数据集:
rcds/swiss_citation_extraction
Swiss Citation Extraction is a multilingual, diachronic dataset of 131K Swiss Federal Supreme Court (FSCS) cases. This dataset is part of a challenging token classification task.
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 | 85K |
French | fr | 38K |
Italian | it | 8K |
decision_id: (string) considerations: (sequence) NER_labels: (sequence) law_area: (string) language: (string) year: (int64) chamber: (string) region: (string)
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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 .
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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} }