metadata
language:
- fr
- de
- en
- it
- nl
- el
- da
- pt
- es
- sv
- fi
- lt
- et
- cs
- hu
- lv
- sl
- pl
- mt
- sk
- ro
- bg
- hr
- ga
license: mit
size_categories:
- 100K<n<1M
pretty_name: Europa Random Split
dataset_info:
features:
- name: celex_id
dtype: string
- name: lang
dtype: string
- name: input_text
dtype: string
- name: keyphrases
sequence: string
splits:
- name: train
num_bytes: 6405779590
num_examples: 159306
- name: valid
num_bytes: 2182262528
num_examples: 53943
- name: test
num_bytes: 2853853947
num_examples: 71708
download_size: 5183354316
dataset_size: 11441896065
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
tags:
- keyphrase-generation
- text-to-text
- legal
Dataset Card for EUROPA
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
EUROPA is a dataset designed for training and evaluating multilingual keyphrase generation models in the legal domain. It consists of legal judgments from the Court of Justice of the European Union (EU) and includes instances in all 24 official EU languages.
Key Features: Multilingual: Covers 24 official EU languages. Domain-Specific: Focuses on legal documents. Source: Derived from Court of Justice of the European Union judgments.
- Curated by: N3 team
- Language(s) (NLP): French, German, English, Italian, Dutch, Greek, Danish, Portuguese, Spanish, Swedish, Finnish, Lithuanian, Estonian, Czech, Hungarian, Latvian, Slovenian, Polish, Maltese, Slovak, Romanian, Bulgarian, Croatian, Irish
- License: MIT License
Dataset Sources
- Paper [optional]: https://arxiv.org/abs/2403.00252
Dataset Structure
- celex_id: unique CELEX identifier inherited from CJEU
- lang: ISO 639-1 language code
- input: judgment transcription or transcription
- keyphrases: keyphrases drafted by the CJEU Registry
Citation
@article{salaun2024europa,
title={EUROPA: A Legal Multilingual Keyphrase Generation Dataset},
author={Sala{\"u}n, Olivier and Piedboeuf, Fr{\'e}d{\'e}ric and Le Berre, Guillaume and Hermelo, David Alfonso and Langlais, Philippe},
journal={arXiv preprint arXiv:2403.00252},
year={2024}
}