README.md

metadata

annotations_creators:
  - expert-annotated
language:
  - deu
  - fra
  - ita
license: cc-by-4.0
multilinguality: multilingual
source_datasets:
  - rcds/swiss_judgment_prediction
task_categories:
  - text-classification
task_ids: []
dataset_info:
  - config_name: de
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 102150270
        num_examples: 35458
      - name: validation
        num_bytes: 11848314
        num_examples: 4705
      - name: test
        num_bytes: 5223575
        num_examples: 2048
    download_size: 52253274
    dataset_size: 119222159
  - config_name: fr
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 95546902
        num_examples: 21179
      - name: validation
        num_bytes: 12843800
        num_examples: 3095
      - name: test
        num_bytes: 10006148
        num_examples: 2048
    download_size: 55552512
    dataset_size: 118396850
  - config_name: it
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 10616543
        num_examples: 3072
      - name: validation
        num_bytes: 1024719
        num_examples: 408
      - name: test
        num_bytes: 2433699
        num_examples: 812
    download_size: 6011252
    dataset_size: 14074961
configs:
  - config_name: de
    data_files:
      - split: train
        path: de/train-*
      - split: validation
        path: de/validation-*
      - split: test
        path: de/test-*
  - config_name: fr
    data_files:
      - split: train
        path: fr/train-*
      - split: validation
        path: fr/validation-*
      - split: test
        path: fr/test-*
  - config_name: it
    data_files:
      - split: train
        path: it/train-*
      - split: validation
        path: it/validation-*
      - split: test
        path: it/test-*
tags:
  - mteb
  - text

SwissJudgementClassification

An MTEB dataset

Massive Text Embedding Benchmark

Multilingual, diachronic dataset of Swiss Federal Supreme Court cases annotated with the respective binarized judgment outcome (approval/dismissal)

Task category	t2c
Domains	Legal, Written
Reference	https://aclanthology.org/2021.nllp-1.3/

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_task("SwissJudgementClassification")
evaluator = mteb.MTEB([task])

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repository.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.

@misc{niklaus2022empirical,
  archiveprefix = {arXiv},
  author = {Joel Niklaus and Matthias Stürmer and Ilias Chalkidis},
  eprint = {2209.12325},
  primaryclass = {cs.CL},
  title = {An Empirical Study on Cross-X Transfer for Legal Judgment Prediction},
  year = {2022},
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("SwissJudgementClassification")

desc_stats = task.metadata.descriptive_stats

{}

---

This dataset card was automatically generated using MTEB