| --- |
| 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 |
| - uk |
| 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 |
| - named-entity-recognition |
|
|
| --- |
| |
| # Dataset Card for LEXTREME: A Multilingual Legal Benchmark for Natural Language Understanding |
|
|
| ## Table of Contents |
|
|
| - [Table of Contents](#table-of-contents) |
| - [Dataset Description](#dataset-description) |
| - [Dataset Summary](#dataset-summary) |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
| - [Languages](#languages) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Instances](#data-instances) |
| - [Data Fields](#data-fields) |
| - [Data Splits](#data-splits) |
| - [Dataset Creation](#dataset-creation) |
| - [Curation Rationale](#curation-rationale) |
| - [Source Data](#source-data) |
| - [Annotations](#annotations) |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) |
| - [Considerations for Using the Data](#considerations-for-using-the-data) |
| - [Social Impact of Dataset](#social-impact-of-dataset) |
| - [Discussion of Biases](#discussion-of-biases) |
| - [Other Known Limitations](#other-known-limitations) |
| - [Additional Information](#additional-information) |
| - [Dataset Curators](#dataset-curators) |
| - [Licensing Information](#licensing-information) |
| - [Citation Information](#citation-information) |
| - [Contributions](#contributions) |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** |
| - **Repository:** |
| - **Paper:** |
| - **Leaderboard:** |
| - **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch) |
|
|
| ### Dataset Summary |
|
|
| The dataset consists of 12 diverse multilingual legal NLU datasets. 6 datasets have one single configuration, 5 datasets have two or three configurations, and 1 dataset has three temporal epoch configurations. This leads to a total of 21 tasks (11 single-label text classification tasks, 5 multi-label text classification tasks and 5 token-classification tasks). |
|
|
| Use the dataset like this: |
| ```python |
| 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](https://huggingface.co/datasets/joelito/brazilian_court_decisions) | |
| | Swiss Judgment Prediction | Judgment Prediction | default | [joelito/swiss_judgment_prediction](https://huggingface.co/datasets/swiss_judgment_prediction) | |
| | German Argument Mining | Argument Mining | default | [joelito/german_argument_mining](https://huggingface.co/datasets/joelito/german_argument_mining) | |
| | Greek Legal Code | Topic Classification | (volume, chapter, subject) | [greek_legal_code](https://huggingface.co/datasets/greek_legal_code) | |
| | Online Terms of Service | Unfairness Classification | (unfairness level, clause topic) | [online_terms_of_service](https://huggingface.co/datasets/joelito/online_terms_of_service) | |
| | Covid 19 Emergency Event | Event Classification | default | [covid19_emergency_event](https://huggingface.co/datasets/joelito/covid19_emergency_event) | |
| | MultiEURLEX | Topic Classification | (level 1, level 2, level 3) | [multi_eurlex](https://huggingface.co/datasets/multi_eurlex) | |
| | LeNER BR | Named Entity Recognition | default | [lener_br](https://huggingface.co/datasets/lener_br) | |
| | LegalNERo | Named Entity Recognition | default | [legalnero](https://huggingface.co/datasets/joelito/legalnero) | |
| | Greek Legal NER | Named Entity Recognition | default | [greek_legal_ner](https://huggingface.co/datasets/joelito/greek_legal_ner) | |
| | MAPA | Named Entity Recognition | (coarse, fine) | [mapa](https://huggingface.co/datasets/joelito/mapa) | |
| | Ukrainian Court Decisions | Judgment Prediction | (pre_war, hybrid_war, full_scale) | [overthelex/ukrainian-court-decisions](https://huggingface.co/datasets/overthelex/ukrainian-court-decisions) | |
| |
| |
| ### 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, uk |
| |
| ## 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](https://github.com/joelniklaus) for adding this dataset. |
|
|
