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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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-
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- The dataset consists of 11 diverse multilingual legal NLU tasks. 6 tasks have one single configuration and 5 tasks 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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-
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-
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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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-
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-
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- ### Languages
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-
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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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-
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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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- [More Information Needed]
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- ### Data Splits
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- [More Information Needed]
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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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- #### Initial Data Collection and Normalization
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- [More Information Needed]
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- #### Who are the source language producers?
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- [More Information Needed]
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- ### Annotations
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- #### Annotation process
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- [More Information Needed]
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-
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- #### Who are the annotators?
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-
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- [More Information Needed]
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-
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- ### Personal and Sensitive Information
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-
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- [More Information Needed]
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- ## Considerations for Using the Data
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-
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- ### Social Impact of Dataset
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-
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- [More Information Needed]
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- ### Discussion of Biases
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- [More Information Needed]
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- ### Other Known Limitations
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-
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- [More Information Needed]
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-
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- ## Additional Information
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-
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- ### Dataset Curators
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- [More Information Needed]
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- ### Licensing Information
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-
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- [More Information Needed]
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-
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- ### Citation Information
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-
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- ```
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- TODO add citation
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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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