G4KMU/LEMUR
Permissive or share-alike
The declared licence (apache-2.0) is classified as permissive.
Licence facts reported by the dataset and its declared sources. Not legal advice, and not a grant of permission to use the dataset.
Evidence
- Declared licence: apache-2.0 (from the tag)
- Licence files: no licence file at the top level of the repository
- Access gated on Hugging Face: no
Declared sources
No source datasets listed in the card metadata.
Signal
See all datasets marked Permissive or share-alike.
Tasks
Citation text from the card
@inproceedings{ahmadi-etal-2026-lemur,
title = "{LEMUR}: A Corpus for Robust Fine-Tuning of Multilingual Law Embedding Models for Retrieval",
author = "Ahmadi, Narges Baba and
Strich, Jan and
Semmann, Martin and
Biemann, Chris",
editor = "Baez Santamaria, Selene and
Somayajula, Sai Ashish and
Yamaguchi, Atsuki",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-srw.18/",
doi = "10.18653/v1/2026.eacl-srw.18",
pages = "248--265",
ISBN = "979-8-89176-383-8",
abstract = "Large language models (LLMs) are increasingly used to access legal information. Yet, their deployment in multilingual legal settings is constrained by unreliable retrieval and the lack of domain-adapted, open-embedding models. In particular, existing multilingual legal corpora are not designed for semantic retrieval, and PDF-based legislative sources introduce substantial noise due to imperfect text extraction. To address these challenges, we introduce LEMUR, a large-scale multilingual corpus of EU environmental legislation constructed from 24,953 official EUR-Lex PDF documents covering 25 languages. We further propose the Lexical Content Score (LCS), a langu
Downloads (30 days): 40502. Likes: 5. Last modified: 2026-03-31T16:41:34.000Z.