datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
super_eurlexSuper-EURLEX dataset containing legal documents from multiple languages.
The datasets are build/scrapped from the EURLEX Website [https://eur-lex.europa.eu/homepage.html]
With one split per language and sector, because the available features (metadata) differs for each
sector. Therefore, each sample contains the content of a full legal document in up to 3 different
formats. Those are raw HTML and cleaned HTML (if the HTML format was available on the EURLEX website
during the scrapping process) and cleaned text.
The cleaned text should be available for each sample and was extracted from HTML or PDF.
'Cleaned' HTML stands here for minor cleaning that was done to preserve to a large extent the necessary
HTML information like table structures while removing unnecessary complexity which was introduced to the
original documents due to actions like writing each sentence into a new object.
Additionally, each sample contains metadata which was scrapped on the fly, this implies the following
2 things. First, not every sector contains the same metadata. Second, most metadata might be
irrelevant for most use cases.
In our minds the most interesting metadata is the celex-id which is used to identify the legal
document at hand, but also contains a lot of information about the document
see [https://eur-lex.europa.eu/content/tools/eur-lex-celex-infographic-A3.pdf] as well as eurovoc-
concepts, which are labels that define the content of the documents.
Eurovoc-Concepts are, for example, only available for the sectors 1, 2, 3, 4, 5, 6, 9, C, and E.
The Naming of most metadata is kept like it was on the eurlex website, except for converting
it to lower case and replacing whitespaces with '_'.multi_eurlexMultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).multi_eurlexMultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).eur-lex-sumThe EUR-Lex-Sum dataset is a multilingual resource intended for text summarization in the legal domain.
It is based on human-written summaries of legal acts issued by the European Union.
It distinguishes itself by introducing a smaller set of high-quality human-written samples,
each of which have much longer references (and summaries!) than comparable datasets.
Additionally, the underlying legal acts provide a challenging domain-specific application to legal texts,
which are so far underrepresented in non-English languages.
For each legal act, the sample can be available in up to 24 languages
(the officially recognized languages in the European Union);
the validation and test samples consist entirely of samples available in all languages,
and are aligned across all languages at the paragraph level.eurlex-multilingual
MultiEURLEXMultilabelClassification
An MTEB dataset
Massive Text Embedding Benchmark
EU laws in 23 EU languages containing annotated labels for 21 EUROVOC concepts.
Task category
t2c
Domains
Legal, Government, Written
Referencehttps://huggingface.co/datasets/coastalcph/multi_eurlex
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/eurlex-multilingual.eurlex_resources
Dataset Card for EurlexResources: A Corpus Covering the Largest EURLEX Resources
Dataset Summary
This dataset contains large text resources (~179GB in total) from EURLEX that can be used for pretraining language models.
Use the dataset like this:
from datasets import load_dataset
config = "de_caselaw" # {lang}_{resource}
dataset = load_dataset("joelito/eurlex_resources", config, split='train', streaming=True)
Supported Tasks and Leaderboards
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/joelniklaus/eurlex_resources.EUR-LEX
EUR-LEX Miner 🇪🇺
A high-performance mining tool on GitHub (https://github.com/do-me/eur-lex) for extracting text and semantic concepts from the European Commission's Cellar database.
Originally based on EuropeanParliament/Eurovoc_2025 and EuropeanParliament/Eurovoc but improved and extended for various reasons:
line breaks are not included that are quite important for semantic chunking approaches and RAG
the preprocessing was way too aggressive and removed too much… See the full description on the dataset page: https://huggingface.co/datasets/do-me/EUR-LEX.multi_eurlexAn non-parallel version of the MultiEURLEX datasets released by Chalkidis et al. (2021).
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
In this version, MultiEURLEX comprises non-parallel documents across 5 languages (English, German, French, Greek,
and Slovakian) including translations from English to the rest of the 4 available languages.eurlexEURLEX57K contains 57k legislative documents in English from EUR-Lex portal, annotated with EUROVOC concepts.eurlex-57k
Dataset Card for "eurlex-57k"
More Information needed
eurlexEURLEX57K contains 57k legislative documents in English from EUR-Lex portal, annotated with EUROVOC concepts.eur-lex
EUR-Lex EN–DA (Parallel Legal Text)
A parallel corpus of EU legal documents in English and Danish. Contains only samples where both languages are present.
Dataset Structure
Features
Field
Type
Description
celex
string
CELEX document identifier
resource_type
string
Type of legal document (caselaw, decision, directive, intagr, recommendation, regulation)
url
string
Source URL
title_en
string
English title
title_da
string
Danish title
text_en… See the full description on the dataset page: https://huggingface.co/datasets/oliverkinch/eur-lex.EUR_Lex_SumNOMOS_EUR_Lex_Sum is a Greek-language legal text summarization dataset derived from official documents of the EUR-Lex portal.
The dataset focuses on extractive summarization of European Union legislative texts written in Greek and was developed in the context of the +NOMOS project
Dataset Structure
The dataset follows a hierarchical folder structure based on EUR-Lex thematic categories.
Top-level folders represent broad policy areas
Intermediate folders represent subtopics
Only the… See the full description on the dataset page: https://huggingface.co/datasets/syn-nomos/EUR_Lex_Sum.multi_eurlex_en
Dataset Card for "multi_eurlex_en"
More Information needed
EURLex-4Keur-lex-bt
EUR-Lex Backtranslation Danish (10k)
Instruction-style backtranslation dataset for Danish legal writing.
Summary
Built from oliverkinch/eur-lex (Danish fields only).
Source filter: text_source_da == html.
Row format: prompt (Danish user instruction) + target (Danish legal text).
Combined from 4 non-overlapping build slices.
Composition
Total rows: 10,210
Columns:
id
prompt
target
sources
meta
multi_eurlex_en_processedeurlexeurlex
eurlex
This is a text classification dataset. It is intended for machine learning research and experimentation.
This dataset is obtained via formatting another publicly available data to be compatible with our AutoIntent Library.
Usage
It is intended to be used with our AutoIntent Library:
from autointent import Dataset
eurlex = Dataset.from_hub("AutoIntent/eurlex")
Source
This dataset is taken from coastalcph/multi_eurlex and formatted with our AutoIntent… See the full description on the dataset page: https://huggingface.co/datasets/DeepPavlov/eurlex.EUR-Lex-Triples
EUR-Lex-Triples: A Legal Relation Extraction Dataset from European Legislation
EUR-Lex-Sum dataset Aumiller, 2022 annotated with triples
Relation Extraction Baselines
Code/RE-Baselines contains the code used to run the RE baselines : Fine-Tuning and Inference.
Results of baseline models for Relation Extraction are :
Model
Precision
Recall
F1-Score
Legal-Bert
0.64
0.59
0.60
Bert
0.58
0.52
0.54
Rebel-Large
0.88
0.75
0.80
Mistral 7b zero-Shot
0.38
0.30… See the full description on the dataset page: https://huggingface.co/datasets/nihedb/EUR-Lex-Triples.eur-lex-europa-merged
Dataset Card for "eur-lex-europa-merged"
More Information needed
eurlexeurlexeurlex-greek-legislation
🇪🇺 EU-RLEX Legislation Dataset (Collected Until June 20, 2025)
📘 Overview
This dataset provides structured and accessible European Union legislation data collected from EUR-Lex up to June 20, 2025. It is maintained by GFOSS – Open Technologies Alliance to support open legal data research, machine learning applications, and transparency initiatives.
The dataset includes:
Metadata in a structured Parquet file
Full-text content of legislation documents in… See the full description on the dataset page: https://huggingface.co/datasets/glossAPI/eurlex-greek-legislation.eurlex_sdg_coverage
EUR-Lex SDG-Annotated Dataset
Dataset Summary
The EUR-Lex SDG-Annotated Dataset enhances the original EUR-Lex dataset by mapping European Union legislation documents to the United Nations Sustainable Development Goals (SDGs). Each document in this dataset has been annotated using a rule-based keyword matching approach, leveraging SDG-related terms and synonyms extracted from an Excel-based term matrix.
This dataset provides researchers, policymakers, and data scientists… See the full description on the dataset page: https://huggingface.co/datasets/razaulhaq/eurlex_sdg_coverage.eur-lex-sum-instructeurobench-multi_eurlex-sleur-lex-sum-secondeur-lex-sum
EUR-Lex-Sum
A dataset of EU legislation paired with legislative summaries from EUR-Lex, covering Danish and English.
Built from the EU Publications Office CELLAR repository using SPARQL-based discovery and XHTML content extraction.
Dataset Description
Each record pairs a full EU legislative document with its official plain-language summary from the EUR-Lex Summaries of EU Legislation collection.
Configs
Config
Records
Description
all… See the full description on the dataset page: https://huggingface.co/datasets/oliverkinch/eur-lex-sum.eurobench-multi_eurlex-sk