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Dataset Card for "LegalGLUE"
Dataset Summary
The "Legal General Language Understanding Evaluation" (LegalGLUE) dataset was created as part of a bachelor thesis. It consists of four already existing datasets covering three task types and a total of 23 different languages.
Supported Tasks
| Dataset | Source | Task Type | Languages |
| German_LER | Leitner et al. | Named Entity Recognition | German |
| LeNER_Br | de Araujo et al., 2018 | Named Entity Recognition | Portuguese |
| SwissJudgmentPrediction | Niklaus et al. | Binary Text Classification | German, French, Italian |
| MultEURLEX | Chalkidis et al. | Multi-label Text Classification | 23 languages (see below) |
Languages
see Split section
Dataset Structure
Data Instances
German_LER
German_LER example
from datasets import load_dataset
dataset = load_dataset('jfrenz/legalglue', 'german_ler')
{
'id': '66722',
'tokens':['4.', 'Die', 'Kostenentscheidung', 'für', 'das', 'gerichtliche', 'Antragsverfahren', 'beruht', 'auf', '§', '21', 'Abs.', '2', 'Satz', '1', 'i.', 'V.', 'm.', '§', '20', 'Abs.', '1', 'Satz', '1', 'WBO', '.'],
'ner_tags': [38, 38, 38, 38, 38, 38, 38, 38, 38, 3, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 38]
}
LeNER-Br
LeNER-Br example
from datasets import load_dataset
dataset = load_dataset('jfrenz/legalglue', 'lener_br')
{
'id': '7826',
'tokens': ['Firmado', 'por', 'assinatura', 'digital', '(', 'MP', '2.200-2/2001', ')', 'JOSÉ', 'ROBERTO', 'FREIRE', 'PIMENTA', 'Ministro', 'Relator', 'fls', '.', 'PROCESSO', 'Nº', 'TST-RR-1603-79.2010.5.20.0001'],
'ner_tags': [0, 0, 0, 0, 0, 9, 10, 0, 3, 4, 4, 4, 0, 0, 0, 0, 11, 12, 12]}
SwissJudgmentPrediction
swissJudgmentPrediction_de example
from datasets import load_dataset
dataset = load_dataset('jfrenz/legalglue', 'swissJudgmentPrediction_de')
{
'id': 48755,
'year': 2014,
'text': "Sachverhalt: A. X._ fuhr am 25. Juli 2012 bei Mülligen mit seinem Personenwagen auf dem zweiten Überholstreifen der Autobahn A1 in Richtung Zürich. Gemäss Anklage schloss er auf einen Lieferwagen auf und schwenkte vom zweiten auf den ersten Überholstreifen aus. Danach fuhr er an zwei Fahrzeugen rechts vorbei und wechselte auf die zweite Überholspur zurück. B. Das Obergericht des Kantons Aargau erklärte X._ am 14. Januar 2014 zweitinstanzlich der groben Verletzung der Verkehrsregeln schuldig. Es bestrafte ihn mit einer bedingten Geldstrafe von 30 Tagessätzen zu Fr. 430.-- und einer Busse von Fr. 3'000.--. C. X._ führt Beschwerde in Strafsachen. Er beantragt, er sei von Schuld und Strafe freizusprechen. Eventualiter sei die Sache an die Vorinstanz zurückzuweisen. ",
'label': 0,
'language': 'de',
'region': 'Northwestern Switzerland',
'canton': 'ag',
'legal area': 'penal law'
}
MultiEURLEX
Monolingual example out of the MultiEURLEX-Dataset
from datasets import load_dataset
dataset = load_dataset('jfrenz/legalglue', 'multi_eurlex_de')
{
'celex_id': '32002R0130',
'text': 'Verordnung (EG) Nr. 130/2002 der Kommission\nvom 24. Januar 2002\nbezüglich der im Rahmen der Auss...',
'labels': [3, 17, 5]}
Multilingual example out of the MultiEURLEX-Dataset
from datasets import load_dataset
dataset = load_dataset('jfrenz/legalglue', 'multi_eurlex_all_languages')
{
'celex_id': '32002R0130',
'text': {
'bg': None,
'cs': None,
'da': 'Kommissionens ...',
'de': 'Verordnung ... ',
'el': '...',
'en': '...',
...
},
'labels': [3, 17, 5]
}
Data Fields
German_LER
id: id of the sampletokens: the tokens of the sample textner_tags: the NER tags of each token
LeNER_Br
id: id of the sampletokens: the tokens of the sample textner_tags: the NER tags of each token
SwissJudgmentPrediction
id: (int) ID of the documentyear: (int) the publication yeartext: (str) the facts of the caselabel: (class label) the judgment outcome: 0 (dismissal) or 1 (approval)language: (str) one of (de, fr, it)region: (str) the region of the lower courtcanton: (str) the canton of the lower courtlegal area: (str) the legal area of the case
MultiEURLEX
Monolingual use:
celex_id: (str) Official Document ID of the documenttext: (str) An EU Lawlabels: (List[int]) List of relevant EUROVOC concepts (labels)
Multilingual use:
celex_id: (str) Official Document ID of the documenttext: (dict[str]) A dictionary with the 23 languages as keys and the corresponding EU Law as values.labels: (List[int]) List of relevant EUROVOC concepts (labels)
The labels lists consists per default of level 1 EUROVOC concepts. Can be changed by adding the label_level parameter when loading the dataset. (available levels: level_1, level_2, level_3, all_levels)
from datasets import load_dataset
dataset = load_dataset('jfrenz/legalglue', 'multi_eurlex_de', label_level="level_3")
Data Splits
| Dataset | Language | ISO code | Number of Documents train/dev/test |
|---|---|---|---|
| German-LER | German | de | 66723 / - / - |
| LeNER-Br | Portuguese | pt | 7828 / 1177 / 1390 |
| SwissJudgmentPrediction | German | de | 35458 / 4705 / 9725 |
| French | fr | 21179 / 3095 / 6820 | |
| Italian | it | 3072 / 408 / 812 | |
| MultiEURLEX | English | en | 55,000 / 5,000 / 5,000 |
| German | de | 55,000 / 5,000 / 5,000 | |
| French | fr | 55,000 / 5,000 / 5,000 | |
| Italian | it | 55,000 / 5,000 / 5,000 | |
| Spanish | es | 52,785 / 5,000 / 5,000 | |
| Polish | pl | 23,197 / 5,000 / 5,000 | |
| Romanian | ro | 15,921 / 5,000 / 5,000 | |
| Dutch | nl | 55,000 / 5,000 / 5,000 | |
| Greek | el | 55,000 / 5,000 / 5,000 | |
| Hungarian | hu | 22,664 / 5,000 / 5,000 | |
| Portuguese | pt | 23,188 / 5,000 / 5,000 | |
| Czech | cs | 23,187 / 5,000 / 5,000 | |
| Swedish | sv | 42,490 / 5,000 / 5,000 | |
| Bulgarian | bg | 15,986 / 5,000 / 5,000 | |
| Danish | da | 55,000 / 5,000 / 5,000 | |
| Finnish | fi | 42,497 / 5,000 / 5,000 | |
| Slovak | sk | 15,986 / 5,000 / 5,000 | |
| Lithuanian | lt | 23,188 / 5,000 / 5,000 | |
| Croatian | hr | 7,944 / 2,500 / 5,000 | |
| Slovene | sl | 23,184 / 5,000 / 5,000 | |
| Estonian | et | 23,126 / 5,000 / 5,000 | |
| Latvian | lv | 23,188 / 5,000 / 5,000 | |
| Maltese | mt | 17,521 / 5,000 / 5,000 |
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
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Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
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Licensing Information
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Citation Information
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Contributions
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