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--- |
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annotations_creators: |
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- other |
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language_creators: |
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- found |
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language: |
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- bg |
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- cs |
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- da |
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- de |
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- el |
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- en |
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- es |
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- et |
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- fi |
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- fr |
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- ga |
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- hr |
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- hu |
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- it |
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- lt |
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- lv |
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- mt |
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- nl |
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- pl |
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- pt |
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- ro |
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- sk |
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- sl |
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- sv |
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license: |
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- cc-by-4.0 |
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multilinguality: |
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- multilingual |
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paperswithcode_id: null |
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pretty_name: "LEXTREME: A Multilingual Legal Benchmark for Natural Language Understanding" |
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size_categories: |
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- 10K<n<100K |
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source_datasets: |
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- extended |
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task_categories: |
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- text-classification |
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- token-classification |
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task_ids: |
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- multi-class-classification |
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- multi-label-classification |
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- topic-classification |
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- text-classification-other-judgement-prediction |
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- named-entity-recognition |
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- named entity recognition and classification (NERC) |
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|
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--- |
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|
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# Dataset Card for LEXTREME: A Multilingual Legal Benchmark for Natural Language Understanding |
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|
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## Table of Contents |
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|
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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|
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## Dataset Description |
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|
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- **Homepage:** |
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- **Repository:** |
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- **Paper:** |
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- **Leaderboard:** |
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- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch) |
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|
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### Dataset Summary |
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|
|
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). |
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|
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Use the dataset like this: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("joelito/lextreme", "swiss_judgment_prediction") |
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``` |
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|
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### Supported Tasks and Leaderboards |
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|
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The dataset supports the tasks of text classification and token classification. |
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In detail, we support the folliwing tasks and configurations: |
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|
|
|
| task | task type | configurations | link | |
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|:---------------------------|--------------------------:|--------------------------------:|-------------------------------------------------------------------------------------------------------:| |
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| Brazilian Court Decisions | Judgment Prediction | (judgment, unanimity) | [joelito/brazilian_court_decisions](https://huggingface.co/datasets/joelito/brazilian_court_decisions) | |
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| Swiss Judgment Prediction | Judgment Prediction | default | [joelito/swiss_judgment_prediction](https://huggingface.co/datasets/swiss_judgment_prediction) | |
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| German Argument Mining | Argument Mining | default | [joelito/german_argument_mining](https://huggingface.co/datasets/joelito/german_argument_mining) | |
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| Greek Legal Code | Topic Classification | (volume, chapter, subject) | [greek_legal_code](https://huggingface.co/datasets/greek_legal_code) | |
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| Online Terms of Service | Unfairness Classification | (unfairness level, claus topic) | [online_terms_of_service](https://huggingface.co/datasets/joelito/online_terms_of_service) | |
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| Covid 19 Emergency Event | Event Classification | default | [covid19_emergency_event](https://huggingface.co/datasets/joelito/covid19_emergency_event) | |
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| MultiEURLEX | Topic Classification | (level 1, level 2, level 3) | [multi_eurlex](https://huggingface.co/datasets/multi_eurlex) | |
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| LeNER BR | Named Entity Recognition | default | [lener_br](https://huggingface.co/datasets/lener_br) | |
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| LegalNERo | Named Entity Recognition | default | [legalnero](https://huggingface.co/datasets/joelito/legalnero) | |
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| Greek Legal NER | Named Entity Recognition | default | [greek_legal_ner](https://huggingface.co/datasets/joelito/greek_legal_ner) | |
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| MAPA | Named Entity Recognition | (coarse, fine) | [mapa](https://huggingface.co/datasets/joelito/mapa) | |
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|
|
|
|
### Languages |
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|
|
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 |
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|
|
## Dataset Structure |
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|
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### Data Instances |
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The file format is jsonl and three data splits are present for each configuration (train, validation and test). |
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|
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### Data Fields |
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|
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[More Information Needed] |
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|
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### Data Splits |
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|
|
[More Information Needed] |
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|
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## Dataset Creation |
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|
|
### Curation Rationale |
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|
|
[More Information Needed] |
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|
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### Source Data |
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|
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#### Initial Data Collection and Normalization |
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|
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[More Information Needed] |
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#### Who are the source language producers? |
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|
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[More Information Needed] |
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### Annotations |
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|
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#### Annotation process |
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|
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[More Information Needed] |
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#### Who are the annotators? |
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|
|
[More Information Needed] |
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|
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### Personal and Sensitive Information |
|
|
|
[More Information Needed] |
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|
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## Considerations for Using the Data |
|
|
|
### Social Impact of Dataset |
|
|
|
[More Information Needed] |
|
|
|
### Discussion of Biases |
|
|
|
[More Information Needed] |
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|
|
### Other Known Limitations |
|
|
|
[More Information Needed] |
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|
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## Additional Information |
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|
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How can I contribute a dataset to lextreme? |
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Please follow the following steps: |
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1. Make sure your dataset is available on the huggingface hub and has a train, validation and test split. |
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2. Create a pull request to the lextreme repository by adding the following to the lextreme.py file: |
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- 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.) |
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- Add your dataset to the BUILDER_CONFIGS list: `LextremeConfig(name="{your_dataset_name}", **_{YOUR_DATASET_NAME})` |
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- Test that it works correctly by loading your subset with `load_dataset("lextreme", "{your_dataset_name}")` and inspecting a few examples. |
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|
|
### Dataset Curators |
|
|
|
[More Information Needed] |
|
|
|
### Licensing Information |
|
|
|
[More Information Needed] |
|
|
|
### Citation Information |
|
|
|
``` |
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@misc{niklaus2023lextreme, |
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title={LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain}, |
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author={Joel Niklaus and Veton Matoshi and Pooja Rani and Andrea Galassi and Matthias Stürmer and Ilias Chalkidis}, |
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year={2023}, |
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eprint={2301.13126}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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|
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### Contributions |
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|
|
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset. |
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|
