Dataset Card for SuperEURLEX

This dataset contains over 4.6M Legal Documents from EURLEX with Annotations. Over 3.7M of this 4.6M documents are also available in HTML format. This dataset can be used for pretraining language models as well as for testing them on legal text classification tasks.

Use this dataset as follows:

from datasets import load_dataset
config = "0.DE" # {sector}.{lang}[.html]
dataset = load_dataset("ddrg/super_eurlex", config, split='train')

Dataset Details

Dataset Description

This Dataset was scrapped from EURLEX. It contains more than 4.6M Legal Documents in Plain Text and over 3.7M In HTML Format. Those Documents are separated by their language (This Dataset includes a total of 24 official European Languages) and by their Sector.

The Table below shows the number of documents per language:

	Raw	HTML
BG	29,778	27,718
CS	94,439	91,754
DA	398,559	300,488
DE	384,179	265,724
EL	167,502	117,009
EN	456,212	354,186
ES	253,821	201,400
ET	142,183	139,690
FI	238,143	214,206
FR	427,011	305,592
GA	19,673	19,437
HR	37,200	35,944
HU	69,275	66,334
IT	358,637	259,936
LT	62,975	61,139
LV	105,433	102,105
MT	46,695	43,969
NL	345,276	237,366
PL	146,502	143,490
PT	369,571	314,148
RO	47,398	45,317
SK	100,718	98,192
SL	170,583	166,646
SV	172,926	148,656

Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]

Dataset Sources [optional]

Repository: https://huggingface.co/datasets/ddrg/super_eurlex/tree/main
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

As Corpus for:

Pretraining of Language Models with self supervised tasks like Masked Language Modeling and Next Sentence Prediction
Legal Text Analysis

As Dataset for evaluation on the following task:

eurovoc-Concepts Prediction i.e. which tags apply? (Muli-Label Classification (large Scale))
- Example for this task is given[below
subject-matter Prediction i.e. which other tags apply (Multi-Label Classification)
form Classification i.e. What Kind of Document is it? (Multi-Class)
And more

Example for Use Of EUROVOC-Concepts

from datasets import load_dataset
import transformers as tr
from sklearn.preprocessing import MultiLabelBinarizer
import numpy as np 
import evaluate
import uuid

# ==================== #
#     Prepare Data     #
# ==================== #
CONFIG = "3.EN" # {sector}.{lang}[.html]
MODEL_NAME = "distilroberta-base"
dataset = load_dataset("ddrg/super_eurlex", CONFIG, split='train')
tokenizer = tr.AutoTokenizer.from_pretrained(MODEL_NAME)

# Remove Unlabeled Columns
def remove_nulls(batch):
  return [(sample != None) for sample in batch["eurovoc"]]
dataset = dataset.filter(remove_nulls, batched=True, keep_in_memory=True)

# Tokenize Text
def tokenize(batch):
  return tokenizer(batch["text_cleaned"], truncation=True, padding="max_length")
# Keep in Memory is optional (The Dataset is large though and can easily use up alot of memory)
dataset = dataset.map(tokenize, batched=True, keep_in_memory=True)

# Create Label Column by encoding Eurovoc Concepts 
encoder = MultiLabelBinarizer()
# List of all Possible Labels 
eurovoc_concepts = dataset["eurovoc"]
encoder.fit(eurovoc_concepts)
def encode_labels(batch):
    batch["label"] = encoder.transform(batch["eurovoc"])
    return batch
dataset = dataset.map(encode_labels, batched=True, keep_in_memory=True)

# Split into train and Test set
dataset = dataset.train_test_split(0.2)

# ==================== #
#  Load & Train Model  #
# ==================== #
model = tr.AutoModelForSequenceClassification.from_pretrained(
    MODEL_NAME,
    num_labels=len(encoder.classes_),
    problem_type="multi_label_classification",
)

metric = evaluate.load("JP-SystemsX/nDCG", experiment_id=uuid.uuid4())
def compute_metric(eval_pred):
    predictions, labels = eval_pred
    return metric.compute(predictions=predictions, references=labels, k=5)

# Set Hyperparameter 
# Note: We stay mostly with default values to keep example short
# Though more hyperparameter should be set and tuned in praxis
train_args = tr.TrainingArguments(
    output_dir="./cache",
    per_device_train_batch_size=16,
    num_train_epochs=20
)
trainer = tr.Trainer(
    model=model,
    args=train_args,
    train_dataset=dataset["train"],
    compute_metrics=compute_metric,
)
trainer.train() # This will take a while
print(trainer.evaluate(dataset["test"]))
# >>> {'eval_loss': 0.0018887673504650593, 'eval_nDCG@5': 0.8072531683578489, 'eval_runtime': 663.8582, 'eval_samples_per_second': 32.373, 'eval_steps_per_second': 4.048, 'epoch': 20.0}

Out-of-Scope Use

[More Information Needed]

Dataset Structure

This dataset is divided into multiple split by Sector x Language x Format

Sector refers to the kind of Document it belongs to:

0: Consolidated acts
1: Treaties
2: International agreements
3: Legislation
4: Complementary legislation
5: Preparatory acts and working documents
6: Case-law
7: National transposition measures
8: References to national case-law concerning EU law
9: Parliamentary questions
C: Other documents published in the Official Journal C series
E: EFTA documents

Language refers to each of the 24 official European Languages that were included at the date of the dataset creation:

BG ~ Bulgarian
CS ~ Czech
DA ~ Danish
DE ~ German
EL ~ Greek
EN ~ English
ES ~ Spanish
ET ~ Estonian
FI ~ Finnish
FR ~ French
GA ~ Irish
HR ~ Croatian
HU ~ Hungarian
IT ~ Italian
LT ~ Lithuanian
LV ~ Latvian
MT ~ Maltese
NL ~ Dutch
PL ~ Polish
PT ~ Portuguese
RO ~ Romanian
SK ~ Slovak
SL ~ Slovenian
SV ~ Swedish

Format refers to plain Text (default) or HTML format (.html)

Note: Plain Text contains generally more documents because not all documents were available in HTML format but those that were are included in both formats

Those Splits are named the following way: {sector}.{lang}[.html]

For Example:

3.EN would be English legislative documents in plain text format
3.EN.html would be the same in HTML Format

Each Sector has its own set of meta data:

Sector 0 (Consolidated acts)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty

Sector 1 (Treaties)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
subject_matter ~ Keywords that provide general overview of content in a document see here for more information
current_consolidated_version ~ date when this version of the document was consolidated Format DD/MM/YYYY
directory_code ~ Information to structure documents in some kind of directory structure by topic e.g. '03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'
eurovoc ~ Keywords that describe document content based on the European Vocabulary see here for more information

Sector 2 (International agreements)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
directory_code ~ Information to structure documents in some kind of directory structure by topic e.g. '03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'
subject_matter ~ Keywords that provide general overview of content in a document see here for more information
eurovoc ~ Keywords that describe document content based on the European Vocabulary see here for more information
latest_consolidated_version ~ Format DD/MM/YYYY
current_consolidated_version ~ Format DD/MM/YYYY

Sector 3 (Legislation)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
directory_code ~ Information to structure documents in some kind of directory structure by topic e.g. '03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'
subject_matter ~ Keywords that provide general overview of content in a document see here for more information
eurovoc ~ Keywords that describe document content based on the European Vocabulary see here for more information
latest_consolidated_version ~ Format DD/MM/YYYY
current_consolidated_version ~ Format DD/MM/YYYY

Sector 4 (Complementary legislation)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
directory_code ~ Information to structure documents in some kind of directory structure by topic e.g. '03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'
subject_matter ~ Keywords that provide general overview of content in a document see here for more information
eurovoc ~ Keywords that describe document content based on the European Vocabulary see here for more information
latest_consolidated_version ~ Format DD/MM/YYYY
current_consolidated_version ~ Format DD/MM/YYYY

Sector 5 (Preparatory acts and working documents)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
directory_code ~ Information to structure documents in some kind of directory structure by topic e.g. '03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'
subject_matter ~ Keywords that provide general overview of content in a document see here for more information
eurovoc ~ Keywords that describe document content based on the European Vocabulary see here for more information
latest_consolidated_version ~ Format DD/MM/YYYY

Sector 6 (Case-law)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
directory_code ~ Information to structure documents in some kind of directory structure by topic e.g. '03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'
subject_matter ~ Keywords that provide general overview of content in a document see here for more information
eurovoc ~ Keywords that describe document content based on the European Vocabulary see here for more information
case-law_directory_code_before_lisbon ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law

Sector 7 (National transposition measures)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
transposed_legal_acts ~ national laws that exist in EU member states as a direct result of the need to comply with EU directives

Sector 8 (References to national case-law concerning EU law)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
case-law_directory_code_before_lisbon ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law
subject_matter ~ Keywords that provide general overview of content in a document see here for more information

Sector 9 (Parliamentary questions)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
directory_code ~ Information to structure documents in some kind of directory structure by topic e.g. '03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'
subject_matter ~ Keywords that provide general overview of content in a document see here for more information
eurovoc ~ Keywords that describe document content based on the European Vocabulary see here for more information

Sector C (Other documents published in the Official Journal C series)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
eurovoc ~ Keywords that describe document content based on the European Vocabulary see here for more information

Sector E (EFTA documents)

celex_id ~ Unique Identifier for each document
text_cleaned (Plain Text) or text_html_raw (HTML Format)
form ~ Kind of Document e.g. Consolidated text, or Treaty
directory_code ~ Information to structure documents in some kind of directory structure by topic e.g. '03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'
subject_matter ~ Keywords that provide general overview of content in a document see here for more information
eurovoc ~ Keywords that describe document content based on the European Vocabulary see here for more information

Dataset Creation

Curation Rationale

This dataset was created for the creation and/or evaluation of pretrained Legal Language Models.

Source Data

Data Collection and Processing

We used the EURLEX-Web-Scrapper Repo for the data collection process.

Who are the source data producers?

The Source data stems from the EURLEX-Website and was therefore produced by various entities within the European Union

Personal and Sensitive Information

No Personal or Sensitive Information is included to the best of our knowledge.

Bias, Risks, and Limitations

We removed HTML documents from which we couldn't extract plain text under the assumption that those are corrupted files. However, we can't guarantee that we removed all.
The Extraction of plain text from legal HTML documents can lead to formatting issues e.g. the extraction of text from tables might mix up the order such that it becomes nearly incomprehensible.
This dataset might contain many missing values in the meta-data columns as not every document was annotated in the same way

[More Information Needed]

Recommendations

Consider Removing rows with missing values for the task before training a model on it

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