README.md

metadata

annotations_creators:
  - other
language_creators:
  - found
language:
  - bg
  - cs
  - da
  - de
  - el
  - en
  - es
  - et
  - fi
  - fr
  - ga
  - hr
  - hu
  - it
  - lt
  - lv
  - mt
  - nl
  - pl
  - pt
  - ro
  - sk
  - sl
  - sv
license:
  - cc-by-4.0
multilinguality:
  - multilingual
paperswithcode_id: null
pretty_name: 'LEXTREME: A Multilingual Legal Benchmark for Natural Language Understanding'
size_categories:
  - 10K<n<100K
source_datasets:
  - extended
task_categories:
  - text-classification
  - token-classification
task_ids:
  - multi-class-classification
  - multi-label-classification
  - topic-classification
  - text-classification-other-judgement-prediction
  - named-entity-recognition
  - named entity recognition and classification (NERC)

Dataset Card for LEXTREME: A Multilingual Legal Benchmark for Natural Language Understanding

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage:
• Repository:
• Paper:
• Leaderboard:
• Point of Contact: Joel Niklaus

Dataset Summary

The dataset consists of 11 diverse multilingual legal NLU datasets. 6 datasets have one single configuration and 5 datasets have two or three configurations. This leads to a total of 18 tasks (8 single-label text classification tasks, 5 multi-label text classification tasks and 5 token-classification tasks).

Use the dataset like this:

from datasets import load_dataset
dataset = load_dataset("joelito/lextreme", "swiss_judgment_prediction")

Supported Tasks and Leaderboards

The dataset supports the tasks of text classification and token classification. In detail, we support the folliwing tasks and configurations:

task	task type	configurations	link
Brazilian Court Decisions	Judgment Prediction	(judgment, unanimity)	joelito/brazilian_court_decisions
Swiss Judgment Prediction	Judgment Prediction	default	joelito/swiss_judgment_prediction
German Argument Mining	Argument Mining	default	joelito/german_argument_mining
Greek Legal Code	Topic Classification	(volume, chapter, subject)	greek_legal_code
Online Terms of Service	Unfairness Classification	(unfairness level, claus topic)	online_terms_of_service
Covid 19 Emergency Event	Event Classification	default	covid19_emergency_event
MultiEURLEX	Topic Classification	(level 1, level 2, level 3)	multi_eurlex
LeNER BR	Named Entity Recognition	default	lener_br
LegalNERo	Named Entity Recognition	default	legalnero
Greek Legal NER	Named Entity Recognition	default	greek_legal_ner
MAPA	Named Entity Recognition	(coarse, fine)	mapa

Languages

The following languages are supported: bg , cs , da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv

Dataset Structure

Data Instances

The file format is jsonl and three data splits are present for each configuration (train, validation and test).

Data Fields

[More Information Needed]

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

How can I contribute a dataset to lextreme? Please follow the following steps:

1. Make sure your dataset is available on the huggingface hub and has a train, validation and test split.
2. Create a pull request to the lextreme repository by adding the following to the lextreme.py file:
   • Create a dict _{YOUR_DATASET_NAME} (similar to _BRAZILIAN_COURT_DECISIONS_JUDGMENT) containing all the necessary information about your dataset (task_type, input_col, label_col, etc.)
   • Add your dataset to the BUILDER_CONFIGS list: LextremeConfig(name="{your_dataset_name}", **_{YOUR_DATASET_NAME})
   • Test that it works correctly by loading your subset with load_dataset("lextreme", "{your_dataset_name}") and inspecting a few examples.

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@misc{niklaus2023lextreme,
    title={LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain},
    author={Joel Niklaus and Veton Matoshi and Pooja Rani and Andrea Galassi and Matthias Stürmer and Ilias Chalkidis},
    year={2023},
    eprint={2301.13126},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Contributions

Thanks to @JoelNiklaus for adding this dataset.