File size: 2,183 Bytes
83d24b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | from __future__ import annotations
from mteb.abstasks import AbsTaskClassification, MultilingualTask
from mteb.abstasks.TaskMetadata import TaskMetadata
class SwissJudgementClassification(MultilingualTask, AbsTaskClassification):
metadata = TaskMetadata(
name="SwissJudgementClassification",
description="Multilingual, diachronic dataset of Swiss Federal Supreme Court cases annotated with the respective binarized judgment outcome (approval/dismissal)",
reference="https://aclanthology.org/2021.nllp-1.3/",
dataset={
"path": "rcds/swiss_judgment_prediction",
"revision": "29806f87bba4f23d0707d3b6d9ea5432afefbe2f",
},
type="Classification",
category="s2s",
eval_splits=["test"],
eval_langs={
"de": ["deu-Latn"],
"fr": ["fra-Latn"],
"it": ["ita-Latn"],
},
main_score="accuracy",
date=("2020-12-15", "2022-04-08"),
form=["written"],
domains=["Legal"],
task_subtypes=[
"Political classification",
],
license="CC-BY-4.0",
socioeconomic_status="mixed",
annotations_creators="expert-annotated",
dialect=[],
text_creation="found",
bibtex_citation="""@misc{niklaus2022empirical,
title={An Empirical Study on Cross-X Transfer for Legal Judgment Prediction},
author={Joel Niklaus and Matthias Stürmer and Ilias Chalkidis},
year={2022},
eprint={2209.12325},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
""",
n_samples={"test": 2048},
avg_character_length={"test": 3411.72},
)
def dataset_transform(self):
for lang in self.hf_subsets:
dataset = self.dataset[lang]["test"]
dataset_dict = {"test": dataset}
subsampled_dataset_dict = self.stratified_subsampling(
dataset_dict=dataset_dict,
seed=42,
splits=["test"],
label="label",
n_samples=min(2048, len(dataset["text"])) - 2,
)
self.dataset[lang]["test"] = subsampled_dataset_dict["test"]
|