | de | 18K |
French | fr | 11K |
Italian | it | 6K |
Romanch | rm | 534 |
English | en | 207 |
Each entry in the dataset is a dictionary with the following keys:
[More Information Needed]
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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.
Metadata is published by the Swiss Federal Supreme Court ( https://www.bger.ch ).
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 .
[More Information Needed]
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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
Please cite our ArXiv-Preprint
@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} }