license: cc0-1.0
task_categories:
- text-classification
language:
- en
tags:
- legal
- law
size_categories:
- n<1K
sequence:
class_label:
names:
'0': state of emergency
'1': restrictions of fundamental rights and civil liberties
'2': restrictions of daily liberties
'3': closures / lockdown
'4': suspension of international cooperation and commitments
'5': police mobilization
'6': army mobilization
'7': government oversight
source_datasets:
- joelniklaus/covid19_emergency_event
pretty_name: mteb-EXCEPTIUS (MTEB version)
EXCEPTIUS-en (MTEB version)
This dataset contains English entries of the test split of the EXCEPTIUS dataset formatted in the Massive Text Embedding Benchmark (MTEB) information retrieval dataset format.
This dataset is intended to facilitate the consistent and reproducible evaluation of multilabel classification models with the mteb embedding model evaluation framework.
More specifically, this dataset tests the ability of multilabel classification models to interpret legislative orders relating to the Covid-19 pandemic.
This dataset has been processed into the MTEB format by Isaacus, a legal AI research company.
Methodology 🧪
To understand how EXCEPTIUS itself was created, refer to their paper.
This dataset was formatted by taking the English entries from the test split, converting the label attribute to a list of intergers (representing the classification) and leaving the text column as is.
Structure 🗂️
As per the MTEB information retrieval dataset format, this dataset comprises two columns, 'text' representing the raw text of the COVID-19 related legislation and 'label' a list of labels relating to the type of exceptional measure the legislation takes.
License 📜
The source dataset is licensed under CC0.
Citation 🔖
@inproceedings{tziafas-etal-2021-multilingual,
title = "A Multilingual Approach to Identify and Classify Exceptional Measures against {COVID}-19",
author = "Tziafas, Georgios and
de Saint-Phalle, Eugenie and
de Vries, Wietse and
Egger, Clara and
Caselli, Tommaso",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nllp-1.5",
pages = "46--62"
}