Datasets:
Tasks:
Text Classification
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
License:
File size: 47,778 Bytes
bf4c71c cd46982 0f037dc d376211 0f037dc bf4c71c 0f037dc cd46982 35e4ee3 bf4c71c cd8d496 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc cd8d496 0f037dc 7e9086d 0f037dc bf4c71c 2020d03 bf4c71c 3a262e5 bf4c71c 0054d72 bf4c71c 0f037dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 |
---
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/multi-eurlex
- **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, S酶gaard 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": "DECISI脫N DEL CONSEJO de 24 de mayo de 1979 sobre ayuda financiera de la Comunidad para la erradicaci贸n de la peste porcina africana en Espa帽a (79/509/CEE)\nEL CONSEJO DE LAS COMUNIDADES EUROPEAS\nVeniendo en cuenta el Tratado constitutivo de la Comunidad Econ贸mica Europea y, en particular, Su art铆culo 43,\n Vista la propuesta de la Comisi贸n (1),\n Visto el dictamen del Parlamento Europeo (2),\nConsiderando que la Comunidad debe tomar todas las medidas adecuadas para protegerse contra la aparici贸n 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 pa铆ses afectados a reforzar sus medidas preventivas; que a tal efecto ya se han concedido a Espa帽a subvenciones comunitarias;\nQue estas medidas han contribuido sin duda alguna a la protecci贸n de la ganader铆a comunitaria, especialmente mediante la creaci贸n y mantenimiento de una zona tamp贸n al norte del r铆o Ebro;\nConsiderando, no obstante, , a juicio de las propias autoridades espa帽olas, las medidas implementadas hasta ahora deben reforzarse si se quiere alcanzar el objetivo fundamental de erradicar la enfermedad en todo el pa铆s;\nConsiderando que las autoridades espa帽olas han pedido a la Comunidad que contribuya a los gastos necesarios para la ejecuci贸n eficaz de un programa de erradicaci贸n total;\nConsiderando que conviene dar una respuesta favorable a esta solicitud concediendo una ayuda a Espa帽a, habida cuenta del compromiso asumido por dicho pa铆s de proteger a la Comunidad contra la peste porcina africana y de eliminar completamente esta enfermedad al final de un plan de erradicaci贸n de cinco a帽os;\nMientras que este plan de erradicaci贸n debe incluir e determinadas medidas que garanticen la eficacia de las acciones emprendidas, debiendo ser posible adaptar estas medidas a la evoluci贸n de la situaci贸n mediante un procedimiento que establezca una estrecha cooperaci贸n entre los Estados miembros y la Comisi贸n;\nConsiderando que es necesario mantener el Los Estados miembros informados peri贸dicamente 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):
漏 European Union, 1998-2021
The Commission鈥檚 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. |