task_categories:text-classification task_ids:multi-label-classification task_ids:topic-classification annotations_creators:found language_creators:found multilinguality:multilingual size_categories:10K<n<100K source_datasets:original language:bg language:cs language:da language:de language:el language:en language:es language:et language:fi language:fr language:hr language:hu language:it language:lt language:lv language:mt language:nl language:pl language:pt language:ro language:sk language:sl language:sv license:cc-by-sa-4.0 arxiv:2109.00904 region:us
MultiEURLEX comprises 65k EU laws in 23 official EU languages (some low-ish resource).
Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU.
As with the English EURLEX, the goal is to predict the relevant EUROVOC concepts (labels);
this is multi-label classification task (given the text, predict multiple labels).
@InProceedings{chalkidis-etal-2021-multieurlex,
  author = {Chalkidis, Ilias
                and Fergadiotis, Manos
                and Androutsopoulos, Ion},
  title = {MultiEURLEX -- A multi-lingual and multi-label legal document
               classification dataset for zero-shot cross-lingual transfer},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods
               in Natural Language Processing},
  year = {2021},
  publisher = {Association for Computational Linguistics},
  location = {Punta Cana, Dominican Republic},
}
---
annotations_creators:
- found
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
- topic-classification
pretty_name: MultiEURLEX
dataset_info:
- config_name: en
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 389250183
    num_examples: 55000
  - name: test
    num_bytes: 58966963
    num_examples: 5000
  - name: validation
    num_bytes: 41516165
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 489733311
- config_name: da
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 395774777
    num_examples: 55000
  - name: test
    num_bytes: 60343696
    num_examples: 5000
  - name: validation
    num_bytes: 42366390
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 498484863
- config_name: de
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 425489905
    num_examples: 55000
  - name: test
    num_bytes: 65739074
    num_examples: 5000
  - name: validation
    num_bytes: 46079574
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 537308553
- config_name: nl
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 430232783
    num_examples: 55000
  - name: test
    num_bytes: 64728034
    num_examples: 5000
  - name: validation
    num_bytes: 45452550
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 540413367
- config_name: sv
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 329071297
    num_examples: 42490
  - name: test
    num_bytes: 60602026
    num_examples: 5000
  - name: validation
    num_bytes: 42766067
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 432439390
- config_name: bg
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 273160256
    num_examples: 15986
  - name: test
    num_bytes: 109874769
    num_examples: 5000
  - name: validation
    num_bytes: 76892281
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 459927306
- config_name: cs
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 189826410
    num_examples: 23187
  - name: test
    num_bytes: 60702814
    num_examples: 5000
  - name: validation
    num_bytes: 42764243
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 293293467
- config_name: hr
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 80808173
    num_examples: 7944
  - name: test
    num_bytes: 56790830
    num_examples: 5000
  - name: validation
    num_bytes: 23881832
    num_examples: 2500
  download_size: 2770050147
  dataset_size: 161480835
- config_name: pl
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 202211478
    num_examples: 23197
  - name: test
    num_bytes: 64654979
    num_examples: 5000
  - name: validation
    num_bytes: 45545517
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 312411974
- config_name: sk
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 188126769
    num_examples: 22971
  - name: test
    num_bytes: 60922686
    num_examples: 5000
  - name: validation
    num_bytes: 42786793
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 291836248
- config_name: sl
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 170800933
    num_examples: 23184
  - name: test
    num_bytes: 54552441
    num_examples: 5000
  - name: validation
    num_bytes: 38286422
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 263639796
- config_name: es
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 433955383
    num_examples: 52785
  - name: test
    num_bytes: 66885004
    num_examples: 5000
  - name: validation
    num_bytes: 47178821
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 548019208
- config_name: fr
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 442358905
    num_examples: 55000
  - name: test
    num_bytes: 68520127
    num_examples: 5000
  - name: validation
    num_bytes: 48408938
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 559287970
- config_name: it
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 429495813
    num_examples: 55000
  - name: test
    num_bytes: 64731770
    num_examples: 5000
  - name: validation
    num_bytes: 45886537
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 540114120
- config_name: pt
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 419281927
    num_examples: 52370
  - name: test
    num_bytes: 64771247
    num_examples: 5000
  - name: validation
    num_bytes: 45897231
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 529950405
- config_name: ro
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 164966676
    num_examples: 15921
  - name: test
    num_bytes: 67248472
    num_examples: 5000
  - name: validation
    num_bytes: 46968070
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 279183218
- config_name: et
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 173878703
    num_examples: 23126
  - name: test
    num_bytes: 56535287
    num_examples: 5000
  - name: validation
    num_bytes: 39580866
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 269994856
- config_name: fi
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 336145949
    num_examples: 42497
  - name: test
    num_bytes: 63280920
    num_examples: 5000
  - name: validation
    num_bytes: 44500040
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 443926909
- config_name: hu
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 208805862
    num_examples: 22664
  - name: test
    num_bytes: 68990666
    num_examples: 5000
  - name: validation
    num_bytes: 48101023
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 325897551
- config_name: lt
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 185211691
    num_examples: 23188
  - name: test
    num_bytes: 59484711
    num_examples: 5000
  - name: validation
    num_bytes: 41841024
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 286537426
- config_name: lv
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 186396252
    num_examples: 23208
  - name: test
    num_bytes: 59814093
    num_examples: 5000
  - name: validation
    num_bytes: 42002727
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 288213072
- config_name: el
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 768224743
    num_examples: 55000
  - name: test
    num_bytes: 117209312
    num_examples: 5000
  - name: validation
    num_bytes: 81923366
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 967357421
- config_name: mt
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 179866781
    num_examples: 17521
  - name: test
    num_bytes: 65831230
    num_examples: 5000
  - name: validation
    num_bytes: 46737914
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 292435925
- config_name: all_languages
  features:
  - name: celex_id
    dtype: string
  - name: text
    dtype:
      translation:
        languages:
        - en
        - da
        - de
        - nl
        - sv
        - bg
        - cs
        - hr
        - pl
        - sk
        - sl
        - es
        - fr
        - it
        - pt
        - ro
        - et
        - fi
        - hu
        - lt
        - lv
        - el
        - mt
  - name: labels
    sequence:
      class_label:
        names:
          '0': '100149'
          '1': '100160'
          '2': '100148'
          '3': '100147'
          '4': '100152'
          '5': '100143'
          '6': '100156'
          '7': '100158'
          '8': '100154'
          '9': '100153'
          '10': '100142'
          '11': '100145'
          '12': '100150'
          '13': '100162'
          '14': '100159'
          '15': '100144'
          '16': '100151'
          '17': '100157'
          '18': '100161'
          '19': '100146'
          '20': '100155'
  splits:
  - name: train
    num_bytes: 6971500859
    num_examples: 55000
  - name: test
    num_bytes: 1536038431
    num_examples: 5000
  - name: validation
    num_bytes: 1062290624
    num_examples: 5000
  download_size: 2770050147
  dataset_size: 9569829914
---

# Dataset Card for "MultiEURLEX"

## 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

- **Repository:** https://github.com/nlpaueb/MultiEURLEX/
- **Paper:** https://arxiv.org/abs/2109.00904
- **Data:** https://doi.org/10.5281/zenodo.5363165
- **Leaderboard:** N/A
- **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk)

### Dataset Summary

**Documents**

MultiEURLEX comprises 65k EU laws in 23 official EU languages. Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. Each EUROVOC label ID is associated with a *label descriptor*, e.g., [60, agri-foodstuffs],  [6006, plant product], [1115, fruit]. The descriptors are also available in the 23 languages. Chalkidis et al. (2019) published a monolingual (English) version of this dataset, called EUR-LEX, comprising 57k EU laws with the originally assigned gold labels.

**Multi-granular Labeling**

EUROVOC has eight levels of concepts. Each document is assigned one or more concepts (labels). If a document is assigned a concept, the ancestors and descendants of that concept are typically not assigned to the same document. The documents were originally annotated with concepts from levels 3 to 8. 
We created three alternative sets of labels per document, by replacing each assigned concept by its ancestor from level 1, 2, or 3, respectively. Thus, we provide four sets of gold labels per document, one for each of the first three levels of the hierarchy, plus the original sparse label assignment. Levels 4 to 8 cannot be used independently, as many documents have gold concepts from the third level; thus many documents will be mislabeled, if we discard level 3.

**Data Split and Concept Drift**

MultiEURLEX is *chronologically* split in training (55k, 1958-2010), development (5k, 2010-2012), test (5k, 2012-2016) subsets, using the English documents. The test subset contains the same 5k documents in all 23 languages. The development subset also contains the same 5k documents in 23 languages, except Croatian. Croatia is the most recent EU member (2013); older laws are gradually translated.
For the official languages of the seven oldest member countries, the same 55k training documents are available; for the other languages, only a subset of the 55k training documents is available.
Compared to EUR-LEX (Chalkidis et al., 2019), MultiEURLEX is not only larger (8k more documents) and multilingual; it is also more challenging, as the chronological split leads to temporal real-world *concept drift* across the training, development, test subsets, i.e., differences in label distribution and phrasing, representing a realistic *temporal generalization* problem (Huang et al., 2019; Lazaridou et al., 2021). Recently, Søgaard et al. (2021) showed this setup is more realistic, as it does not over-estimate real performance, contrary to random splits (Gorman and Bedrick, 2019).

### Supported Tasks and Leaderboards

Similarly to EUR-LEX (Chalkidis et al., 2019), MultiEURLEX can be used for legal topic classification, a multi-label classification task where legal documents need to be assigned concepts (in our case, from EUROVOC) reflecting their topics. Unlike EUR-LEX, however, MultiEURLEX supports labels from three different granularities (EUROVOC levels). More importantly, apart from monolingual (*one-to-one*) experiments, it can be used to study cross-lingual transfer scenarios, including *one-to-many* (systems trained in one language and used in other languages with no training data), and *many-to-one* or *many-to-many* (systems jointly trained in multiple languages and used in one or more other languages).

The dataset is not yet part of an established benchmark.

### Languages

The EU has 24 official languages. When new members join the EU, the set of official languages usually expands, except the languages are already included. MultiEURLEX covers 23 languages from seven language families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). EU laws are published in all official languages, except Irish, for resource-related reasons (Read more at https://europa.eu/european-union/about-eu/eu-languages_en). This wide coverage makes MultiEURLEX a valuable testbed for cross-lingual transfer. All languages use the Latin script, except for Bulgarian (Cyrillic script) and Greek. Several other languages are also spoken in EU countries. The EU is home to over 60 additional indigenous regional or minority languages, e.g., Basque, Catalan, Frisian, Saami, and Yiddish, among others, spoken by approx. 40 million people, but these additional languages are not considered official (in terms of EU), and EU laws are not translated to them.

## Dataset Structure

### Data Instances

**Multilingual use of the dataset**

When the dataset is used in a multilingual setting selecting the the 'all_languages' flag:

```python
from datasets import load_dataset
dataset = load_dataset('multi_eurlex', 'all_languages')
```

```json
{
  "celex_id": "31979D0509",
  "text": {"en": "COUNCIL DECISION  of 24 May 1979  on financial aid from the Community for the eradication of African swine fever in Spain  (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,",
           "es": "DECISIÓN DEL CONSEJO de 24 de mayo de 1979 sobre ayuda financiera de la Comunidad para la erradicación de la peste porcina africana en España (79/509/CEE)\nEL CONSEJO DE LAS COMUNIDADES EUROPEAS\nVeniendo en cuenta el Tratado constitutivo de la Comunidad Económica Europea y, en particular, Su artículo 43,\n Vista la propuesta de la Comisión (1),\n Visto el dictamen del Parlamento Europeo (2),\nConsiderando que la Comunidad debe tomar todas las medidas adecuadas para protegerse contra la aparición de la peste porcina africana en su territorio;\nConsiderando a tal fin que la Comunidad ha emprendido y sigue llevando a cabo acciones destinadas a contener los brotes de este tipo de enfermedades lejos de sus fronteras, ayudando a los países afectados a reforzar sus medidas preventivas; que a tal efecto ya se han concedido a España subvenciones comunitarias;\nQue estas medidas han contribuido sin duda alguna a la protección de la ganadería comunitaria, especialmente mediante la creación y mantenimiento de una zona tampón al norte del río Ebro;\nConsiderando, no obstante, , a juicio de las propias autoridades españolas, las medidas implementadas hasta ahora deben reforzarse si se quiere alcanzar el objetivo fundamental de erradicar la enfermedad en todo el país;\nConsiderando que las autoridades españolas han pedido a la Comunidad que contribuya a los gastos necesarios para la ejecución eficaz de un programa de erradicación total;\nConsiderando que conviene dar una respuesta favorable a esta solicitud concediendo una ayuda a España, habida cuenta del compromiso asumido por dicho país de proteger a la Comunidad contra la peste porcina africana y de eliminar completamente esta enfermedad al final de un plan de erradicación de cinco años;\nMientras que este plan de erradicación debe incluir e determinadas medidas que garanticen la eficacia de las acciones emprendidas, debiendo ser posible adaptar estas medidas a la evolución de la situación mediante un procedimiento que establezca una estrecha cooperación entre los Estados miembros y la Comisión;\nConsiderando que es necesario mantener el Los Estados miembros informados periódicamente sobre el progreso de las acciones emprendidas.",
           "de": "...",
           "bg": "..."
  },
  "labels": [
    1,
    13,
    47
  ]
}
```

**Monolingual use of the dataset**

When the dataset is used in a monolingual setting selecting the ISO language code for one of the 23 supported languages. For example:

```python
from datasets import load_dataset
dataset = load_dataset('multi_eurlex', 'en')
```

```json
{
  "celex_id": "31979D0509",
  "text": "COUNCIL DECISION  of 24 May 1979  on financial aid from the Community for the eradication of African swine fever in Spain  (79/509/EEC)\nTHE COUNCIL OF THE EUROPEAN COMMUNITIES\nHaving regard to the Treaty establishing the European Economic Community, and in particular Article 43 thereof,\nHaving regard to the proposal from the Commission (1),\nHaving regard to the opinion of the European Parliament (2),\nWhereas the Community should take all appropriate measures to protect itself against the appearance of African swine fever on its territory;\nWhereas to this end the Community has undertaken, and continues to undertake, action designed to contain outbreaks of this type of disease far from its frontiers by helping countries affected to reinforce their preventive measures ; whereas for this purpose Community subsidies have already been granted to Spain;\nWhereas these measures have unquestionably made an effective contribution to the protection of Community livestock, especially through the creation and maintenance of a buffer zone north of the river Ebro;\nWhereas, however, in the opinion of the Spanish authorities themselves, the measures so far implemented must be reinforced if the fundamental objective of eradicating the disease from the entire country is to be achieved;\nWhereas the Spanish authorities have asked the Community to contribute to the expenses necessary for the efficient implementation of a total eradication programme;\nWhereas a favourable response should be given to this request by granting aid to Spain, having regard to the undertaking given by that country to protect the Community against African swine fever and to eliminate completely this disease by the end of a five-year eradication plan;\nWhereas this eradication plan must include certain measures which guarantee the effectiveness of the action taken, and it must be possible to adapt these measures to developments in the situation by means of a procedure establishing close cooperation between the Member States and the Commission;\nWhereas it is necessary to keep the Member States regularly informed as to the progress of the action undertaken,",
  "labels": [
    1,
    13,
    47
  ]
}
```

### Data Fields

**Multilingual use of the dataset**

The following data fields are provided for documents (`train`, `dev`, `test`):

`celex_id`: (**str**)  The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.\
`text`: (dict[**str**])  A dictionary with the 23 languages as keys and the full content of each document as values.\
`labels`: (**List[int]**) The relevant EUROVOC concepts (labels).


**Monolingual use of the dataset**

The following data fields are provided for documents (`train`, `dev`, `test`):

`celex_id`: (**str**)  The official ID of the document. The CELEX number is the unique identifier for all publications in both Eur-Lex and CELLAR.\
`text`: (**str**)  The full content of each document across languages.\
`labels`: (**List[int]**) The relevant EUROVOC concepts (labels).


If you want to use the descriptors of the EUROVOC concepts, similar to [Chalkidis et al. (2020)](https://aclanthology.org/2020.emnlp-main.607/), please download the relevant JSON file [here](https://raw.githubusercontent.com/nlpaueb/multi-eurlex/master/data/eurovoc_descriptors.json).
Then you may load it and use it:
```python
import json
from datasets import load_dataset

# Load the English part of the dataset
dataset = load_dataset('multi_eurlex', 'en', split='train')

# Load (label_id, descriptor) mapping 
with open('./eurovoc_descriptors.json') as jsonl_file:
    eurovoc_concepts =  json.load(jsonl_file)

# Get feature map info
classlabel = dataset.features["labels"].feature

# Retrieve IDs and descriptors from dataset
for sample in dataset:
  print(f'DOCUMENT: {sample["celex_id"]}')
  # DOCUMENT: 32006D0213
  for label_id in sample['labels']:
    print(f'LABEL: id:{label_id}, eurovoc_id: {classlabel.int2str(label_id)}, \
            eurovoc_desc:{eurovoc_concepts[classlabel.int2str(label_id)]}')
    # LABEL: id: 1, eurovoc_id: '100160', eurovoc_desc: 'industry'
```

### Data Splits
<table>
<tr><td> Language </td> <td>   ISO code </td> <td>  Member Countries where official </td> <td>  EU Speakers [1] </td> <td>   Number of Documents [2] </td> </tr> 
<tr><td> English     </td> <td> <b>en</b>   </td> <td>  United Kingdom (1973-2020), Ireland (1973), Malta (2004)   </td> <td> 13/ 51% </td> <td>  55,000 / 5,000 / 5,000 </td> </tr> 
<tr><td> German    </td> <td>  <b>de</b>   </td> <td> Germany (1958), Belgium (1958), Luxembourg (1958)  </td> <td> 16/32% </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> 
<tr><td> French    </td> <td>  <b>fr</b>   </td> <td> France (1958), Belgium(1958), Luxembourg (1958)  </td> <td> 12/26% </td> <td> 55,000 / 5,000 / 5,000 </td> </tr> 
<tr><td> Italian     </td> <td>  <b>it</b>   </td> <td> Italy (1958)   </td> <td>  13/16% </td> <td>  55,000 / 5,000 / 5,000 </td> </tr> 
<tr><td> Spanish     </td> <td>  <b>es</b>   </td> <td> Spain (1986)  </td> <td>  8/15% </td> <td>  52,785 / 5,000 / 5,000 </td> </tr> 
<tr><td> Polish      </td> <td>  <b>pl</b>   </td> <td> Poland (2004)  </td> <td>  8/9% </td> <td>  23,197 / 5,000 / 5,000 </td> </tr>  
<tr><td> Romanian    </td> <td>  <b>ro</b>   </td> <td> Romania (2007)  </td> <td>  5/5% </td> <td>  15,921 / 5,000 / 5,000 </td> </tr>  
<tr><td> Dutch       </td> <td>  <b>nl</b>   </td> <td> Netherlands (1958), Belgium (1958)  </td> <td>  4/5% </td> <td>  55,000 / 5,000 / 5,000 </td> </tr>  
<tr><td> Greek       </td> <td>  <b>el</b>   </td> <td> Greece (1981), Cyprus (2008) </td> <td>  3/4% </td> <td>  55,000 / 5,000 / 5,000 </td> </tr>  
<tr><td> Hungarian   </td> <td>  <b>hu</b>   </td> <td> Hungary (2004)  </td> <td>  3/3% </td> <td>  22,664 / 5,000 / 5,000 </td> </tr>  
<tr><td> Portuguese  </td> <td>  <b>pt</b>   </td> <td> Portugal (1986)  </td> <td>  2/3% </td> <td>  23,188 / 5,000 / 5,000 </td> </tr>  
<tr><td> Czech       </td> <td>  <b>cs</b>   </td> <td> Czech Republic (2004)  </td> <td>  2/3% </td> <td>  23,187 / 5,000 / 5,000 </td> </tr>  
<tr><td> Swedish     </td> <td>  <b>sv</b>   </td> <td> Sweden (1995)  </td> <td>  2/3% </td> <td>  42,490 / 5,000 / 5,000 </td> </tr>  
<tr><td> Bulgarian   </td> <td>  <b>bg</b>   </td> <td> Bulgaria (2007)  </td> <td>  2/2% </td> <td>  15,986 / 5,000 / 5,000 </td> </tr>  
<tr><td> Danish      </td> <td>  <b>da</b>   </td> <td> Denmark (1973)  </td> <td>  1/1% </td> <td>  55,000 / 5,000 / 5,000 </td> </tr>  
<tr><td> Finnish     </td> <td>  <b>fi</b>   </td> <td> Finland (1995)  </td> <td>  1/1% </td> <td>  42,497 / 5,000 / 5,000 </td> </tr>  
<tr><td> Slovak      </td> <td>  <b>sk</b>   </td> <td> Slovakia (2004)  </td> <td>  1/1% </td> <td>  15,986 / 5,000 / 5,000 </td> </tr>  
<tr><td> Lithuanian  </td> <td>  <b>lt</b>   </td> <td> Lithuania (2004)   </td> <td>  1/1% </td> <td>  23,188 / 5,000 / 5,000 </td> </tr>  
<tr><td> Croatian    </td> <td>  <b>hr</b>   </td> <td> Croatia (2013) </td> <td>  1/1% </td> <td>  7,944 / 2,500 / 5,000 </td> </tr>  
<tr><td> Slovene     </td> <td>  <b>sl</b>   </td> <td> Slovenia (2004) </td> <td>  <1/<1% </td> <td>  23,184 / 5,000 / 5,000 </td> </tr>  
<tr><td> Estonian    </td> <td>  <b>et</b>   </td> <td> Estonia (2004) </td> <td>  <1/<1% </td> <td>  23,126 / 5,000 / 5,000 </td> </tr> 
<tr><td> Latvian     </td> <td>  <b>lv</b>   </td> <td> Latvia (2004) </td> <td>  <1/<1% </td> <td>  23,188 / 5,000 / 5,000 </td> </tr>  
<tr><td> Maltese     </td> <td>  <b>mt</b>   </td> <td> Malta (2004) </td> <td>  <1/<1% </td> <td>  17,521 / 5,000 / 5,000 </td> </tr>  
</table>

[1] Native and Total EU speakers percentage (%) \
[2] Training / Development / Test Splits 

## Dataset Creation

### Curation Rationale

The dataset was curated by Chalkidis et al. (2021).\
The documents have been annotated by the Publications Office of EU (https://publications.europa.eu/en).

### Source Data

#### Initial Data Collection and Normalization

The original data are available at the EUR-LEX portal (https://eur-lex.europa.eu) in unprocessed formats (HTML, XML, RDF). The documents were downloaded from the EUR-LEX portal in HTML. The relevant EUROVOC concepts were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql). 
We stripped HTML mark-up to provide the documents in plain text format.
We inferred the labels for EUROVOC levels 1--3, by backtracking the EUROVOC hierarchy branches, from the originally assigned labels to their ancestors in levels 1--3, respectively.

#### Who are the source language producers?

The EU has 24 official languages. When new members join the EU, the set of official languages usually expands, except the languages are already included. MultiEURLEX covers 23 languages from seven language families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). EU laws are published in all official languages, except Irish, for resource-related reasons (Read more at https://europa.eu/european-union/about-eu/eu-languages_en). This wide coverage makes MultiEURLEX a valuable testbed for cross-lingual transfer. All languages use the Latin script, except for Bulgarian (Cyrillic script) and Greek. Several other languages are also spoken in EU countries. The EU is home to over 60 additional indigenous regional or minority languages, e.g., Basque, Catalan, Frisian, Saami, and Yiddish, among others, spoken by approx. 40 million people, but these additional languages are not considered official (in terms of EU), and EU laws are not translated to them.


### Annotations

#### Annotation process

All the documents of the dataset have been annotated by the Publications Office of EU (https://publications.europa.eu/en) with multiple concepts from EUROVOC (http://eurovoc.europa.eu/). EUROVOC has eight levels of concepts. Each document is assigned one or more concepts (labels). If a document is assigned a concept, the ancestors and descendants of that concept are typically not assigned to the same document. The documents were originally annotated with concepts from levels 3 to 8.
We augmented the annotation with three alternative sets of labels per document, replacing each assigned concept by its ancestor from level 1, 2, or 3, respectively. 
Thus, we provide four sets of gold labels per document, one for each of the first three levels of the hierarchy, plus the original sparse label assignment.Levels 4 to 8 cannot be used independently, as many documents have gold concepts from the third level; thus many documents will be mislabeled, if we discard level 3.


#### Who are the annotators?

Publications Office of EU (https://publications.europa.eu/en)

### Personal and Sensitive Information

The dataset contains publicly available EU laws that do not include personal or sensitive information with the exception of trivial information presented by consent, e.g., the names of the current presidents of the European Parliament and European Council, and other administration bodies.

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

MultiEURLEX covers 23 languages from seven language  families (Germanic, Romance, Slavic, Uralic, Baltic, Semitic, Hellenic). This does not imply that no other languages are spoken in EU countries, although EU laws are not translated to other languages (https://europa.eu/european-union/about-eu/eu-languages_en).

## Additional Information

### Dataset Curators

Chalkidis et al. (2021)

### Licensing Information

We provide MultiEURLEX with the same licensing as the original EU data (CC-BY-4.0):

© European Union, 1998-2021

The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.

The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.

Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \
Read more:  https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html

### Citation Information

*Ilias Chalkidis, Manos Fergadiotis, and Ion Androutsopoulos.*
*MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer.*
*Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic. 2021*
```
@InProceedings{chalkidis-etal-2021-multieurlex,
  author = {Chalkidis, Ilias  
                and Fergadiotis, Manos
                and Androutsopoulos, Ion},
  title = {MultiEURLEX -- A multi-lingual and multi-label legal document 
               classification dataset for zero-shot cross-lingual transfer},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods
               in Natural Language Processing},
  year = {2021},
  publisher = {Association for Computational Linguistics},
  location = {Punta Cana, Dominican Republic},
  url = {https://arxiv.org/abs/2109.00904}
}
```

### Contributions

Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset.