Datasets:
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
other
Source Datasets:
extended
ArXiv:
Tags:
License:
| 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 | |
| - 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 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: | |
| ```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) | | |
| ### 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](https://github.com/joelniklaus) for adding this dataset. | |